SMEX02 Experiment Plan
SOIL MOISTURE
EXPERIMENTS IN 2002
(SMEX02)
Experiment Plan
June 2002
TABLE OF CONTENTS
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Executive Summary
Overview and Scientific Objectives
1.1 Soil Moisture Mission (EX-4a)
1.2 Global Water and Energy Cycle (GWEC)
1.3 Advanced Microwave Scanning Radiometer (AMSR)
1.4 Soil Moisture Experiments in 2002 (SMEX02)
SMACEX-Soil Moisture Atmosphere Coupling EXperiment
2.1 Background
2.2 Scientific Approach and Expected Results
Satellite Observing Systems
3.1 Aqua Advanced Microwave Scanning Radiometer (AMSR-E)
3.2 Special Sensor Microwave Imager (SSMI)
3.3 European Radar Satellite (ERS-2)
3.4 Envisat Advanced Synthetic Aperture Radar (ASAR)
3.5 Radarsat
3.6 Terra Sensors
3.7 Landsat Thematic Mapper
3.8 Advanced Very High Resolution Radiometer (AVHRR)
3.9 Geostationary Operational Environmental Satellites (GOES)
3.10 SeaWinds Quickscat
Aircraft Remote Sensing Instruments
4.1 Polarimetric Scanning Radiometer (PSR)
4.2 Passive and Active L and S Microwave Instrument (PALS)
4.3 Electronically Scanned Thinned Aperture Radiometer (ESTAR)
4.4 Airborne Synthetic Aperture Radar (AIRSAR)
4.5 Global Positioning System (GPS) Technique
4.6 Utah State University Visible and Infrared Sensors
Remote Sensing Aircraft Mission Design
5.1 NCAR C-130
5.2 NASA P-3B
5.3 NASA DC-8
5.4 Canadian Twin Otter
5.5 Utah State University Piper
Iowa Study Region
6.1 Watershed Sites
6.2 Regional Sites
Schedule
Ground Data Collection
8.1 Tower Based Surface Flux Measurements
8.2 Lidar/Sodar/Radiosondes
8.3 Sun Photometer
8.4 Vegetation and Land Cover
8.5 Soil Moisture
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8.6 Soil and Surface Temperature
8.7 Soil Surface Roughness
8.8 Ground Based Microwave Radiometer
Regional Meteorological and Climate Networks
9.1 Soil Climate Analysis Network (SCAN)
9.2 NSTL Meteorological Stations
9.3 Iowa Environmental Mesonet
SMEX02/SMACEX Tower Flux Measurements
10.1 Eddy Covariance Measurements
10.2 Ancillary Measurements
10.3 Intercomparison
10.4 Instrument Height/Depth and Position
10.5 Selected Test Sites
Protocols for Ground Sampling
11.1 General Guidelines for Field Sampling
11.2 Watershed Site Surface Soil Moisture and Temperature
11.3 Regional Site Surface Soil Moisture and Temperature
11.4 Theta Probe Soil Moisture Sampling and Processing
11.5 Gravimetric Soil Moisture Sampling with the Scoop Tool
11.6 Gravimetric Soil Moisture and Bulk Density Sampling with the Coring Tool
11.7 Gravimetric Soil Moisture Sample Processing
11.8 Watershed Site Bulk Density and Surface Roughness
11.9 Hydra Probe Soil Moisture and Apogee Surface Temperature Installations
11.10 Watershed Site Vegetation Sampling
11.11 Plant Canopy Analyzer Measurements
11.12 Global Positioning System (GPS) Coordinates
References
Investigator Abstracts
Contact List
Logistics
15.1 Security
15.2 Safety
15.3 Hotels
15.4 Shipping Information
15.5 Directions
15.6 Local Contacts
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EXECUTIVE SUMMARY
Soil moisture field experiments have been very successful at addressing a broad range of science
question, focusing technology development and demonstration, and providing educational
experiences for undergraduate and graduate students. The data have been used in studies that
went well beyond the algorithm research, primarily due to an emphasis on developing map-based
products.
For 2002, a soil moisture field experiment (SMEX02) is proposed that would support the science
needs of the NASA Land Surface Hydrology Program Soil Moisture Mission (EX-4a), the
NASA Global Water and Energy Cycle Research Program, the EOS Aqua Advanced Microwave
Scanning Radiometer, and NOAA-DOD prototype land parameter algorithms utilizing data from
the Special Sensor Microwave Imager (SSM/I). The objectives of SMEX02 are to understand
land-atmosphere interactions, extension of instrument observations and algorithms to a broader
range of vegetation conditions, validation of land surface parameters retrieved from SSM/I and
potentially AMSR data, and the evaluation of new instrument technologies for soil moisture
remote sensing. We have chosen to address the combined objectives with
ground/aircraft/spacecraft observations over sites in Iowa during the summer of 2002.
This report describes the elements of SMEX02 in detail. Coverage includes the aircraft and
satellite soil moisture sensors, the land atmosphere experiments, aircraft missions, ground data
collection, regional networks and test sites. A set of abstracts describing the research goals of the
individual investigators is also included.
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1
OVERVIEW AND SCIENTIFIC OBJECTIVES
The significance of a hydrologic state variable is expressed well in the recent description of
NASA’s Global Water and Energy Cycle research program. Water is at the heart of both the
causes and the effects of climate change. Ascertaining the rate of cycling of water in the Earth
system, and detecting possible changes, is a first-order problem with regard to the renewal of
water resources and hydrologic hazards. A more complete understanding of water fluxes,
storage, and transformations in the land, atmosphere, and oceans will be the central challenge
to the hydrological sciences in the 21st century. Improved knowledge and prediction of the water
cycle can yield large benefits for resource management and regional economies if variability
and uncertainties can be understood, quantified and communicated effectively to decisionmakers and to the public. The overarching objective is to improve the understanding of the
global water cycle to the point where useful predictions of regional hydrologic regimes can be
made. This predictive capability is essential for practical applications to water resource
management and for validating scientific advances through the test of real-life prediction.
Soil moisture is the key state variable in hydrology: it is the switch that controls the proportion of
rainfall that percolates, runs off, or evaporates from the land. It is the life-giving substance for
vegetation. Soil moisture integrates precipitation and evaporation over periods of days to weeks
and introduces a significant element of memory in the atmosphere/land system. There is strong
climatological and modeling evidence that the fast recycling of water through evapotranspiration
and precipitation is the primary factor in the persistence of dry or wet anomalies over large
continental regions during summer. As a result, soil moisture is the most significant boundary
condition that controls summer precipitation over the central U.S. and other large mid-latitude
continental regions, and essential initial information for seasonal predictions.
A common goal of a wide range of agencies and scientists is the development of a global soil
moisture observing system (Leese et al. 2001). Providing a global soil moisture product for
research and application remains a significant challenge. Precise insitu measurements of soil
moisture are sparse and each value is only representative of a small area. Remote sensing, if
achievable with sufficient accuracy and reliability, would provide truly meaningful wide-area
soil wetness or soil moisture data for hydrological studies over large continental regions.
Development and implementation of the remote sensing component of a global soil moisture
observing system will require advancements in science and technology. Many aspects of the
research require validation and demonstration, which can only be accomplished through
controlled large-scale field experimentation. Large-scale field experimentation requires
significant resources to be successful that are usually contributed from several programs.
Through a series of workshops and research announcements science and technology priorities for
soil moisture remote sensing have been identified. Elements requiring field experimentation were
identified and, to the extent possible, combined into Soil Moisture Experiments for 2002
(SMEX02). This model has worked well in soil moisture research in the past and will be applied
in 2002. SMEX02 will focus on microwave remote sensing of soil moisture in an agricultural
setting. Issues not addressed in this experiment will be the focus of future field experiments.
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At the present time there are three programs that significantly influence the direction of research
and the requirements of a soil moisture field experiment. These are the Soil Moisture Mission
(EX-4a), Global Water & Energy Cycle (GWEC) Research and Analysis, the Advanced
Microwave Scanning Radiometers (AMSR) on Aqua and ADEOS-II. The relevant science needs
of each program are described in the following sections. These were merged into the SMEX02
experiment plan.
1.1
Soil Moisture Mission EX-4a
Soil moisture is recognized by the NASA Post 2002 program as a critical measurement As a
result, several scientific reviews were conducted to define a Soil Moisture Mission. The final
report can be found at http://maximus.ce.washington.edu/~tempcm/Post2002/smm3.html. This mission is
based on a scientific consensus that an L band microwave remote sensing with high spatial
resolution (<10 km) is needed for soil moisture. Technology development will be needed before
such a mission can be implemented. Many of the science issues related to this mission can be
addressed immediately. These include:
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Conduct a field experiment to collect passive microwave data to extend the calibration and
validation to agricultural crops at peak biomass.
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Conduct a field experiment to collect passive microwave data to validate algorithm
performance in regions with diverse topography.
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Conduct field experiments to collect passive microwave data to explore their usefulness in
different types of forest canopies.
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Soil moisture retrieval algorithms that rely on ancillary data or multichannel data need to be
compared
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Evaluations of soil moisture retrieval techniques with C band data can be performed using
near future satellite missions such as EOS-PM and ADEOS-II. Conduct field experiments to
collect aircraft and ground passive microwave data concurrent with AMSR over passes that
will allow the validation of algorithms and definition of the scaling behavior of the
measurements.
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Establish a series of validation sites where high quality ground data will be collected
consistently.
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Airborne simulators for each proposed space instrument need to be built for pre-mission
studies and mission-current validation flights.
As part of this research we must also consider the contributions it can make to the ESA Soil
Moisture Ocean Salinity (SMOS) mission and how EX-4a can in turn benefit. SMOS is a passive
microwave L band soil moisture measurement mission with a 50-km spatial resolution.
Although it will not have the desired EX-4a spatial resolution, such a mission would provide a
first experience and a valuable science data product. At the present time, the launch is
anticipated in 2006 (http://www-sv.cict.fr/cesbio/smos/). SMOS will utilize two dimensional
synthetic aperture radiometry and will employ a variation on soil moisture algorithms that has
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not been rigorously calibrated and validated. The instruments utilized and field experiments
conducted in SMEX02 are highly relevant to the SMOS project.
1.2
Global Water & Energy Cycle (GWEC)
The most recent NASA initiative relevant to soil moisture research is the Global Water & Energy
Cycle (GWEC) program. A current focus of this program is to explore the connection between
weather-related fast dynamical/physical processes that govern energy and water fluxes, and
climate responses and feedbacks. The objective of this research is to address the water and
atmospheric energy cycles as a single integrated problem. This approach includes exploring the
response of regional hydrologic regimes (precipitation, evaporation, and surface run-off) to
changes in atmospheric general circulation and climate, and the influence of surface hydrology
(soil moisture, snow accumulation and soil freezing/thawing) on climate.
Key scientific questions of this program are listed below along with specific issues that can be
addressed by SMEX02.
Is the global cycling of water through the atmosphere accelerating?
• Assessment of large-scale variability patterns and/or global trends in the occurrence of
extreme hydrologic events (e.g., floods and droughts), based on the analysis of global remote
sensing and insitu observational data.
• Estimation of evaporation fluxes over the land and oceans, based on the assimilation of
relevant observational data, and advanced parameterizations of model sub-grid scale
processes (e. g. planetary boundary layer dynamics).
• Diagnostics of spatial and temporal changes in the distribution of surface energy and water
storage; diagnostics of atmospheric responses to changes in ocean and land boundary
conditions.
What are the effects of clouds and surface hydrologic processes on climate change?
• Use satellite remote sensing to improve land surface process modeling and the understanding
of soil-vegetation-atmosphere interactions at regional or greater scales.
• Establish the interrelationships and feedbacks among clouds, precipitation, boundary layer,
and land surface processes using improved coupled land-atmosphere models and assimilated
data.
• Determine how land-atmosphere interactions, as affected by orography, vegetation, and soil,
affect the predictability of large-scale terrestrial hydrology and atmospheric systems,
including precipitation and runoff.
How are variations in local weather, precipitation, and water resources related to global
climate change?
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Analysis of the effect of spring and early summer hydrologic anomalies (snow accumulation,
soil moisture, soil freezing and thawing) on subsequent weather and precipitation patterns,
and hydrologic phenomena (impacts on runoff, water storage, and inland water bodies), and
how climate change might affect such anomalies in the future.
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Establish the scientific justification for future space-based observations of soil moisture,
snow, surface water, or other hydrologic variables, through scientific analysis and field
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investigations, including the improvement in our understanding of the global water and
energy cycle, floods and droughts, and climate change.
•
1.3
Determine techniques for transferring regional (e.g. GAPP) hydrologic process
understanding and prediction tools to other areas of the world, using remotely sensed and
emerging Coordinated Enhanced Observing Period (CEOP) observations scheduled for 2001
to 2003.
Advanced Microwave Scanning Radiometer (AMSR)
While it will be years before a spaceborne L band instrument will be available, a major opportunity
exits to maximize the AMSR instruments that are part of the recently launched Aqua satellite and
ADEOS-II (2002-2003). A critical element of the AMSR program is validation of the soil moisture
products. In addition, there are gaps in the knowledge base available for algorithm development,
especially over vegetation. The EOS Aqua AMSR-E science team is highly involved in the
SMEX02 program.
AMSR includes C band channels that offer improved capabilities for soil moisture sensing over
current satellite options (even though it is less optimal than the proposed L band radiometers
proposed for SMOS and EX-4a). The spatial resolution will be significantly better than its
predecessor SMMR.
All AMSR research will contribute to efforts to understand and validate soil moisture retrievals
from EX-4a and SMOS. Validating large footprint observations is a difficult task and in the past
it has been neglected. We have to commit to collecting real soil moisture data, not surrogate
variables, and these should correspond to the sensor measurement depth. What we learn in
attempting this for AMSR will be of great benefit to EX-4a.
1.4
Soil Moisture Field Experiment for 2002 (SMEX02)
Field experiments, in particular the series that has been conducted at the Southern Great Plains
(SGP) site, have been very successful at addressing a broad range of science and instrument
questions. The data have been used in studies that went well beyond the algorithm research,
primarily due to an emphasis on developing map-based products.
For 2002, a field experiment is proposed that would support the science needs of EX-4a, GWEC,
and AMSR. Main elements of the experiment are to understand land-atmosphere interactions,
validation of AMSR brightness temperature and soil moisture retrievals, extension of instrument
observations and algorithms to more challenging vegetation conditions, and the evaluation of
new instrument technologies for soil moisture remote sensing. We have chosen to address the
combined objectives with ground/aircraft/spacecraft observations over sites in Iowa during the
summer of 2002.
This report describes the elements of SMEX02 in detail. Coverage includes the aircraft and
satellite soil moisture sensors, the boundary layer experiments, aircraft missions, ground data
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collection, regional networks and test sites. A set of abstracts describing the research goals of the
individual investigators is also included.
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2
SMACEX-Soil Moisture Atmosphere Coupling EXperiment
A number of field experiments in the past, including SGP97 and SGP99, have been designed to
investigate land surface-atmosphere coupling and the role of remote sensing in Land Atmosphere
Transfer Schemes (LATS). However, in these studies, methodologies for upscaling and aggregation
have not been adequately developed, implemented and tested because the necessary measurements,
models and other tools to perform these tasks either have not existed and/or have not merged in the
necessary way. Moreover data quality depends to a degree on the conditions encountered in a
particular experiment. Hence building a diverse knowledge base needed to understand the complex
interaction of the land surface and atmosphere requires a continued effort in collecting the necessary
field observations over different land cover types and climatic conditions. An integral part of
SMEX02 is an experiment designed to address these concerns. The SMACEX project is designed to
collect atmospheric and remote sensing data over a range of spatial and temporal scales necessary to
investigate local and regional scale impacts of landscape heterogeneity on water and energy
exchanges.
2.1
Background
SMACEX will address several timely research foci in the area of water and energy cycling
across the land-atmosphere interface (see below). With additional support for flying time and
data processing, the Twin-Otter can collect surface-layer and atmospheric boundary-layer (ABL)
flux data. Support for two other remote sensing activities, namely aircraft-based high resolution
optical remote sensing data and ground-based Lidar observations of wind and water vapor
concentrations in the ABL, will provide simultaneous landscape and atmospheric properties
covering a wide range of temporal and spatial scales. Combining these observations together
with a network of 15-20 tower-based flux observations will result in a complete set of distributed
surface and atmospheric data, allowing for LATS and Large Eddy Simulation (LES) model
validation and development and testing of methodologies to bridge the scales from local to
regional. A schematic diagram summarizing the measurement and modeling activities
(experimental logistics) proposed for the project and the overall framework addressing upscaling issues is given in Figure 1. This figure also illustrates the interdependency of the
proposed activities and that all components of the project are required in order to achieve
proposal goals and objectives.
The expected advances with the coupled measurement and modeling program will address one of
NASA’s core missions of seeking to rigorously bridge between remotely sensed data and
operational forecast models, including advances in operational data assimilation schemes.
The overall objective of this work is to use a direct-measurement/remote sensing/modeling
approach to understand how horizontal heterogeneities in vegetation cover, soil moisture and
other land-surface variables influence the exchange of moisture and heat with the atmosphere.
The field observations will support the analysis of heterogeneities ranging from within field or
patch to the regional scales that are commensurate with prediction models of weather and
climate. The unique in-situ and aircraft measurements of atmospheric and soil variables and
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fluxes to be provided in the SMACEX data set are of primary importance. They will be used
both to validate fluxes diagnosed using remote sensing methods at various scales, and in
evaluating results from the LES-remote sensing model that will be used to develop horizontal
scaling relationships. The experimental approach is thus also an "up-scaling" endeavor,
investigating how remote sensing data at different horizontal and temporal scales may be utilized
for both diagnosis and prediction of the surface energy exchanges from patch to regional scales.
In particular, we focus on the effects of observation and model scale on the importance and
effectiveness of assimilation techniques.
Figure 1. Schematic summarizing measurement and modeling activities and their
interrelationships. Validation (blue arrows) of both the high resolution (10 to 100 m) output
from LATS and LES remote sensing models is performed with the flux towers and Lidar data,
while up-scaling techniques (red arrows) to the coarser resolutions ( > 100 m to 10 km) validated
with Lidar and aircraft fluxes. Validation of the prognostic LATS is achieved through validated
up-scaled diagnostic LATS remote sensing models.
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2.2
Scientific Approach and Expected Results
2.2.1
Large Eddy Simulation Investigations
The effects of surface heterogeneity on atmospheric turbulence and mean air properties and
resulting feedbacks on the land surface fluxes can be captured in a modeling framework using
LES. LES models simulate the space and time dynamics of ABL turbulence and the interactions
with the land surface using a numerical solution of the Navier-Stokes equations (e.g., Albertson
and Parlange, 1999). Most studies addressing land surface heterogeneity using LES have
described surface boundary conditions as predefined fluxes with artificial variability (e.g.
Hadfield et al., 1991, 1992; Avissar et al., 1998; Avissar and Schmidt, 1998; Cai, 1999), or with
spatial variability defined to match the surface flux fields estimated from experimental data at a
particular site (e.g. Hechtel et al., 1990; Eastman et al., 1998). The questions of how the surface
heterogeneity affects ABL heterogeneity, and how the surface and air properties in turn affect the
flux fields that develop over a region with heterogeneous surface properties are left unanswered
in most LES studies.
The LES-remote sensing model recently developed by Albertson et al (2001) couples remotely
sensed surface temperature and soil moisture fields (2D) to the dynamic (4D) ABL variables via
a LATS model, which includes separate and explicit contributions from soil and vegetation (i.e.
two sources) to mass and energy exchanges. This is a merger of active lines of research: the use
of remotely-sensed land surface properties to study water and energy fluxes, and the use of LES
to study the impacts of surface variability on ABL processes. This LES-remote sensing model
can run over a ~10 km2 domain at relatively high spatial resolution (~100 m) with remotely
sensed vegetation cover, surface soil moisture and temperature defining surface heterogeneities
governing atmospheric exchanges/interactions with the land surface. Typically, LATS are either
driven by a network of surface meteorological observations, or use energy conservation
principles applied to ABL dynamics to deduce air temperature (Anderson et al., 1997).
However, neither approach considers the resulting impact/feedback of surface heterogeneity on
atmospheric turbulence and the resulting spatial features of the mean air properties, particularly
at the patch or local scale. LES predictions will provide a benchmark for assessing the impact of
a range of surface heterogeneity features on LATS predictions neglecting such coupling.
To validate the results of LES turbulence and flux simulations as the basis for later up-scaling
parameterizations, the fields of LES-derived air properties will be compared to Lidar
observations (providing multidimensional “snapshots” of the turbulent fields in the ABL), and
the LES-derived spatially-distributed heat fluxes will be evaluated in the context of the network
of tower–based flux measurements. The tower-based flux observations, the high-resolution
optical remote sensing data, and the Lidar observations will not only provide patch-scale
validation of the LES predictions, but also allow us to investigate the interactions of highresolution surface fields with high-resolution turbulence fields. Such observations and analyses
are needed to ultimately account for the subgrid processes needed to link remotely-sensed land
surface fields with coarser-scale atmospheric models. The aircraft-based measurements will
validate flux predictions on scales commensurate with the nominal grid size of weather
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forecasting models (i.e., ~ 10 x 10 km (mesoscale models), to ~ 100 x 100 km (climate models
scales)).
LES results and Lidar/flux observations will support a thorough investigation of spatial scaling
relationships and identification of variables or combinations of variables that can be scaled to
weather and climate models, providing accurate grid-averaged fluxes. In fact, some initial insight
into scaling relationships between the surface and the lower atmosphere has been reported in the
results from Albertson et al. (2001). It was found that the correlation between time-averaged
maps of surface and air temperatures is dependent on the length scale of the heterogeneous
surface features, and that the horizontal standard deviation of time-averaged air temperature
decreased logarithmically with height in the atmospheric surface layer. Moreover, the mean air
temperature contains spatial variability induced preferentially from variations in surface
temperature occurring at scales greater than 500-1000 m. Hence, the feedback strength between
the land and the atmosphere is shown to be scale-dependent for the range of length scales (i.e.
≤ 101 km ) studied here.
2.2.2
LATS Investigations
The investigators have developed several related LATS models that have been used in a
diagnostic mode, where a combination of visible, near-IR, thermal, and microwave remote
sensing data are used to estimate land surface fluxes, soil moisture and other characteristics over
various spatial and temporal scales. The versions of the diagnostic models are complementary,
in that they use remote sensing data measured over different space and time scales and produce
flux estimates over these scales. Recent work has merged model versions such that flux
estimates made at larger scales (5-10 km) can be successfully "disaggregated" to the 30-m scale
of remote sensing data from aircraft, Landsat and potentially other high resolution satellite (e.g.,
ASTER) instruments. The various LATS models that can operate at the patch scale will be
validated using tower-based flux measurements, fluxes derived indirectly from Lidar and LES
output, while those LATS operating at the 5-10 km scale will be compared to aircraft, and LES
aggregated values. Incorporation of microwave data in combination with visible, near-infrared
and thermal-infrared is considered to have great potential in constraining two-source LATS
predictions. This is because the microwave-derived surface soil moisture may permit an
independent determination of the soil evaporation component, critical in assessing energy
partitioning between soil and vegetation components in partial canopies. The result of not
directly considering feedback effects of surface heterogeneity on atmospheric turbulence and
mean air properties will be deduced from the comparison of the LATS fluxes (with no local
feedbacks) with the LES output, Lidar observations and flux measurements (which include
feedbacks).
The vegetation/land surface scheme (called the “two-source” approach) employed is the common
thread among the various LATS versions and related remote sensing parameterizations employed
in this project. This two-source scheme, where the two sources refer to fluxes originating from
the individual soil and vegetation sub-components, is essential for interpreting the relationships
between aerodynamic and radiometric temperature as a function of vegetation cover and viewing
angle of the remotely-sensed infrared brightness temperatures (Norman et al, 1995, Kustas et al.,
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2000). This two-source-remote sensing modeling framework is considered a major advancement
over single-layer schemes (e.g., Hall et al., 1992; Vining and Blad, 1992), which do not consider
the individual soil and vegetation contributions to the total or composite heat fluxes and soil and
vegetation temperatures to the radiometric temperature measurements (Norman et al., 1995). In
areas of partial vegetation cover the remotely sensed surface temperature is a mixture of
vegetation and soil temperatures. As the soil dries these temperatures can differ greatly.
Biophysical processes related to the coupling of water and carbon cycles through plants depend
on the foliage temperature while the soil processes, such as decomposition and respiration,
depend on the soil temperature. The work proposed in this project will expand the impact of the
remote sensing data as the combination of the two-source model with microwave and infrared
data enable the partitioning of the surface temperature into the vegetation and soil components,
as needed to accurately assess the water and carbon cycling in vegetation and soils.
At the larger scales, an Atmospheric-Land-EXchange-Inverse model (ALEXI, Anderson et al.,
1997; Mecikalski et al., 1999) has been developed to diagnose surface latent and sensible heat
fluxes at regional scales at a resolution of 5 to 10 km. A strength of this model is that it uses
time-differencing (made approximately 4 hours apart) of radiometric temperatures from GOES
satellites as input rather than single-time measurements. Such differencing has been shown to be
less sensitive than single-time measurements to the effects of view angle (Diak, 1990),
emissivity and atmospheric corrections (Anderson et al., 1997). A second important feature is
that energy closure is achieved using an Atmospheric Boundary Layer (ABL) closure scheme
and ABL temperature profiles that can be obtained from radiosonde data, or even from a forecast
model. This avoids the use of screen-level atmospheric measurements, which suffer from errors
of representativeness (made at about 100-km intervals and at potentially unrepresentative sites)
and also are made too close to the surface, so that errors from any surface source will produce
large errors in the estimated fluxes. Using the ABL closure, ALEXI derives local estimates of
air temperature at an interface height of about 50m. As part of complementary NASA and
NOAA funding, an ALEXI version based in the mathematics of statistical interpolation (SIALEXI) has been put into a real-time mode and linked with the CIMSS mesoscale forecast
model. Because the predictive version of ALEXI (ALEX) is used as its land-surface component,
the CIMSS model has a built-in compatibility with ALEXI. The SI enhancement enables
multiple signals of the surface water/energy balance, to be incorporated into ALEXI. Such
inputs include low-level measurements of air temperature and humidity (e.g., Mahfouf, 1991;
Yang et al., 1994; Mecikalski et al., 1997), radiometric temperatures (Mecikalski et al., 1999), as
well as microwave estimates of near-surface soil moisture (Kustas et al., 1998).
A relatively simple dual-temperature difference (DTD) approach for using time rate of change in
radiometric and air temperature observations to compute the surface heat fluxes was recently
derived and evaluated using data from several different field sites covering a wide range of
environmental conditions (Norman et al., 2000a). Similar to ALEXI, the approach reduces the
effect of errors associated with radiometer calibration, emissivity variations and use of non-local
air temperature and wind speed data. Comparisons with heat fluxes predicted by ALEXI
indicates that the DTD method has potential for regional scale applications using satellite data
and a synoptic weather station network. It is also computationally very efficient taking 1/30 the
computational time of ALEXI (Kustas et al., 2000).
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A drawback of ALEXI and DTD approaches, however, is that the source of thermal-IR data
(GOES) and the atmospheric boundary layer closure dictate the larger resolution of 5 to 10 km,
and for many applications, including examining scaling issues, we need to estimate fluxes at
much finer spatial scales. Additionally, temporal changes of infrared temperatures are only
available from GOES satellites at a maximum resolution of ~5 km, but not from satellites with
higher spatial resolution. The framework of the two-source model outlined by Norman et al
(1995) will be the bridge between ALEXI and the high-resolution vegetation and thermal remote
sensing data available from such satellite platforms as Landsat, ASTER, AVHRR and MODIS.
The original version of this model applied to remotely sensed imagery incorporated remotelysensed single-time radiometric temperatures (Schmugge et al., 1998), while Kustas et al. (1998;
1999a) have recently modified the two-source model to utilize microwave-derived surface soil
moisture amounts to estimate the local energy balance on the 100-m scale. In these schemes,
however local atmospheric measurements of air temperature were available and assumed
uniform over the domain. Generally this is not an appropriate assumption for remote sensing data
taken over regional spatial scales at any resolution and can lead to significant errors depending
on the reference height adopted (Kustas et al., 1999b).
A solution to this problem was introduced by Norman et al. (2000b), who developed a scheme
for "disaggregating" ALEXI 5-km flux estimates (called DisALEXI) to the 30-m scale using
high-resolution remotely sensed vegetation and thermal-infrared data, and the local 50-m air
temperature estimate provided by ALEXI as the important atmospheric boundary condition in
temperature. Although, this scheme makes use of energy conservation principles applied to ABL
dynamics to deduce air temperature via ALEXI, it still does not consider the resulting
impact/feedback of surface heterogeneity on atmospheric turbulence and the resulting spatial
features of the mean air properties at the local or patch scale. The impact of these effects will be
captured in a modeling framework with the adoption of the LES-remote sensing model.
The diagnostic techniques embodied in these schemes (e.g., ALEXI, DTD, DisALEXI) can be
adapted to provide hourly fluxes throughout the day based on clear sky observations, whether the
remaining day is clear or not (Anderson et al., 1997).
2.2.3
Prognostic Modeling
The role of the CIMSS forecast model in this proposed work is to evaluate the potential of
remotely-sensed data and up-scaling procedures for these data in a forecasting environment. The
CIMSS forecast model is run in near real-time twice daily (the model initialization times being
the synoptic times of 00 and 12 UTC and for forecast durations of 48 hours) in support of
regional agricultural applications (Diak et al., 1998; Anderson et al., 2000). Various applications
entail model runs at resolutions of 50 km (with 46 vertical levels) for most of North America and
15 km for the upper Midwest region and northern Mexico. Other versions of the model have
been used to examine surface and boundary layer parameterizations (Diak et al., 1987), for
observing system simulation experiments involving satellite sounding instruments and other
satellite data experiments (Diak, 1987; Diak et al., 1992; Diak et al., 1994; Wu et al., 1994; Wu
15
and Smith, 1992; Burns et al., 1997; Bayler et al., 2000) and to investigate various other physical
parameterizations and numerical techniques (Raymond, 1988; Raymond, 1993; Raymond, 1994).
The prognostic component of the LATS within the CIMSS model (the prognostic version of
ALEXI, termed ALEX) uses a powerful and versatile parameterization for the photosynthetic
process that is based on principles of light-use efficiency (LUE; Anderson et al., 2000). LUE is
defined as the ratio of net CO2 assimilation to absorbed photosynthetically-active radiation
((APAR); Monteith 1977; Norman and Arkebauer 1991). In ALEX, LUE replaces the large
number of vegetation-dependent parameters required by many other models (e.g., SiB2; Sellers
et al. 1996). It allows an analytical solution for canopy resistance, including the effect of vapor
pressure deficit, significantly improving computational efficiency and reducing input data
requirements, without sacrificing generality. Since this LUE formulation deals with vegetation
variables scaled to the canopy level, it is also amenable to the input of remote sensing data,
inherently single-level information. Development and testing of the LUE parameterization is
progressing under existing NASA and NOAA support and will leverage the efforts in this
proposal.
Prognostic CIMSS model runs will be made at resolutions ranging between 10 and 100 km (a
typical range going from mesoscale model to climate model resolution) to evaluate the influence
of remote sensing data and up-scaling procedures. Fine-scale LES flux outputs will be used to
evaluate areal-averaged fluxes from the 10-km mesoscale model, and 10-km mesoscale model
outputs will be averaged to the coarser resolutions for similar evaluations of the those outputs.
The ALEXI (flux diagnosis model) and complementary flux disaggregation tools will be used to
validate fluxes produced by the CIMSS and LES models at all scales. Flux station data from the
field program, as well as other ground-based measurements (air temperature, moisture, etc.) will
also be used to validate the various forecasts. To isolate the effects of land-surface variables on
forecast quality (reduce potential errors from other model physical parameterizations), highresolution measured surface solar and longwave radiation streams (both from GOES satellite
data: Diak et al. 1996; Diak et al. 2000) will be used as boundary conditions (forcing) for model
integrations.
2.2.4
Scaling Investigations
Fluxes are non-linearly related to most LATS input variables so that using variables defined at
scales significantly larger than the patch-scale can introduce large errors in landscape-scale and
regional-scale flux calculations. The effects of surface heterogeneity on LATS predictions and
methods for representing subgrid scale heterogeneity are reviewed in detail by Giorgi and
Avissar (1997). While much of the findings are from model simulations, a review of
observational results from large scale field experiments suggests that relatively simple
aggregation procedures can be used in areas where heterogeneity is not “organized”; “organized”
heterogeneity is loosely defined as a region containing a patchwork of homogeneous land types
> 101 km (Giorgi and Avissar, 1997). However, the observational data typically do not contain
all the necessary information at the different scales to make any definitive conclusions as to the
factors/processes which permit such simple aggregation procedures to be applied without
resulting in large errors. Instead the development of aggregation/up-scaling procedures that
16
preserve energy conservation principles and are mathematically rigorous in scaling-up model
parameters and variables have been applied to artificial surfaces to assess up-scaling errors (see
Hu et al., 1999 for a review).
The experimental design being proposed will allow for a more thorough investigation into the
effects of subgrid/subpixel variability on LATs predictions and lead to a more fundamental
understanding of the relationships between scale of heterogeneity and the relative impact on
LATS predictions using aggregated input. The study area and surrounding region is primarily
row crops (corn and soybean) distributed in a patchwork pattern with fields > 1 hectare in size.
Thus the main length scale of heterogeneity is well defined by the field dimensions. With 20
tower flux stations, a high density of flux tower measurements will permit adequate coverage of
the variation in energy fluxes across the watershed and additionally be representative of the
region. The Lidar observations of atmospheric profiles will permit evaluating the effects of field
scale heterogeneity in roughness, fractional cover and energy flux partitioning on atmospheric
dynamics of the surface layer. The aircraft flux observations will assist in the evaluation of
aggregated flux estimation from LATS using input data at different pixel resolutions. The high
resolution remote sensing data will permit separating soil and vegetation components, thus
allowing for evaluating the effects of up-scaling important LATS inputs, such as fractional
vegetation cover, on LATS-derived fluxes. Although the microwave data will be at much
coarser resolution, the combination of these data with the optical will allow for additional
constraints to be imposed on LATS and LES model predictions by providing a means of
estimating the soil evaporation component.
Besides the variation in energy fluxes due to soil moisture/soil texture differences, the
phenological differences between the two main agricultural crops and differences in management
practices (i.e., conventional till, no till and ridge till) can result in significant variations in the
land-surface energy exchanges across the watershed. Due to partial canopy coverage and varying
crops and tillage practices, the interpretation of remotely-sensed data (including thermal data and
vegetation indices) and subsequent estimation of soil and vegetation energy exchanges will be
challenging (Norman et al., 1995). The magnitude of these variations are currently being
assessed with energy balance measurements that have been made over several growing seasons
at six locations over the watershed with varying crop (corn and soybean) and tillage history. Both
the nitrogen treatment and soil type are found to significantly influence water use and hence the
surface energy fluxes; differences in cumulative water use varied by as much as 400 mm by the
end of the growing season. Net carbon exchanges have been monitored since 1998 over both
corn and soybean.
In 2000 these measurements were expanded to include CO2 fluxes over two different nitrogen
management regimes in corn and included a soil respiration component for the purpose of being
able to relate the soil component as a source for canopy uptake. These measurements cover the
entire growing season in all years. Until 2000 the CO2 flux was estimated using Bowen ratio
systems and in 2000 the LI-7500 has been added for one site. For the soil component,
respiration estimates come from the LI-Cor 6200 units. Plans are to expand the effort and
include three more LI-7500 to match with the 3-D sonic anemometers for measuring CO2 flux
17
using eddy covariance and matching soil units for each site. These will be placed in different
soils within both corn and soybean for 2002.
18
3
SATELLITE OBSERVING SYSTEMS
3.1
Aqua Advanced Microwave Scanning Radiometer (AMSR-E)
Two versions of the AMSR instrument will be launched in 2002/2003 on the Aqua (AMSR-E)
(http://wwwghcc.msfc.nasa.gov/AMSR/)
and
ADEOS-II
platforms
(http://adeos2.hq.nasda.go.jp/default_e.htm). The NASA EOS Aqua platform (http://eospm.gsfc.nasa.gov/) was launched in May 2002. A picture of Aqua is shown on the cover of this
plan. If the AMSR-E implementation proceeds as scheduled, it is likely that the data will be
available for SMEX02. SMEX02 is designed to support AMSR related algorithm development and
validation; however, the experiment has a broad set of objectives.
AMSR is not the optimal solution to mapping soil moisture but it is the best possibility in the near
term. As shown in Table 1, the lowest frequency is 6.9 GHz (C band). The viewing angle will be
55o. Details on AMSR-E can be found at http://wwwghcc.msfc.nasa.gov/AMSR/. Based on the
results of SMMR and supporting theory (Wang, 1985, Ahmed, 1995, and Njoku and Li, 1999), we
anticipate that this instrument will be able to provide soil moisture information in regions of low
vegetation cover, less than 1 kg/m2 vegetation water content.
There are very few data sets that have been obtained that include the low frequencies of the AMSR
instruments, especially dual polarization at off nadir viewing angles. Early research efforts did
examine these frequencies in limited ground and aircraft experiments (Wang et al. 1983 and
Jackson et al. 1984). Several of these data sets can be found at the following web site
http://hydrolab.arsusda.gov/.
Table 1. AMSR-E Characteristics (Aqua)
Frequency
Polarization
Horizontal
(GHz)
Resolution (km)
6.925
V, H
75
10.65
V, H
48
18.7
V, H
27
23.8
V, H
31
36.5
V, H
14
89.0
V, H
6
Swath
(km)
1445
1445
1445
1445
1445
1445
Besides AMSR-E, Aqua includes several other instruments of potential value to investigators in
the 2002 experiment. The Atmospheric Infrared Sounder (AIRS) is a high-resolution instrument,
which measures upwelling infrared (IR) radiances at 2378 frequencies ranging from 3.74 and
15.4 micrometers.
The Advanced Microwave Sounding Unit (AMSU) is a passive scanning microwave radiometer
consisting of two sensor units, A1 and A2, with a total of 15 discrete channels operating over the
frequency range of 50 to 89 GHZ. The AMSU operates in conjunction with the AIRS and HSB
instruments to provide atmospheric temperature and water vapor data both in cloudy and cloud-free
areas.
19
Clouds and the Earth's Radiant Energy System (CERES) is a broadband scanning radiometer, with
three detector channels, 0.3 to 5.0 micrometers, 8.0 to 12.0 micrometers and 0.3 to 50 micrometers.
Two CERES instruments are in the Aqua payload suite. One instrument operates in the cross track
mode for complete spatial coverage from limb to limb; the other with a rotating scan plane (biaxial)
mode to provide angular sampling. Both instruments are capable of operating in either mode.
Humidity Sounder for Brazil (HSB) is a passive scanning microwave radiometer with a total of 5
discrete channels operating in the range of 150 to 183 GHz. The HSB data are used in conjunction
with the AIRS data to provide humidity profile corrections in the presence of clouds.
Moderate Resolution Imaging Spectroradiometer (MODIS) is a passive imaging spectroradiometer.
The instrument scans a cross-track swath of 2330 km using 36 discrete spectral bands between 0.41
and 14.2 micrometers.
It is likely that AMSR-E and HSB data will be collected during SMEX02. However, the other
instruments will be undergoing initialization shortly before and even during the experiment. This
makes the likelihood of data acquisition low.
3.2
Special Sensor Microwave Imager (SSM/I)
SSM/I satellites have been collecting global observations since 1987. The SSM/I satellite data can
only provide soil moisture under very restricted conditions because the frequencies (see Table 2)
were not selected for land applications (Jackson, 1997, Jackson et al. 2000, Teng et al. 1993). The
viewing angle of the SSM/I is 53.1o.
Frequency (GHz)
19.4
22.2
37.0
85.5
Table 2. SSM/I Characteristics
Polarization
Spatial Resolution (km)
H and V
69 x 43
V
60 x 40
H and V
37 x 28
H and V
15 x 13
Swath (km)
1200
1200
1200
1200
At the present time, there may be four satellites with the SSM/I on board in operation during
SMEX02. The ascending equatorial crossing times (UTC) of the three satellites are F13 (17:54),
F14 (20:46), and F15 (21:20). SSM/I data are useful in some aspects of algorithm development,
serving as a prototype of the data stream that AMSR will provide and providing a cross reference to
equivalent channels on the TMI and AMSR instruments. As part of SMEX02 an attempt will be
made to provide validation of exploratory soil wetness and temperature products. SSM/I data are
freely available to users through http://www.saa.noaa.gov/. As in past experiments, the data will be
subset and repackaged for this experiment.
3.3
European Radar Satellite (ERS-2)
ERS-2 includes a C-band Active Microwave Instrument (AMI) that can operate with two modes:
synthetic aperture radar (SAR) operating at VV polarization and wind scatterometer. The SAR
20
mode has a fixed incidence angle of 23o. Additional information on ERS-2 can be found at
http://earth1.esrin.esa.it/ERS/. It is anticipated that ERS-2 data will be acquired during the
experiment; however, individual research groups must obtain these data. Possible overpass dates
and times are listed in Table 3.
Table 3. ERS-2 SAR Coverage of the SMEX02
Area
Date
Time
Walnut Creek
5/31/02
16:59
Walnut Creek
7/0502
16:59
Walnut Creek
8/1002
16:59
The ERS AMI scatterometer mode operates also at C-band and VV polarization, with three
antennae generating radar beams looking 45o forward, sideways, and 45o backwards with respect
to the satellite's flight direction. These beams continuously illuminate a 500 km wide swath as
the satellite moves along its orbit. The wind scatterometer is originally designed for ocean
surface wind speed and direction retrieval, but there have been some applications on land surface
soil moisture retrieval and other studies related large scale monitoring recently.
Despite of the coarse spatial resolution (50 km) of the wind scatterometer, it has been
demonstrated that the spatial and temporal variability of a variety of geophysical parameters
could be measured and monitored over the land surfaces with it (Wen and Su, 2001). The
excellent calibration and maintenance of the instrument guarantee high quality data, which, for
the first time, allow a precise evaluation of the spatial and temporal variability of the NRCS over
the global land surface. The ERS wind scatterometer provides a near global coverage within 3 to
4 days and thus is well suitable for a wide range of operational monitoring tasks.
Currently two datasets are available. The Institute For Applied Remote Sensing of Germany
(IFARS) produced a database (CDROM) of Global C-Band Radar backscattering coefficient
with a spatial resolution of 50 km and three months in temporal resolution, while the monthly
land surface backscattering coefficient and its slope are also available for a single pixel. Raw
ERS scatterometer data are available via a FTP server one month after their acquisition at the
receiving stations.
3.4
Envisat Advanced Synthetic Aperture Radar (ASAR)
The Envisat satellite was launched by the European Space Agency in March 2002
(http://envisat.esa.int/). It is designed to provide Earth observations using a suite of remote sensing
instruments. Of particular interest to soil moisture and hydrology is the inclusion of the Advanced
Synthetic Aperture Radar (ASAR) that will provide both continuity to the ERS-1 and ERS-2
mission SARs and next generation capabilities.
Envisat will be in a sun synchronous polar orbit with a descending node mean local solar time of
10:00 am. The repeat cycle is 35 days.
21
The ASAR will be a C band instrument with a wide variety of observing modes. It is the alternating
polarization mode that is of greatest interest to soil moisture. In this mode two polarization
combinations can be obtained (i.e. HH and VV). It is anticipated that this additional information will
enhance soil moisture retrieval. Swath width is 100 km and the pixel size is 30 m.
Data takes must be scheduled and are limited to approved investigations. If possible, requests will
be made for coverage over Iowa during SMEX02. Projected dates of coverage are listed in Table 4
for narrow beam coverage. The satellite was launched in March 2002 and the instruments are
scheduled to be in the commissioning phase during SMEX02. Although we have requested data, it
is unlikely any will be provided.
Table 4. Envisat ASAR Narrow Swath Coverage of SMEX02
Area
Date
Time
All
6/29/02
16:19
WC
7/02/02
16:25
All
7/0302
03:42
All
7/06/02
03:48
All
7/15/02
16:16
3.5
Radarsat
Radarsat is operated by the Canadian Space Agency. It is a C band SAR with HH polarization. It
is in a sun-synchronous orbit at an altitude of 798 kilometers above the Earth, at an inclination of
98.6 degrees to the equatorial plane. The sun-synchronous orbit also means that the satellite
overpasses are always at the same local mean time. As opposed to the other radar satellites,
Radarsat can provide a variety of beam selections. It has the ability to shape and steer its beam
from an incidence angle of less than 20 degrees to more than 50 degrees, in swaths of 35 to 500
kilometers, using resolutions ranging from 10 to 100 meters. Coverage will be requested,
however, the type and availability have not been determined yet.
3.6
Terra Sensors
The NASA Terra spacecraft (http://terra.nasa.gov/About/) includes several instruments of value to
the soil moisture investigations proposed here. Of particular interest are the Moderate-resolution
Imaging Spectroradiometer (MODIS) and Advanced Spaceborne Thermal Emission and Reflection
Radiometer (ASTER).
MODIS can view the entire surface of the Earth every 1-2 days. MODIS is a whisk broom scanning
imaging radiometer consisting of across-track scan mirror, collecting optics, and a set of linear
arrays with spectral interference filters located in four focal planes. MODIS has a viewing swath
width of 2330 km (the field of view sweeps ± 55° cross-track) and will provide high-radiometric
resolution images of daylight-reflected solar radiation and day/night thermal emissions over all
regions of the globe. Its spatial resolution ranges from 250 m to 1 km at nadir, and the broad
spectral coverage of the instrument (0.4 - 14.4 µm) is divided into 36 bands of various bandwidths
optimized for imaging specific surface and atmospheric features. The observational requirements
22
also lead to a need for very high radiometric sensitivity, precise spectral band and geometric
registration, and high calibration accuracy and precision. Coverage time is about 16:15 UTC. Dates
are summarized in Table 5.
ASTER can obtain high-resolution (15 to 90 m) images of the Earth in the visible, near-infrared
(VNIR), shortwave-infrared (SWIR), and thermal-infrared (TIR) regions of the spectrum. ASTER
consists of three distinct telescope subsystems: VNIR, SWIR, and TIR. It is a high spatial, spectral,
and radiometric resolution, 14-band imaging radiometer. Spectral separation is accomplished
through discrete bandpass filters and dichroics. Each subsystem operates in a different spectral
region and has its own telescope. Unlike the other instruments aboard Terra, ASTER does not
collect data continuously; rather, it will collect an average of 8 minutes of data per orbit. ASTER
data takes must be requested and potential coverage is limited by the satellite track and repeat.
Date
June 21
June 23
June 28
June 30
3.7
Table 5. Terra MODIS Coverage for SMEX02
Track
Frame
Date
Track
20
31
July 7
20
18
31
July 9
18
21
31
July 14
21
19
31
July 16
19
Frame
31
31
31
31
Landsat Thematic Mapper
The Landsat Thematic Mapper (TM) satellites collect data in the visible and infrared regions of the
electromagnetic spectrum. Data are high resolution (30 m) and are very valuable in land cover and
vegetation parameter mapping. Band 8 (panchromatic) for Landsat 7 has a 10 m resolution.
Additional details on the Landsat program and data can be found at
http://geo.arc.nasa.gov/sge/landsat/landsat.html.
The Iowa site is located on both path 26 and 27 row 31. For path 27 the northern portion is not well
covered, however, the Walnut Creek area is included. It may be necessary to acquire row 30 for
complete coverage. At the present time coverage by both the Landsat 5 and 7 satellites results in
frequent temporal coverage. Coverage dates are listed in Table 6 and shown in Figure 2.
Date
May 13
May 14
May 21
May 22
May 29
May 30
June 6
June 7
June 14
Table 6. Landsat TM Coverage for SMEX02
Landsat No.
Path Date
Landsat No.
5
27
June 15
7
7
26
June 22
7
7
27
June 23
5
5
26
June 30
5
5
27
July 1
7
7
26
July 8
7
7
27
July 9
5
5
26
July 16
5
5
27
July 17
7
Path
26
27
26
27
26
27
26
27
26
23
Figure 2. Map showing the SMEX02 region and Landsat TM frame coverage. Blue lines are
Landsat scenes, gray area is the SMEX02 region and black lines are Ease Grids. The yellow box is
50 km by 50 km and is used to represent scale.
24
3.8
Advanced Very High Resolution Radiometer (AVHRR)
This is a TIROS-N series satellite designed to operate in a near-polar, sun-synchronous orbit.
During SMEX02 it is anticipated that 2 satellites may be providing AVHRR data Currently these
NOAA 15 (morning coverage) and NOAA 16 (afternoon coverage). The AVHRR sensor collects
data in the visible and infrared regions of the electromagnetic spectrum (see Table 7) and has a
spatial resolution of approximately 1 km. Additional information on these data can be found at
http://www.saa.noaa.gov/.
Data can be acquired in three formats from the satellite. High Resolution Picture Transmission
(HRPT) data are full resolution image data transmitted to a ground station as they are collected.
The average instantaneous field-of-view of 1.4 milliradians yields a HRPT ground resolution of
approximately 1.1 km at the satellite nadir from the nominal orbit altitude of 833 km (517 mi).
Local Area Coverage (LAC) are full resolution data that are recorded on an onboard tape
recorder for subsequent transmission during a station overpass. Resolution is the same as HRPT.
Global Area Coverage (GAC) data are derived from a sample averaging of the full resolution
AVHRR data. Four out of every five samples along the scan line are used to compute one average
value and the data from only every third scan line are processed, which results in a 1.1 km by 4 km
resolution
Table 7. AVHRR Bands
Band No.
Wavelengths
(micrometers)
1
0.58 - 0.68
2
0.725 - 1.10
3
3.55 - 3.93
4
10.3 - 11.3
5
11.5 - 12.5
3.9
Geostationary Operational Environmental Satellites (GOES)
GOES satellites provide continuous monitoring in selected visible and infrared electromagnetric
channels. Coverage of Iowa is currently provided by GOES-8. Of particular interest to this project is
the imager. This is a multichannel instrument (see Table 8) that senses radiant energy and reflected
solar energy from the Earth's surface and atmosphere. The resolution is 1 km for the visible and 4
km for the infrared channels. Additional information can be found at http://www.saa.noaa.gov/.
Table 8. GOES Imager Bands
Band No.
Wavelengths
(micrometers)
1
0.65
2
3.9
3
6.7
4
11
5
12
25
3.10
SeaWinds QuikSCAT
SeaWinds is a radar scatterometer on QuikSCAT. It was launched in 1999 and was designed to
measure near-surface wind speed over the Earth's oceans. A second instrument is scheduled to be
launched on ADEOS-2 in November of 2002, and is expected to be operational by mid-2003.
SeaWinds uses a rotating dish antenna with two spot beams that sweep in a circular pattern. The
antenna spins at a rate of 18 rpm, scanning two pencil-beam footprint paths at incidence angles
of 46o (H-pol) and 54o (V-pol). The antenna radiates microwave pulses at a frequency of 13.4
gigahertz across broad regions on Earth's surface with an 1800 km swath. QuikSCAT sampling at
the latitude of Iowa is approximately two times daily; and ADEOS-2 is anticipated to have similar
coverage.
QuikSCAT is in a sun-synchronous, 803-kilometer, circular orbit with a local equator crossing time
at the ascending node of 6:00 A.M. +/- 30 minutes. The SeaWinds antenna footprint is an ellipse
approximately 25-km in azimuth by 37-km in the look (or range) direction. There is
considerably overlap of these footprints, with approximately 8-20 of these ellipses with centers
in a 25x25 km box on the surface. Signal processing provides commandable variable range
resolution of approximately 2- to 10-km. The nominal resolution is approximately 6 km—an
effective range gate of 0.5 msec.
Additional information is available at
http://podaac.jpl.nasa.gov/quikscat/qscat_doc.html.
Gridded (0.2x0.2 degree) daily observations are available through BYU. Documentation is available
at http://podaac.jpl.nasa.gov:2031/DATASET_DOCS/dLongSigBrw.html, and data orders can be
placed through linked pages. Non-binned data are available, on tapes and through FTP, from the
PO.DAAC (http://podaac.jpl.nasa.gov/quikscat/qscat_data.html).
26
4
AIRCRAFT REMOTE SENSING INSTRUMENTS
Aircraft remote sensing will include visible, infrared, and microwave instruments. Visible and
infrared measurements will be provided by the Utah State University aircraft over the watershed
area. A total of four different aircraft microwave instruments may contribute to SMEX02. Two of
these instruments are very important to the broad objectives of the experiment: PALS and PSR.
The inclusion of the new two dimensional synthetic aperture radiometer is also a very high priority.
Descriptions of these instruments are provided in the following sections.
4.1
Polarimetric Scanning Radiometer (PSR)
The PSR is an airborne microwave imaging radiometer operated by the NOAA Environmental
Technology Laboratory (Piepmeier and Gasiewski 2001) for the purpose of obtaining polarimetric
microwave emission. It has been successfully used in several major experiments including SGP99
(Jackson et al. 2002).
A typical PSR aircraft installation is comprised of four primary components: 1) scanhead, 2)
positioner, 3) data acquisition system, and 4) software for instrument control and operation. The
scanhead houses the PSR radiometers, antennas, video and IR sensors, A/D sampling system,
and associated supporting electronics. The scanhead can be rotated in azimuth and elevation to
any arbitrary angle. It can be programmed to scan in one of several modes, including conical,
cross-track, along-track, and spotlight. The positioner supports the scanhead and provides
mechanical actuation, including views of ambient and hot calibration targets. The PSR data
acquisition system consists of a network of four computers that record several asynchronouslysampled data streams, including navigation data, aircraft attitude, scanhead position, radiometric
voltage, and calibration target temperatures. These streams are available in-flight for quick-look
processing.
During SMEX02, the PSR/CX scanhead will be integrated onto the NASA WFF P-3B aircraft in
the aft portion of the bomb bay. The PSR/CX scanhead is an upgraded version of the previously
successful PSR/C scanhead used during SGP99 (Figure 3 and Table 9). The installation will
utilize the NOAA P-3 bomb bay fairing, and will locate the PSR immediately aft of the NASA
GSFC ESTAR L-band radiometer. The upload will commence at NASA WFF as soon as
possible after the P3-B returns from the mission scheduled before SMEX02.
The PSR/CX scanhead will have the polarimetric channels listed in Table 8 for SMEX02. The
system will be operated in two imaging modes, both using conical scanning. Mapping
characteristics are described in Table 10. Figure 4 shows the results of one day of mapping
brightness temperature using the PSR in SGP99.
At the end of a each set of flight lines a steep (~60 degree) port roll will be requested for the
purpose of calibrating the PSR radiometers using cold sky looks. Additional details on the PSR
not presented here can be found at http://www1.etl.noaa.gov/radiom/psr/.
27
Figure 3. PSR/C scanhead installed on the NASA P3-B aircraft during the SGP99 experiment.
Table 9. PSR/CX Channels for SMEX02
Frequency (GHz)
Polarizations
5.82-6.15
V,h
6.32-6.65
V,h
6.75-7.10 *
v,h,U,V
7.15-7.50
V,h
10.6-10.8 *
v,h,U,V
10.68-10.70 *
V,h
9.6-11.5 um IR
V+h
* Indicates close to an AMSR-E channel.
Beamwidth
10o
10o
10o
10o
7o
7o
7o
28
Table 10. PSR Flightline and Mapping Specifications for SMEX02
Wide Area Imaging
High-Resolution
Imaging
Location
Iowa Region
Walnut Creek
Watershed Site
Altitude (AGL) in m
7300
1800
Number of parallel flight lines
4
4
Flight line length (km)
150
50
Flight line spacing (km)
19
4.75
Scan period (seconds)
8
3
Incidence angle (deg)
55
55
3-dB footprint resolution
3.0 km at 6 GHz
750 m at 6 GHz
2.0 km at 10 GHz
500 m at 10 GHz
Sampling
Oversampling above
Nyquist
Nyquist
Figure 4. SGP99 PSR/C conically-scanned brightness temperature imagery 7.325 GHz channel, Hpolarization, North looking.
29
4.2
Passive and Active L and S Band Microwave Instrument (PALS)
In order to evaluate the potential of alternative approaches to soil moisture retrieval, a new L and S
band
integrated
passive/active
instrument
has
been
developed
(http://eis.jpl.nasa.gov/msh/mission+exp/pals.html) (Figure 5). PALS provides single beam
observations at L and S bands, dual polarized, passive and active simultaneously (radar is
polarimetric). The incidence angle is selectable between 30 and 50 degrees. Additional details are
described in Table 11. This instrument offers many interesting opportunities for algorithm
development and evaluation that have not been available; dual polarization, off nadir viewing
typical of conical scanning systems, multifrequency, and both active and passive observations.
From these observations we hope to obtain a better understanding of the frequency and polarization
characteristics of land surfaces in the L to C-band range, leading to potential improvements in future
spaceborne system designs and retrieval algorithms. Additional details on PALS can be found in
Wilson et al. (2001). The PALS instrument was flown successfully in SGP99. Figure 6 shows a
map product data set generated from the PALS data. PALS will be flown at low altitudes in
SMEX02 over the Walnut Creek watershed flightlines.
Table 11.
Parameter
Frequencies
Polarization
Sensitivity
Incidence angle
Spatial resolution
(@ 1000 m alt)
Description of the JPL PALS Instrument
Radiometer
Radar
1.41 and 2.69 GHz
1.26 and 3.15 GHz
V and H
VV, VH, HH
0.2 K
0.2 dB
30 to 50 deg (preselected) 30 to 50 deg (preselected)
400 m
400 m
Figure 5. PALS antennas installed on the NCAR C-130 during SGP99.
30
TB LH
34.95
34.91
34.91
34.95
Latitude
Sigma0 LVV
34.95
-98.30
-98.20
-98.10
-98.00 34.95
34.91
34.95
-98.30
-98.20
-98.10
-98.00 34.95
-98.20
-98.10
-98.00
-98.30
-98.20
-98.10
-98.00
-98.30
-98.20
-98.10
-98.00
-98.30
-98.20
-98.10
-98.00
-98.30
-98.20
-98.10
-98.00
July 11
July 12
34.91
-98.30
-98.20
-98.10
-98.00
34.95
July 13
34.91
34.91
34.95
-98.30
34.91
34.91
34.95
July 09
-98.30
-98.20
-98.10
-98.00
34.91
34.95
July 14
34.91
-98.30
-98.20
-98.10
-98.00
Longitude
210 220 230 240 250 260 270 280 290
Kelvin
-23
-21
-19
-17
-15
-13
dB
Figure 6. PALS brightness temperature and radar backscatter image products from SGP99.
4.3
Electronically Scanned Thinned Aperture Radiometer (ESTAR)
ESTAR is a synthetic aperture, passive microwave radiometer operating at a center frequency of
1.413 GHz and a bandwidth of 20 MHz. As installed in the SMEX02 mission it is horizontally
polarized.
Aperture synthesis is an interferometric technique in which the product (complex correlation) of
the output voltage from pairs of antennas is measured at many different baselines. Each baseline
produces a sample point in the Fourier transform of the scene, and a map of the scene is obtained
after all measurements have been made by inverting the transform. ESTAR is a hybrid real and
synthetic aperture radiometer that uses real antennas (stick antennas) to obtain resolution alongtrack and aperture synthesis (between pairs of sticks) to obtain resolution across-track (Le Vine
et al., 1994). This hybrid configuration could be implemented on a spaceborne platform.
The effective swath created in the ESTAR image reconstruction (essentially an inverse Fourier
transformation) is about 45o wide at the half power points. The field of view is restricted to 45o
to avoid distortion of the beam but could be extended to wider angles if necessary. The image
reconstruction algorithm in effect scans this beam across the field of view in 2o steps. The beam
width of each step varies depending on look angle from 8 to 10o, therefore, the individual
original data are not independent, since each data point overlaps its neighbors. Contiguous beam
positions can be achieved by averaging the response of several of these data points. This results
in approximately nine independent beam positions. For this experiment the swath will be
restricted to approximately 35o. Another approach to using the data, especially in a mapping
mode, is to interpret each of the original nonindependent observations as a sample point and then
use a grid overlay to average the data. The final product of the ESTAR is a time referenced series
of data consisting of the set of beam position brightness temperatures at 0.25 second intervals.
31
Calibration of the ESTAR is achieved by viewing two scenes of known brightness temperature.
By plotting the measured response against the theoretical response, a linear regression is
developed that corrects for gain and bias. Scenes used for calibration include black body, sky,
and water. During aircraft missions, a black body is measured before and after the flight and a
water target during the flight. Water temperature is determined using a thermal infrared sensor.
The match in level and pattern is quite good and in general the ESTAR calibration should be
considered accurate and reliable. For interpretation purposes it should be noted that the
sensitivity of soil moisture to brightness temperature is 1% for 3oK.
ESTAR has demonstrated the potential of L band radiometry and STAR technology (Levine et
al. 1994 and 2001, Jackson et al., 1995 and 1999). Figure 7 is an example of the type of product
produced after processing the ESTAR data. Details on ESTAR and soil moisture products can be
found
at
the
following
web
site
http://daac.gsfc.nasa.gov/CAMPAIGN_DOCS/SGP97/estar.html. Including ESTAR in SMEX02
is important because it will extend the range of vegetation types that the instrument has been
applied to and it will complement the AIRSAR for data fusion and integrated algorithm studies.
Figure 7. ESTAR brightness temperature image from the SGP97 experiment (Jackson et al.
1999).
32
4.4
Airborne Synthetic Aperture Radar (AIRSAR)
AIRSAR is a side-looking radar instrument developed by the Jet Propulsion Lab
http://airsar.jpl.nasa.gov/. It has several operating modes. In SMEX02 the polarimetric
(POLSAR) will be used. In POLSAR mode, fully polarimetric data are acquired at all three
frequencies C-, L-, and P-band. Fully polarimetric means that radar waves are alternatively
transmitted in horizontal (H) and vertical (V) polarization, while every pulse is received in both
H and V polarizations. Therefore, there are four combinations; HH, VV, HV and VH. Basic
parameters of the AIRSAR are listed in Table 12. It is anticipated that the 20 MHz bandwidth
data will be requested.
Table 12. AIRSAR Parameters
Channel
C
Frequency GHz
5.29875
Pixel Spacing 20 MHz Bandwidth (m)
Swath Width 20 MHz Bandwidth m
Pixel Spacing 40 MHz Bandwidth (m)
Swath Width 40 MHz Bandwidth (m)
4.5
L
1.2375
10 x 10
15
5x5
10
P
0.4275
Global Positioning System (GPS) Reflectivity Technique
The GPS satellite constellation currently broadcasts a civilian-use carrier signal at 1575.42 MHz,
which is bi-phase modulated by satellite-specific pseudorandom noise codes. The signals are
encoded with timing and navigation information so that the receiver can calculate the positions
of the transmitting satellites and solve for its own position and time by measurement of
pseudoranges from at least four satellites. These direct signals (Figure 8) are normally received
by a low-gain, hemispherical, zenith antenna. These same GPS signal transmissions also reflect
off of the Earth's surface and can be measured with a nadir-viewing antenna at longer delays than
the direct signal. The reflected signal is modified by the roughness and dielectric properties of
the scattering surface. If the roughness is known a priori or is assumed constant over some time,
the ratio of reflected signal power to the direct signal power is an indicator of the dielectric
constant of the surface. Therefore, this ratio can be used to temporally sense changes in soil
moisture in the top 5 cm of the surface. Additionally, the polarization of the RHCP direct signal
is predominantly LHCP upon surface reflection for most incidence angles. Because the
geometry is variable depending upon the slowly changing transmitter and receiver positions, a
hemispherical nadir antenna has been used in past ocean reflection research. In SMEX02, a
higher gain nadir antenna is anticipated to achieve a better SNR at the expense of tracking
multiple satellites.
The received signals are cross-correlated with a replica signal (1 ms code length) to produce a
narrower, approximate 1 µs correlation pulse. This procedure is similar in design to pulse
compression radar receivers. Our previous efforts have been focused on the distribution or
spreading of the reflected signal power over time delay, which is an indicator of the roughness of
the reflecting surface. For soil moisture sensing, the observable is the ratio of the magnitudes of
the reflected and direct signal powers.
33
GPS Transmitters
24 sats
L1: 1.5, L2: 1.2 GHz
PRN coding
Dire
ct Sig
RCP
nal
Re
fl e
cte
dS
ign
LC
P
al
GPS Receiver
Zenith & nadir antennas
h≈
δ
2 sin(θ )
Range cells
Rou
glis gh su
r
ten
ing face
zon
e
θ
Figure 8. Bistatic radar configuration of reflected GPS signals.
In bistatic radar systems, scattering is mainly forward, and the radar cross section is expressed as
a normalized bistatic cross section. For the specific case of an aircraft receiving direct and landreflected GPS signals, we use an analytical scattering model developed by Zavorotny and
Voronovich (Z-V model) (2000). The model is based upon physical optics and will employ a
rough surface estimated from the SMEX02 terrain.
The current GPS bistatic radar receiver is based upon a modified Plessey 12-channel C/A code
receiver built by NASA Langley Research Center. New receivers are currently being developed
for GPS bistatic radar applications, and their use in SMEX02 is possible. The Plessey receiver is
comprised of a single board containing two RF front-ends and a correlator, which is connected to
a PC-104 computer in a small, lightweight chassis (20x15x15 cm). The RF front-ends perform
automatic gain control, down conversion, and IF sampling. The PC-104 computer serves as the
controller and data logger for both GPS navigation functions and recording the signal power.
The GPSBuilder-2 software allows access to the correlator power measurements.
In the Delay Mapping Receiver (DMR) mode of operation, five channels track direct signals in a
conventional, closed-loop fashion. The pseudorange and Doppler measurements made by these
channels are used to form navigation solutions. The other 14 correlators (two for each of seven
channels) are controlled in an open-loop mode to measure reflected signal power at specific
delays relative to one or more of the direct signal channels. For each of the slaved reflection
correlators, one hundred 1 ms correlator samples are averaged to produce an estimate of reflected
signal power at a rate of 10 Hz. The reflected signal power is sampled in discrete bins around
34
the time delay corresponding to the arrival of the signal from the specular reflection point. The
direct and reflected signal power measurements are stored on internal disk for later analysis. In
the DMR mode of operation, the bistatic radar receiver can operate for long periods without user
intervention.
In SMEX02, the GPS bistatic radar receiver will operate as a proof-of-concept technology
onboard the NCAR C-130 aircraft. The GPS-based measurements will be evaluated in
conjunction with the observations made by the JPL PALS instrument and the ground sampling of
soil moisture. The GPS bistatic radar measurement parameters are presented in Table 13. These
tests will guide future development of optimized receivers and processing algorithms to retrieve
soil moisture and surface roughness information from GPS bistatic radar measurements.
Table 13. GPS Bistatic Radar Parameters
Parameter
GPS Bistatic Radar
Frequency
1.56 GHz
Polarization
LHCP
Incidence Angle
0-60 deg (predicted by satellite geometry)
Spatial Resolution
Variable w/ height & angle (first Fresnel zone)
The GPS instrument research will be conducted by the Colorado Center for Astrodynamics
Research at the University of Colorado at Boulder. For more information on GPS remote
sensing using land and ocean reflections, visit http://ccar.colorado.edu/~dmr.
4.6
Utah State University Visible and Infrared Airborne System
The Remote Sensing Services Laboratory at Utah State University (USU) will support the
experiment with a series of over flights using its airborne system of short wave and long wave
imagers mounted in a light twin-engine Piper Seneca II. The system consists of three Kodak
Megaplus 4.2i digital cameras, with interference filters forming narrow spectral bands centered
in the green (0.55 µm), red (0.67 µm) and near-infrared (0.80 µm) portions of the
electromagnetic spectrum. These filters are interchangeable so different bandwidths could be
used for this experiment if desired by the research community involved. An onboard GPS
system is used to navigate along pre-planned flight lines over the site, as well as to geo-reference
the approximate center of each set of digital images. The cameras are mounted inside a
specially designed graphite composite cylinder with adjustable aluminum mounts that are
installed through a hole in the belly of a Piper Seneca II twin-engine aircraft. The adjustable
mounts allow for the alignment of the cameras, which are usually set to view a very distant
target.
The system also supports an Inframetrics 760 thermal infrared scanner that is mounted through a
separate porthole for the acquisition of thermal infrared imagery in the 8 - 12 µm range. This
imagery is stored on S-VHS videotape and later frame-grabbed in the laboratory. A color video
camera is used to acquire color imagery of the flight. GPS position information is encoded on the
bottom of this imagery, which are also stored on videotape.
35
5
REMOTE SENSING AIRCRAFT MISSION DESIGN
The PSR and ESTAR instruments will be installed on the NASA WFC P3-B aircraft. PALS will be
installed on a C-130 aircraft operated by NCAR (http://raf.atd.ucar.edu/). A GPS instrument will be
part of the C-130 instrumentation and there may also be one on the P3-B. AIRSAR operates from
the DC-8.
An aircraft briefing will be conducted each night at the hotel selected in Des Moines (Embassy
Suites). Arrangements will be made for a phone link to a single location in Ames to allow
communication with any aircraft operations based there.
As in previous missions, the goals of the experiment design are to collect data for both algorithm
development/verification and soil moisture mapping. The extent and scale of the mapping must
satisfy the range of objectives of the land-atmosphere and AMSR components of SMEX02. Unlike
recent experiments, low altitude flightlines will be emphasized. The following sections describe the
flight missions of the five aircraft that will take part in SMEX02.
5.1
NCAR C-130
This aircraft is operated by the National Center for Atmospheric Research (NCAR). Details on the
aircraft can be found at http://raf.atd.ucar.edu/. It will have the PALS and GPS sensor systems on
board. In addition, there is meteorological instrumentation available on the aircraft that may be of
value.
The primary mission of the C-130 is to fly low altitude flightlines over the Walnut Creek Watershed
area with the PALS instrument. The mission design is similar to SGP99. It will utilize a series of
basically East-West lines. These are 800 m apart and offset from the road network by 400 m. Roads
are approximately on a square 1600 m grid. With a nominal field size of 800 m and sensor footprint
size of 400 m, this procedure provides a reliable sample for the study sites and allows interpolation
for mapping.
The flightlines are listed in Table 14 and shown in Figure 9. In past experiments, these types of
lines have been flown sequentially in alternating East-West directions. However, the investigators
may encounter local RFI and may need to alter the sequencing or design. In SGP99, the RFI was
directional and all lines had to be flown in a single direction. The major impact of this type of
change is on flight hours. If longer individual flights are required it may be necessary to reduce the
number of flights.
Flights will be conducted in the morning. Each flight will be approximately 2.5 hours in duration. It
is anticipated that nine flights will be conducted. The aircraft is expected to arrive in Des Moines on
Thurs. June 20 and be based out of DMS airport. It is scheduled to depart on July 8.
36
Figure 9. SMEX02 Walnut Creek watershed area and microwave aircraft flightlines. Blue lines
are the low altitude microwave flightlines; yellow squares indicate intensive soil moisture
sampling sites (those with red text are also flux tower sites). The large yellow square is 2 km by
2 km and is used to illustrate scale.
Line Altitude
No.
(km)
1
1
2
1
3
1
4
1
5
1
6
1
7
1
8
1
9
1
10
1
5.2
Table 14. SMEX02 C-130 Flightlines
Length Description
Start Lat. Start Lon.
(km)
33
C-130
41.925
-93.4
33
C-130
41.932
-93.8
33
C-130
41.940
-93.4
33
C-130
41.947
-93.8
33
C-130
41.954
-93.4
33
C-130
41.962
-93.8
33
C-130
41.969
-93.4
33
C-130
41.976
-93.8
33
C-130
41.983
-93.4
33
C-130
41.991
-93.8
Stop Lat.
41.925
41.932
41.940
41.947
41.954
41.962
41.969
41.976
41.983
41.991
Stop Lon.
-93.8
-93.4
-93.8
-93.4
-93.8
-93.4
-93.8
-93.4
-93.8
-93.4
NASA P-3B
The P-3B operates from the NASA Wallops Flight Facility in Wallops Island, VA. Details on the
aircraft can be found at http://www.wff.nasa.gov/. It will have the PSR and ESTAR sensor systems
on board. There may also be a GPS sensor system installed during SMEX02.
The primary mission of the P3-B is to collect both low and high altitude data over the Iowa study
region with the PSR instrument. High altitude lines are more important than the low altitude lines.
37
Another very important objective is to collect data with the ESTAR over the region. Mission design
and operations are dependent on the PSR and not the ESTAR.
Flightlines are listed in Table 15 and plotted in Figure 10. The low altitude and water calibration
lines correspond to those listed for the C-130. High altitude lines will provide coverage of an area
that is approximately 40 km wide (East-West) and 95 km long (North-South).
Line Altitude
No.
(km)
3
1.5
7
1.5
Table 15. SMEX02 P-3B Flightlines
Length Description
Start Lat. Start Lon.
(km)
33
P3-B
41.940
-93.4
33
P3-B
41.969
-93.4
11
12
13
14
8.5
8.5
8.5
8.5
95
95
95
95
P3-B
P3-B
P3-B
P3-B
15
0.5
12
Water Calibration
Stop Lat.
Stop Lon.
41.940
41.969
-93.8
-93.8
41.75
42.7
41.75
42.7
-93.7
-93.567
-93.433
-93.3
42.7
41.75
42.7
41.75
-93.7
-93.567
-93.433
-93.30
41.6756
-93.6648
41.7813
-93.7279
Flights will be conducted during the mid day in order to match the nominal Aqua overpass time of
1330. Each flight will be approximately 2.5 hours in duration. It is anticipated that eleven high
altitude flights will be conducted. The aircraft is expected to arrive in Des Moines on Mon. June 24
and be based out of DMS airport. It is scheduled to depart on July 12.
5.3
NASA DC-8
The AIRSAR instrument will be flown on NASA's Douglas DC-8. This is a four jet engine
aircraft
operated
out
of
the
Dryden
Flight
Center
in
California
http://www.dfrc.nasa.gov/airsci/dc-8/dc8page.html. AIRSAR flights for SMEX02 will be flown
at an altitude of 8 km.
The primary mission of the DC-8 is to collect AIRSAR data close in time with passive
microwave measurements made by instruments on the C-130 and P-3B aircraft. The objectives
of including AIRSAR in SMEX02 are:
•
•
•
Collect AIRSAR and passive microwave observations (PALS, ESTAR, and PSR) at
different spatial resolutions that will allow the validation of passive and active data fusion
concepts.
Collect AIRSAR observations concurrent with high quality ground observations over a
range of soil moisture and vegetation conditions that will allow the extension and validation
of the current radar soil moisture algorithms to vegetated surfaces with higher biomass
levels.
Obtain AIRSAR observations concurrent with Envisat ASAR, ERS-2 and Radarsat
measurements to evaluate the quality and value of these sensors in soil moisture applications
38
Figure 10. SMEX02 regional flightlines and mapping area. Regional mapping flightlines are
indicated in black, regional soil moisture sites are shown as a red X. The yellow box is 10 km by 10
km and is used to illustrate scale.
39
In order to accomplish these objectives, we have prepared a flight with the following key
features:
•
•
•
•
•
Flights between July 1 and July 8, 2002 in order to maximize overlap with PALS on the
C-130. It is also anticipated that we will be able to obtain data to capture several wet and dry
conditions for corn and soybeans at different stages of growth. It is anticipated that there will
be four flight dates. The arrival and departure dates can also be science data collection dates.
Flights will be concentrated over the Walnut Creek Watershed, an area 10 km North-South
and 40 km East-West where PALS and intensive ground sampling will be conducted. Swath
coverage areas are summarized in Table 16 and illustrated in Figure 11. Multiple flightlines
are desired in order to produce a composite image of the watershed with a nominal incidence
angle range close to 40 degrees (the PALS and proposed HYDROS incidence angle). This
will facilitate the disaggregation studies and use with PALS. In addition, this design will
result in multiple incidence angle observations over the test sites, which will allow the
exploration of new algorithm concepts. Note that the flightlines are defined by swath
corners.
Regional coverage data sets will be collected for extrapolation of the watershed results to
larger scales typical of satellite radiometer footprints. Swath coverage areas are summarized
in Table 16 and illustrated in Figure 11.
If Envisat data can be scheduled, concurrent flights will be made to acquire Envisat ASAR
data during SMEX02.
Flights will be conducted between 1100 and 0100 local time. It is possible that the radar
could contaminate the passive measurements being made on the C-130 and the P-3.
Therefore, the aircraft will be scheduled to reduce this possibility.
Table 16. NASA DC-8 AIRSAR Flightline Parameters
Swath Corners (Degrees)
Flight
Flight Altitude
Heading Length Latitude Latitude Latitude Latitude
line
(km)
(Deg.)
(km) Longitude Longitude Longitude Longitude
1
8
180
110
41.74823 41.74823 42.7026 42.7026
-93.5634 -93.3082 -93.5653 -93.3063
2
8
360
110
41.74823 41.74823 42.7026 42.7026
-93.7962 -93.5411 -93.7981 -93.5391
3
8
90
60
41.89276 42.08381 41.89276 42.08381
-93.2379 -93.2379 -93.962 -93.962
4
8
90
60
41.91476 42.1058 41.91476 42.1058
-93.2365 -93.2365 -93.9634 -93.9634
5
8
270
60
42.00123 41.81018 42.00123 41.81018
-93.9647 -93.9647 -93.2354 -93.2354
6
8
270
60
42.02324 41.83219 42.02324 41.83219
-93.966 -93.966 -93.2341 -93.2341
40
Figure 11. SMEX02 AIRSAR coverage areas. Regional study area is outlined as red/black. The
watershed area will be covered by four swaths. A larger region will also be mapped by two NorthSouth swaths.
41
5.4
Canadian Twin Otter
The Twin-Otter aircraft (Figure 12) operated by personnel from the National Research Council
of Canada (MacPherson et al., 2001) will be available for making aircraft-based flux
observations over several transects surrounding the watershed. Surface layer flux measurements
(~30 m agl) will be conducted using an east-west transect starting west of the watershed and
ending west of the interstate (Figure 13 and Table 17). The frequency and timing of the fluxaircraft observations will be subject to the flying schedule for the microwave observations.
Ideally, flights in the mid-morning (~1030 local time), around the time of EOS Terra and
Landsat 7 overpasses, would be the most useful. Mid-morning is also the typical time for the
ALEXI output and when the frequency of clouds via boundary layer convective activity is minor.
With length of the flight transects ~ 20 km and flying at ~30 m agl, transect-average fluxes
represent aggregated values of length scales ~ 10 km. However, sub-sampling the transects for
comparisons with tower-based observations has also been successful.
Figure 12. Twin Otter atmospheric research aircraft configured for flux studies.
42
Figure 13. Twin Otter flightlines for SMEX02. Aircraft flux lines are in blue with letter codes. The
yellow squares indicate sites with flux towers. The large yellow square is 2 km by 2 km and is used
to illustrate scale.
Line Altitude
No.
(km)
A
.03
B
.03
C
.03
D
.03
E
.03
F
.03
G
.03
H
.03
J
.03
5.5
Length
(km)
6.4
11.9
10.0
8.5
9.0
6.2
9.5
7.5
6.3
Table 17. SMEX02 Twin Otter Flightlines
Description
Start Lat. Start Lon.
Aircraft Flux
Aircraft Flux
Aircraft Flux
Aircraft Flux
Aircraft Flux
Aircraft Flux
Aircraft Flux
Aircraft Flux
Aircraft Flux
41.94333
41.92983
41.95466
41.97300
41.97933
41.99316
42.00000
41.97200
41.93733
-93.66600
-93.78883
-93.75000
-93.65433
-93.74216
-93.70033
-93.76850
-93.73666
-93.53650
Stop Lat.
41.88600
41.93300
41.95833
41.89683
41.89833
41.93850
41.95733
41.97200
41.99333
Stop Lon.
-93.66600
-93.64466
-93.62900
-93.65433
-93.73950
-93.71516
-93.68083
-93.64616
-93.52600
Utah State University Piper Seneca
High-resolution airborne multispectral imagery will be acquired with the USU airborne digital
system (Neale and Crowther, 1994). Christopher Neale will coordinate missions with NCAR C130, NASA WFC P3-B, NASA DC-8 and Twin Otter teams. Short-wave imagery will be
acquired over the study area from 3200 meters above ground level, using Nikon 20 mm lenses,
resulting in a pixel size of approximately 1.5 meters. Each image consists of approximately
2000 x 2000 pixels, so the resulting swath will be 3.0 km. The flightlines, see Table 18, were
planned to cover the entire study area, with a side overlap of 30% between imagery. Along each
flight line, the images will be acquired with a 60% overlap to facilitate the mosaicking of the
43
imagery during post-processing. This flight line plan will result in approximately 128 images to
be processed for each mission. The pixel resolution of the optical data will be ~ 1.5 m pixel size.
Table 18. Systematic Coverage of Walnut Creek Watershed Area
Flightline
Latitude
Long. Start
Latitude
Long. End
U1
41.93111
93.8
41.93111
93.4
U2
41.95003
93.4
41.95003
93.8
U3
41.96894
93.8
41.96894
93.4
U4
41.98786
93.4
41.98786
93.8
The flight plan may vary for the different sensors, due to differences in the footprint of the shortwave band digital cameras and the thermal infrared Inframetrics scanner. It is expected during
this period of rapid vegetation growth that there will be a need for a set of high-resolution shortwave imagery covering the study area at the beginning, middle and end of the experimental
period to capture the changes in biomass and albedo throughout the period. USU will try and
acquire imagery to coincide with the LANDSAT overpasses depending on the weather
conditions. However, the thermal infrared image acquisition will be closely tied to the PALS
and Twin Otter flights and will also include post-dawn flights with subsequent flights throughout
the day to capture the thermal inertia of the system and for testing the time-rate-of-change
algorithms at high pixel resolutions. Some transects at even higher pixel resolutions (~ 0.5 m)
will be obtained (using lower flight lines) to capture the effects of incomplete covered vegetation
systems and to aid in the calibration of the imagery.
The optical images provide distributed surface cover characterizations that are necessary for
calculation of water and energy exchange rates. Due to the high spatial resolution of the imagery,
the system lends itself well to monitor sparse or incomplete cover crops, as it can resolve the soil
background and vegetation canopy. The highest resolution imagery will support the ground
measurements of LAI and fractional vegetation cover in certain cropped fields. These data add
significant value to the planned soil moisture observations.
The over flights are presently budgeted to cover approximately 17 hours of flight time over the
site. The flights will be planned, usually for the morning hours to avoid excessive cloud cover,
and to support different project activities that will include:
•
•
•
Systematic coverage of the larger research area with a combination of short wave (1.5
meter resolution) and long wave (5 meter resolution) measurements to coincide with
airborne microwave flights (see Table 18, Figure 14). Flight altitude ~ 3200 m.
Higher resolution (0.5 meter short wave, 1 meter long wave) imagery over the fields
containing the flux stations and lidar equipment (see Table 19, Figure 14). Flight altitude ~
1000 m.
Higher resolution flight lines over sites where biophysical canopy properties will be
measured and intensive soil moisture measurements will be conducted.
44
On the day of each flight, a calibrated barium sulfate reflectance panel with known bi-directional
reflectance properties will be set up leveled at a central location within the study area. The
incoming irradiance reflected by the panel will be continuously monitored using an Exotech 4-
Figure 14. Utah State University (USU) flightlines. Dashed lines are designed for mapping the
watershed area and solid lines are for low altitudes flights over flux tower sites.
Flightline
U5
U6
U7
U8
U9
U10
U11
U12
U13
U14
U15
U16
U17
U18
U19
Table 19. USU Low Altitude Flightlines
Latitude
Longitude
Latitude
Stop
Start
Start
(Deg.)
(Deg.)
(Deg.)
41.9331
93.7667
41.9331
41.9356
93.7667
41.9356
41.9393
93.7667
41.9393
41.9456
93.697
41.9456
41.9493
93.697
41.9493
41.9529
93.697
41.9529
41.9586
93.69
41.9586
41.9623
93.69
41.9623
41.9722
93.75
41.9722
41.9762
93.75
41.9762
41.9798
93.65
41.9798
41.9918
93.55
41.9918
41.9881
93.55
41.9881
41.9437
93.5481
41.9437
41.94
93.5481
41.94
Longitude
Stop
(Deg.)
93.64
93.64
93.64
93.634
93.634
93.634
93.634
93.634
93.640
93.640
93.640
93.515
93.515
93.5222
93.5222
45
band radiometer with Thematic Mapper bands TM1-4 and a 21X data logger. The radiometer
used will be the same instrument used to obtain the absolute calibration of the digital cameras in
a separate lab experiment. The reflectance of some large uniform representative surfaces within
the study area will also be measured to check the image calibration, using a second Exotech
radiometer and Polycorder data logger.
At the beginning or at the end of each flight mission, the aircraft will acquire thermal infrared
imagery over a nearby reservoir, where water temperatures are routinely measured. These data
will be used to check the accuracy of the surface temperature estimates and atmospheric
corrections using MODTRAN.
Three sets of flightlines will be utilized. Table 18 shows the systematic coverage of the
watershed area, where the images will be acquired from 3200 meters above ground level (10500
feet). Considering the average ground elevation of the study area, the aircraft will be positioned
at 11500 feet while acquiring images for these flight lines. The swath width of the short-wave
imagery will be approximately 3000 meters. These image acquisition campaigns will coincide
with the Landsat satellite overpasses whenever weather permits. We estimate a minimum of
three and a maximum of six systematic coverage flights of the entire watershed study area.
The lower elevation flightlines (Table 19) have been planned to cover the all the flux stations
located within the experimental area. These are parallel east-west flight lines, spaced
approximately 410 meters apart, covering the flux stations and the area to the south-southwest of
each station, which is the prevailing upwind direction. Additional flight lines could be added to
include important vegetation sampling and soil moisture monitoring sites. The flight elevation
will be 1067 meters (3500 feet) above ground level, or 4500 feet. This will result in a shortwave image swath width of 1000 meters and a thermal swath width of 587 meters. These flight
lines will be flown whenever the weather permits, at different times of the day. At least one predawn campaign of these flight lines will be conducted for thermal inertia estimates.
The third set of flightlines will be used to match several of the Twin Otter flightlines for
matching fluxes at this scale. The lines selected are A, D, E, F, and J described in Table 17.
These will be flown at an altitude of 2100 m. The swath width of the thermal imagery will be
approximately 1200 m and the pixel size will be 2 m. The shortwave imagery will have a pixel
size of 1 m.
The USU team will arrive on June 12th to initiate the image acquisition. The campaign will be
divided into two periods: (1) from June 12th to June 23rd and (2) from July 1st to July 9th. During
the period from June 23rd to July 1st, the aircraft will return to Utah to obtain data for other
committed projects as well as to have the annual inspection of the aircraft. The USU aircraft
maintenance personnel will be pre-planning the purchase of items necessary for the annual
inspection in order to expedite this procedure within three days.
46
6
IOWA STUDY REGION
In order to satisfy the requirements of the diverse research projects making up SMEX02 it was
necessary to include a test site that would provide a data set for the development and verification
of alternative soil moisture retrieval algorithms under significant biomass levels associated with
agricultural crops and satisfy the land atmosphere investigations described in other sections. It is
essential that multi-parameter microwave observations be obtained over a range of soil moisture
conditions with moderate to high vegetation biomass conditions. A study site in Iowa was
selected. Within this region, is a small watershed, Walnut Creek just south of Ames, IA This
watershed has been the focus of research by the USDA ARS National Soil Tilth Lab (NSTL)
http://www.nstl.gov/.
Nearly 95% of the region and watershed is used for row crop agriculture. Corn and soybean are
grown on approximately 80% of the row crop acreage, with greater than 50% in corn, 40-45% in
soybean and the remaining 5-10% in forage and grains.
The watershed is representative of the Des Moines Lobe, which covers approximately 1/4 of the
state of Iowa. The climate is humid; with an average annual rainfall of 835 mm. SMEX02 is
tentatively planned from mid June through mid-July. At the outset corn will be in early stages of
growth and most soybean fields will be essentially bare soil. By the end of June in a typical
growing season, corn biomass is expected to range between 3 and 4 kg m-2, while soybean will
have a biomass of less than 1 kg m-2. This translates to leaf-area index (LAI) values on the order
of 2 and 0.5 and fractional canopy cover about 0.75 and 0.5 respectively, for corn and soybean.
The area around central Iowa is considered the pothole region of Iowa because of the undulating
terrain. This area on the Des Moines lobe represents the youngest of soils in the United States.
Two features standout in this terrain. First, the lack of a surface stream channel except for the
areas near streams and rivers. Second, the large variation of soil types within a field. Surface
organic matter contents often range from 1-2 % to over 8% in a transect from the pothole areas to
the eroded knolls within the same field. This is also coupled with a variation in rooting depth.
These features create a potential condition in the spring and extremely wet summers of a soil
surface covered with random water-filled potholes. Typically, however, these potholes are dry
by early spring due to subsurface drainage and farmers are able to plant without any problems.
This variation, however, presents a challenge when field sampling to ensure that the surface
conditions within the field are adequately sampled. The NSTL has been developing a library of
soils maps for a number of fields along with a differential GPS to measure topography at the 1015 cm contour interval.
Additional
regional
information
can
be
found
http://mcc.sws.uiuc.edu/Introduction/micis.html
http://www.exnet.iastate.edu/Information/weather.html
at
the
following
sites
and
The heaviest precipitation months are May and June (about 1/3 of the annual total) Rainfall
events in the spring and summer are often thunderstorms, providing brief and intense showers.
The topography is characterized by low relief and poor surface drainage. “Prairie potholes” are a
common feature of the region. Figure 15 is one orthophoto image collected under bare soil
47
conditions that clearly illustrates this phenomena. The soils are loams and silty clay loams, with
generally low permeability. Figure 16 shows the general soil texture distribution in the region.
Anthropogenic forces have significantly modified the hydrologic character of the basin. Over the
past 100 years most of the “prairie potholes” have been drained, much of the land cultivated and
many of the agricultural fields have been tile drained to assist in subsurface drainage (tile flow).
Conventional tillage is most widely used, however no tillage and ridge tillage have been recently
introduced.
Figure 15. USGS orthophoto image from the Walnut Creek watershed area.
Figure 16. SMEX02 regional soil textures (green=loam and brown=silty clay loam).
Within the Walnut Creek watershed area there are 20 recording rain gauges separated by 1-mile
intervals or sections and air temperature is also recorded at these sites. There are two
48
meteorological stations located in the watershed measuring air temperature, relative humidity,
wind speed and direction, soil temperature and solar radiation. There are five stream gauging
locations in the watershed designed to isolate water flow and water quality for three subwatersheds and the entire basin.
Figures 2 and 10 show the regional study area. Figure 17 is a false color Landsat TM image
obtained on July 27, 2000. Nearly all fields rotate between corn and soybeans each year;
therefore, we expect to see a similar spatial pattern in 2002 (Bright red=soybeans and dark
red=corn). Both row and drilled soybeans planting practices are in use. Figure 18 shows row
soybean and corn conditions on June 27th 2000. Figure 19 is a higher resolution Landsat TM
image of the watershed area.
6.1
Watershed Sites
Sites were identified within the Walnut Creek watershed (code=WC) to satisfy the data requirement
of the PALS and surface/aircraft flux components of the experiment. To the extent possible the sites
selected had the following characteristics:
•
•
•
•
•
•
•
•
•
•
•
At least 800 m in the NS direction
At least 800 m in the EW direction
Centered on one of the low altitude C-130 flight lines
Single cover condition
Single management unit
Balance of corn and soybeans
Dominant soil types
Wind direction
Flux aircraft constraints
Geographic domain
Permission to use
Figure 9 shows the locations plotted on a road map. Table 20 summarizes these sites and describes
the expected crop type and row direction.
49
Figure 17. SMEX02 portion of a Landsat 7 July 27, 2000 false color composite.
50
a)
b)
Figure 18. Walnut Creek, Iowa a) typical corn canopy and b) typical row soybeans on June 28,
2000.
Figure 19. SMEX02 Walnut Creek watershed portion of a Landsat 7 July 27, 2000 false color
composite.
51
Site
WC01
WC02
WC03
WC04
WC05
WC06
WC07
WC08
WC09
WC10
WC11
WC12
WC13
WC14
WC15
WC16
WC17
WC18
WC19
WC20
WC21
WC22
WC23
WC24
WC25
WC26
WC27
WC28
WC29
WC30
WC31
WC32
WC33
Table 20. Walnut Creek Watershed Sites
Special
Crop
Row
Reference Coordinates
Observations
Direction Latitude (Deg.) Longitude (Deg.)
S
E
41.967688
-93.760127
Dropped
Flux Tower
S
N
41.982193
-93.754082
S
E
41.977215
-93.740728
C
N
41.962795
-93.740382
Flux Tower
C
N
41.933098
-93.746235
SCAN
Grass
42.010903
-93.732817
C
N
41.924171
-93.722218
S
N
41.924171
-93.703281
Flux Tower
S
X
41.976228
-93.690514
Flux Tower
C
N
41.972237
-93.694506
C
N
41.961937
-93.684345
Flux Tower
S
X
41.952925
-93.689670
Flux Tower
S
N
41.947517
-93.694786
Flux Tower
C
E
41.939278
-93.663560
Flux Tower
S
E
41.933784
-93.663560
C
E
41.962280
-93.655708
C
N
41.948161
-93.655847
C
E
41.933699
-93.642918
C
E
41.923742
-93.642688
S
E
41.969147
-93.634000
S
N
41.947174
-93.636310
Flux Tower
S
E
41.991806
-93.537476
Flux Tower
C
N
41.991806
-93.529163
Flux Tower
S
E
41.943741
-93.537014
C
N
41.976700
-93.508414
S
E
41.961851
-93.459338
C
N
41.925030
-93.449522
C
E
41.991034
-93.431625
C
E
41.967773
-93.422760
C
N
41.967430
-93.409542
GBMR
S
E
41.977820
-93.644950
GBMR
C
E
41.974730
-93.644950
Flux Tower
Crop: C=Corn, S=Soybean
Row Direction: N=North-South, E=East-West, X=Flex Coil
52
6.2
Regional Sites
Regional sites were selected to provide representative coverage over an area large enough to include
several AMSR sized footprints. Factors considered in selection included:
•
•
•
•
•
Geographic distribution
Travel time and access
Balance of corn and soybeans
Dominant soil types
Permission to use
Figure 10 shows the locations plotted on a road map and Table 21 summarizes features of these
sites including the expected crop type and row direction.
Site
Crop
IA01
IA02
IA03
IA04
IA05
IA06
IA07
IA08
IA09
IA10
IA11
IA12
IA13
IA14
IA15
IA16
IA17
IA18
IA19
IA20
IA21
IA22
IA23
IA24
IA25
C
C
S
C
C
S
C
S
C
S
S
C
C
S
S
S
S
S
S
S
S
S
S
S
S
Table 21. Iowa Regional Sites
Reference Coordinates
Row Direction
Latitude (Deg.) Longitude (Deg.)
N
42.659783
-93.717814
E
42.588000
-93.714000
N
42.503700
-93.716480
E
42.411000
-93.725000
E
42.340279
-93.728789
E
42.253890
-93.699959
N
42.168060
-93.700690
N
42.091170
-93.700000
N
41.972620
-93.695410
N
41.923056
-93.699974
N
41.848000
-93.719500
N
41.773300
-93.679350
N
42.664375
-93.526641
N
42.591591
-93.545908
E
42.513227
-93.550464
N
42.428000
-93.556000
E
42.355814
-93.563255
E
42.251500
-93.558500
E
42.163000
-93.557500
N
42.094000
-93.539000
E
41.992000
-93.537000
E
41.942300
-93.539000
N
41.848000
-93.562000
N
41.761500
-93.561000
E
42.659000
-93.424000
53
IA26
IA27
IA28
IA29
IA30
IA31
IA32
IA33
IA34
IA35
IA36
IA37
IA38
IA39
IA40
IA41
IA42
IA43
IA44
IA45
IA46
IA47
IA48
C
C
S
C
C
C
S
C
C
S
C
S
S
S
C
C
S
C
C
S
C
C
N
N
N
E
N
E
N
E
N
N
N
N
E
N
E
N
E
E
N
N
42.599000
42.513227
42.438000
42.340000
42.254105
42.180204
42.094631
41.992000
41.927000
41.833000
41.788000
-93.419000
-93.424145
-93.425000
-93.422000
-93.426222
-93.427836
-93.427482
-93.432000
-93.451000
-93.407000
-93.392000
42.587500
42.497950
42.443790
42.351952
42.257000
42.179775
42.090769
42.003000
41.948891
41.848040
41.782036
-93.279000
-93.294638
-93.293000
-93.294223
-93.312000
-93.293235
-93.293608
-93.292000
-93.292729
-93.292306
-93.303000
54
7
SCHEDULE
Table 22. SMEX02 Schedule
9-Jun
Surface Flux/Lidar
10
AH 8:00 am
11
Setup
12
13
Setup
Setup
Installation
Installation
Arrive
Test Flights
Set 1
Set 1
14
15
Ground Soil Moisture
C-130
P3-B
Return
Installation
Installation
L5
L7, A
DC-8
Twin Otter
USU
Satellite Data Sets
Vegetation
16
17
18
19
Set 1
20
Set 1
21
22
Surface Flux/Lidar
Ground Soil Moisture
Arrive
C-130
P3-B
Installation
Installation
Installation
Check
Installation
Arrive
Test Flights
Check Flight
Installation
DC-8
Twin Otter
USU
Satellite Data Sets
Vegetation
L7, A
Set 1
Set 1
Set 1
23
24
25
26
27
28
29
Surface Flux/Lidar
Ground Soil Moisture
AH 8:00 am
C-130
AH 4:00 pm
P3-B
AH 4:00 pm
DC-8
Twin Otter
USU
Satellite Data Sets
AH 4:00 pm
Depart
AH 4:00 pm
L5
ES
Vegetation
AH 1:00 pm
30
1-Jul
2
Set 2
3
Set 2
4
Set 2
5
6
Surface Flux/Lidar
Ground Soil Moisture
C-130
P3-B
Arrive
DC-8
Twin Otter
USU
Satellite Data Sets
Vegetation
Arrive
L5
L7, A
ES
ES
Set 2
Set 2
Set 2
Set 3
7
8
9
Set 3
10
E2
ES
Set 3
Set 3
11
12
13
Surface Flux/Lidar
Ground Soil Moisture
Depart
C-130
Depart
P3-B
Depart
DC-8
Twin Otter
USU
Satellite Data Sets
Vegetation
Set 3
L7, A, ES
L5, ES
Set 3
Set 3
Set 3
55
8
GROUND BASED OBSERVATIONS
8.1
Tower-based Flux Measurements
Through this project and collaborative relationships we will deploy a number of eddy covariance
systems through the study area, with each system consisting primarily of Campbell Scientific
CSAT3 3-D sonic anemometer and KH20 krypton hygrometer, measuring momentum flux and
sensible and latent heat fluxes between the land and the atmosphere across the watershed. Figure
20 illustrates a typical tower installation. These observations will be representative at the “patch”
or local scale (i.e., length scales ~ 102 m). Investigators from USDA-ARS, Utah State
University, University of Virginia, University of Iowa, and Texas A&M will be involved. These
systems will also have a picture of the complete energy balance by including net radiation, soil
heat flux, and radiometric surface temperature measurements. There is also an effort planned by
NSTL and Iowa State University scientists to make detailed soil heat flux measurements at
several locations within the watershed at varying landscape positions to assess within canopy
scale variability. In addition, there will be several systems, which will also be measuring net
carbon exchange by eddy covariance with the 3D sonic and LiCor LI-7500 open path CO2/H20
sensors. This will permit a very detailed assessment of water-energy-carbon fluxes and controls
as a function of crop type and amount of cover and tillage practices. For selected sites with a
significant fractional bare soil component, there are also plans to make measurements of soil
respiration using LiCor LI-6200 sensors. Details on the flux tower measurements are provided
in Chapter 10.
Figure 20. Typical surface flux tower.
56
8.2
Lidar/Sodar/Radisonde Measurements
Several ground-based atmospheric sensing systems are proposed for deployment for
investigating the role of land surface heterogeneity on atmospheric properties and processes.
The Raman scanning Lidar from Los Alamos National Lab (LANL) will provide water vapor
concentration fields in the lower boundary layer, and a scanning wind Lidar from the University
of Iowa (UI) will provide horizontal winds throughout the boundary layer. A scanning elastic
Lidar also from UI will map winds in the area, boundary layer height, entrainment zone
properties and cloud information. A sodar and radar/RASS system from LANL will be used to
measure meso-synoptic scale atmospheric conditions. The Lidar measurements will be
coordinated with tower-based flux measurements conducted over several fields having
significant differences in roughness and and/or fractional vegetative cover due to differences in
planting dates and/or planting method (i.e., drilled versus row planting). The Lidar data provide
distributed water vapor and wind fields over the mapped surface temperature, moisture, and
cover data. This would be the first time that such detailed data are collected simultaneously, and
will provide the basis for assessing the injection of spatial heterogeneity form the land surface
and into the lower atmosphere.
Radiosondes are a key element in upper air observation systems. Balloon-borne radiosondes
measure upper air temperature, humidity and pressure during their ascent to the upper
atmosphere. Radiosonde signals are received and processed by ground equipment, which
automatically computes wind speed and direction using global navigation networks.
8.3
Sun Photometer
The NASA Aeronet, which is led by Brent Holben, will provide SMEX02 with an eight channel
(Cimel) sun photometer. The sun photometer is designed to view the sun and sky at
preprogrammed intervals for the retrieval of aerosol optical thickness and water vapor amounts,
particle size distribution, aerosol scattering, phase function, and single scattering albedo. It
measures the intensity of sunlight arriving directly from the Sun. Although some Sun
photometers respond to a wide range of colors or wavelengths of sunlight, most include special
filters that admit only a very narrow band of wavelengths. These measurements are used to
radiometrically correct satellite imagery in the visible and infrared bands. By radiometrically
correcting these images it is then possible to quantitatively extract physical parameters and
compare multiple dates. The instrument will be installed at a central location to provide data
appropriate for the intensive site and for the regional area studies.
8.4
Vegetation and Land Cover
Vegetation biomass and soil moisture sampling will be performed for all watershed (WC) sites.
The measurements that will be made are:
•
•
•
Plant height
Ground cover
Stand density
57
•
•
•
Phenology
Leaf area (LAI)
Green and dry biomass
Non-destructive sampling of LAI using LiCor LAI-2000 instruments will be conducted. Since
the experimental period is likely to be during the active growing stages for both corn and
soybeans, efforts will be made to make LAI measurements several times during the study period,
including at the beginning and end of the study.
8.5
Soil Moisture
Ground based soil moisture measurements will be made for a variety of investigations. The three
primary objectives are:
•
•
•
Provide field (~800 m) average surface volumetric soil moisture for the development and
validation of microwave remote sensing soil moisture retrieval algorithms at a range of
frequencies primarily from aircraft platforms. This will be called Watershed sampling.
Provide footprint scale (~ 50 km) average surface volumetric soil moisture for the development
and validation of satellite microwave remote sensing soil moisture retrieval algorithms at a
range of frequencies. This will be called Regional sampling.
Provide calibrated continuous soil moisture for water and energy balance investigations. This
will be called Tower sampling.
8.5.1
Watershed Sampling
The goal of soil moisture sampling in the Watershed sites is to provide a reliable estimate of the
mean and variance of the volumetric soil moisture of the surface soil moisture for fields that are
approximately 800 m by 800 m. These measurements are used primarily to support the aircraft
based microwave investigations, which will be conducted between 0900 and 1200 local time. This
determines the time window for the Watershed site sampling.
The primary measurement made will be the 0-6 cm dielectric constant (voltage) at fourteen
locations in each field using the Theta Probe (TP). Dielectric constant is converted to volumetric
soil moisture using a calibration equation. There are built in calibration equations, however, we will
develop field specific relationships using supplemental gravimetric soil moisture and bulk density
sampling. At four standard locations in each site the gravimetric soil moisture (GSM) will be
sampled on each day of sampling. A 0-6 cm scoop tool will be used. This GSM sample be split into
0-1 cm and 1-6 cm samples providing a rough estimate of the site average 0-1 cm GSM. GSM is
converted to volumetric soil moisture (VSM) by multiplying gravimetric soil moisture and bulk
density of the soil. Bulk density will be sampled one time at each of these four locations using an
extraction technique. The composite set of VSM samples and TP dielectric constants will be used to
calibrate the TP for each site. It is anticipated that individual investigators may conduct more
detailed supplemental studies in specific sites.
TPs consist of a waterproof housing which contains the electronics, and, attached to it at one end,
58
four sharpened stainless steel rods that are inserted into the soil. The probe generates a 100 MHz
sinusoidal signal, which is applied to a specially designed internal transmission line that extends
into the soil by means of the array of four rods. The impedance of this array varies with the
impedance of the soil, which has two components - the apparent dielectric constant and the ionic
conductivity. Because the dielectric of water (~81) is very much higher than soil (typically 3 to
5) and air (1), the dielectric constant of soil is determined primarily by its water content. The
output signal is 0 to1V DC for a range of soil dielectric constant, ε, between 1 and 32, which
corresponds to approximately 0.5 m3 m-3 volumetric soil moisture content for mineral soils.
More details on the probe are provided in the sampling protocol section of the plan.
8.5.2
Regional Sampling
The goal of soil moisture sampling in the Regional sites is to provide a reliable estimate of the VSM
mean and variance within a single satellite passive microwave footprint (~50 km) at the nominal
time of the Aqua AMSR overpass (1330 local time). The exact center location and orientation of the
satellite footprint will vary with each overpass. A grid of 48 individual sites will be sampled each
day that covers a domain of approximately 50 km by 100 km (4 by 12 sites). A single location in
each of these 48 sites will be sampled. As noted, these measurements are used primarily to support
the Aqua AMSR based microwave investigations, therefore, the Regional sampling will be
conducted between 1200 and 1500 local time.
The primary measurement made will be the 0-6 cm dielectric constant at a single location in each
site using the Theta Probes described above Dielectric constant is converted to volumetric soil
moisture using a calibration equation. There are built in calibration equations, however, we will
develop field specific relationships using supplemental gravimetric soil moisture and bulk density
sampling. A different approach will be used for the Regional sites than the Watershed sites. Each
sampling day, a coring tool will be used to extract a single VSM sample of the 0-1 cm and 1-6 cm
soil layers. The composite set of VSM samples and TP dielectric constants will be used to calibrate
the TP for each site. It is anticipated that individual investigators may conduct more detailed
supplemental studies in specific sites.
8.5.3
Tower Sampling
Tower sampling is intended to provide continuous measurements of the surface soil moisture at the
locations of the surface flux towers. A single Vitel Hydra capacitance sensor will be installed at a
depth of 5 cm. To insure accurate calibration of these devices, the TP and GSM measurements will
be made near these locations on each sampling date. This effort will include the SCAN site.
Each surface flux tower will include instruments to measure the surface layer soil moisture and
temperature and the surface temperature. This will be a continuous record at a single point within
the field site. Cross referencing to the watershed site sampling will be done by collecting Theta
Probe soil moisture, gravimetric soil moisture, soil temperature and surface temperature at a
location in the vicinity of the tower each time sampling is conducted.
59
Soil moisture and temperature for the surface layer will be measured using Vitel Type A Hydra
Probes. This version is compatible with Campbell CR-10 data loggers, the temperature output
voltage never exceeds 2.5 volts.
The Hydra Probe (HP) soil moisture probe determines soil moisture and salinity by making a
high frequency (50 MHz) complex dielectric constant measurement. A complex dielectric
constant measurement resolves simultaneously the capacitive and conductive parts of a soil's
electrical response. The capacitive part of the response is most indicative of soil moisture while
the conductive part reflects predominantly soil salinity. Temperature is determined from a
calibrated thermistor incorporated into the probe head.
As a soil is wetted, the low dielectric constant component, air, is replaced by water with its much
higher dielectric constant. Thus as a soil is wetted, the capacitive response (which depends upon
the real dielectric constant) increases steadily. Through the use of appropriate calibration curves,
the dielectric constant measurement can be directly related to soil moisture.
The dielectric constant of moist soil has a small, but significant, dependence on soil temperature.
The soil temperature measurement that the Hydra probe makes can be used to remove most of
the temperature effects.
The Hydra probe has three main structural components, a multiconductor cable, a probe head,
and probe sensing tines. The probes will be installed horizontally in the soil with the center tine
at a depth of 5 cm. Additional details on the Hydra probe are provided in the sampling protocol
section of this plan.
The measured raw electrical parameters determined by the HP are the real and imaginary
dielectric constants. These two parameters serve to fully characterize the electrical response of
the soil (at the frequency of operation, 50 MHz). These are both dimensionless quantities.
Because both the real and imaginary dielectric constants will vary somewhat with temperature, a
temperature correction using the measured soil temperature is applied to produce temperature
corrected values for the real and imaginary dielectric constant. The temperature correction
amounts to calculating what the dielectric constants should be at 25oC.
The dielectric constants are used to calculate soil moisture with conversion equations. The
manufacturer provides these, however, through the ground sampling component it should be
possible to refine these for each site.
8.6
Soil and Surface Temperature
The objectives of the soil and surface temperature are nearly identical to those of soil moisture.
There are a few differences related to the spatial and temporal variability of temperature versus soil
moisture. Typically the soil temperature exhibits lower spatial variability, especially at depth. On
the other hand surface temperature can change rapidly with changes in radiation associated with
clouds. In addition, it can be difficult to correctly characterize surface temperature at satellite
60
footprint scales (30 m – 1 km) using high resolution ground instruments. This is especially true
when there is partial canopy cover.
The surface temperature will be sampled using handheld infrared thermometers (IRT). The soil
temperatures will be obtained using a temperature probe inserted to depths of 1 cm, 5 cm, and 10
cm depths.
8.6.1
Watershed Sampling
Temperature sampling will be conducted at the four locations selected for GSM sampling. These
will be distributed over the each site.
8.6.2
Regional Sampling
Temperature sampling will be conducted at the specific single location selected for sampling in the
site.
8.6.3
Tower Sampling
Tower sampling is intended to provide continuous measurements of the surface temperature and 2.5
cm soil temperature at the locations of the surface flux towers. The Vitel HP sensor also provides
temperature at 5 cm. An Apogee infrared sensor will be installed on each tower and will provide
surface observations. This device provides the measured surface temperature and the sensor
housing temperature. This second observation can be used to adjust for diurnal effects. These
will be installed at a height of 2 m on the tower at an angle of 30 degrees. More information can
be found in the protocols section of the plan. When GSM is sampled at the towers the surface and
soil temperatures will also be sampled. This effort will include the SCAN site. The temperature
measurement provided by the Hydra probe is in degrees Celsius.
8.7
Surface Roughness
Each Watershed site will be characterized one time during the time frame. The grid board
photography method employed in previous experiments will be used.
8.8
Ground Based Microwave Radiometer
The University of Tokyo in cooperation with the Japanese ADEOS-II AMSR program will
deploy a ground based microwave radiometer (GBMR) at a site in the Iowa study area. This will
most likely be in the watershed. A version of this instrument was part of SGP99.
Table 23 describes the basic parameters of the GBMR and Figure 21 shows the likely instrument
configuration for SMEX02.
The observation strategy is to leave the GBMR at a single location for the duration of SMEX02
and collect diurnal data over several adjacent fields (or plots). This location will likely be at the
61
border of sites WC32 and WC33. One option is illustrated in Figure 22 and might include
soybeans and bare soil with two roughness conditions. The required field area (~750 m2). The
site would be co-located in an intensive surface flux field.
Table 23. Features of the GBMR.
Frequencies
6.925 GHz, 10.65 GHz and 18.7 GHz
Polarization
V and H
Absolute Accuracy:
0.5K, 0.3K(RMS) over 10 min
Resolution
0.2K min
Antenna Beam Width
10 degree
Beam efficiency
90% min
Side lobe Level
-40dB max
Cross Polarization
3% max
Positioner:
Azimuth 360 degree, Elevation 90 degree
Calibration:
Hot (ambient hot load) and Cold (liquid nitrogen)
Figure 21. Ground Based Microwave Radiometer (GBMR-6Ch) system.
62
In addition to the microwave observations, this group will also collect the following
observations:
• Surface and 2.5 cm soil/vegetation temperature
• Soil moisture at 1 cm, 2.5 cm and 5 cm.
• Diurnal cycles: 2 hours interval operation for 24 hours for all footprints
• Vegetation sampling
• Roughness and bulk density: one sample per day at one location near footprint.
• Atmospheric forcing data collection (Downward shortwave radiation, Downward
longwave radiation, Relative humidity (Reference height), Air temperature (Reference
height), Wind velocity (Reference height), and Precipitation)
30m
GBMR-6Ch
~2.5m
θ =55
25m
~13.5m
Vegetation
Bare soil:
Roughness
Study
θ =65
AWS
Note: Clear Surrounding
Figure 22 Ground Based Microwave Radiometer (GBMR-6Ch) footprints
63
9
REGIONAL NETWORKS AND GENERAL SITE CONDITIONS
9.1
USDA Soil Climate Analysis Network (SCAN)
The USDA NRCS has initiated nationwide soil moisture and soil temperature (SMST) analysis
network called SCAN. Details and data can be obtained at the following web site
http://www.wcc.nrcs.usda.gov/smst/smst.html. Hourly observations are provided to the public on
the Internet in real time. Each system provides hourly observations of:
Air temperature
Barometric pressure
Wind speed
Precipitation
Relative humidity
Solar radiation
Soil temperature at 5, 10, 20, 50 and 100 cm
Soil moisture at 5, 10, 20, 50 and 100 cm
A SCAN site was installed near Ames, IA at Latitude: 42.00o, Longitude: 93.74o and Elevation:
1073 Feet on 09/23/2001. Figure 23shows the site conditions.
Figure 23 SCAN site at Story County, IA
64
9.2
NSTL Meteorological Stations
NSTL operates rain gages, stream gages, and meteorological stations within the Walnut Creek
watershed. All are on data loggers, which are downloaded on a weekly basis. The locations of
these are shown in Figure 24 Data for the SMEX02 time period will be provided following the
experiment. Other periods of record may be obtained by contacting NSTL.
Figure 24 Rain rages (stars) and steam gages (flags) within the Walnut Creek watershed. Other
meteorological stations are indicated as triangles. The large yellow box is 2 km by 2 km and is
used to represent scale.
Two of the rain gage sites include additional meteorological observations (701 and 702).
Measurements are made every minute and recorded every hour. End of day max-min air temp
and daily total rainfall and max wind speed are recorded. Observations made are:
•
•
•
•
•
•
•
•
•
solar radiation (kJ/m2)
air temperature (C)
saturated vapor pressure (kPa)
actual vapor pressure (kPa)
4 cm soil temperature (C)
20 cm soil temperature (C)
wind speed (m/s)
wind direction (degrees)
hourly total rainfall (mm)
65
9.3
Iowa Environmental Mesonet
The Iowa Environmental Mesonet (IEM) collects environmental data from cooperating members
with observing networks. The data is stored and available on the following website.
http://mesonet.agron.iastate.edu/. Contributors are Iowa State University, the National Weather
Service, the Iowa Department of Transportation and local sponsored school networks. Nearby
station locations in the Iowa State Agroclimate portion of the network are shown in Figure 24
Iowa State AgClimate stations provide measurements of
Precipitation
Solar Radiation
Air Temperature
Soil Temperature (10 cm)
Wind speed
Wind Direction
Relative Humidity
Time
Inches
Kilo calories per meter squared
Fahrenheit
Fahrenheit
MPH
Degrees
%
local time, either CST or CDT
These data are available as real time plots or can be extracted from archives.
The NWS operates three networks of interest. Automated Surface Observing System (ASOS).
Stations are located at airports and measurements are made every minute of temperature, dew
point, wind, altimeter setting, visibility, sky condition, and precipitation. They also operate the
Automated Weather Observing System (AWOS), which provides wind speed and direction,
temperature and dew point, visibility, cloud heights and types, precipitation, and barometric
pressure. Finally, limited observations of precipitation and temperature are available through the
NWS Cooperative Observer Program [COOP] in which daily observations are reported by
volunteer observers.
66
10
SMEX02/SMACEX TOWER FLUX MEASUREMENTS
10.1
Eddy Covariance Measurements
The tower flux team is composed of individuals from eight (8) locations. The primary
participants include; John Albertson (Duke Univ.), Tony Cahill (Texas A&M), Dan Cooper (Los
Alamos National Lab), Bill Eichinger (Univ. of Iowa), Larry Hipps (Utah State Univ.), Bill
Kustas (USDA-ARS-HRSL), John Norman (Univ. of Wisconsin) and John Prueger/Jerry
Hatfield (USDA-ARS-NSTL).
Instruments
The instruments that are being in used in the study are provided by these individuals and are
listed in Table 21. At present fifteen (15) CSAT3 sensors (Campbell Scientific 3-D sonic
anemometer) and eleven (11) LI7500 (Li-Cor CO2/H2O analyzer) are available. This will give
the ability to mount 11 eddy covariance (EC) systems in the study that will provide us with time
series (high frequency) data of wind (u, v, and w components), sonic temperature (TS), air
temperature (TA) if a fine wire thermocouple is mounted with the CSAT3, water vapor and CO2
from the LI7500. The remaining 4 CSAT3 will be used with KH20 sensors (Campbell Scientific
1-D Krypton Hygrometers/ H2O sensors) to provide the same data as above except for CO2. With
one CSAT3/KH20 system reserved as backup, a total of 14 EC systems will in operation during
the study.
Logging Measurements
Each of the EC systems will have a Campbell 23X data logger to execute the time series
commands. Twelve (12) of the EC systems will have a Libretto (Toshiba) 30, 50 or 70 mini
computer to store all the raw high frequency data in “time series mode”. Each mini computer
will have a PCMCIA card of either 80 or 128 Mb storage capacity. In short, the mini computers
will be communicating with the 23X grabbing the data from the 23X and storing it onto the
PCMCIA cards. Approximately every 24 hours, and most likely at the beginning of each day, the
PCMCIA cards will be exchanged at each EC tower with new PCMCIA cards. During this time
input location channels of the EC components will be viewed online and inspected for instrument
performance and instantaneous data integrity. If the EC systems are performing without error,
normal time series acquisition will be resumed with no loss of data. If a problem is encountered,
it will be solved, noted in field logbooks and data acquisition resumed. The 2 remaining EC
systems will be running in “flux mode” with 10-minute output of the fluxes being stored on the
23X.
Note: Laboratory EC tests at the NSTL during the month of February and March 2002 have
found that the high frequency (20 Hz) data acquisition works best with only the CSAT + LI7500
or CSAT + KH20. Attempts have been made to include radiometric measurements with the
Apogee IRT in the EC data stream. The results have consistently been loss of data in the 20 Hz
stream. No loss of data has occurred with doing only the EC instruments. Therefore, radiometric
67
temperature and all other non-EC measurements will be recorded separately on a 21X data
logger.
Table 24 lists the inventory of eddy covariance/ancillary measurement instrumentation
committed by the various investigators.
Table 24 List of Instrumentation Supplied by Investigators for the Flux Tower Observations.
INVESTIGA CSA LI KH 23X 21X AM NR CNR IRTS HFT LIBR 3 m Solar
TOR
T3 7500 20
25T Lite 1
-P
ETTO Tower Panels
NSTL
2
4
W. Kustas
W. Eichenger
T. Cahill
L. Hipps
J. Albertson
J. Norman
D. Cooper
2
3
2
2
2
1
1
2
1
2
2
TOTALS
15
11
8
7
12
2
4
1
2
2
2
2
4
1
15
20
1
1
2
4
6
8
4
12
2
5
8
13
10
12 - 8-80 mb,
MSX 60 12-128 mb
12-128 mb
3
1
2 - 40W
1- 75W
4 - 40W
2
4
PCMCIA
7
10
15
12
12
3
7
30
52
14
20
19
32
As a reminder the NSTL will provide two deep cycle batteries per site as well as Libretto
computers and customized enclosures for the 23x. s it stands now (April. 05, 2002) we can field
14 eddy covariance systems (11 w/ LI 7500 and 3 w/ KH20) for 20 Hz data acquisition.
Important points to keep in mind; There will be two inter-comparisons of eddy covariance and
net radiation instrumentation, one in May, and one at the end of the experiment (July). The intercomparison will be in flux mode 20-30 minute average LE, H, CO2 and Rn. Results of the first
inter-comparison will be quickly distributed among the investigators (as in days after the intercomparison) for investigator review. We will need all the instrumentation in the above Table at
the NSTL by the end of April for instrument check out and fitting to the enclosures. The NSTL
will be keeping meticulous notes of serial numbers of instrumentation/owners. When you send in
your instruments we will be sending a verification list of what the NSTL received as soon as we
receive the instrumentation.
The NSTL has just purchased a dedicated computer to archive all of the flux data. 2.0 GHz
Pentium III 512 mb RAM, 80 G HD, double PCMCIA card reader, CD-RW, DVD etc…the idea
is simple, PCMCIA cards will be exchanged every morning at all sites following daily sensor
check. PCMCIA cards will be brought to the NSTL. NSTL techs will transfer data from the
PMCIA cards to the computer, (the data will be in binary form), techs. will convert binary to
ASCII and perform the 2 scan offset correction for T, CO2, and H2O signals (sync up with the
CSAT data) and then package the data into convenient 1 hr blocks of 20 Hz ASCII data. This
process will complete the archive process and will take place daily. After the archive process the
techs will run a Mathematica notebook to compute 30 minute fluxes from the 20 Hz data, (this
68
will include Webb-Pearman-Leuning corrections so that investigators will be able to look at the
data within a few hours after it has been archived. As the end of the experiment comes to a close,
all archived data will be burned onto DVD's.
Sampling Frequency and Output Averages
EC systems (CSAT3 with either LI7500 or KH20) will be sampled at 20 Hz. For 12 of the 14
EC systems no averaging of output will be performed with the data recorded in binary. The
remaining 2 EC systems will have 10-min averages recorded.
10.2
Ancillary Measurements
Ancillary measurements will include the remaining energy balance components, namely net
radiation (RN), and soil surface heat flux (GS) which includes soil heat flux across the heat flow
transducer (G), and heat transfer of the soil layer above the transducers, the storage term (S), so
that GS=G+S. The magnitude of S is dependent on soil heat capacity, which is a function of soil
texture and soil moisture, and the temporal trace of soil temperature (TSOIL). The most accurate
estimate of S will be obtained when the data is post-processed, however, nominal values of S
could be computed and stored in the output files using the existing NSTL soil texture database
for the field sites. Radiometric temperature observations of the soil-vegetation canopy system or
composite surface temperature, (TRAD,C) and one of the bare soil surface (TRAD,S), will be made at
each site. Mean air temperature (TA) and relative humidity (RH) and soil moisture (W) using a
heat capacity probe. Depending on availability, measurements of mean wind speed (cup
anemometer), wind direction (wind vane) and total precipitation (tipping bucket rain gage) may
also be made at some of the sites.
Instruments
Net radiation will be made using primarily one of three RN sensors, the Kipp&Zonen CNR1 or
NR Lite and the Radiation and Energy Balance (REBS) Q*7 series. There are seven (7) CNR1
and three (3) NR Lite sensors, requiring the remaining four (4) sites to be instrumented with
REBS Q*7 series sensors. Since there are issues associated with instrument response and
differing sensitivity to long and short wave radiation among RN sensors, we will conduct an intercomparison among the sensors at the beginning and end of the study and evaluate differences. In
addition we will assign priority levels to the flux sites so as to insure that the highest priority
sites receive the “A” suite of instruments, i.e. the CSAT3 with LI7500 and a CNR1 net
radiometer. For the sites having the CNR1 net radiometer, all four-radiation components will be
recorded, namely incoming and outgoing short and long wave radiation. Soil flux G will be
measured using REBS HFT3 soil heat flow transducers (plates) and the soil temperature
measurements of the soil layer above the HFT3 sensors for estimating S will be made using typeT soil thermocouples manufactured by NSTL. The TRAD,C and TRAD,S observations will be made
using Apogee precision radiometers (model IRTS-P). Mean TA and RH will be made using
Vaisala temperature/RH probe (model HMP45C) enclosed in a radiation shield. Values of W
will be estimated using the Vitel Hydra (Model type A) soil heat capacity probe.
69
Logging Measurements
For the 12 sites in “time series mode”, ancillary measurements will be made using a Campbell
21X data logger connected to a Campbell AM 25T multiplexer (see Table 24). The two
remaining sites running in “flux mode” will have an additional 23X to collect the ancillary
measurements.
Sampling Frequency and Output Averages:
The TRAD and RN measurements will be sampled at 1 Hz or 1-sec with 60 seconds or 1-min
average output. TA, RH, G, and TSOIL will be sampled every 0.1 Hz or 10-sec with a 10-min
average output.
10.3
Intercomparison
Two intercomparisons are planned for study, one before the experiment and one after. The
intercomparisons will include eddy covariance instruments, net radiation, and IRTs. The purpose
of the intercomparison will be to assess measurement difference and variance when made over a
uniform surface. This will provide guidance when determining if, for example, a 25-50 W m-2
difference in turbulent heat flux or net radiation between two or more sites is greater than the
uncertainty in the measurements. We need to establish confidence limits in our measurements.
Two intercomparisons are planned because of the significant vegetative differences the fields
will undergo during the course of this study.
10.4
Instrument Height/Depth And Position
See Table 22 for a summary of the measurement height/depth of the sensors.
Eddy Covariance Sensors
Triangular towers (radio towers) will be used to mount the EC sensors and instrumentation for
the ancillary measurements. A 6 meter tower (combining two 3-m triangular tower sections)
anchored by 3 guy wires will be placed in the corn field sites while a single 3-m triangular tower
section will be used for the soybean field sites. For the soybean crop, the vegetation height is not
expected to exceed ~0.5-1 m by the end of the experimental period in mid-July. The corn crop,
however, may be significantly taller since this season, an earlier than normal planting date (midApril) is being scheduled due to relatively dry winter and early warm spring conditions.
Depending on rainfall amounts after planting and degree-days, the corn height could be greater
than 1 m by mid-June and reach ~2-3 m by the end of the experiment in mid-July. To maintain
an acceptable measurement height above the canopy for the EC sensors and at the same time
remain within the upwind source-area or fetch of the individual field sites, we propose
maintaining a ~1.5 m height above the corn and soybean canopies during the experimental
period. Given the measurement configuration for the two tower heights, and typical size of the
fields (~400 x 400 m) this is a reasonable compromise for both corn and soybean canopies.
70
The NSTL will provide ten (10) 3-m sections of radio tower while Los Alamos and University of
Iowa can provide seven (7) and three (3) 3-m sections of radio tower, respectively (see Table
25). With a total of twenty (20) 3-m sections we can have at a minimum six (6) EC systems in
corn on 6-m radio towers. This will permit the corn canopy to reach its maximum height of ~3
m during the study, and still maintain EC sensors at 1.5 m above the canopy. This leaves us with
eight (8) 3-m radio tower sections for use in the soybean field sites. The 3-m towers will be
sufficiently tall for the soybean crop since the maximum height by mid-July would be ~1 m.
Soybeans could reach a canopy height of ~1.5 m, but only under extraordinary conditions and
this height wouldn’t be attained until later in August. Since the predominant wind directions in
the summer growing season are from the south and southwest, the EC sensors will be pointing
south.
Table 25. A summary of sensor height/depth for the flux towers in the corn and soybean
field sites
Sensor/Instrument
Sensor Height/Depth for Sensor Height/Depth for
Corn
Soybean
EC System/CSAT3-LI7500 or KH20
Net Radiometer/CNR1, NR-Lite, Q*7
Surface Temperature/IRTS-P
~2 to 4.5 m AGL*
~6 m AGL
~6 m and ~0.4 m AGL@
~1.5 to 2 m AGL*
~3 m AGL
~3 m and ~0.4 to 0.1 m
AGL@
~1.5 to 2 m AGL*
Air Temperature-Relative Humidity/HMP~2 to 4.5 m AGL*
45C
Soil Heat Flux Plate/HFT
-0.06 m
-0.06 m
Soil Temperature/Type-T Thermocouple
-0.02 and –0.04 m
-0.02 and –0.04 m
Soil Moisture/Hydra Type A
-0.05 m#
-0.05 m#
*
The EC and air temperature/relative humidity sensors will be moved to maintain ~1.5 m height above the
crop.
@
There will be two (2) IRT sensors, one nadir viewing from the top of the tower and the other within the
canopy at ~45 degree viewing angle measuring soil surface temperature at a height primarily as a function of
row spacing.
#
The soil moisture sensor will have the center of the sensor at -0.05 cm so that the sensor is effectively
sampling a bulk near-surface (~0-0.07 m) soil moisture.
Ancillary Instrumentation
Net Radiation: Net radiation instruments will be mounted at the top of the radio towers oriented
to the south at a fixed height near or at the top of the radio towers, specifically at ~6 m and 3 m
above ground level (AGL). They will be positioned further away (south) from the tower than the
EC system positioned below, to avoid instrument shadowing and influences of EC sensors on
reflected/upwelling radiance measurements.
Radiometric Surface Temperature: The two Apogee IRT sensors will be positioned at two
heights and viewing angles. The sensor for measuring a composite temperature, TRAD,C will be
positioned at the top of the tower with a nadir viewing angle. The IRTS-P model has a
nominally 3:1 field of view (FOV) so that at 3 m AGL the FOV is a 1 m diameter circle. To
minimize any contamination from the tower and instrumentation, the nadir-viewing IRT will be
71
positioned on the east side of the tower and located ~2 m away from the tower. The other IRTSP for estimating TRAD,S will be placed in the center of the row crop on a short pole with a
measurement height of ~ 0.1 to 0.4 m AGL and nominally a 45 degree view angle from nadir.
The sensor will view the soil surface parallel to the row direction (either north-south or eastwest) to obtain a spatial representation of the average bare surface soil temperature. The actual
TRAD,S measurement height will depend primarily on crop row spacing with ~ 0.4 m height for the
30-in row crops, and ~0.1 m height for the 8-in row crops.
Air Temperature/Relative Humidity: The air temperature/relative humidity sensor will be
positioned on the north facing side of the tower at the same height as the EC sensors or ~1.5 m
above the canopy. Therefore, it will be repositioned each time the EC sensors are moved.
Soil Heat Flux Plates and Soil Thermocouples: Soil heat flux plates will buried at a depth of
0.06 m below the soil surface with Type-T thermocouples at 0.02 and 0.04 m below the soil
surface but above the associated soil heat flux plate. Four (4) soil heat flux plates and eight (8)
soil temperature sensors will be used for obtaining a spatially-representative value of GS. Each
sensor cluster (1 soil heat flux plate and 2 soil temperature sensors) will occupy ¼ of the row,
with the cluster placed in the center of each ¼-section. For corn planted in 30-in or ~ 0.76 m
row, the ¼-sections will be 0.19 m wide. The row width for soybeans will be vary anywhere
from 30-in to 8-in or ~ 0.20 m (drilled soybean) to no real row-structure (flex-coil soybean). For
the 0.20 m rows, the ¼-sections will be 0.05 m wide, while for flex-coil soybeans, sensors will
need to be placed in a more random fashion. Within the row, the sensor clusters should be
placed at different distances from the tower in order to obtain a better spatial sampling of soil
and vegetation conditions. In Figure 25, an example of the sampling strategy for a 30-in row
crop is illustrated.
Soil Moisture Sensor: The soil moisture probe will be buried such that the center of the probe
is at 0.05 m depth. This will give a sampling of the near-surface soil moisture condition. The
probe will be positioned within the row at a location that approximates a area-weighted sample.
For the 30-in (0.76 m) row spacing, the location would be at 0.19 m or ¼ the distance from the
row (see Figure 24).
10.5
Selected Flux Sites
Twelve (12) of the soil moisture sampling field sites were selected to contain flux towers. Two
(2) field sites, one corn and the other soybean, will contain 2 EC systems for investigating within
field variability and will also be field sites monitored with the Lidar system. In Table 26 is a
listing of the field sites, crop type, row direction, number of EC systems, whether in flux or time
series mode, whether using LI7500 or KH20, net radiometer type (i.e., CNR1 or REBS/NR Lite)
and Lat/Long location.
72
~76 cm
~19 cm
~1-2 m
Row
Row
Figure 25. Schematic (planar view) illustrating sampling design for soil heat flux/soil
temperature sensor clusters (solid circles) and location of soil moisture probe (solid square) for a
30-in or 0.76 m row crop.
Table 26. Flux tower field sites, cover crop, row direction, number of systems/towers, data
collection mode, water vapor and/or CO2 sensor, net radiometer model and reference
coordinates.
Field
Crop
Row
Dir.
WC03
WC06
WC10
WC11
WC13
WC14
WC15
WC16
WC23
WC24
WC25
WC33
S
C
S
C
S
S
C
S
S
C
S
C
N
N
X
N
X
N
E
E
E
N
E
E
Flux (F) or
LI7500 or
CNR1 or
Time Series
KH20
REBS/NR Lite
(T) Mode
1
T
LI7500
CNR1
1
T
LI7500
CNR1
1
F
KH20
REBS/NR Lite
1
T
LI7500
REBS/NR Lite
1
T
LI7500
REBS/NR Lite
1
T
KH20
REBS/NR Lite
2
T(2)
LI7500(2)
CNR1(2)
2
T(2)
LI7500(2)
CNR1(2)
1
T
LI7500
REBS/NR Lite
1
T
LI7500
CNR1
1
F
KH20
REBS/NR Lite
1
T
LI7500
REBS/NR Lite
Crop: C=Corn, S=Soybean
Row Direction: N=North-South, E=East-West, X=Flex Coil
No. of
Towers
Latitude
(Deg)
Longitude
(Deg)
41.982193
41.933098
41.976228
41.972237
41.952925
41.947517
41.939278
41.933784
41.991806
41.991806
41.943741
41.974730
-93.754082
-93.746235
-93.690514
-93.694506
-93.689670
-93.694786
-93.663560
-93.663560
-93.537476
-93.529163
-93.537014
-93.644950
73
11
SAMPLING PROTOCOLS
11.1
General Guidance on Field Sampling
•
•
•
•
•
•
•
•
•
•
•
Sampling is conducted every day. It is canceled by the group leader if it is raining, there are
severe weather warnings or a logistic issue arises.
Know your pace. This helps greatly in locating sample points and gives you something to
do while walking.
If anyone questions your presence, politely answer identifying yourself as a scientist working
on a NASA/USDA soil moisture study with satellites. If you encounter any difficulties just
leave and report the problem to the group leader.
Although gravimetric and vegetation sampling are destructive, try to minimize your impact
by filling holes. Leave nothing behind.
Always sample or move through a field along the row direction to minimize impact on the
canopy.
Please be considerate of the landowners and our hosts. Don’t block roads, gates, and
driveways. Keep sites, labs and work areas clean of trash and dirt.
Watch your driving speed, especially when entering towns. Be courteous on dirt and gravel
roads, lower speed=less dust.
Avoid parking in tall grass, catalytic converters can be a fire hazard.
Close any gate you open as soon as you pass.
Work in teams of two. Carry a cell phone.
Be aware that increased security at government facilities may limit your access. Do not
assume that YOU are exempt.
11.2
Watershed Site Surface Soil Moisture and Temperature
Watershed site sampling will take place between 8:30 am and 11:30 am.
Soil moisture and temperature sampling of the watershed area sites is intended to estimate the
site average and standard deviation. It is assumed here that most of these sites will be quarter
sections (800 m by 800 m), however, there will be a number of variations that may require
adaptation of the protocol. The variables that will be measured or characterized are:
•
•
•
•
•
•
•
•
0-6 cm soil moisture using the Theta Probe (TP) instrument
0-1 and 1-6 cm gravimetric soil moistures using the scoop tool
0-6 cm soil bulk density (separate team)
Surface temperature using a hand held infrared thermometer
1 cm soil temperature
2.5 cm soil temperature
10 cm soil temperature
GPS locations of all sample point locations (one time)
74
Preparation
•
•
•
•
•
•
•
•
Arrive at the field headquarters at assigned time. Check in with group leader and review
notice board.
Assemble sampling kit
o Bucket
o Theta Probe and data logger (use the same probe each day, it will have an ID)
o Scoop tool
o 8 cm spatula
o 4 cm spatula
o Notebook
o Pens
o Box of cans (see note below)
o Soil thermometer
o Handheld infrared thermometer
o Extra batteries (9v, AA, AAA)
o Screwdriver
o First aid kit (per car)
o Phone (if you have one)
For the WC sites, each team should take one box of 18 cans. The cans will be numbered
XX01-XX18. Use only boxes labeled AA through BZ.
Verify that your TP, data logger, infrared instrument, and soil thermometer are working.
Check weather
The first time you sample, it will help to use flags to mark your transect rows and sample
point locations. Use only plastic flags and mark with the site ID and point ID (i.e. WC05-02)
All sample points should be located with a GPS once during the experiment. Points will be
referenced by Site “WC##” and Point “##”.
Use a new notebook page each day. Take the time to draw a good map and be legible.
These notebooks belong to the experiment, if you want your own copy make a zerox.
Procedure
•
•
•
•
Upon arrival at a site, note site id (WC##), your name(s) and time in notebook. Draw a
schematic of the field (It might be a good idea to do this before you go out for the day).
Indicate the TP ID you are using.
Assemble 8 sequential cans and indicate on schematic where they will be used. Odd
numbered cans are used for the 0 – 1 cm sample and even numbered cans are used for the
1 – 6 cm sample. See Figure 26 as an example of such a diagram.
Use cans sequentially.
From a reference point for the site (usually a corner), measure 200 m along one side to locate
the first transect.
o Transects should be parallel with the row direction.
o If possible, select a row that is a tractor row to walk in.
75
•
•
•
•
From this location initiate a sampling transect across the site. Take the first sample at 100 m
and repeat every 100 m until you are 100 m from the edge of the site. For a standard quarter
section site this will result in 7 samples along the transect.
o Sample in the row adjacent to the row you are working in, it is suggested that this
be the row to your right.
o At all points collect three TP samples across the row as suggested in Figure 27.
See the Theta Probe protocol for how to use the instrument and data logger.
o At points labeled ALL in Figure 26 (four per site) collect
One gravimetric soil moisture sample for 0-1 cm and 1-6 cm following the
procedures described using the scoop, enter can numbers on diagram in
book (See Gravimetric Sampling with the Scoop Tool protocol)
One soil temperature (Degrees C) for 1 cm, 2.5 cm and 10 cm using the
probe, enter values in book (See Temperature Sampling protocol)
One averaged surface thermal infrared temperature (Degrees C) using
infrared thermometer, enter value in book (See Temperature Sampling
protocol)
After completing this transect move 400 m perpendicular into the site and initiate a new
transect. This will result in a total of 14 sampling points.
o Exit the field before attempting to move to the second transect.
As you move along the transect note any anomalous conditions on the schematic in your
notebook, i.e. standing water.
Record your stop time and place cans in box. Try to keep them cool.
100 m
8
All
9
All
10
11
12
13
14
400 m
All 100 m
All
7
6
5
4
3
2
1
200 m
Row direction
Start
Figure 26. Schematic of layout of samples in a watershed site.
76
Theta Probe
Sample Points
Tractor
Row
Sampling
Row
Figure 27. Schematic of layout of Theta Probe sample points.
Sample Data Processing
•
•
•
•
•
•
Return to the field headquarters immediately upon finishing sampling.
For each site, weigh the gravimetric samples and record on the data sheets (Figure 28) that
will be provided. Use a single data sheet for all your samples for that day and record cans
sequentially.
Transfer temperature and other requested data to data sheets (same sheet used for GSM).
Place cans in (in box) “TO OVENS” area and data sheet in collection box.
Turn in your TP and data logger to the person in charge. They will be responsible for
downloading data.
Clean your other equipment.
11.3
Regional Site Surface Soil Moisture and Temperature
Regional sampling will take place between 12:00 pm and 3:00 pm.
Soil moisture and temperature sampling of the region near Ames Iowa is intended to estimate the
site average and standard deviation at the scale of passive microwave satellite footprints and grid
cells. The variables that will be measured or characterized are:
•
•
•
•
•
•
•
0-6 cm soil moisture using the Theta Probe (TP) instrument
0-1 and 1-6 cm gravimetric soil moistures and bulk density using the coring tool
Surface temperature using a hand held infrared thermometer
1 cm soil temperature
2.5 cm soil temperature
10 cm soil temperature
GPS locations of all sample point locations (one time)
77
Preparation
•
•
•
•
•
•
•
•
Arrive at the field headquarters at assigned time. Check in with group leader and review
notice board.
Assemble sampling kit
o Bucket
o Theta Probe and data logger (use the same probe each day, it will have an ID)
o Coring tool and coring tool hammer
o 8 cm spatula
o 4 cm spatula
o Funnel
o Bottle brush
o Notebook
o Pens
o Box of cans (see note below)
o Soil thermometer
o Handheld infrared thermometer
o Extra batteries
o Screwdriver
o First aid kit (per car)
o Phone
Verify that your TP, data logger, infrared instrument, and soil thermometer are working.
For the IA sites, each team should take one box of 24 cans. The cans will be numbered
XX01-XX24. Use only boxes labeled CA through CZ.
Check weather
The first time you sample, it will help to use flags to mark the field entry point and sample
point location. Use only plastic flags and mark with the site ID (i.e. IA05)
All sample points should be located with a GPS once during the experiment. Points will be
referenced by Site “IA##”.
Check weather
Procedure
•
•
•
Upon arrival at a site, note site id (IA##), your name(s), TP ID, and time in notebook. Draw
a schematic of the field (It might be a good idea to do this before you go out for the day).
Assemble 2 sequential cans. Odd numbered cans are used for the 0 – 1 cm sample and
even numbered cans are used for the 1 – 6 cm sample.
Go to the pre-established sampling location.
o Sample in the row adjacent to the row you are working in, it is suggested that this
be the row to your right.
o At the location collect three TP samples across the row as suggested in Figure 27.
Always start with the sample directly in the plant row and move out.
o Using the coring tool collect one gravimetric soil moisture sample for 0-1 cm and
1-6 cm following the Coring Tool Sampling protocol, enter can numbers in book
78
o One soil temperature (Degrees C) for 5 cm and 10 cm using the probe, enter
values in book
o One averaged surface thermal infrared temperature (Degrees C) using infrared
thermometer, enter value in book
o Record your stop time and place cans in box. Try to keep them cool.
Sample Data Processing
•
•
•
•
•
•
Return to the field headquarters immediately upon finishing sampling.
Weigh the gravimetric samples and record on the data sheets that will be provided. Use a
separate sheet for each date and record cans sequentially. (see Figure 28)
Transfer temperature and other requested data to data sheets (same sheet used for GSM).
Place cans in “TO OVENS” area and data sheet in collection box.
Turn in your TP and data logger to the person in charge. They will be responsible for
downloading data.
Clean your other equipment.
Gravimetric Soil Moisture Sampling
Date
Time
Observers
Sites
Site ID
Sample
ID
Wet
Weight
WC01
WC01
WC01
WC01
WC01
WC01
WC01
WC01
WC02
.
WC02
AB01
AN02
AB03
AB04
AB05
AB06
AB07
AB08
AB09
.
AB16
210.15
Dry
Surface
Weight Temperature
25
Soil Temperature
1 cm
22
2.5 cm
20
10 cm
18
Figure 28. Example of the gravimetric soil moisture sampling data sheet.
79
11.4
Theta Probe Soil Moisture Sampling and Processing
There are two types of TP configurations; Type 1 (Rod) (Figure 29) and Type 2 (Handheld)
(Figure 30). They are identical except that Type 1 is permanently attached to the extension rod.
Figure 29. Theta Probe Type 1 (with extension rod).
Figure 30. Theta Probe Type 2.
Each unit consists of the probe (ML2x) and the data logger or moisture meter (HH2). The HH2
reads and stores measurements taken with the ThetaProbe (TP) ML2x soil moisture sensors. It
can provide milliVolt readings (mV), soil water (m3.m-3), and other measurements. Readings
are saved with the time and date of the reading for later collection from a PC.
The HH2 is shown in Figure 31. It applies power to the TP and measures the output signal
voltage returned. This can be displayed directly, in mV, or converted into other units. It can
convert the mV reading into soil moisture units using conversion tables and soil-specific
parameters. Tables are installed for Organic and Mineral soils, however, greater accuracy is
possible by developing site-specific parameters. For SMEX02, all observations will be recorded
as mV and processed later to soil moisture.
80
Use of the TP is very simple - you just push the probe into the soil until the rods are fully
covered, then using the HH2 obtain a reading. Some general items on using the probe are:
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
One person will be the TP coordinator. If you have problems see that person.
A copy of the manual for the TP and the HH2 will be available at the field HQ. They are
also available online as pdf files at http://www.dynamax.com/#6, http://www.delta-t.co.uk
and http://www.mluri.sari.ac.uk/thetaprobe/tprobe.pdf..
Each TP will have an ID, use the same TP in the same sites each day.
The measurement is made in the region of the four rods.
Rods should be straight.
Rods can be replaced.
Rods should be clean.
Be careful of stones or objects that may bend the rods.
Some types of soils can get very hard as they dry. If you encounter a great deal of resistance,
stop using the TP in these fields. Supplemental GSM sampling will be used.
Check that the date and time are correct and that Plot and Sample numbers have been reset
from the previous day.
Disconnect sensor if you see the low battery warning message.
Protect the HH2 from heavy rain or immersion.
The TP is sensitive to the water content of the soil sample held within its array of 4 stainless
steel rods, but this sensitivity is biased towards the central rod and falls off towards the
outside of this cylindrical sampling volume. The presence of air pockets around the rods,
particularly around the central rod, will reduce the value of soil moisture content measured.
Do not remove the TP from soil by pulling on the cable.
Do not attempt to straighten the measurement rods while they are still attached to the probe
body. Even a small degree of bending in the rods (>1mm out of parallel), although not
enough to affect the inherent TP accuracy, will increase the likelihood of air pockets around
the rods during insertion, and so should be avoided. See the TP coordinator for replacement.
Figure 31. HH2 display.
81
Before Taking Readings for the Day Check and configure the HH2 settings
1. Press Esc to wake the HH2.
Check Battery Status
2. Press Set to display the Options menu
3. Scroll down to Status using the up and down keys and press Set.
4. The display will show the following
Mem %
Batt %
Readings #.
• If Mem is not 0% see the TP coordinator.
• If Battery is less than 50% see TP coordinator for replacement. The HH2
can take approximately: 6500 TP readings before needing to replace the
battery.
• If Readings is not 0 see the TP coordinator
5. Press Esc to return to the start-up screen.
Check Date and Time
6. Press Set to display the Options menu
7. Scroll down to Date and Time using the up and down keys and press Set.
8. Scroll down to Date using the up and down keys and press Set to view. It should be
in MM/DD/YY format. If incorrect see the TP coordinator or manual.
9. Press Esc to return to the start-up screen.
10. Press Set to display the Options menu
11. Scroll down to Date and Time using the up and down keys and press Set.
12. Scroll down to Time using the up and down keys and press Set to view. It should be
local (24 hour) time. If incorrect see the TP coordinator or manual.
13. Press Esc to return to the start-up screen.
Set First Plot and Sample ID
14. Press Set at the start up screen to display the Options Menu.
15. Scroll down to Data using the up and down keys and press Set.
16. Select Plot ID and press Set to display the Plot ID options.
17. The default ID should be A. If incorrect scroll through the options, from A to Z, using
the up and down keys, and press Set to select one.
18. Press Esc to return to the main Options menu.
19. Scroll down to Data using the up and down keys and press Set
20. Scroll down to Sample and press Set to display available options. A sample number
is automatically assigned to each reading. It automatically increments by one for each
readings stored. You may change the sample number. This can be any number
between 1 and 2000.
21. The default ID should be 1. If incorrect scroll through the options, using the up and
down keys, and press Set to select one.
22. Press Esc to return to the main Options menu.
Select Device ID
23. Each HH2 will have a unique ID between 0 and 255. Press Set at the start up or
readings screen to display the main Options menu.
24. Scroll down to Data using the up and down keys and press Set.
82
25. Select Device ID and press Set to display the Device ID dialog.
26. Your ID will be on the HH2 battery cover.
27. Scroll through the options, from 0 to 255, and press Set to select one.
28. Press Esc to return to the main menu.
To take Readings
1. Press Esc to wake the HH2.
2. Press Read
If successful the meter displays the reading, e.g.ML2
Store?
32.2%vol
3. Press Store to save the reading.
The display still shows the measured value as follows:
ML2
32.2%vol
Press Esc if you do not want to save the reading. It will still show on the display but
has not been saved.
ML2
32.2%vol
4. Press Read to take the next reading or change the optional meter settings first. such as
the Plot ID. Version 1 of the Moisture Meter can store up to 863 if two sets of units
are selected.
Troubleshooting
Changing the Battery
• The HH2 unit works from a single 9 V PP3 type battery. When the battery reaches 6.6V,
(~25%) the HH2 displays :
*Please Change
Battery
• On receiving the above warning have your data uploaded to the PC next, or replace the
battery. Observe the following warnings:
o WARNING 1: Disconnect the TP, immediately on receiving this low battery
warning. Failure to heed this warning could result in loss of data.
o WARNING 2: Allow HH2 to sleep before changing battery.
o WARNING 3: Once the battery is disconnected you have 30 seconds to replace
it before all stored readings are lost. If you do not like this prospect, be reassured
that your readings are safe indefinitely, (provided that you do disconnect your
sensor and you do not disconnect your battery). The meter will, when starting up
after a battery change always check the state of its memory and will attempt to
recover any readings held. So even if the meter has been without power for more
than 30 seconds, the meter may still be able to retain any readings stored.
83
Display is Blank
The meter will sleep when not used for more than 30 seconds. This means the display will go
blank.
• First check that the meter is not sleeping by pressing the Esc key. The display should become
visible instantly.
• If the display remains blank, then try all the keys in case one key is faulty.
• Try replacing the battery.
• If you are in bright light, then the display may be obscured by the light shining on the
display. Try to move to a darker area or shade the display.
Incorrect Readings being obtained
• Check the device is connected to the meter correctly.
• Has the meter been set up with the correct device.
Zero Readings being obtained
• If the soil moisture value is always reading zero, then an additional test to those in the
previous section is to check the battery.
Settings Corrupt Error Message
• The configurations such as sensor type, soil parameters, etc. have been found to be corrupt
and are lost. This could be caused by electrical interference, ionizing radiation, a low battery
or a software error.
Memory Failure Error Message
• The unit has failed a self-test when powering itself on. The Unit’s memory has failed a self
test, and is faulty. Stop using and return to HQ.
Some Readings Corrupt Error Message
• Some of the stored readings in memory have been found to be corrupt and are lost. Stop
using and return to HQ.
Known Problems
• When setting the date and time, an error occurs if the user fails to respond to the time and
date dialog within the period the unit takes to return to itself off. (The solution is to always
respond before the unit times out and returns to sleep).
• The Unit takes a reading but fails to allow the user to store it. (This can be caused if due to
electrical noise, or if calibrations or configurations have become corrupted. An error message
will have been displayed at the point this occurred.
84
11.5
•
•
•
•
•
•
•
•
•
•
Gravimetric Soil Moisture Sampling with the Scoop Tool
Remove vegetation and litter.
Use the large spatula (6 cm) to cut a vertical face at least 6 cm deep (Figure 32a).
Push the GSM tool into this vertical face. The top of the scoop should be parallel with the
soil surface. (Figure 32b).
Use the large spatula to cut a vertical face on the front edge of the scoop (Figure 32c).
Use the small spatula to cut the sample into a 0-1 and a 1-6 cm depth sample.
Place each sample depth in a separate can, the small spatula aids extraction (Figure 32d).
Remember that the odd numbered cans are for the top layer and the even are for the deeper layer
(remember to use cans sequentially and odd numbers for the 0-1 and even for 1-6 cm samples).
Record these can numbers in the field notebooks at the point location on the map.
A video clip showing the gravimetric sampling technique can be downloaded from an
anonymous ftp site hydrolab.arsusda.gov/pub/sgp99/gsmsamp.avi.
At the specific sampling points where it is required, measure the soil temperature at 5 and 10 cm
depths using the digital thermometer provided. Record these values in degrees C to one decimal
point in the field notebooks at the point location on the map.
At the specific sampling points where it is required, measure the surface temperature. Record
these values in degrees C to one decimal point in the field notebooks at the point location on the
map.
Figure 32. How to take a gravimetric soil moisture sample.
85
11.6
•
•
•
•
•
Gravimetric Soil Moisture and Bulk Density Sampling with the Coring Tool
The tool is called a 200-A soil core sampler.
Figure 33 shows the parts of the apparatus.
The thin cutting tip will be used in SMEX02 because we anticipate that the soils will be
uniform and rock-free. This can be replaced using a spanner tool to unscrew it from the
barrel.
The cap is unscrewed to insert or remove rings.
The ring volumes are 1 cm=x cm3 and 5 cm=y cm3.
Procedure for Taking a Sample
•
•
•
•
•
•
•
•
•
•
•
Insert three sample rings and the extractor ring in the following order starting from the
cutting end
o Extractor
o 2 cm
o 5 cm
o 1 cm
Replace cap with handle
Insert the coring tool into the soil. If necessary, the hammer tool can be used. The hammer
rod is inserted into the handle.
Stop when the lip of the cap reaches the surface, try not to compact the sample.
Remove the core and inspect the tip to make sure the notches on the extractor ring are clear.
Use the extractor tool (Figure 33) to assist in cleaning these notches. Also inspect for
separation within the coring tool by looking for protruding soil at the bottom of the tool. If
there is separation, dispose of this sample and start again.
Remove the cap.
Use the extractor tool (from the bottom or cutting edge) to push the rings out the top or cap
end.
Using a wide spatula, cut the rings apart into the soil moisture cans using the soil funnel to
insure capture of the entire sample..
Place the 1 cm and 5 cm samples in cans (Remember odd-1 cm and even-5 cm).
It can be difficult to extract a perfect surface 1 cm sample for bulk density. However, it s still
useful as a GSM sample. Please make a note on the sample quality in your notebook.
Clean all rings.
Computing the Volumetric Soil Moisture and Bulk Density
•
•
•
Compute the sample GSM and dry mass
Divide the mass of the soil by the volume of the cylinder (1 or 5 cm) hole to obtain the
sample bulk density
Compute VSM=GSM*BD
86
Figure 33. Coring tool parts.
87
11.7
Gravimetric Soil Moisture Sample Processing
All GSM samples are processed to obtain a wet and dry weight. It is the sampling teams
responsibility to deliver the can, fill out a sample set sheet, and record a wet weight at the field
headquarters. A lab team will transport the samples to NSTL and place the samples in the drying
ovens. They will perform the removal of samples from the oven, dry weighing, and can cleaning.
All gravimetric soil moisture (GSM) samples taken on one day will be collected from the field
headquarters in late afternoon or early the following morning. These samples will remain in the
ovens until the morning of the second day (approximately 24 hours).
Wet Weight Procedure
1.
2.
3.
4.
5.
6.
Turn on balance.
Tare.
Obtain wet weight to two decimal places and record on sheet.
Process your samples in sample numeric order.
Place the CLOSED cans back in the box. Arrange them sequentially.
Place box and sheet in assigned locations.
Dry Weight Procedure
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
Each day obtain a balance reference weight on the wet weight balance and the dry
weight balance.
Pick up all samples from field headquarters.
Turn off oven and remove samples for a single data sheet and place on tray.
These samples will be hot. Wear the gloves provided
Turn on balance.
Tare.
Obtain dry weight to two decimal places and record on sheet.
Process your samples in sample numeric order.
All samples should remain in the oven for approximately 20-22 hours at 105oC.
Try to remove samples in the order they were put in.
Load new samples into oven.
Turn oven on.
Clean all cans that were removed from the ovens and place empty cans in boxes.
Check that can numbers are readable and replace any damaged or lost cans with
spares.
Return the clean cans to the field HQ before 8:00 am the following day.
Data Processing
1.
Enter all data from the sheets into an Excel spreadsheet. One file per day, one
worksheet per site.
88
2.
3.
4.
5.
There will be a summary file for each day that will contain the means and
standard deviations.
All files are backed up with a floppy disk copy.
The summary file will be transmitted to a central collection point on a daily basis.
You may keep copies of raw data for any site that you actually sample at this
stage. You may not take any other data until quality control has been conducted
89
11.8
Watershed Site Soil Bulk Density and Surface Roughness
All sites involved in gravimetric soil moisture sampling will be characterized for soil bulk
density. The method used is a volume extraction technique that has been employed in most of
the previous experiments and is especially appropriate for the surface layer. Four replications
are made for each site.
The Bulk Density Apparatus
The Bulk Density Apparatus itself consists of a 12" diameter plexiglass piece with a 6" diameter
hole in the center and three 3/4" holes around the perimeter. Foam is attached to the bottom of
the plexiglass. The foam is three inches high and two inches thick. The foam is attached so that
it follows the circle of the plexiglass. Figure 34 shows the basic components.
Other Materials Required for Operation:
Three 12" threaded dowel rods and nuts are used to secure the apparatus to the ground.
A hammer or mallet is used to drive the securing rods into the ground.
A bubble level is used to insure the surface of the apparatus is horizontal to the ground.
A trowel is used to break up the soil and to remove the soil from the hole.
Oven-safe bags are used to hold the soil as it is removed from the ground. The soil is left
in the bag when it is dried in the oven.
Water is used to determine the volume of the hole.
A plastic gas can is used to carry the water to the site.
One gallon plastic storage bags are used as liners for the hole and to hold the water.
A 1000 ml graduated cylinder is used to determine the volume of the water. Plastic is
best because glass can be easily broken in the field.
A hook-gauge is used to insure water fills the apparatus to the same level each time.
Selecting and Preparing an Appropriate Site
1.
2.
Select a site. An ideal site to conduct a bulk density experiment is: relatively flat,
does not include any rocks or roots in the actual area which will be tested and has
soil which has not been disturbed.
Ready the site for the test. Remove all vegetation, rocks and other debris from the
surface prior to beginning the test. Remove little or no soil when removing the
debris.
90
Figure 34. How to take a bulk density sample.
Bulk Density Procedure
Securing the Apparatus to the Ground
1.
2.
3.
Place the apparatus foam-side-down on the ground.
Place the three securing rods in the 3/4" holes of the apparatus.
Drive each dowel into the ground until they do not move easily vertically or
horizontally. (Figure 34a)
Leveling the Apparatus Horizontally to the Ground
1.
2.
Tighten each of the bolts until the apparatus appears level and the foam is
compressed to 1-1/2" to 2".
Place the bubble level on the surface of the apparatus and tighten or loosen the
bolts in order to make the surface level. Place the level in at least three directions
and on three different areas of the surface of the apparatus.
Determining the Volume from the Ground to the Hook Gauge
1.
2.
3.
Pour exactly one liter of water into the graduated cylinder.
Pour some of the water into a plastic storage bag.
Hold the plastic bag so that the water goes to one of the lower corners of the bag.
91
4.
5.
6.
7.
8.
9.
10.
Place the corner of the bag into the hole. Slowly lower the bag into the hole
allowing the bag and the water to snugly fill all of the crevasses.
Slightly raise and lower the bag in order to eliminate as many air pockets as
possible.
Lay the remainder of the bag around the hole.
Place the hook-gauge on the notches on the surface of the apparatus.
Add water to the bag until the surface of the water is just touching the bottom of
the hook on the hook-gauge. A turkey-baster works very well to add and subtract
small volumes of water. Be sure not to leave any water remaining in the turkeybaster. (Figure 34b)
Place the graduated cylinder on a flat surface. Read the cylinder from eye-level.
The proper volume is at the bottom of the meniscus. Read the volume of the
water remaining in the graduated cylinder. Subtract the remaining volume from
the original 1000 ml to find the volume from the ground surface to the hookgauge.
Carefully transfer the water from the bag to the graduated cylinder. Hold the top
of the bag shut, except for two inches at either end. Then use the open end as a
spout. (It is best to reuse water, especially when doing multiple tests in the field.)
Loosening the Soil and Digging the Hole
1.
2.
3.
4.
Label the oven-safe bag with the date and test number and other pertinent
information using a permanent marker.
Loosen the soil. The hole should be approximately six cm deep and should have
vertical sides and a flat bottom. (The hole should be a cylinder: with surface area
the size of the hole of the apparatus and height of six inches.)
Remove the soil from the ground and very carefully place it in the oven-safe bag.
(Be careful to lose as little soil as possible.) (Figure 34c and d)
Continue to remove the soil until the hole fits the qualifications.
Finding the Volume of the Hole
1.
2.
Determine the volume from the bottom of the hole to the hook-gauge as described
in Determining the Volume from the Ground to the Hook-Gauge. Reusing
the water from the prior measurement presents no potential problems and is
necessary when performing numerous experiments in the field.
Subtract the volume of the first measurement from the second volume
measurement. The answer is the volume of the hole.
Calculating the Density of the Soil
1.
2.
3.
Dry the soil in an oven for at least 24 hours.
Determine the mass the soil.
Divide the mass of the soil by the volume of the hole. The answer is the density
of the soil.
92
Potential Problems and Solutions
After I started digging I hit a rock. What should I do?
The best solution is to start over in another location. Also, you can remove the rock from the soil
and subtract the volume of the rock from the total volume of the water. You should never
include a rock in the density of the soil. Rocks have significantly higher densities than soil and
will invalidate the results. Roots, corn cobs, ants and even mole holes will also invalidate the
results. If you find any of these things the best thing to do is start the test again at another site.
After I began digging the hole I noticed one of the dowels wasn’t the apparatus firmly in
place. Do I have to start over?
Unfortunately, if you have already started digging you do have to start the experiment again.
Replacing the dirt to find the volume between the ground surface and the hook-gauge will give
an inaccurate volume and thus an inaccurate soil density.
I noticed that the bag holding the water has a small leak. Is there anything I can do? If the
leak began after you had already found the volume, it is not necessary to start again. The volume
is being measured in the graduated cylinder. If you have already removed the appropriate
volume of water leaks in the bag, it will not affect the results of the test. However, if you noticed
the leak before finding the volume, you will have to start again.
Surface Roughness
•
•
Take a photo along and across the rows at each BD location with the grid board.
The site and sample ID should be indicated in the photo.
93
11.9
Hydra Probe Soil Moisture and Apogee Temperature Sensor Installations
Figure 35 shows a close up of the Hydra probe. As with the installation of any soil moisture
measuring instrument, there are two prime considerations: the location the probe is to be
installed at, and the installation technique. A copy of the instruction manual for the HP will be
available at he field HQ and can also be found at http://hydrolab.arsusda.gov.
Figure 35. The Hydra probe used at the tower locations.
Selecting a Location for the HP
•
•
The probe installation site should be chosen carefully so that the measured soil parameters
are "characteristic" of the site.
Make sure that the site will be out of foot traffic and is carefully marked and flagged.
Installation of the HP
•
The installation technique aims to minimize disruption to the site as much as possible so that
the probe measurement reflects the “undisturbed site” as much as possible.
o Dig an access hole. This should be as small as possible.
o After digging the access hole, a section of the hole wall should be made relatively
flat. A spatula works well for this.
o The probe should then be carefully inserted into the prepared hole section. The
probe should be placed into the soil without any side to side motion which will
result in soil compression and air gaps between the tines and subsequent
measurement inaccuracies. The probe should be inserted far enough that the
plane formed where the tines join the probe head is flush with the soil surface.
94
•
o After placing the probe in the soil, the access hole should be refilled.
o For a near soil surface installation, one should avoid routing the cable from the
probe head directly to the surface. A horizontal cable run of 20 cm between the
probe head and the beginning of a vertical cable orientation in near soil surface
installations is recommended.
Other general comments are below.
o Avoid putting undue mechanical stress on the probe.
o Do not allow the tines to be bent as this will distort the probe data
o Pulling on the cable to remove the probe from soil is not recommended.
o Moderate scratches or nicks to the stainless steel tines or the PVC probe head
housing will not affect the probe's performance.
Installation of the Apogee Surface Temperature Sensor
A copy of the instruction manual for the Apogee sensor (Figure 36) will be available at he field
HQ and can also be found at http://hydrolab.arsusda.gov.
•
•
•
Height
Target
Angle
Figure 36. Apogee thermal infrared sensor.
95
11.10 Vegetation Sampling
Purpose of Sampling
The purpose of vegetative sampling is to provide an estimate of the variation in the vegetative
components in the corn and soybean fields across the SMEX02 study sites.
Parameters
1.
2.
3.
4.
5.
6.
Plant height
Ground cover
Stand density
Phenology
Leaf area
Green and dry biomass
Sampling Locations
Three sites within each field will be sampled during the course of the study to quantify the full
range of vegetative cover. A minimum of three sampling times will be considered for SMEX02,
a week before the study during the first week, and during the last week. Sites will be determined
through aerial surveys of the sampling fields conducted during an early May and early June
overflight. This is necessary since the spatial pattern across the field will depend upon the soil
water distribution. For example, in the case of a wet spring the soils in the potholes of the
landscape will be water stressed because of excess water and growth will be slowed, conversely
in a dry year growth may be enhanced in the lower areas because of the increased soil water
availability.
Site Identification
Sites will be identified with a unique site id made of the field number, within field site and row
number, e.g., field number = V09 plus site 1 plus row 2 yields an id of V0912. The V will
denote a vegetative sampling site to avoid any potential confusion with other measurement sites
within the same field.
Sampling Layout
Each site will be identified with a flag in the right hand corner and a pole that extends above the
crop height to aid in locating the site. Each sampling site will consist of a 10 row area by 10 m.
This will provide adequate area for all sampling dates (see Figure 37).
Sites will be located with GPS units and coordinates recorded for the corners of the site prior to
the first sample collection. Sampling will not require the use of GPS but the right hand row of
the sampling site will be flagged at the end row of the field so the sampling crews can travel
down the rows.
96
Row direction
X 10
9
X 8
7
X 6
5
X 4
3
X 2
1
Flag
3m
First sample
3m
Second sample
X denotes sampling row
3m
Third sample
Figure 37. Vegetation sampling layout.
Sampling Scheme
Sampling sites are designed to provide a representative sample from the area of the field. One
plant will be sampled from every other row for phenological stage and green and dry biomass (5
plants total). These same rows will be sampled for height, stand density, and row cover. Leaf
area will be measured at four locations (in-row, ¼ across row, ½ across row, ¾ across row) with
LI-2000. The first sampling position will be between the first row (flag location and the row to
the left.
The first sampling time will begin on the end next to the flag, the second sampling time will
begin 3 m down the row from the flag, and the third 6 m from the flag.
Protocols
1. Stand density
Stand density will be determined by placing meter stick along the row in each of the 5 rows
sampled. The meter stick will be placed at the center of a plant stem and that stem counted
as the first plant. All plants within the one-meter length are to be counted. If a plant is at the
end of the meter stick and more than half of the stalk extends beyond the end of the meter
stick it is not counted. Counts are recorded onto the sampling sheet.
2. Phenology
Phenological stage for corn and soybean will be determined using the standard phenological
guide. The guide is attached as an appendix to this protocol. Plants from the sampling rows
will be measured and recorded.
97
3. Height
Height will be measured by placing a measuring rod on the soil surface in the row and
recording the height of the foliage using a horizontal bar that just touches the upper leaves.
One person will hold the measuring stick and the other determine the proper position of the
horizontal bar.
4. Green and Dry Biomass
To measure biomass a plant will be cut at the ground surface from each sampling row. The
five plants for the sampling site will be placed into a plastic bag with a label for the sampling
site. A separate tag with the sampling site id will be placed into the bag as additional
insurance against damaged labels. These plants will be transported to the field facility for
separation of the plant material into stalks and leaves for corn and stems and leaves for
soybean. Corn plants can be separated into leaves and stalks in the field for easier transport
to the laboratory. These plant parts will be placed into a bag for drying and marked with
sample site id.
Green biomass will be measured for both components (stalks or stems and leaves) by
weighing the sample immediately after separation of the components. If the biomass has
excess of moisture on the leaves and stalks this will be removed by blotting with a paper
towel prior to weighing. Dry biomass will be determined after drying the plant components
in ovens at 75C for 48 hours.
5. Leaf Area
Leaf area will be measured with a LI-2000 (Figure 38) in the inter-row region at least one
meter away from where the biomass sample was taken (5 sets of 4 across-row
measurements). The LI-2000 will be set to average 4 locations into a single value so one
observation is taken above the canopy and 4 beneath the canopy; in the row, ¼ of the way
across the row, ½ of the way across the row and ¾ of the way across the row. This gives a
good spatial average for row crops of partial cover. The observer always stands with their
back to the sun if it is shining and a lens cap that blocks ¼ of the sensor view is always in
place and positioned so the sun and the observer are never in the view of the sensor. The
observer should always note if the sun was obscured during the measurement, whether the
sky is overcast or partly cloudy with the sun behind the clouds. If no shadows could be seen
during the measurement, then the measurement is marked “shaded”, if shadows could be
seen during the measurement then the measurement is marked “sunny”. Conditions should
not change from cloudy to sunny or sunny to cloudy in the middle of measurements. When
measurements are made under sunny conditions, then someone will return to the site during a
diffuse sky condition (near sunrise or sunset or when it is overcast within 2 days of the
original measurement) and repeat the measurement to make sure that the presence of direct
sun did not compromise the accuracy of the LAI measurement. Sunlit conditions can cause
underestimates of LAI when LAI is low so factors must be obtained by which to adjust LAI
measurements under sunny conditions. This does not necessarily mean all sites will have to
be re-measured, but only sites representative of the sites measured.
98
Figure 38. The LAI-2000 instrument.
6. Photographs
Photographs will be taken of plot area at the time of sampling. These will be collected with a
digital camera. A marker board will be used to mark the plot, field location, and date.
Photographs will be collected at an oblique angle (30-45º from horizontal) and at nadir at a
height of a minimum of 1 m above the canopy. Cameras will be fixed to a telescoping pole
to allow positioning above the canopy and a remote trigger to collect data. Three photos will
be taken in each plot in this order; marker board, oblique, and nadir.
7. Ground reflectance
Ground reflectance will be measured at each vegetative sampling site with a
spectroradiometer. The spectroradiometer will be positioned above the plot area at a height
to obtain a 50-100 cm2 viewing area. These data will be collected during the period from
10:00 – 1330 CST on clear days. These data will be recorded by plot with five readings per
plot at different positions along row 10 of the plot. Data will be electronically recorded and
stored and transferred into a ground reflectance database at the end of each day. Reflectance
panel data are collected with a Spectrolon panel throughout the day to estimate reflectance
values. Data would be collected with an Exotech radiometer mounted on a high-boy tractor
in selected corn and soybean fields.
Data Recording
Data will be recorded onto the sampling sheet illustrated in Figure 39. Each field will have a
separate notebook and data sheets for each sampling plot within the field. Each blank on the
sheet will be filled in during the observation period. Data sheets will be maintained as part of the
permanent experimental record to verify the data once it is entered into the computer.
99
Vegetative Sampling
Date
Observers
Time
Row Spacing
Row Direction
Crop
Photographs Taken
Sample
id
Stand
density
Height
(plants/m)
(m)
Phenology
Ground
Cover
Green (g)
Biomass
(%)
Stem Leaf
x
x
x
x
x
x
x
x
V10102
V10104
V10106
V10108
V10110
Dry (g)
Biomass
Stem
x
x
x
x
Leaf Sun/shade
Area
Index
Leaf
x
x
x
x
Figure 39. Example of the vegetation sampling data sheet.
11.11 Plant Canopy Analyzer Measurements
Setup
•
•
•
•
•
•
Connect sensor to X-connector (on left as viewed from keypad). Clean the lens carefully
with a lens brush.
Setup List:
o Verify X Cal data for X port. Serial No. should match sensor on X-connector.
(FCT 01; 02)
o Set Resolution = HIGH.
o Make sure time and date are correct (FCT 05)
Operating Mode:
o Set Op Mode= 1 sensor X (FCT 11)
o Sequence= 1 above and 5 below (FCT 12)
o Reps= 1
o Bad Reading= BEEP AND IGNORE (FCT 16)
Verify that each ring (X1 thru X5) is responding to light. (BREAK)
View cap = 90 degrees.
Sky conditions:
o Diffuse illumination is ideal, but measurements can be made on sunny days with
the following precautions:
o Make all Above and Below readings with your back to the sun and with the view
cap blocking the sensor's view of you and the sun.
100
o Shade the sensor with your body to prevent reflections of the sun from
influencing the readings.
o Shade the part of the canopy which is visible to the sensor with the umbrella.
Sunlit leaves cause the sensor to underestimate LAI.
Sampling
1. Press LOG. Enter site number for SITE= prompt, e.g., LW01 or ER08. Enter plot+sample
for the SAMPLE= prompt, e.g., A10, A20, or C20. Add a zero to the sample number to
indicate measurement is within the sampling frame.
2. Level the sensor above the canopy, shade the sensor from direct sun, and press the button on
the sensor handle.
3. Note:Two beeps will be heard: one when the button is pressed and the other when the reading
has been completed. Between the two beeps, keep the sensor level.
4. Put the sensor beneath the vegetation and level it. The sensor should view the same
direction as the Above canopy reading. Press the button on the sensor handle.
5. Move the sensor about 15 cm diagonally (relative to the field of view of the sensor) and take
another beneath the canopy reading. Repeat for 5 beneath the canopy readings. After the last
reading the display will show COMPUTING....
6. Note: The first set of LAI measurements should be within the sampling frame. The
subsequent measurements should be within 3 m of the frame.
7. Move to a new area outside the sampling frame and repeat steps #6-9 four times. SITE =
stays the same (press enter to retain the value). SAMPLE= the last digit increments by one,
e.g, A11, A12, A13, A21, A22, etc.
Downloading LAI-2000 files to a PC (see chapters 6 and 9 of Instruction Manual)
1. Use FCT 31 to set
• BAUD =
4800
• DATA BITS =
7
• PARITY =
None
• Xon/Xoff =
No
2. Run communications program, PROCOMM, on personal computer. Configure the
computer's RS-232 port to match the LAI-2000. Connect the computer and LAI-2000 with
the appropriate cable.
Specify the destination for the incoming data.
c:\SMEX02\LAI\yymmddn.ext where yymmddn = year, month, day, name of team leader
(c=Curry, r=Russ, w=Ward)..ext = format of output (.std = standard, .spr = spreadsheet
format)
3. Output the LAI-2000 data files in the standard (.STD) and Spreadsheet (.SPR). formats.
Backup files to a floppy disk.
4. Print the spreadsheet format files.
5. Clear the files after verifying that all files have been transferred.
101
11.12 Global Positioning System (GPS) Coordinates
The acquisition of geographic coordinates at all sample point locations (e.g., WC and IA points,
vegetation sites, and flux towers) is necessary for mapping of data in a Geographic Information
System (GIS). A Garmin eTrex “sportsman” GPS will be used to collect location data. This unit
has the capacity to store up to 500 geographic coordinates or waypoints and it is designed so that
all key entries can be performed with the left hand alone. Accurate GPS data can be acquired 24
hours a day under all weather conditions. The only restraint is that the eTrex antenna--location
determination is made at the site of the internal antenna--must have a clear view of the sky in all
directions. Once accurate location data at a particular sample site has been acquired and
confirmed, no additional measurements at that site will be needed.
•
All sampling points will be located using a handheld GPS.
o WC points
o IA points
o Vegetation samples
o Flux towers
General Information
Record eTrex ID number (etched on back cover), site and point ID, and latitude and longitude
coordinates in field notebook.
Watershed and regional sites should be labeled as follows:
Watershed: Site WC## and point ## (e.g., WC05-02)
Regional: Site IA##
Carry at least two (2) extra AA alkaline batteries. The eTrex is configured to run in Battery Save
mode which automatically turns the GPS receiver on and off to conserve power. In this mode,
the eTrex should operate for approximately 22 hours. A “Battery Low” message will appear at
the bottom of the screen when the unit has ten (10) minutes of battery life remaining.
eTrex GPS Features (see Figure 40)
UP/DOWN ARROW buttons: used to select options.
ENTER button: used to confirm selections or data entry.
PAGE button: switches between display screens (or “pages”) and functions as escape key.
POWER button: turns eTrex GPS as well as display backlight on and off.
102
Internal
Antenna
Up Arrow
PAGE
Down
Arrow
Power
Enter
Data cable and
Connector
Battery Cover
AA
Alkaline
Figure 40. GPS features.
All eTrex operations are carried out from the four (4) “pages” (or display screens) Shown in
Figure 41. The PAGE key is used to switch between pages. (The Map and Pointer Pages are
used for navigation and will not be discussed further.)
Satellite
Map
Pointer
Menu
Figure 41. GPS display screens or “pages”.
103
Setup at Headquarters Prior to Data Collection
1. Power unit on: Depress and hold power button until eTrex welcome screen appears and
Satellite Page is displayed.
2. Confirm configuration parameters:
• PAGE to Menu screen; ARROW to Setup; press ENTER (Figure 42)
• Use the following key sequence to check configuration parameters:
- ARROW to first parameter; press ENTER;
- confirm values (see configuration values below);
- press PAGE to return to Setup menu;
- ARROW to next parameter, etc.
• The following are the parameters and required settings;
- Time = Format: 24 Hour; Zone: US-Central; (UTC Offset: -6:00);
Daylight Saving: Auto
- Display = Timeout: 15 sec.
- Units = Position Format: hddd.dddddo; Map Datum: WGS 84; Units:
Metric; North Reference: True
- Interface = I/O Format: Garmin
- System = Mode: Battery Save
Figure 42. GPS Menu page.
3. Turn eTrex off after GPS data collection by depressing and holding POWER button until
screen blanks.
Important Note: Geodetic datums mathematically describe the size and shape of the earth and
provide the origin and orientation of coordinate systems used in mapping. Hundreds of datums
are currently in use and particular attention must be paid to what datum is used during GPS data
collection. The Global Positioning System is based on the World Geodetic System of 1984
(WGS84). However, popular map products such as USGS 1:24,000 topo sheets originally used
the North American Datum of 1927 (NAD27). Most of the maps in this series have been
updated to the North American Datum of 1983 (NAD83). Fortunately, there is virtually no
practical difference between WGS84 and NAD83. Yet significant differences exists between
104
NAD27 and NAD83. (In Iowa, a north-south displacement of approximately 215m occurs
between NAD27 and NAD83.) All geographic coordinates collected with the eTrex GPS should
be acquired using the following parameters: latitude/longitude (decimal degrees), WGS84
datum, meters, true north. Various coordinate conversion software packages such as the
Geographic Calculator ($500) or NOAA’s Corpscon (free) exist which allow the conversion of
geodetic (latitude and longitude) coordinates into planar (UTM or State Plane) coordinates for
GIS mapping.
GPS Field Data Collection
1. Power unit on: Depress and hold power button until eTrex welcome screen appears and
Satellite Page is displayed (Figure 43). Wait until text box at top of screen reads
“READY TO NAVIGATE” before continuing.
Wait until “READY TO NAVIGATE is displayed
AND
Accuracy is <= 10 m
(Up to 5 minutes at a new location;
15-45 seconds on subsequent visits.)
Figure 43. GPS Satellite Page.
2. Adjust screen backlight and contrast, if necessary.
• Turn backlighting on by quickly pressing and releasing POWER button from any
screen. (To save power, the backlight remains on for only 30 seconds.); AND/OR,
• Adjust screen contrast by pressing UP (darker) and DOWN (lighter) buttons from
Satellite Page.
3. Initiate GPS point data collection:
• PAGE to Menu screen (Figure 42); Arrow to Mark; press ENTER. (Shortcut: press
and hold ENTER button from any screen to get to Mark Waypoint page below.)
• ARROW to alphanumeric ID field (Figure 44); press ENTER. Use ENTER and
UP/DOWN buttons to edit ID, if necessary. (Waypoint ID increments by one (1)
automatically.)
• Record latitude (North) and longitude (West) coordinates displayed at bottom of
screen into field notebook. Do not rely on electronic data download to save data
points!
• ARROW to OK prompt; press ENTER to save point coordinates electronically.
105
ID field; edit ID using ENTER
and UP/DOWN buttons
ARROW to OK prompt; press
ENTER to store coordinates.
Copy geographic coordinates
into field notebook
Figure 44. GPS Mark Waypoint Page.
4. Turn eTrex off after GPS data collection by depressing and holding POWER button until
screen blanks.
Electronic Data Downloading
Electronic data downloading will be performed at field headquarters by assigned person.
Connect PC data cable by sliding keyed connector into shoe at top rear of eTrex (under flap);
power eTrex on.
Launch Waypoint.exe
GPS => Port => Com?
Waypoints => Download
File => Save => Waypoint
Select Save as type: Comma Delimited Text File
106
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13
INVESTIGATOR ABSTRACTS
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Flux Measurement and Large Eddy Simulation of Land-Atmosphere Exchange
John D. Albertson and William P. Kustas
John D. Albertson, Civil and Environmental Engineering, Duke University, Box 90287, Hudson
Hall, Durham, NC 27708-0287, john.albertson@duke.edu
We will field several eddy covariance towers to measure water, energy, and carbon fluxes in
representative fields within the footprint of the aircraft operating over the Walnut Creek
Watershed in Ames, Iowa. Our measurements will be conducted in coordination with the other
surface flux efforts, as overseen by John Prueger. Furthermore, through our participation in the
Kustas et al. NASA project our measurements will be coordinated with the aircraft flux, aircraft
remote sensing, and Lidar operations. Also, under the Kustas et al. NASA project we will be
conducting Large Eddy Simulation (LES) of the Atmospheric Boundary Layer (ABL) dynamics
over the remotely sensed land surface fields.
Our tower measurements will include: upward and downward directed short-wave, long-wave,
and photosynthetically-active radiation; sensible heat, latent heat, and CO2 fluxes; radiometric
surface temperature; air temperature and humidity; wind speed, direction, and friction velocity
(u*); precipitation; soil moisture; soil temperature; soil heat flux.
The LES work will be based on our LES-Remote Sensing approach (Albertson et al., WRR, 37,
1939-1953, 2001). We will simulate land surface flux fields over the region and ABL
development and structure. Detailed atmospheric structure simulated by the LES will be
evaluated in the context of the Lidar observations. Joint analysis of the LES results and Lidar
data will be conducted to explore mixing of the ABL over heterogeneous terrain, assess feedback
effects of the heterogeneity on land-atmosphere coupling, and test spatial scaling hypotheses
over complex terrain. Remotely sensed fields of surface cover, temperature, and moisture, and
balloon and lidar profiles of the ABL are critical data needs to support the LES work.
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115
Operational Use of Scatterometer Data over Land to Improve Hydro-Meteorological
Forecasts
Mark A. Bourassa1, James J. O’Brien1, David E. Weissman2, Jeffrey Tongue3, Tom Adams4
1. Center for Ocean-Atmospheric Studies, Florida State University, Tallahassee FL 32306-2840,
bourassa@coaps.fsu.edu, (850) 644-6923,FAX: (850) 644-4841
obrien@coaps.fsu.edu, (850) 644-4581
2. Hofstra University, eggdew@hofstra.edu, (516) 463-5546, FAX: 631-269-5920
3. National Weather Service WFO, 175 Brookhaven Ave., Upton, NY 11973,
Jeffrey.Tongue@noaa.gov, (516) 924-0593 x 224, FAX: (516) 924-0593 x 224
4. Ohio River Forecast Center, National Weather Service, 1901 S. State Route 134, Wilmington,
OH 45177, (937) 383-0527, FAX: (937) 383-0038
This project will create a new capability for remote sensing of surface moisture, and study the
use of these observations in weather forecasting, hydrology, and forestry. The combinations of
satellite-derived surface moisture measurements, which have good temporal coverage (the wide
1800 km swath width and orbit selection results is approximately twice daily sampling) and high
spatial resolution, with other National Weather Service resources are expected to lead to major
enhancements land weather forecasts. Results of this research will enable conventional rain data
(e.g., NEXRAD precipitation) to be used to improve the knowledge (and hence models) of
retention and runoff in surface hydrology. The SMEX02 data desired for our study are ground
water content observations, which can them be used for validation/calibration of our algorithms.
The National Aeronautics and Space Administration recently launched a new, polar orbiting
satellite radar, named “SeaWinds” on the QuikSCAT spacecraft. This instrument is a specialized
microwave radar designed to measures near surface wind speed and direction under all weather
and cloud conditions over the earth’s oceans; however, it also operates over land. The swath
width of the radar is 1800 km. Wind vector estimates are averaged over each 25x25km square
area within the swath; however, high-resolution techniques have been used to obtain 10x10km
backscatter products from a single pass over land (Spencer et al. 2000; Early and Long 2001).
Surface locations are illuminated an average of 2 times each day with one satellite. Another
SeaWinds instrument is scheduled to be launched in November 2002, and it is anticipated that
the orbits will be designed to provide ~4 times daily sampling in ~6 hour intervals. These
examples of sampling are appropriate for the latitudes of the United States of America; sampling
will be a little worse in the tropics, and much better near the poles. Through a cooperative effort
between NASA/JPL and NOAA/NESDIS Office of Research and Applications near real-time
data is available for rapid transfer to selected NWS operational offices. We have identified an
unanticipated application of scatterometer observations: there is a signal related to the moisture
in the upper several centimeters of the ground. A principal objective of our project is to facilitate
the implementation and evaluation of these observations, at the level of the individual Weather
Forecast Offices, state climatologists (the PI, O’Brien, is the Florida State climatologist), and
numerical weather prediction (NWP). This project will implement mutual education and training
between NWS Scientific & Operations Officers, hydrologists, and Scatterometer researchers in
ways that had not been envisioned when the satellite was designed.
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Preliminary results (see figure below) indicate that there is a substantial signal in the backscatter
(the signal returned to the satellite) related to moisture in the surface or in vegetation. The
integrated depth of surface moisture to which the satellite instrument responds will be
approximately 1cm: it will measure the moisture content very close to the surface. Such
observations should be ideal for NWP initialization, for which knowledge of surface moisture
content is poorly known, and for which improvements in accuracy have been anticipated to lead
to substantial improvements in NWP accuracy. We anticipate that the value of such observations
will increase with time, as the resolution of NWP models increases, and local changes in surface
moisture content are expected to have greater impacts. We will explore a theoretically-based
model, and calibrate this model with in situ ground observations products through several
projects. The resulting estimates of moisture content will be used studies involving NWP,
hydrology, moisture climatology (drought impacts), forestry (fire risk), and estimates of
mosquito-related problems. The theoretical developments will be lead by David Wiessman,
NWP and hydrology applications will be pursued by the NWS, and the applications to drought,
forestry, and mosquitoes will be undertaken a COAPS and related to our routine activities in
these areas.
Stronger backscatter
from wet ground.
-2.5 -2.0 –1.5 –1. -0.5 –.25 2.5 0.5 1.0 1.5 2.0
Change in H-pol σo (dBZ)
(Wet period – dry period)
TS Alison
NEXRAD radar image of TS
Alison and associated rain.
Figure - Change in QSCAT H-pol so (after a dry period; wet case minus dry case) due to TS
Alison. Backscatter is binned in 0.25 degree bins, from 3 hour periods on separate days.
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Scaling Characteristics of Remotely Sensed Vegetation, Surface Radiometric Temperature,
and Derived Surface Energy Fluxes
N. A. Brunsell and R. R. Gillies
Nate Brunsell, Dept. of Plants, Soils, and Biometeorology, Agricultural Science 322, Utah State
University 4820 University Blvd., Logan, UT 84322-4820
The proposed research entails examining the spatial variability observed over multiple scales of
remotely sensed data. These will include airborne, Landsat TM, and AVHRR. Wavelet
multiresolution analysis will be used to examine the characteristics of vegetation and surface
temperature over the range of scales. A Soil-Vegetation-Atmosphere-Transfer (SVAT) model
will be used to derive surface energy fluxes using the "Triangle" method and the scaling
characteristics of the resultant fluxes will also be examined. In addition, the co-spectra will be
used to examine the correlation between remotely sensed input and the derived surface fluxes.
The proposed research will aid in the assimilation of coarse resolution (e.g., AVHRR) remotely
sensed data into regional meteorological models.
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119
Optimizing Land-Atmosphere Interaction Models For Use With Data Assimilation
Anthony Cahill
Department of Civil Engineering, Texas A&M University, 3136 TAMU, College Station, TX
77843-3136, Phone: 979-862-3858, Fax: 979-862-1542
This project seeks to explore what approach to modeling the land-atmosphere interaction is best
for use with data assimilation. There are a number of different models for estimating the energy
fluxes and the moisture and temperature states at the land surface. These models are of varying
complexity, and simulation the transport processes in different ways. Which land-atmosphere
interaction models give the best results with different methods of data assimilation is an open
question. In fact, how to define "best" is itself an open question.
In this work, the PI will develop a method a estimating the merit of a data assimilation-land
surface model set, and using the data collected in the SMEX 02 experiment, will determine
which approaches are best. The PI will be part of the flux tower data collection team at SMEX
02, and this data will be used in the analysis proposed here. Other data to be used will include
the aircraft flux and soil brightness temperature measurements.
It is assumed that a means of determining the "best" set of methods can be done by minimizing a
discrepancy measure, such as is done with AIC. Other factors that must be kept in mind in the
analysis include the different scales on which the measurements are taken, and the difficulty of
estimating measurement error for remotely sensed measurements. The overall contribution of
this project will be guidance for future work in the application data assimilation of satellite
measurements in land surface hydrology, when the satellite measurements become available.
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121
Assessing the Role of Distributed Hydrologic Modeling for Validation of AMSR-E Soil
Moisture Products
Wade Crow
USDA-ARS Hydrology and Remote Sensing Laboratory, 104 Bldg. 007, BARC-West,
Beltsville, MD, 20705.
The coarse spatial resolution of spaceborne microwave radiometers (> 30 km) poses a severe
challenge for efforts to validate soil moisture products derived from such observations.
Validation based on point-scale observations require upscaling strategies (e.g. kriging,
interpolation, weighted averaging) capable of converting local observations into meaningful
predictions of soil moisture at the footprint-scale. The success of any given strategy hinges
largely on obtaining an accurate of the underlying autocorrelation within the soil moisture field
and the manner in which soil moisture variability is cross-correlated with observable land surface
attributes (i.e. topography, soil and vegetation cover).
Given the practical difficulties in obtaining in situ soil moisture data of sufficient length and
extent to provide such descriptions, recent interest has focused on the use of distributed
hydrologic models to bridge the scale gap between in situ observations and footprint-scale
retrievals. The assumption underlying such approaches is that hydrologic models are capable of
realistically capturing landscape-scale soil moisture heterogeneity. Regional soil moisture
observations during SMEX02 provide an excellent opportunity to test such an assumption and
clarify the potential role of distribution hydrologic modeling for validation of spaceborne
retrievals and/or the design of optimal in situ observation networks.
Specific goals include:
1) Intercomparison of statistical and explicit representations of surface soil moisture
heterogeneity obtained from field observations, retrievals, and hydrologic models during
SMEX02.
2) Development and testing of model-based strategies for accurately upscaling a subset of
regional soil moisture observations up to the AMSR-E footprint scale.
122
123
Energy Balance and Crop Yield Studies at Walnut Creek Watershed
Paul Doraiswamy, William Kustas
Hydrology and Remote Sensing Laboratory, USDA- ARS, Beltsville Maryland
Jerry Hatfield and John Prueger
National Soil Tilth Laboratory, USDA-ARS, Ames, Iowa
Objective 1: Daily Energy Balance and Evapotranspiration mapping for the Watershed using a
dynamic energy balance model
Model required Inputs: Hourly/Daily climate data, vegetation classification, LAI, Canopy
reflectance (ground and airborne), surface temperature (ground and airborne) and TM Landsat
imagery.
Outputs: Parameter maps for the Watershed of ET, Energy Balance and Crop Water Stress.
Objective 2: Corn and Soybean Crop Yields using parameters retrieved from remote sensing
with crop simulation models.
Inputs:
Daily climatic data, vegetation classification , LAI, soil classification
Baseline nitrogen levels, Management practices and crop yields at fields
selected for monitoring vegetation parameters.
Outputs: Crop yields at watershed and county levels
Objective 3: Scaling up ET and Crop Yields from field to watershed.
Data acquired for Objectives 1 and 2 will be used in models to scale up to the watershed level.
Inputs: Daily climatic data, crop phenology, vegetation classification, leaf area index, soil
classification, baseline nitrogen levels. Satellite and available aircraft imagery for visible, near
IR and thermal.
Outputs: ET and crop yield maps for the watershed.
Planned Ground Measurements at Walnut Creek
• Vegetation classification
• Corn and soybean canopy architecture parameters (one time in July)
• Canopy reflectance (one time in July)
Planned satellite data acquisition and processing
• ETM Landsat- 30 m visble/near IR sharpened to 15 m
- 90 m surface temperature
• MODIS- 250 m visible and near IR
- 1 km surface temperature
• AVHRR- 1 km (surface temperature and visible/near IR)
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Measurements Required from Other Investigators
Crop measurements
LAI, biomass, crop phenological, crop yields
Soil measurements
Soil physical properties at field or water shed levels
Soil chemical (C,N) properties at field or water shed scales
Soil moisture
Surface Measurements
Water and CO2 fluxes from tower measurements
Climate measurements from towers
Aircraft Measurements
Visible near IR reflectances
Surface temperature
Soil moisture
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126
127
Field Observations of Soil Moisture Variability from the Point to Remote Sensing
Footprint Scale
Jay Famiglietti, UC Irvine, jfamigli@uci.edu, tel: 949-824-9434; fax: 949-824-3874
Graduate student participants:
Aaron Berg, UC Irvine, berga@uci.edu
Sally Holl, UC Irvine, sholl@uci.edu
Dongryeol Ryu, UC Irvine, dryu@uci.edu
Ki-Weon Seo, UT Austin, kiweon@speer.geo.utexas.edu
Our field studies will build on our previous work during SGP97 and SGP99. In 1997 we made
detailed observations of surface soil moisture variations within a dozen ground truth (quarter
section) sites, and carefully analyzed the data in 6 of these fields. We found that soil moisture
variability increased with increased drying and that the distributions followed predictable forms.
In 1999 we studied soil moisture variability across scales, from 2.5 x 2.5 m plots to a 1.6 x 1.6
km (full section) area, quadrupling the size of the areas studied in 1997. Results indicated that
variability increases with increasing drying, confirming the 1997 results, and that variability
increases with increasing spatial scale. Additionally, the impact of different land use treatments
within the 800 m quarter-section fields on the full (1.6 km) section distributions was evident.
During the SMEX02 campaign, in conjunction with the local ARS staff, we will undertake
regional-scale sampling in an area that is approximately 50km by 50km, or as large as one or
more future soil moisture satellite footprints. We will continue to explore the scaling behavior of
soil moisture variability and distributions, and investigate whether this behavior follows any
predicable patterns across scales.
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129
Dual C- and X-band High-Resolution Imagery of Soil Moisture
Albin J. Gasiewski1, Aleksandre Yevgrafov1, Marian Klein1, and Thomas J. Jackson2
1
NOAA Environmental Technology Laboratory, 325 Broadway, R/ET1, Boulder, CO 803053328, (303) 497-7275 (O), (303) 497-3577 (F), al.gasiewski@noaa.gov
2
USDA-ARS Hydrology Lab, 104 Bldg 007 BARC West, Beltsville MD 20705, (301) 504-8511
(O), (301) 504-8931 (F), tjackson@hydrolab.arsusda.gov
Additional NOAA/ETL Participants: Dr. Vladimir Irisov, Dr. Gary Wick, Dr. Vladimir Leuskiy,
Lee Church, Robert Zamora, Dr. Valery Zavorotny, Michael Falls
The Polarimetric Scanning Radiometer was originally developed at the Georgia Institute of
Technology starting with a concept proposal in 1995, and first operated on the NASA P-3B
aircraft in 1997 for the Labrador Sea Ocean Winds Imaging (OWI) experiment. Since this initial
deployment it has been upgraded and successfully operated by the NOAA Environmental
Technology Laboratory (ETL) during several airborne campaigns (Piepmeier and Gasiewski
1996) [1], including the Third Convection and Moisture Experiment (CAMEX-3) in August and
September 1998, Southern Great Plains Experiment (SGP99) during July 1999, and Meltpond
2000 during June 2000. As a result of these campaigns the PSR has provided the first airborne
passive microwave imagery of ocean surface wind vector fields, the first multiband conicalscanned imagery of hurricanes and intense convection, the first high-resolution C-band imagery
of soil moisture, and the first high-resolution conical-scanned imagery of sea ice.
During the 2002 Soil Moisture Experiment (SMEX02), the PSR/CX scanhead will be integrated
onto the NASA WFF P-3B aircraft in the aft portion of the bomb bay. The PSR/CX scanhead is
an upgraded version of the previously successful PSR/C scanhead used during SGP99. The
installation will utilize the NOAA P-3 bomb bay fairing, and will locate the PSR immediately aft
of the NASA GSFC ESTAR L-band radiometer on the NASA P-3. The upload will commence at
NASA WFF around June 17, 2002. Flights in Iowa will occur starting around June 24, and
flights in Oklahoma around July 8. Transit back to WFF will occur around July 15. A total of
approximately nine 3-4 hour data flights are planned in Iowa, and ~three in Okahoma. Flights
will be coordinated to the extent possible with the NCAR C-130, which will support the JPL
PALS L- and S-band radiometer/scatterometer.
The primary hypotheses to be studied using the PSR/CX data during SMEX02 are:
•
•
Can C-band imagery be used to reliably measure surface soil moisture in the presence of
agricultural biomass?
Can coincident C- and X-band imagery be used to improve single-band measurements by
compensating for vegetation-induced brightness perturbations?
The first of these hypotheses was answered in the affirmative for grassland and agricultural
regions cultivated with low-canopied crops during the PSR SGP99 campaign, wherein good
correlation was obtained between PSR-estimated soil moisture and surface truth over Oklahoma
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during mid-summer conditions. During SMEX02 we will study the estimation of soil moisture
using C-band for taller crops, specifically corn (~1-2 m height) and soybeans (~20-30 cm
height). The second of these hypotheses as not been studied extensively to date, and the PSR/CX
and ESTAR airborne imagery will provide a unique data set by which to study the relative
penetration capabilities of L-, C-, and X-band.
In addition to the above hypotheses, the following issues will be studied:
• Applications of sub-watershed soil moisture mapping, both in water management, flashflood and wildfire potential estimation, and surface emission modeling.
• Algorithm development for C- and combined C- and X-band soil moisture retrieval.
The successful development of dual C- and X- band radiometry for airborne soil moisture
mapping is expected to help improve the ability to manage the distribution of water as well as the
prediction of hazardous conditions.
Ancillary data required for the above investigations include in-situ soil moisture samples, crop
type maps, watershed terrain maps, and canopy height and biomass samples. Soil moisture
samples measured by R. Zamora of NOAA/ETL will made using time domain reflectometry and
will be intercompared with available measurements from gravimetric and other techniques.
[1] See http://www1.etl.noaa.gov/radiom/psr/ .
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Spatial Variation of Surface Soil Water and Crop Growth across Production Scale Fields
J.L. Hatfield, J.H. Prueger, and C. Walthall
J.L. Hatfield, NSTL, 2150 Pammel Drive, Ames, Iowa 50011-4419, 515-294-5723, 515-2948125 (fax), email: hatfield@nstl.gov
Background: Soil water content in the upper surface varies with soil surface properties and is
critical for a plant germination and emergence. Of particular importance after the emergence of
the economic crop is the impact of soil surface water content on the emergence of weeds. Recent
studies have shown the effect of elevation and soil type on crop growth and yield but there has
been little quantification of these factors on soil water or early season crop growth. Soil heat
flux was documented to vary across small scales (1-2 m) in the early season with the amount of
variation related to the development of leaf area in the corn canopy. Observations of canopy
growth have suggested a direct relationship between leaf area development and soil water
holding capacity. Each of these observations suggest that we need to quantify the spatial
variation that exists within production scale fields to be able to relate these to yield patterns at
harvest.
Hypothesis: 1) Variation of surface soil water content is independent of soil type and elevation
across fields. 2) Variation of leaf area index during the growing season is independent of soil
water holding capacity.
Approach: Two fields with corn and soybean crops will be measured for surface soil water
content in transects over a range of soil types. The spacing of the transect will range from 1 to
10 m for a length of 300 m. This field will be co-located with the micrometeorology
experiments. Prior to planting a detailed RTK survey to obtain a 10-20 cm DEM will be
conducted on these two fields. Soil type will be obtained from digitized soil survey maps and
verified with satellite imagery from the archives in central Iowa. Soil water content will be
measured with surface probes daily before and after a rainfall event to obtain the changes in
spatial variation.
Leaf area index will be measured within defined areas of the field to capture differences in soil
water holding capacity. Pedon data exists for these soils based on measurements made by the
NRCS Soil Survey Laboratory to obtain soil water holding capacity information. Leaf area will
be measured with a LI-2000 and related to observations made with aircraft mounted radiometers.
Aircraft flights will occur in early June and July as part of another experiment to obtain a field
scale map of leaf area and biomass to relate to the ground observations. Observations of leaf
area with the LI-2000 and dry biomass will be made biweekly for this field during the SMEX02
experiment.
Data Analysis: Transect data for soil water content will be analyzed for any changes in the
spatial patterns over time and co-kriged with elevation and soil type data. These patterns will be
examined for both crops and patterns evaluated relative to amount of ground cover. Leaf area
relationships relative to soil type will be evaluated by comparison of the leaf area means with
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soil water holding capacity to determine if these relationships are valid. The aircraft data will
permit the extension over the field or to other fields.
Data requirements: Surface soil water, leaf area, and biomass from observations within
selected areas of the intensive micromet field. Combine these observations with requirements of
Parkin CO2 study to link growth or water content to CO2 fluxes.
Personnel: NSTL and cooperators will supply personnel to complete these measurements.
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Determination of Surface Fluxes and Coupling to the ABL
Lawrence Hipps
Plants, Soils, & Biometeorology
Utah State University
Logan, UT 84322-4820
(435) 797-2009
Larry@claret.agsci.usu.edu
The proposed activities will involve participation in the flux measurements during the field
study, as well as later analyses and interpretation of the results. The coupling between the surface
and atmosphere will be determined for each site to quantify the processes that govern the
evaporation. Any spatial variations in coupling that are associated with variations in surface
properties will be identified.
Flux Measurements and Analysis
Two eddy covariance stations will be supplied by USU. Each will consist of a CSAT 3-D sonic
anemometer and a LiCor 7500. There will also be a CR23X, and a Kipp & Zonen NR Lite net
radiometer for each station. These sites will be configured to sample the turbulence instruments
at 20 Hz and save the time series data.
A number of corrections need to be made to the data to yield the best possible flux estimates. A
correction for density effects is the obvious first adjustment. However, there are other corrections
that are sometimes important. These include a coordinate rotation to force mean vertical velocity
to zero and orient the axes according to the mean wind, accounting for the limited frequency
response of instruments, correcting for the finite path length of instruments, effects of separation
of sensors, band pass filters as appropriate to remove frequencies not associated with the
turbulence fluxes, and choice of a proper time average. A recent paper from an Ameriflux
workshop by Lee and Massman discusses most of these corrections. In consultation with other
investigators, they will be applied to all the flux data as appropriate.
Energy balance closure values will be determined for each station to address the reliability of the
flux estimates. Input will be made into a group decision about how to address the issues arising
from energy balance closure values. For example, a decision must be made about whether or not
to adjust the fluxes to force closure of the energy balance.
Spatial Variations in Surface-Atmosphere Coupling
Depending upon spatial variations in surface properties, various fields may be coupled
differently to the ABL. Effects of horizontal advection may vary as well. These processes may
affect the energy and water balance of different surfaces. The coupling factor can be determined
for each site, to determine the importance of the key processes that govern the evaporation. The
goal is to determine what changes in coupling are associated with spatial variations in the surface
134
properties. Such information will allow us to determine which biophysical factors are governing
the surface fluxes.
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Improving Hydrologic Models Performance by Better Prediction of Soil Moisture
Temporal Variability
Investigators: Wael Khairy1, Teferi Tsegaye1, Wubishet Tadesse1, Tommy Coleman1, Ali
Sadeghi2, and Gregory McCarty2
1
Center for Hydrology, Soil Climatology, and Remote Sensing, Alabama A and M University,
Normal, AL.
2
Environmental Quality Laboratory, USDA-ARS, BARC-West, Beltsville, MD.
Contact Information: Wael Khairy, voice (256) 858-4218, Email: wkhairy@aamu.edu, Mailing
Address: Center for Hydrology, Soil Climatology, and Remote Sensing, Alabama A and M
University, 4900 Meridian Street, P.O. Box 1208, Normal, AL 35762.
Description: Variability of soil moisture with time and space depends mainly on the vegetation
cover, soil properties, climate conditions, and hydrologic conditions. Many hydrologic processes
and land system simulation tools depend on nonlinear surface soil moisture equations. Therefore,
soil moisture variability must be better understood to enable full utilization of the regional-scale
remotely sensed data. Our focus in this research study will primarily be on assessing the
temporal variability of near-surface soil moisture. Thus, the relationship of soil moisture change
under different vegetation and soil properties against time will be better estimated for the Iowa
soils. This will result in a more accurate soil moisture temporal change formulation that can be
introduced in the hydrological modeling tools. Soil moisture profile at 2, 3, 4, 5, and 6 cm depths
will be determined at the selected locations in four fields (two with corn and two with soybeans)
using a soil moisture Impedance Probe (IP). Soil moisture sampling will be conducted two times
a day; one in the morning and one in the afternoon during the SMEX02 period. The soil moisture
change versus time relationship will be estimated at two fields (corn and soybeans fields) and
then verified using data from the other two fields. Ground soil moisture observations obtained
from the near-surface layer (0-6cm) will be used to address the following two specific objectives:
1) calibration and validation of the regional-scale hydrologic models; and 2) verification of the
microwave radiometer soil moisture algorithms. The remotely sensed soil moisture values
determined at the same times and locations of the ground soil moisture sampling will be
compared to verify their consistency. Findings of this study will provide quantitative validation
of the algorithms used for AMSR soil moisture estimation.
Hypothesis: A better understanding of soil moisture change with time under different vegetation
cover and soil properties will certainly improve the temporal variability simulation of soil
moisture using the regional-scale hydrologic models.
Data Needs: The parameters that will be measured include: 1) soil moisture profile in the nearsurface layer (0-6 cm) and near-surface temperature in the morning and afternoon of each day; 2)
soil bulk density; 3) porosity; 4) hydraulic conductivity; 5) soil texture; and 6) vegetation cover
type and density. The remotely sensed surface soil moisture data in the areas covering the
sampling spots will be collected through the SMEX02 management team. The prevailing climate
conditions (air temperature, humidity, solar radiation, and wind speed) will be collected from the
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nearest weather station to the study area. Rainfall depth and duration, if occurred during the
experiment period, will be collected from the nearest rain gauge or weather station to the study
area. Soil properties and soil moisture data will be collected in cooperation with Dr. Tsegaye
since our research studies will need the same soil datasets. Vegetation data needed for this
research study will be obtained from Dr. Tadesse since he will focus on the characterization of
vegetation parameters for soil moisture estimation. Drs. Sadeghi and McCarty will assist in
assessing the temporal variability of near-surface soil moisture.
Contribution to the SMEX02: It is expected that, this research study will contribute to the
overall SMEX02 gravimetric soil moisture and vegetation sampling programs. In addition, this
study will help verifying the microwave radiometer soil moisture algorithms through comparing
the remotely sensed soil moisture values with the measured ground soil moisture values. Also,
this research study will help in improving the performance of regional-scale hydrological
modeling tools used by NASA.
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Calibration Of SVAT-Microwave Models And Soil Moisture Signature Scaling Behavior
Under Higher Vegetation Biomass Conditions
Edward J. Kim
NASA/GSFC
Mailstop 975
Greenbelt, MD 20771
ph: 301-614-5653; fax: 301-614-5558; edward.j.kim.1@gsfc.nasa.gov
Research Interests:
1) Calibration of forward SVAT-microwave models under conditions different from those of
Oklahoma—specifically: different profiles of soil properties & moisture, greater vegetation
column density (2-6 kg/m2), different vegetation structure, and multiple microwave frequencies.
Work would be initially at the scale of homogeneous fields, and then at larger footprint sizes to
explore scaling behavior.
2) Investigation of disaggregation approaches in mixed-pixel areas, such as using active
microwave observations for resolution enhancement.
Data needed:
satellite: AMSR if available, SSM/I
airborne: all geolocated passive & active microwave observations.
ground truth: profiles of soil texture, porosity, bulk density, moisture* (including regional-scale
sampling), & temperature*; soil surface roughness.
vegetation truth: total column biomass* (wet, dry) per field, height*, crop type, LAI*, perhaps
more detailed plant structure statistics.
micromet: standard energy balance quantities*.
*quantities needed as a function of time during the experiment
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139
Ground Based Microwave Radiometer Experiments in SMEX02
Toshio Koike (tkoike@hydra.t.u-tokyo.ac.jp) and Mahadevan Pathmathevan (devan@hydra.t.utokyo.ac.jp)
River and Environmental Engineering Laboratory (REEL), Department of Civil Engineering,
The University of Tokyo
Accurate assessment of soil moisture is one of the most important and mathematically
challenging issue in the current research field. Heterogeneity is the most critical issue in this
research. Remote sensing observations, used in conjunction with in situ measurements, can
provide information of value in modeling, validations and monitoring the biophysical processes
governing balances of energy and water at the land surface. Observations from space are
uniquely suited to studying the spatial and temporal variabilities of these processes over a wide
range of scales due to their wide-swath mapping and orbital sampling capabilities. When the use
of remote sensing observations as inputs for the development and operation of land surface flux
models, parameterizations of surface characteristics and fluxes must be matched to
parameterizations of radiative transfer.
We developed a Land Data Assimilation System, can act as a resistant to control the effects of
uncertainties and noise inherent in the observational data and asses the model parameters through
minimization techniques. In addressing linkages between surface fluxes and remote sensing
observations, spatial homogeneity (one component) or heterogeneity (multi components) must be
integrated with in the remote sensors footprint. The above two basic challenges have to be
addressed in assimilating remote sensing data into hydrological and atmospheric circulation
models. The SMEXP02 can provide us an opportunity to upgrade our land surface models by
completing the following two experimental tasks.
Land Data Assimilation System (LDAS) for vegetated/bare soil field:
•
We propose an observation strategy to parameterizes and validate our land surface and
radiative transfer models. This study can define appropriate parameterizations for
individual surface components (one kind of vegetation area (Soya bean), natural surface
of bare soil and smooth surface of bare soil) for linking remote sensing and surface flux
models. Our research group can carry out these observations. Surface brightness
temperature observations can be made by a 6 channel Ground Based Microwave
Radiometer (GBMR-6).
Additionally in this case, we would like to use the observations from co-research groups; low
frequency (1.4Ghz) microwave brightness temperature by aircrafts sensors (Lower altitude:
single surface component and higher altitude: surface heterogeneity) and turbulent fluxes.
140
Land Data Assimilation System (LDAS) for field with heterogeneity: Scaling up
This study must be carried out for accounting the scaling up the heterogeneity effects, especially
for covering remote sensing observations from large footprints. At present stage we did not
propose any large-scale experiments. Higher altitude aircraft observations will be much helpful
to us for further modification in our modeling efforts. The potentials of TRMM/TMI and a
possible combination of ADEOS-II AMSR and AQUA AMSRE are expected to play important
role in these studies.
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Soil Moisture-Atmosphere Coupling Experiment (SMACEX)
William P. Kustas, John D. Albertson, Nate Brunsell , Anthony T. Cahill, Daniel J. Cooper,
George R. Diak, William Eichinger, Jerry L. Hatfield, Lawrence E. Hipps, J. Ian MacPherson,
Christopher Neale, John M. Norman, John H. Prueger
William P. Kustas, USDA-ARS Hydrology and Remote Sensing Lab, Bldg 007 BARC-West,
Beltsville, MD 21045, Bkustas@hydrolab.arsusda.gov
The proposed field study (SMACEX) greatly expands the objectives of the Soil Moisture
Experiment 2002 (SMEX02). The main objective of SMEX02 is to provide a data set for the
development and verification of alternative passive microwave soil moisture retrieval algorithms
for regions with the significant biomass amounts characteristic of agricultural crops. The field
campaign will be conducted in the Walnut Creek Watershed, Iowa (centered at 41.96 N. Lat.
93.6 W. Long.) encompassing a region approximately 10 x 20 km. The measurement campaign
will run from nominally June 10 – July 8, 2002. The planned experiment will provide a data set
ideally suited to addressing several timely research foci in the area of water and energy cycling
across the land-atmosphere interface. The Canadian Twin-Otter aircraft will collect surface-layer
and atmospheric boundary-layer (ABL) flux data. Support for two other remote sensing
activities, namely aircraft-based high resolution optical remote sensing data and ground-based
Lidar observations of wind and water vapor concentrations in the ABL, will provide
simultaneous landscape and atmospheric properties covering a wide range of temporal and
spatial scales. Combining these observations together with a network of 15 tower-based flux
observations will result in a complete set of distributed surface and atmospheric data, allowing
for Land-Atmosphere-Transfer-Schemes (LATS) and Large Eddy Simulation (LES) model
validation and development and testing of methodologies to bridge the scales from local to
regional. A schematic diagram summarizing the measurement and modeling activities
(experimental logistics) proposed for the project and the overall framework addressing upscaling issues is given in Figure 1. This figure also illustrates the interdependency of the
proposed activities and that all components of the project are required in order to achieve
proposal goals and objectives. Abstracts outlining research plans by individual investigators of
SMACEX are provided.
The SMACEX program provides the necessary framework for linking the relevance of soil
moisture monitoring to water and energy cycling. However, with a modest additional
investment, NASA will foster fundamental scientific advances well beyond the existing
instrument validation goals of SMEX02. The expected advances with the coupled measurement
and modeling program will address one of NASA’s core missions of seeking to rigorously bridge
between remotely sensed data and operational forecast models, including advances in operational
data assimilation schemes.
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143
Soil Moisture Retrieval Using the PALS Sensor
Venkat Lakshmi
Department of Geological Sciences, University of South Carolina, Columbia SC 29208
(803)-777-3552, (803)-777-6449 (fax), vlakshmi@geol.sc.edu
(1) Validation of PALS retrieved soil moisture using the in-situ collected data
We will use the in-situ data along with the ancillary data to employ a radiative transfer model
as well as a scattering model to retrieve the soil moisture using the L and S band radar and
radiometer. This work is similar to the SGP99 study. However, we expect a harsher
vegetation environment for the Iowa study due to greater vegetation biomass.
(2) Validation of a hydrological model for the Walnut River basin
A hydrological model can be run for the Walnut river basin for the duration of the
experiment at pre-specified temporal and spatial resolutions in order to provide a third basis
(in-situ and aircraft/satellite are the other two) for comparison and validation studies. This
can be used to study spatial scaling relationships and comparisons with satellite and aircraft
data.
(3) Relationship between the in-situ soil moisture and large scale microwave observations
There are a few microwave sensors in orbit – SSM/I, ERS-1 etc. We propose to use these
in-situ observations to carry out regressions and validation of satellite derived soil moisture
with these sensors. In addition, they will provide a good basis for understanding the scaling
properties of soil moisture, in-situ – model – aircraft – satellite.
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145
Validation of the AMSR-E Brightness Temperature and Soil Moisture Products
Chip Laymon, Bill Crosson, Ashutosh Limaye, Frank Archer, Global Hydrology and Climate
Center, 320 Sparkman Dr., Huntsville, AL 35805
Chip Laymon, Global Hydrology and Climate Center, 320 Sparkman Dr., Huntsville, AL 35805,
256.961.7885, charles.laymon@msfc.nasa.gov
Project Description:
We plan to utilize a coupled hydrologic/radiobrightness model (H/RM) to provide “best
estimates” of footprint-scale mean volumetric soil moisture and C and X band TB with associated
variance and confidence limits. This information will provide quantitative validation of AMSRderived soil moisture. The high spatial and temporal resolution of the model output relative to
AMSR observations will permit us to evaluate a.) the errors associated with using a limited
number of point-scale measurements to estimate footprint-scale mean soil moisture, b.) the errors
associated with asynchronous sampling times, and c.) the relationship between surface moisture
(~1 cm) and profile moisture. These analyses are necessary to characterize the accuracy of the
AMSR data products at footprint scale. Our interest in SMEX02 is to obtain data necessary to
conduct these AMSR validation studies. For the most part, the data necessary for hydrologic
modeling will be collected as part of the overall SMEX02 sampling/measurement strategy that is
presented in the experiment plan. To this extent, we plan to support the gravimetric soil moisture
and vegetation sampling programs. The TB derived from our model will be used to validate the
aircraft-derived TB. Conversely, the aircraft data can be assimilated into the model periodically
to improve model performance.
In addition, we wish to address two specific topics: 1) upscaling surface roughness and
vegetation properties from point to footprint scale using remote sensing, and 2) to define the bias
between 0-1 cm and 0-5 cm GSM. Passive microwave remote sensing measurements are highly
sensitive to surface roughness and vegetation water content. Previous experiments have used a
simple correlation table based on NDVI and land cover to scale point measurements of
vegetation water content and surface roughness up to aircraft pixel scales. The Iowa study site
offers us a good opportunity to explore some potentially viable direct remote sensing
measurements of the spatial variability of vegetation biomass and surface roughness. We will
acquire MODIS, ASTER, and Radarsat data for the regional-scale study area. We will also
acquire Quickbird II data at 2.5 m resolution (multispectral) for a 256 km2 area. In addition, we
are exploring the possibility of acquiring hyperspectral data with Hyperion and the CASI
instrument from ITRES Research Ltd. The use of radar data to provide a proxy for surface
roughness will be reevaluated. The use of multi- and/or hyperspectral data fused with radar data
as a proxy for vegetation biomass will also be investigated. These remote sensing products will
be used to investigate upscaling from field measurements of biomass and Neale's vegetation
products to scales consistent with an AMSR footprint. This scale transformation is an important
piece of the puzzle leading to the exclusive use of satellite-based sensors such as Landsat,
MODIS, ASTER, Hyperion, Radarsat, etc. for parameter retrieval in conjunction with AMSR
soil moisture retrieval algorithms.
146
To define the bias between 0-1 and 0-5 cm GSM, we will utilize the sliced core method
developed during the Huntsville '98 experiment to sample the gravimetric soil moisture at 1 cm
intervals in the upper 6 cm of soil. We anticipate collecting samples at 3-4 sites on 4 fields in
conjunction with the standard daily GSM sampling. On approximately three occasions, we will
collect samples on an hourly basis throughout the day until late at night to characterize the
diurnal cycle of the near surface profile. We are investigating possibilities for automated
measurements of the near-surface profile, but plans for this are still sketchy at this time. These
data will compliment the more detailed mapping of the near-surface profile that Dr. Tsegaye is
planning for two fields.
Data requirements for hydrologic modeling:
Time-dependent input: wind speed, air temperature, relative humidity, rainfall, atmospheric
pressure, downwelling solar radiation, downwelling longwave radiation.
Static variables: slope, saturated hydraulic conductivity, saturated matric potential, soil
wilting point, rooting depth, soil porosity, canopy height, fractional vegetation cover,
minimum stomatal resistance, leaf area index, reflectance properties.
Contributions:
1.) support for surface GSM and vegetation sampling
2.) characterize the near-surface soil moisture profile, its diurnal variations, and the bias between
0-1 and 0-5 cm
3.) modeled, spatially-distributed soil moisture and temperature profiles over regional domain
(resolution TBD).
4.) modeled, spatially-distributed TB over regional domain.
5.) remotely sensed proxies for surface roughness and vegetation biomass over regional domain
147
Soil Moisture Measurements Using Synthetic Aperture Radiometry
D. M. Le Vine and T. J. Jackson
David M. Le Vine, Code 975, Goddard Space Flight Center, Greenbelt, Maryland 20771, Phone:
301-614-5640; FAX: 301-614-5558, email: dmlevine@priam.gsfc.nasa.gov
One possible approach to obtaining global maps of soil moisture from space with resolution of
10 km or better, is the use of aperture synthesis. Aperture synthesis is an interferometric
technology for passive microwave sensing, that has been successfully demonstrated in one
dimension with the L-band instrument, ESTAR. Research to extend synthesis to both
dimensions is underway. An aircraft instrument, called 2D-STAR, is currently being built under
NASA’s Instrument Incubator Program. Based upon mission requirements only ESTAR will be
flown in SMEX02.
The specific goals of the measurements with the ESTAR instrument are:
•
Facilitate progress toward a robust algorithm for remote sensing of soil moisture. SMEX02
provides a wide range of vegetation than past experiments. In addition, this will be the first
extended AIRSAR/ESTAR mission and these data sets will be critical to future space
mission design.
•
The instrument will be used to map large areas for soil moisture to provide data to the
SMEX02 research community for studies of land-atmosphere interactions.
Aperture synthesis is an interferometric technology for passive microwave remote sensing
designed to address the problem of putting large apertures in space (Le Vine et al. 1994). The
technique is similar in principle to earth rotation synthesis employed in radio astronomy
(Thompson et al. 1986). The technique uses the product of pairs of small antennas and signal
processing to create the resolution of a single large aperture. In aperture synthesis, the coherent
product (correlation) of the signal from pairs of antennas is measured at different antenna-pair
separation (baselines). The product at each baseline yields a sample point in the Fourier
transform of the brightness temperature map of the scene. By making measurements at many
baselines, one in effect samples the Fourier transform of the scene and to make an image, the
sampled transform is inverted (e.g. Le Vine et al. 1994).
This technique has been successfully demonstrated for remote sensing with an instrument called,
ESTAR, which uses synthesis in one dimension (Le Vine et al. 1990, 1994). ESTAR, is a hybrid
which uses aperture synthesis in the cross track dimension and a real aperture in the along track
dimension. This hybrid works well (Jackson et al. 1995) and is suitable for remote sensing from
space (Le Vine et al. 1989). However, it does not take full advantage of the thinning possible
with aperture synthesis and it is not easily adapted to conical scanning.
148
149
Relation of Soil Dielectric Properties to Soil Water Content
Sally Logsdon
NSTL, 2150 Pammel Dr., Ames, IA 50010; logsdon@nstl.gov; (515)294-8265.
Background: Soil dielectric properties are assumed to be related to water content, based on the
notion that the relative real dielectric number for water is around 80, for soil particles is around
4, and for air is 1. If this were true, then the dielectric properties would be dominated by water,
and could easily be related to soil water content. The problem is that the real dielectric number
for water is not 80 over all frequencies. The SMEX documentation so far expressed interest in
using data at frequencies ranging from 1.4 up to 89 GHz! Because of the free water relaxation,
the real dielectric number drops off dramatically much above 1 GHz. The frequency 89 GHz is
much beyond the free water relaxation, so the real dielectric number is much less than 80. At
frequencies below 1 GHz, the bound water relaxations start to become significant (an important
consideration for those advocating the Theta probe, which operates at 0.1 GHz). Both the free
water and bound water relaxations are influenced by temperature. At operating frequencies
above the relaxation frequency, the real dielectric increases as temperature increases. At
operating frequencies below the relaxation frequency, the real dielectric decreases as temperature
increases. Because of variation in canopy cover, and sunny vs. cloudy days impact on incomplete
canopy cover, surface soil temperatures vary greatly in June, and are the highest in June (which
greatly increases electrical conductivity).
Hypothesis: Soil dielectric numbers are influenced by other factors in addition to soil water,
which complicates developing a relationship for soil water content as a function of the real soil
dielectric number at a particular measurement frequency. At the very least, the calibration should
include temperature.
Data needs: It is absolutely essential that those testing and calibrating the theta probe include the
effect of temperature. I am greatly concerned that those pushing the use of this probe to increase
the amount of data collected will only end up with useless data based on some quicky lab
calibration. Dr. Mohanty presented data to show that fewer data points are needed if the points
are carefully selected.
Contributions to project: I am willing to contribute directly or indirectly as needed. I will even
volunteer to help with the lab testing of theta probes, to make sure that a variation of
temperatures is included. I have taken samples for Dr. Tsegaye, and will provide other local
support and background data as needed (i.e. physical characterization of local soils).
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151
Aircraft Flux Program as Part of SMACEX (Soil Moisture Atmosphere Coupling
EXperiment)
Ian MacPherson (NRC) and Bill Kustas (USDA)
Ian MacPherson, Flight Research Laboratory, National Research Council of Canada,
U-61, Montreal Rd., Ottawa, Ontario, K1A 0R6, Phone: (613)-998-3014, fax: (613)-952-1704,
Email: ian.macpherson@nrc.ca
As part of the Soil Moisture Experiments in 2002 (SMEX-02), the Twin Otter atmospheric
research aircraft, operated by the National Research Council of Canada, will make boundary
layer flux measurements in the Walnut Creek watershed near Ames, Iowa.
These flux
measurements will form an important link between surface-based measurements and remotelysensed data to be collected by three other instrumented aircraft and a series of satellites.
The primary focus of SMEX-02 is to improve the understanding of the global water cycle, with a
focus on soil moisture and its exchange with the atmosphere. A principal objective is to continue
the development of algorithms to remotely measure soil moisture distributions in an area with
denser vegetation than Oklahoma, where previous studies have been conducted (e.g., SGP97).
The flux aircraft data will be used to ground-truth modeled evaporation rates and carbon uptake
based on the remotely sensed data. In particular, the aircraft data, in conjunction with the
surface tower and LIDAR measurements, will form a multi-scale dataset with which to evaluate
Land-Atmosphere-Transfer-Schemes (LATS) that have been developed to directly integrate the
spatial information provided by remotely sensed data.
Approximate dates for the flux aircraft field program are June 20 to July 6, 2002. The flight
program will consist of 30 project hours, 2 test-flight hours, and 12 transit hours. Ideally, most
flights will occur in the mid-morning (~1030 local time), around the time of EOS Terra and
Landsat 7 overpasses. Mid-morning is also the typical time for the ALEXI output and when the
frequency of clouds via boundary layer convective activity is minor. Flights will consist of
repeated low-altitude (30-50 m) flux runs on several tracks close to the flux towers and the
LIDAR, given the restrictions imposed by the proximity of dwellings and highways, and eyesafety issues for the LIDAR. Atmospheric profiles to above the top of the mixed layer will be
done at the start and end of each flight. A data playback system will be transported to the project
area, and printouts of preliminary flux data will be available to collaborating scientists a few
hours after each flight. A more complete re-computation of the flux data will be accomplished in
Ottawa after the field campaign, with the flux runs segmented by type of underlying vegetation
(corn, soybeans) or proximity to surface towers.
The NRC Twin Otter atmospheric research aircraft (MacPherson, et al., 2001) combines
immersion sensing with remote sensing capabilities. It is instrumented with an accurate 3-axis
wind sensing system along with various gas analyzers, which enables it to measure the vertical
fluxes of sensible and latent heat, momentum, CO2, and ozone. Remote sensing instruments are
used to record incident and reflected solar radiation and infrared surface temperature, and to
152
characterize the underlying surface vegetation. Figure 12 shows the basic configuration of the
instrumentation aboard the aircraft for flux experiments. A new net radiometer system will be
installed in the port wingtip for SMEX02. All data are recorded on a removable hard drive at a
rate of 32 Hz. The flux of N2O has also been successfully measured with this aircraft utilizing
the Relaxed Eddy Accumulation technique and equipment provided by Agriculture and AgriFood Canada (Flēchard et al., 2001). It is possible that this system will be used in SMEX-02 to
study the relationship between the N2O flux and the rate of fertilizer application along the
selected tracks. In that case, Ray Desjardins of Agriculture and Agri-Food Canada will join the
project team.
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GPS Bistatic Radar in SMEX02
D. Masters, P. Axelrad, University of Colorado, Boulder
V. Zavorotny, NOAA Environmental Technology Laboratory
Use of Global Positioning System (GPS) signals reflecting off of land and ocean surfaces is
under research as new, potentially inexpensive remote sensing tools. Simultaneous measurement
of both direct and surface-reflected GPS signals constitutes a bistatic radar system, with
transmitters located at GPS satellites and a separate receiver located above the surface of the
Earth. Researchers at the University of Colorado, Boulder (UCB), in collaboration with NASA
Langley Research Center (LaRC), have in the past focused on remotely sensing ocean surface
roughness to estimate wind speed and direction (Armatys et al. 2000), but UCB is now also
investigating GPS bistatic radar for soil moisture sensing and terrain avoidance applications
(Masters et al. 2000 and 2001). The SMEX02 campaign will provide an opportunity to gather
and investigate a controlled data set of land-based GPS reflections in the company of other
instruments attempting to remotely sense soil moisture.
GPS Bistatic Radar Concept
The GPS satellite constellation currently broadcasts a civilian-use carrier signal at 1575.42 MHz,
which is bi-phase modulated by satellite-specific pseudorandom noise codes. The signals are
encoded with timing and navigation information so that the receiver can calculate the positions
of the transmitting satellites and solve for its own position and time by measurement of
pseudoranges from at least four satellites. These direct signals are normally received by a lowgain, hemispherical, zenith antenna. These same GPS signal transmissions also reflect off of the
Earth's surface and can be measured with a nadir-viewing antenna at longer delays than the direct
signal. The reflected signal is modified by the roughness and dielectric properties of the
scattering surface. If the roughness is known a priori or is assumed constant over some time, the
ratio of reflected signal power to the direct signal power is an indicator of the dielectric constant
of the surface. Therefore, this ratio can be used to temporally sense changes in soil moisture in
the top 5 cm of the surface. Additionally, the polarization of the RHCP direct signal is
predominantly LHCP upon surface reflection for most incidence angles. Because the geometry
is variable depending upon the slowly changing transmitter and receiver positions, a
hemispherical nadir antenna has been used in past ocean reflection research. In SMEX02, a
higher gain nadir antenna is anticipated to achieve a better SNR at the expense of tracking
multiple satellites.
The received signals are cross-correlated with a replica signal (1 ms code length) to produce a
narrower, approximate 1 µs correlation pulse. This procedure is similar in design to pulse
compression radar receivers. Our previous efforts have been focused on the distribution or
spreading of the reflected signal power over time delay, which is an indicator of the roughness of
the reflecting surface. For soil moisture sensing, the observable is the ratio of the magnitudes of
the reflected and direct signal powers.
154
In bistatic radar systems, scattering is mainly forward, and the radar cross section is expressed as
a normalized bistatic cross section. For the specific case of an aircraft receiving direct and landreflected GPS signals, we use an analytical scattering model developed by Zavorotny and
Voronovich (2000) (Z-V model). The model is based upon physical optics and will employ a
rough surface estimated from the SMEX02 terrain.
GPS Bistatic Radar Receiver
The current GPS bistatic radar receiver is based upon a modified Plessey 12-channel C/A code
receiver built by NASA Langley Research Center. New receivers are currently being developed
for GPS bistatic radar applications, and their use in SMEX02 is possible. The Plessey receiver is
comprised of a single board containing two RF front-ends and a correlator, which is connected to
a PC-104 computer in a small, lightweight chassis (20x15x15 cm). The RF front-ends perform
automatic gain control, down conversion, and IF sampling. The PC-104 computer serves as the
controller and data logger for both GPS navigation functions and recording the signal power.
The GPSBuilder-2 software allows access to the correlator power measurements.
In the Delay Mapping Receiver (DMR) mode of operation, five channels track direct signals in a
conventional, closed-loop fashion. The pseudorange and Doppler measurements made by these
channels are used to form navigation solutions. The other 14 correlators (two for each of seven
channels) are controlled in an open-loop mode to measure reflected signal power at specific
delays relative to one or more of the direct signal channels. For each of the slaved reflection
correlators, one hundred 1 ms correlator samples are averaged to produce an estimate of reflected
signal power at a rate of 10 Hz. The reflected signal power is sampled in discrete bins around
the time delay corresponding to the arrival of the signal from the specular reflection point. The
direct and reflected signal power measurements are stored on internal disk for later analysis. In
the DMR mode of operation, the bistatic radar receiver can operate for long periods without user
intervention.
In SMEX02, the GPS bistatic radar receiver will operate as a proof-of-concept technology
onboard the NCAR C-130 aircraft. The GPS-based measurements will be evaluated in
conjunction with the observations made by the JPL PALS instrument and the ground sampling of
soil moisture. The GPS bistatic radar measurement parameters are presented in Table 11. These
tests will guide future development of optimized receivers and processing algorithms to retrieve
soil moisture and surface roughness information from GPS bistatic radar measurements.
155
Evolution of Multi-Scale Soil Hydrologic Processes and its Impact on Land-Atmosphere
Interaction
Binayak P. Mohanty, Department of Biological and Agricultural Engineering, Texas A&M
University, College Station, Texas, Douglas A. Miller, EMS Environmental Institute, Penn State
University, University Park, Pennsylvania, Todd H. Skaggs, George E. Brown Salinity
Laboratory, Riverside, California
Binayak P. Mohanty, 301C Scoates Hall, Dept. Biological and Agricultural Engineering, Texas
A&M University, College Station, TX 77843-2117, Tel: 979-458-4421, Fax: 979-845-3932,
Email: bmohanty@tamu.edu
NASA-GWEC Project Description
Soil moisture content at the land surface and subsurface is important for global water balance
calculations and land-atmosphere interaction in terms of partitioning upward and downward
water and energy fluxes at the land surface. Soil moisture is controlled by factors such as soil
type, topography, vegetation, and climate. The planned global-scale land surface mission of the
AMSR-E - AQUA (PM) satellite platform, and other insitu-, point-, field-, and aircraft-based soil
moisture measurement campaigns present a unique opportunity to study the evolution of multiscale soil hydrologic processes and controls across the conterminous USA at a range of spatial
and temporal scales. We propose analyzing spatio-temporal soil moisture data using exploratory
data analyses, geostatistical analyses, time-stability analyses, scaling, and subsurface flow
modeling. Our approach will integrate several high resolution continental-scale databases (1-10
kilometers) of soil (CONUS-SOIL, GCIP), topography (DEM, USGS), land cover (AVHRRderived), and climate (NEXRAD-based and AMSR-E-based) with a comprehensive numerical
variably-saturated subsurface flow simulation model (HYDRUS-1D). The estimated surface soil
moisture of AQUA AMSR-E will be used as the top boundary condition for the numerical model
as well as for calibration and assimilation purposes. Simulation model parameters appropriate for
the scale of AQUA AMSR-E footprint (56 km X 56 km) will be derived using Pedo-TopoVegetation Transfer Functions (PTVTFs) that are currently being developed based on data from
SGP97. Simulation results will be tested against historical subsurface soil moisture data sets
collected at various climatological networks including the Oklahoma Mesonet, Micronet, DOE
ARM-CART, and Illinois Water Survey. Overall the results of this data analysis-assimilation
research will help establish the effective use of land surface data products of space-borne remote
sensors to further our understanding of soil-vegetation-atmosphere interaction, and related
climate dynamics. Among other important benefits, time stability analysis will identify potential
areas for establishing strategic ground-based soil moisture monitoring network necessary for
biosphere-feedback to climate modeling.
Hypothesis
156
Soil, topography, vegetation, and climate interactively control space-time distribution of soil
moisture at different space and time scales. We propose to gain quantitative understanding of
their individual and interactive contributions.
SMEX02 Data Needs and Contributions
We will collect surface soil moisture data at multiple scales cutting across various soil, land
cover, and topographic features in Iowa during SMEX02 (June-July, 2002) campaign.
Our specific research contributions during the SMEX02 will include:
1.
Characterization of spatial distribution and process controls (i.e., soil, topography,
vegetation) on land-surface and subsurface soil moisture within space-borne remote
sensor footprints.
2.
Developing a framework for comparing methods for estimating space-borne remote
sensor footprint-scale mean soil moisture content from point, field-averaged, and airborne remote sensor data collected during SMEX02 experiment.
3.
Identify areas for establishing strategic soil moisture monitoring network based on
significant process controls.
4.
Testing a newly developed suite of PTVTF upscaling techniques from point, ground, or
air-borne remote sensor footprint (e.g., ESTAR, 800 m X 800 m) to space-borne AMSRE footprint (56 km X 56 km) scale.
Acknowledgment: This project is funded by NASA-GWEC program.
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Aircraft Remote Sensing and Energy Balance Based Flux Fields
Christopher Neale, Dept. Bio. & Irrigation Eng.,Utah State Univ., Logan, UT 84322-4105435797-3689
The Remote Sensing Services Laboratory at Utah State University will support the experiment
with a series of over flights using its airborne system of short wave and long wave imagers
mounted in a light twin-engine Piper Seneca II. The lab will be involved in the analysis of the
imagery and production of calibrated datasets to support other ongoing experiments. In
particular, we are interested in producing energy balance flux fields and comparing these with
the extensive ground-based flux measurements, including footprint analysis and extrapolation to
daily flux values.
USU Airborne Digital System
The system consists of three Kodak Megaplus 4.2i digital cameras, with interference filters
forming narrow spectral bands centered in the green (0.55 µm), red (0.67 µm) and near-infrared
(0.80 µm) portions of the electromagnetic spectrum. These filters are interchangeable so
different bandwidths could be used for this experiment if desired by the research community
involved. An onboard GPS system is used to navigate along pre-planned flight lines over the
site, as well as to geo-reference the approximate center of each set of digital images. The
cameras are mounted inside a specially designed graphite composite cylinder with adjustable
aluminum mounts that are installed through a hole in the belly of a Piper Seneca II twin-engine
aircraft. The adjustable mounts allow for the alignment of the cameras, which are usually set to
view a very distant target.
The system also supports an Inframetrics 760 thermal infrared scanner that is mounted through a
separate porthole for the acquisition of thermal infrared imagery in the 8 - 12 µm range. This
imagery is stored on S-VHS videotape and later frame-grabbed in the laboratory. A color video
camera is used to acquire color imagery of the flight. GPS position information is encoded on the
bottom of this imagery, which are also stored on videotape.
Image Acquisition Over flights
The over flights are presently budgeted to cover approximately 17 hours of flight time over the
site. The flights will be planned, usually for the morning hours to avoid excessive cloud cover,
and to support different project activities that will include:
1. Systematic coverage of the larger research area with a combination of short wave (1.5
meter resolution) and long wave (3 meter resolution) measurements to coincide with
airborne microwave flights.
2. Higher resolution (0.5 meter short wave, 1 meter long wave) imagery over the fields
containing the flux stations and lidar equipment
3. Higher resolution flight lines over sites where biophysical canopy properties will be
measured and intensive soil moisture measurements will be conducted
158
On the day of each flight, a calibrated barium sulfate reflectance panel will be set up
leveled at a central location within the study area. The incoming irradiance reflected by
the panel will be continuously monitored using an Exotech 4-band radiometer with
Thematic Mapper bands TM1-4. The radiometer used will be the same instrument used
to obtain the absolute calibration of the digital cameras in a separate lab experiment. The
reflectance of some large uniform representative surfaces within the study area will also
be measured to check the image calibration.
At the beginning or at the end of each flight mission, the aircraft will acquire thermal infrared
imagery over a nearby reservoir, where water temperatures are routinely measured. These data
will be used to check the accuracy of the surface temperature estimates.
Image Analysis
Spectral imagery in each band will be corrected for geometrical lens radial distortions and
vignetting brightness effects and registered into a 3-band image in ERDAS Imagine format.
Short wave band images will be calibrated to a reflectance standard using the panel data.
Thermal infrared imagery will be calibrated into apparent temperatures using the system
calibration and then adjusted for atmosphere using MODTRAN. This will require temperature
and humidity profile measurements with radiosondes close to the over flight times. Adjustments
for surface emissivity will also be conducted.
Flux estimations and Comparisons
The short wave and long wave imagery will be used to obtain spatially distributed energy
balance fluxes over the field-scale research sites. We hope to work closely with the research
group that will be responsible for gathering canopy biophysical variables, in order to obtain
relationships between image-based vegetation indices and these canopy variables, and allow for
the production of spatial maps of these variables over the different fields. We also would like to
compare the remotely sensed fluxes with the ground-based measurements of fluxes under
different configurations of upwind footprints and under different conditions. Finally, we hope to
be involved in the comparison of the thermal infrared temperature fields and latent heat estimates
with the soil moisture measurements using microwave radiometry and radar.
Data Needs
•
•
•
•
•
•
reservoir surface temperatures for the research period
radiosonde temperature and humidity measurements at the research site on over flight days,
close to acquisition times
canopy biophysical parameter data and transect positions
energy balance flux data from selected ground stations
weather station data during the experiment period
soil moisture data of selected transects and sites
159
Soil Moisture Measurements Over Agricultural Fields in SMEX02 Using the Airborne
Passive and Active L- and S-band Sensor (PALS)
E. Njoku1, S. Dinardo1, W.Wilson1, S. Yueh1 T. Jackson2, V. Lakshmi3
1
Jet Propulsion Laboratory, Pasadena, CA
2
USDA ARS Hydrology and Remote Sensing Lab, Beltsville, MD
3
University of South Carolina, Columbia, SC
Contact: E. Njoku, M/S 300-233, Jet Propulsion Laboratory, 4800 Oak Grove Drive, Pasadena,
CA 91109, Tel: (818) 354-3693, Fax: (818) 354-9476; Email: eni.g.njoku@jpl.nasa.gov
The Passive and Active L- and S-band (PALS) airborne sensor is a multi-frequency, multipolarization, passive and active instrument designed for soil moisture and ocean salinity studies
[Wilson et al. 2001]. The radiometer operates at 1.41 and 2.69 GHz frequencies with V and H
polarizations; the radar operates at 1.26 and 3.15 GHz with VV, HH and VH polarizations. The
PALS instrument is non-scanning and views the surface at approximately 40° from nadir. It
operates on the NSF/NCAR C-130 aircraft. The PALS instrument was flown for the first time
during the SGP99 experiment over the Little Washita Watershed, Oklahoma in June/July 1999
and acquired data over predominantly bare soil and grassland conditions
http://daac.gsfc.nasa.gov/CAMPAIGN_DOCS/SGP99/air_rem.shtml.
Our objectives for acquiring and analyzing PALS data in SMEX02 are to:
(1) Study the passive and active multi-polarization signatures of land surfaces, and improve
the parameterizations of microwave radiative transfer and backscatter models
(2) Develop and evaluate candidate soil moisture retrieval algorithms and approaches for
future L-band spaceborne missions
(3) Investigate improvements in retrievals obtainable by combining radiometer and radar
data
(4) Provide high-quality microwave signature and soil moisture datasets for use in
complementary hydrometeorological investigations, including studies of surface
heterogeneity, spatial scaling, and boundary-layer processes.
PALS data will be acquired during SMEX02 over higher biomass conditions than encountered
during the SGP99 experiment in Oklahoma, with a focus on corn and soybean crops. The
combined SGP99 and SMEX02 datasets will cover a range of agricultural soils and vegetation
covers with biomass up to ~4 kg m-2. Approximately 30 hours of C-130 flight time will be
available to generate mapping coverage of the Walnut Creek, Iowa watershed at about 400 m
resolution over an approximately two-week period from June 24 through July 6. Processed
PALS data will be made available to SMEX02 participants for complementary hydrological and
meteorological studies.
The PALS data, and the ground-sampled surface moisture, temperature, biomass, texture, and
topography information acquired during SMEX02 will provide us a basis for improved
microwave modeling at L- and S-bands and for development of multichannel retrieval
approaches. We will combine our analyses of PALS data with C-band data from the PSR-C
160
airborne sensor, as part of the Aqua-AMSR algorithm development and validation program. The
data will permit examining the frequency dependence (L-, S-, and C-bands) of sensitivity to soil
moisture and vegetation characteristics over varied terrain. Scaling analyses will also be carried
out using hydrologic modeling and spatial aggregation techniques to examine the performance of
the retrieval algorithms at varying spatial scales..
161
Diagnosing Surface Fluxes from Scales of Meters to Megameters Using Remote
Thermal/Optical Observations
John Norman1, Martha Anderson1, John Mecikalski2, George Diak2 and William Kustas3
1
Department of Soil Science, University of Wisconsin-Madison, Madison, Wisconsin
Cooperative Institute for Meteorological Satellite Studies, Space Science and Engineering
Center, University of Wisconsin-Madison, Madison, Wisconsin
3
USDA-ARS Hydrology and Remote Sensing Lab Beltsville, Maryland
2
Dr. Martha Anderson, 1525 Observatory Drive, University of Wisconsin, Madison, WI 53705
608-265-3288, email: anderson@emily.soils.wisc.edu
Surface Flux Modeling
Remote sensing data from ground, aircraft, and satellite platforms will be used to diagnose
surface fluxes at pixel resolutions from meters to kilometers covering spatial domains from field,
to watershed to state-wide areas. This will be done using the two-source (i.e., soil + vegetation)
land surface scheme (TSM) described by Norman et al. (1995) applied to the full range of spatial
resolutions provided by these platforms. This scheme has been imbedded in a suite of related
models designed to operate over a range of spatial scales (see Figure that follows).
At the regional scale, ALEXI (Atmosphere Land EXchange Inverse – Anderson et al., 1997;
Mecikalski et al., 1999) is a coupled planetary-boundary-layer-land surface model that uses
regional scale operational weather inputs (atmospheric sounding and surface winds) with GOES
and AVHRR satellite data to estimate surface fluxes on a 5 km grid. Using near-surface air
temperature estimates from ALEXI, a disaggregation approach (DisALEXI – Norman et al.,
2000) is used with high-resolution, remotely-sensed surface temperature and vegetation cover to
evaluate surface fluxes at finer scales.
At larger scales, estimates of surface fluxes from ALEXI will be compared to aircraft
measurements and other model estimates. The disaggregated surface fluxes will be compared to
tower observations, serving as a basis for evaluating both ALEXI and DisALEXI. Statewide
daily surface-flux maps (at 5 km resolution) will be generated from the ALEXI output.
Vegetation Data Collection
In addition to the surface flux modeling described above, our group will coordinate the collection
of vegetation data for the SMEX02 experiment. Measurements of the following vegetation
characteristics will be made on a weekly basis during the experiment: height, green (wet)
biomass, dry biomass, leaf area index, vegetation type and specific characteristics such as stand
density, phenology, and crop management. Sampling will be done at all flux-tower sites and soil
moisture collection sites.
162
Diagram of the suite of models that is used to estimate surface fluxes from a combination of
vegetation maps, operational weather data and satellite observations over a range of spatial scales
from tens of meters to megameters.
163
Validation Work for SSM/IS Land Surface Temperature and Soil Moisture EDRs During
SMEX02
Peggy O’Neill and Manfred Owe, Hydrological Sciences Branch / Code 974
NASA Goddard Space Flight Center, Greenbelt, MD 20771
Tom Jackson, Hydrology Laboratory, Agricultural Research Service
U.S. Dept. of Agriculture, Beltsville, MD 20705
Pat Starks, Grazinglands Research Laboratory, Agricultural Research Service
U.S. Dept. of Agriculture, El Reno, OK 73036
The Defense Meteorological Satellite Program’s Special Sensor Microwave Imager (SSM/I)
satellites have been collecting global measurements from space for the last 15 years. With the
impending launch of the latest F16 SSM/IS satellite now planned for early 2002, it is anticipated
that SSM/IS will have completed its initial on-orbit checkout phase and be fully operational by
the time of the SMEX02 experiment. This schedule allows the leveraging of SMEX02 activities
for validation of SSM/IS soil moisture and land surface temperature Environmental Data
Records (EDRs).
Past research studies have demonstrated the capabilities of low frequency microwave sensors for
estimation of land surface soil moisture and temperature. While papers in the refereed literature
have indicated the potential of SSM/I frequencies in retrieving temperature, accurate estimates of
soil moisture generally require the use of lower frequency microwave channels (preferably L
band at 1.4 GHz). Because the lowest frequency on SSM/IS is at the much higher frequency of
19 GHz (1.55 cm wavelength), theoretical limitations will restrict the ability of SSM/IS to
retrieve soil moisture/soil wetness to bare or lightly vegetated surfaces. The challenge for
validation of the SSM/IS soil moisture EDR will be to document (in a quantified repeatable way)
the accuracy of the soil moisture retrievals under specific conditions of surface vegetation and
roughness.
Validation Activities
The main thrust of the SSM/IS validation approach will be to leverage
off of NASA/USDA AMSR cal/val activities to the maximum extent possible. These activities
consist of two major programs: the extensive SMEX02 soil moisture field campaign and the
continuous collection of relevant data from instrument networks in four USDA watersheds
across the U.S. for year-long validation.
The SMEX02 soil moisture field campaign is currently scheduled for June/July, 2002, and will
include aircraft overflights with AESMIR sensors at the SSM/IS channels. Since the AMSR-E
frequencies of 6.6 and 10 GHz will respond to soil moisture in a deeper surface layer (~1-2 cm)
than 19 GHz, most of the ground validation data will be collected at 2.5 and 5 cm depths. The
relationship between soil moisture at these depths and the shallower SSM/IS sampling depth
(0.3-0.5 cm) will be examined. It is likely that planned gravimetric soil moisture sampling will
be augmented in limited areas to collect a 1 cm sample to supplement the SSM/IS validation.
164
Spatial scaling between point samples and the area response, which will be a part of the AMSR
analysis, is directly relevant to SSM/IS because of the similar spatial extent of the SSM/IS 19
GHz and AMSR 6.6 GHz footprints.
Volumetric Soil Moisture (%)
35
30
25
20
1.4 GHz model
15
19 GHz model
10
1.4 GHz 1994
5
19 GHz 1994
0
0.7
1.4 GHz 1992
19 GHz 1992
0.8
0.9
Emissivity
1.0
Figure - Soil moisture-microwave relationship for Oklahoma grasslands in 1992-1994, showing
the large difference in sensitivity in the microwave-soil moisture relationship between the 19
GHz SSM/I sensor and equivalent data collected at 1.4 GHz using an aircraft sensor.
Long-term observations in diverse environments are needed to understand possible variations in
the soil moisture – microwave relationships that arise from seasonal variations in vegetation
cover and temperature. This will be accomplished by utilizing the planned upgrades under the
AMSR cal/val program to in situ observations in four USDA instrumented watersheds across the
U.S. (Georgia, Oklahoma, Arizona, and Idaho) to better characterize moisture and temperature
conditions in the shallow surface layers. In addition, the instrument network in the Oklahoma
watershed will be augmented by SMEX02 for SSM/IS temperature EDR validation work by
adding fixed-mount thermal infrared thermometers to the 17 AMSR-upgraded stations in the 610
km2 Oklahoma watershed.
The Oklahoma watershed currently includes 42 Micronet
meteorological stations, 14 ARS SHAWMS stations (which collect 2.5 and 5 cm soil moisture
and soil temperature data as part of their soil profile measurements), 2 ARM/CART stations, and
1 NRCS SCAN station. Data are recorded at 30- and 60-minute timesteps continuously yearround to provide information to assess diurnal and seasonal relationships with the satellite
microwave measurements. All stations will undergo initial site-specific calibration and crosscalibration between sites for all of the soil moisture and soil temperature sensors. These
calibrations will be periodically rechecked to insure data quality.
165
Use of Regional Microwave-Derived Soil Moisture in Land Data Assimilation and
Atmospheric Boundary Layer Studies
Peggy O’Neill, Paul Houser, Christa Peters-Lidard, Xiwu Zhan
Hydrological Sciences Branch / 974, NASA Goddard Space Flight Center, Greenbelt, MD
20771
The Hydrological Sciences Branch at NASA / GSFC has a long-standing and comprehensive
interest in the application of microwave remote sensing to land surface hydrology. Research
within the Branch ranges from investigation of more accurate soil moisture retrieval algorithms
to technology development of 2D STAR techniques to application of remote sensing variables in
data assimilation and atmospheric boundary layer studies. It is anticipated that Branch members
will participate in all of these areas as part of the SMEX02 experiment, especially in support of
ground vegetation and soil moisture sampling.
Soil Moisture Retrieval Algorithms
SMEX02 will offer the opportunity to compare the accuracy of soil moisture retrieved using the
standard single-channel algorithm to soil moisture retrieved using a multiple-channel approach in
both a more challenging vegetated environment (Iowa) and a well-characterized low vegetation
environment (Oklahoma). This work has direct bearing on future soil moisture mission
planning.
Land Data Assimilation
The overall goal of a recently funded AMSR calibration/validation effort is (1) to quantify the
accuracy and provide validation for soil moisture retrieved from AMSR-E on a variety of time
scales, (2) to quantify the geographical, seasonal, and environmental sensitivities of the accuracy
characteristics, and (3) to analyze the effects of these uncertainties on the predictability of the
global surface water and energy balance using land surface data assimilation techniques in near
real-time. The use of data assimilation methods for satellite validation is the next logical step in
NASA’s ongoing efforts to understand the Earth’s land-surface and to extend NASA’s emerging
observations for application to a vast array of socially-relevant land-surface issues. A nearly
real-time operational land data assimilation system will be developed that will monitor the
spatial-temporal AMSR soil moisture quality, so as to provide feedback to mission operators of
observational problems. This system will also extend AMSR-E products in time and space to
produce consistent data assimilation land surface fields that will be valuable for use in
subsequent analysis and application. The in situ and airborne land surface observations from
SMEX02 will be used initially as a surrogate for AMSR data in model runs of the Iowa and
Oklahoma regions as a precursor to the actual AMSR cal/val field activities in 2003. For the
determination of land surface biophysical parameters such as NDVI, leaf area index, land
cover/use type, etc., optical remote sensing data from satellites like MODIS, ASTER, Ikonos,
ETM+ and/or AVHRR will also be needed and ingested into the modeling approach.
166
Atmospheric Boundary Layer Studies
Atmospheric boundary layer measurement activities planned for SMEX02 include flux towers,
aircraft, lidar and ground-based sampling. The focus of the GSFC contribution to this effort will
be support for ground sampling, with a special focus on soil moisture and temperature profiles,
as well as LAI and soil hydraulic and thermal properties within the regional and high resolution
sampling grids. This effort will support future coupled land surface-ABL modeling activities in
the Iowa region.
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An Agroecosystem Water Management Model Prediction and Calibration during SMEX02
Investigators: Z. Pan1, R. Horton1, J.H. Prueger2, D.P. Todey1, M. Segal1, and E.S. Takle1
1
2
Dept. of Agronomy, Iowa State University, Ames, IA 50011
NSTL, 2150 Pammel Drive, Ames, Iowa 50011
Contact: Zaitao Pan, 3010 Agronomy, Iowa State University, Ames, IA50011, panz@iastate.edu,
Tel: (515)294-0264, Fax: (515)294-2619
Background
In a project funded by the Iowa State University Agronomy Endowment Fund, we have been
developing an agroecosystem water management model. This soil-plant-atmosphere coupled
modeling system includes a regional climate model (MM5), soil/hydrology model (SWAT), and
crop development models (CERES-Maize and CROPGRO-Soybean). The coupled modeling
system will be run in forecast mode, projecting seasonal crop-available soil moisture thereby
allowing evaluation of alternative cropping strategies. We have run the model for springsummer 2001, and plan to have real-time soil moisture prediction for the growing-season 2002.
The SMEX02 provides a unique opportunity for us to validate and calibrate our soil moisture
forecasting model, and at the same time, our model simulation can complement SMEX02 nearsurface soil moisture by providing simulation of deeper soil moisture profiles which can be
useful for agricultural and hydrology applications.
Experiment Objectives
The overall goal of the proposed project is to use SMEX02 near-surface soil moisture
observations, both in-situ and airborne, to validate and calibrate our soil moisture forecast model
while dynamically interpolating observed data over the entire watershed. This will complement
observed near-surface soil moisture by providing model generated root-zone data. Specifically
the project has following objectives:
(1) To use SMEX02 soil moisture data to validate/calibrate our coupled model and improve
model parameterization for the soil-plant-atmosphere water budget.
(2) To assimilate the soil moisture into the coupled model and generate four-dimensional soil
moisture that is consistent with the observed.
(3) To dynamically generate root-zone soil moisture based on the near-surface in-situ
observations via our coupled model.
(4) To evaluate effects of field-scale variability in soil wetness and energy fluxes at the
surface on atmospheric boundary-layer processes, particularly convective activity.
Strategies
We will adopt a multi-scale domain nesting and interdisciplinary component coupling approach.
The nested regional climate model will use a GCM forecast as the initial and boundary
conditions. The GCM-regional model nesting is one-way whereas within our coupled model, we
168
will have two-way interactive nesting, allowing information on finer grids to feed back to coarser
grids. The planned coarsest (finest) grid spacing appropriate for this project will be about 9.0
(1.0) km. The coarse domain spans the Midwest while the fine domain, which is freely movable,
covers only the Walnut Creek watershed. This triple nesting will have relatively fine grid
resolution to provide detailed spatial heterogeneity while embracing large-scale dynamics of the
atmosphere.
Before SMEX02 starts (middle June), we will run our model to predict soil moisture distribution
and temporal variation over the entire Walnut Creek watershed. The projected soil moisture will
be based on the forecast precipitation, atmospheric condition, and soil water content at the time.
This soil moisture forecast can also serve as a first-guess for SMEX02 participants. During the
SMEX02 as the observed soil moisture is reported, we will continuously run the model while
nudging the model-generated soil moisture towards the SMEX02 observation. After SMEX02,
we will calibrate our model with more comprehensive and refined data.
Data needed from SMEX02
•
•
•
Soil and vegetation distribution
Near-surface soil moisture and temperature
Radiative properties of vegetation, LAI, CO2 concentration
Contribution to the overall SMEX02
Provide a spatio-temporal distribution of model-generated soil moisture and energy fluxes that
are consistent with SMEX02 observations over the entire Walnut Creek watershed.
Extend the soil moisture profile deeper into root zone by using the coupled model based on water
and energy budgets, broadening future usage of SMEX02 soil moisture data by agriculture and
hydrology communities.
Provide an example of the SMEX02 data application in driving, validating, and calibrating landsurface schemes of climate models.
If needed, we could possibly provide one or two actual soil profile sampling.
169
Spatial and Temporal Controls of Soil CO2 Flux
T.B. Parkin, USDA-ARS- National Soil Tilth Lab, Z. Senwo, Alabama A&M University
T.B. Parkin, voice (515) 294-6888, Email parkin@nstl.gov
Rational: Assessment of soil C storage requires precise knowledge of C inputs and losses. In
agricultural systems C inputs can be estimated from crop yield data, however C losses are more
difficult to estimate. A major portion of the C lost from soil occurs as CO2 produced from
microbial action on plant material and soil organic matter. This soil-derived CO2 is not only a
reflection of soil C degradation processes, but also represents a pool of CO2 that may be reincorporated into the growing plants. Prediction of the magnitude of this process and
development of an understanding of the factors controlling soil CO2 flux are critical to
assessment of the impact of agricultural activities on soil C storage.
Hypothesis: The relationships between temperature, soil water content and soil CO2 flux are
dependant upon landscape position.
Approach: Soil CO2 flux will be measured in transects corresponding to flight lines. Three
fields along each of 3 flight lines will be sampled. In each field a stratified sampling scheme will
be employed whereby three ‘replicate’ locations of each of 3 general landscape elements will be
identified (Hilltop, Side-slope, and Depression), resulting in 9 locations/field. The distance
between locations will depend upon specific field topography. At each landscape element
location soil CO2 flux, soil temperature (surface and 5 cm) and soil water content (surface 5 cm Theta probe) will be measured at 3 separate locations located 1 meter apart. Soil samples will
also be collected for laboratory analysis of soil microbial enzymes (to be conducted by Z.
Senwo, Alabama A & Univ.). The resulting sampling plan will result in data collected at 27
locations within each field, (81 locations on each transect : 243 locations/watershed). It is
estimated that it will require a 2 person team 2 hours to sample each field, thus 3 teams of 2
people will be able to conduct sampling on the 3 transects in approximately 6 hours. Sampling
will be performed twice during the experiment. Soil CO2 flux will be measured using soil
chambers (27 cm diameter). Chambers will be placed over the soil and the chamber headspace
gas recirculated through portable infrared gas analyzers to quantify increases in CO2
concentration over a 2 minute period. To assess temporal variations in CO2 flux 3 automated
chambers will be placed in a single field, one on each landscape element. Automated chambers
will collect soil CO2 flux (along with soil water content, soil temperature, air temperature, and
rainfall) during 5 minute periods every hour over the duration of the experiment. These data will
be used to determine diurnal responses of soil CO2 flux in order to account for time-of-day
influences associated with the point-in-time CO2 transect data.
170
Data analysis: A jack-knifing procedure will be used. Data set will be split in thirds. Data
from one set will be used to generate relationships between temperature, soil water content,
landscape position, soil texture, and respiration. Data from the remaining 2 sets will be used as
validation sets to assess accuracy of predicted CO2 fluxes. This process will be repeated 3 time,
using a different model set each time. If there is consistency in the mathematical relationships
over all three model sets, the remote temperature and soil water content data along with elevation
and soil maps will be used to construct estimates of field and watershed CO2 fluxes. The
magnitudes of these estimates will be compared with CO2 fluxes obtained by eddy correlation. It
is not anticipated that soil CO2 flux and eddy correlation fluxes will be exactly comparable, since
these methods measure fundamentally different processes. The chambers will only yield
information on soil contribution to CO2 flux while Eddy correlation gives an aggregated
assessment of plant and soil contributions. However, evidence in the literature suggests that
recycling of soil derived CO2 may be recycled into the crop, and that this component is not
accounted for by most micro-met assessments of CO2 flux measurements made above the crop
canopy. Determination of the relative magnitudes of soil CO2 flux a total CO2 flux will be
valuable in assessing the importance of soil-derived CO2 which has the potential to be recycled
into the crop canopy. Since the transect CO2 flux will only be collected during the daytime,
diurnal patterns in CO2 flux will be derived from automated chamber data. Relationships
between diurnal air temperature changes and diurnal CO2 fluxes will be developed from the
automated chambers, and used to estimate average daily CO2 flux at each of the transect
measurement positions.
Data Contribution: At select locations: surface soil temperature, 5 cm soil temperature, soil
water content (theta probe), soil CO2 flux, selected soil enzymes.
Data requirements. Plant biomass assessments (plant height) at each soil CO2 flux location.
Remote sensed soil temperature and water content.
Personnel: 5 Student Hourly workers for 2 days (In addition to Parkin, Senwo, and Parkin’s
technician).
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Turbulence Mechanisms for Heat, Water and CO2 Exchange over Midwest Corn Soybean
Fields.
Investigators: J.H. Prueger, J.L. Hatfield, W.P. Kustas, and L.E. Hipps
Contact Information: J.H. Prueger, NSTL, 2150 Pammel Drive, Ames, Iowa 50011-4419, 515294-7694, 515-294-8125 (fax), email: prueger@nstl.gov
Background: The upper Midwest region represents a major sector of production agriculture for
corn and soybeans. Radiant energy partitioning into the four components of net radiation (Rn),
soil heat flux (G), sensible heat (H) and latent heat (evaporation, LE) is strongly influenced by
surface roughness, soil water content, soil type, and landscape position. Mechanically and
thermally driven turbulence contributes to the transport of surface scalars (H, LE and CO2).
Intermittent eddies of varying spatial and temporal scales can affect the energy partitioning
particularly between fields having significant changes in surface roughness such as bordering
soybean and corn fields. Carbon dioxide exchanges from the soil-plant atmosphere continuum
are similarly affected. The vast areas of corn/soybean production represent a potential sink for C
uptake. Little information is available on the role of intermittent turbulence and C uptake for
Midwest cropping systems.
Hypothesis: 1) Exchange of scalar fluxes of H, LE and CO2 can be related to soil type, soil
water content and intermittent eddy transport events. 2) Intermittent transport events of scalars
are an important component of daily energy flux exchange.
Approach: Two fields, one planted in corn and the other in soybeans will be instrumented with a
horizontal (north-south) transect of 4 eddy covariance systems, two in corn and two in soybeans.
Both fields represent typical soil associations and topography for Midwest cropping systems.
Each eddy covariance system will be comprised of a 3-Dimensioanl sonic anemometer, and an
LI 7500 CO2 / H2O open path analyzer to provide measurements of the three wind components
(u, v, w), water vapor density (ρv) air temperature (T) and CO2 concentration. Each system will
be erected within a specific soil type and will include measurements of Rn and G. Eddy
covariance instrumentation will be sampled at 10 Hz while those of Rn and G will be at 1 Hz.
Radiometric temperatures (surface temperatures) will also be made at each of the sites.
Data Analysis: Characteristics of the turbulent exchange related to specific flux transport events
will be determined via time series analysis. This will include power spectra and co-spectra of the
measured high frequency data components. These results will be used to evaluate the frequency
distribution of energy containing eddies. Wavelet analysis will be used to decompose wind,
water vapor, and CO2 signals to identify intermittent events during periods of non-stationarity
and determine the effect on energy balance and CO2 flux computations.
Data requirements: High frequency (10 Hz) measurements of the three wind components u, v,
w, water vapor density, temperature, and CO2 concentration.
Personnel: NSTL, Hydrology and Remote Sensing Laboratory and Utah State University
scientists will supply personnel to complete these measurements.
172
173
Coupled Heat and Water Flow in Surface Soil Layers
T. J. Sauer (NSTL), T. E. Ochsner, and R. Horton (ISU)
Dr. Thomas J. Sauer, USDA-ARS, National Soil Tilth Laboratory, 2150 Pammel Drive, Ames,
IA 50011-4420, sauer@nstl.gov, Ph. (515)294-3416, Fax (515)294-8125
Traditional surface energy balance measurement protocols involve the measurement of soil heat
flux (G) using flux plates buried near the soil surface. The plates make a direct measurement of
G at the depth of placement. Heat storage in the soil above the plate can be significant, often
even greater in magnitude than the measured flux during daytime under sparse canopies. Heat
storage in a soil layer changes not only with soil temperature but also with the soil heat capacity
(C), which changes dramatically with soil water content (θ). Large changes in surface soil
temperature and water content, therefore, result in rapid and substantial changes in heat storage
in shallow soil layers.
In order to obtain an accurate measurement of G at the soil surface, a correction for heat storage
in the soil above the plate must be made. The objective of this project is to improve the
accuracy of surface G measurements by developing continuous, real-time heat storage
corrections for flux plate measurements using data from in-situ soil heat capacity sensors.
An array of identical heat flux plates will be deployed at 0.06 m depth at 2 of the intensely
monitored surface flux sites. At both locations, dual probe soil thermal property sensors will be
installed in the soil above the flux plates. These sensors provide a direct and continuous estimate
of soil volumetric heat capacity. Performance of this new, automated system will be compared
with the traditional method of estimating C from de Vries’ equation and measured θ. Special
attention will be given to intervals following rainfall when C is changing rapidly as the soil dries.
Additional sensors (infrared thermometers, net radiometers, pyranometers, and quantum sensors)
will be deployed to characterize the canopy microclimate, especially radiation penetration
through the canopy.
174
175
Characterization of Vegetation Parameters Within a Footprint Area Of SMEX02
W. Tadesse, T. Coleman, T. Tsegaye, W. Khairy, and Bridget Sanghadasa, Alabama, A&M
University, Center for Hydrology, Soil Climatology, and Remote Sensing
W. Tadesse, 256-858-4252, wtadesse@aamu.edu
Rationale:
Leaf area index (LAI) defined as the one-sided leaf area per unit ground area, normalized differences
of the vegetation index (NDVI), and fraction of photosynthetically active radiation (fPAR) are key
vegetation parameters that describe a canopy structure. Soil moisture estimation models use these
parameters. Despite large developments over the past years, the main problem is still the acquisition of
these vegetation parameters that are needed to input in the models. These parameters vary on a field
scale and undergo rapid changes with time. In the last decade, NDVI derived from NOAA-AVHRR
and Landsat Thematic Mapper (TM) have been some of the widely used sources of satellite data
available to monitor the activity of vegetation from space. New sensors such as MODIS, having
increased spatial resolution as well as spectral and directional properties, will provide improved land
cover estimates and vegetation properties, including fPAR. Estimation of these variables from satellite
and aircraft based remote sensing data require ground data for validation and testing for bias.
Therefore, the objectives of this study are: (1) to measure the LAI and fPAR of soybean and corn at
field scale using the AccuPAR Model PAR-80 (Decagon Devices, Inc. Pullman, WA), (2) to validate
the Landsat TM, AVHRR, and MODIS derived LAI, NDVI, and fPAR parameters using field scale
measurement.
Hypothesis:
Surface biophysical parameters (LAI, fPAR, and biomass) can be accurately estimated from satellite
and aircraft remotely sensed data.
Data Collection:
The protocol used to measure vegetation parameters during the Southern Great Plains (SGP97)
Experiments, with some modification to fit the type of crops (soybean, corn and others) at Ames,
Iowa, will be utilized. Sampling locations will be coordinated with ground based flux stations and sites
that will be used by Alabama A&M University (HSCaRS) scientists. The LAI and fPAR measurement
will be taken on a grid from the corn and soybean field using the AccuPAR Model PAR-80 (Decagon
Devices, Inc. Pullman, WA) device. Spectral reflectance measurement will be done using a hand held
radiometer that will measure the incident and reflected light. NDVI will be calculated using the
surface spectral measurement and satellite data. The sampling locations on the field will be recorded
using differential GPS unit. The biophysical parameters measured on grid scale will be interpolated
across the footprint using geostatistical kriging method. The normalized difference vegetation index
(NDVI) will be derived from Landsat TM, AVHRR, and MODIS sensors and relationships will be
established with the measured surface biophysical parameters (LAI, fPAR, and biomass).
176
Contribution to SMEX02:
Biophysical parameters (LAI, fPAR, and NDVI) measurements obtained from the field scale will be
used for watershed, and regional scale soil moisture estimation.
177
Integration of Depth Dependent Soil Moisture, Flux, and ESTAR Data to Better
Characterize Soil Moisture Distribution under Corn and Soybean Fields
Teferi D. Tsegaye, Wael Khairy, Wubishet Tadesse, and Karnita Golson, Center for Hydrology,
Soil Climatology, and Remote Sensing, Alabama A&M University
Teferi D. Tsegaye, voice (256) 858-4219, Email ttsegaye@aamu.edu
Soil moisture is a critical component of many regional and global climate studies. It’s spatial and
temporal distribution within a field and watershed is affected by sources of the hydrological
conditions and variability mostly associated with management-related factors. Furthermore, the
relative significance of spatial, temporal, and management-induced sources of variation are not
well known, nor are they typically accounted for in the soil moisture modeling and mapping
efforts. Knowledge of their significance is nevertheless important for the development of both
efficient sampling protocols and proper parameterization scenarios. The objectives of this
research are: 1) evaluate the variance structure associated with soil moisture within a corn and
soybean field; and 2) determine the emitting depth by correlating the near-surface soil moisture
data with ground based flux and remotely sensed measurements.
Hypothesis: Evaluating the relative significance of the variance structure of near-surface soil
moisture improves the validation process of remotely sensed data.
Sample collection: During the SMEX02 field experiment, repeated soil moisture measurements
will be collected from corn and soybean fields. Soil moisture measurements will be done twice a
day within-row and side-row of the row crops. Occasionally, soil moisture measurements will be
done on a 100 X 100 m grid within these fields. The data collection will be coordinated with
ground based flux measurements and measurements will be done at least from five soil depths
including 2, 3, 4, 5, and 6 cm and in four fields (two corn and two soybean). A site-specific
calibration will be performed before and after the field experiment to compare the soil moisture
data collected from each field.
Contribution to SMEX02 field experiment: In addition to the flux stations data, robust spatial
and temporal characteristics of these data should provide important insight into soil moisture
distribution under corn and soybean fields. Data collected from this study can be used to couple
remotely sensed data with hydrology models and will also assist to validate remotely collected
active and passive data.
178
179
Evaluation of Regression Tree Algorithm (RTA) and Artificial Neural Networks (ANNs)
for Developing Pedotransfer Functions of Soil Hydraulic Parameters
Teferi D. Tsegaye1, Wael Khairy1, Wubishet Tadesse1, Karnita Golson1, Yakov Pachepsky2, and
Binayak Mohanty3
1
2
Center for Hydrology, Soil Climatology, and Remote Sensing, Alabama A&M University,
USDA-ARS Beltsville, MD, 3Texas A&M University
Teferi D. Tsegaye, voice (256) 858-4219, Email ttsegaye@aamu.edu
Knowledge of the spatial and temporal variability of hydraulic properties at different scaling
levels is essential to effectively apply many research and management tools. In addition, this will
improve our ability to estimate and quantify the spatial distribution of soil moisture. Most
hydrologic models that simulate soil hydrologic processes and their impacts on crop growth
depend on accurate characterization of such properties. The lack of such information is often
considered to be a major obstacle to effectively utilize these tools. It is generally recognized that
soil hydraulic properties are affected by numerous sources of variability mostly associated with
spatial, temporal, and management related factors. Soil type is considered the dominant source of
variability, and parameterization is typically based on soil survey databases. We are trying to
identify a set of potential algorithms to predict water retention characteristics from more readily
available soil data such as texture, structure, bulk density, organic matter, and Cation Exchange
Capacity (CEC). Therefore, the purpose of this study is to evaluate the relationships between soil
types and their hydraulic properties at the field scale and to develop a relationship between easily
measured soil properties, vegetation characteristics, and hydrologic processes for up scaling, as
well as improve the performance of the hydrologic modeling.
The objective of this research is to: 1) develop hierarchical pedotransfer functions (PTFs) using
soil, topography, and plant properties; 2) compare two types of PTF models (Artificial Neural
Network, Regression Tree Algorithm and estimate soil hydraulic properties at different spatial
scales (point and field); and 3) develop a soil database that can be used as an input for watershed
or regional scale hydrology models.
Hypothesis: Successful prediction of near surface hydraulic parameters and incorporation of
such parameters with existing hydrologic models will improve the model performance at the
watershed and regional scales.
Sample collection and analysis: Disturbed and undisturbed soil samples will be collected from
the 0-10 cm depth on a grid from two or more corn and soybean fields. The soil samples will be
crushed and passed through a 2-mm sieve. The disturbed soil samples will be analyzed for
particle size (texture), organic matter, Cation Exchange Capacity (CEC), and pH. Undisturbed
soil cores, 7.6 cm long by 7.6 cm diameter will be collected using Uhland core sampler. The
samples will be trimmed, wrapped in plastic bags, and stored in a refrigerator at 4°C prior to
analysis. They will then be used for the determination of soil physical properties including
saturated hydraulic conductivity, water retention, bulk density, and porosity.
180
Contribution to the SMEX02 Field Experiment: Research outcomes and the resulting
database from this work will be used in the watershed and regional hydrologic modeling work.
181
14
Contacts
Name
Affiliation
Email
Jackson
Tom
HRSL
tjackson@hydrolab.arsusda.gov
Bindlish
Rajat
HRSL
bindlish@hydrolab.arsusda.gov
Cosh
Mike
HRSL
mcosh@hydrolab.arsusda.gov
Hsu
Ann
HRSL
hsu@hydrolab.arsusda.gov
Nijenhuis
Mathieu
HRSL
mrnijenhuis@hydrolab.arsusda.gov
Kuijper
Marijn
HRSL
marijnk@hydrolab.arsusda.gov
McKee
Lynn
HRSL
lmckee@hydrolab.arsusda.gov
Dulaney
Wayne
HRSL
wdulaney@hydrolab.arsusda.gov
Walthall
Charlie
HRSL
cwalthal@hydrolab.arsusda.gov
Doraiswamy
Paul
HRSL
pdoraisw@hydrolab.arsusda.gov
Crow
Wade
HRSL
wcrow@hydrolab.arsusda.gov
Lakshmi
Venkat
USC
vlakshmi@geol.sc.edu
Bolten
John
USC
jbolten@geol.sc.edu
Jec0913@aol.com
Cashion
James
USC
Srinivasan
Raja
USC
Schiedt
Stephen
USC
Famiglietti
Jay
UCI
jfamigli@uci.edu
Berg
Aaron
UCI
berg@uci.edu
Holl
Sally
UCI
sholl@uci.edu
Ryu
Dongrryeol
UCI
dryu@uci.edu
Seo
Ki-Weon
UCI
kiweon@speer.geo.utexas.edu
Hatfield
Jerry
NSTL
hatfield@nstl.gov
Logsdon
Sally
NSTL
logsdon@nstl.gov
Hehr
Dee
NSTL
hehr@nstl.gov
Kulisky
Shannon
NSTL
kulisky@nstl.gov
Oesterriech
Wolfgang
NSTL
oesterriech@nstl.gov
Sauer
Tom
NSTL
sauer@nstl.gov
Parkin
Tim
NSTL
parkin@nstl.gov
Miller
Doug
PSU
Voortman
Jon
PSU
miller@essc.psu.edu
jjv@essc.psu.edu
Heffelfinger
Stephen
PSU
steveheff11@yahoo.com
Calamito
Anthony
PSU
arc167@psu.edu
Bills
Brian
PSU
miller@essc.psu.edu
Mohanty
Binayak
TAMU
bmohanty@cora.tamu.edu
Jacobs
Jennifer
UF
jjaco@ce.ufl.edu
Whitfield
Brent
UF
Ripo
Gerard
UF
Tien
Calvin
UF
ktien@agen.ufl.edu
182
Tsegaye
Teferi
AAMU
ttsegaye@aamu.edu
Tadesse
Wubishet
AAMU
wtadesse@aamu.edu
Khairy
Wael
AAMU
wkhairy@aamu.edu
Senwo
Zachary
AAMU
zsenwo@aamu.edu
Coleman
Tommy
AAMU
tcoleman@aamu.edu
Laymon
Chip
GHCC
Charles.Laymon@msfc.nasa.gov
Crosson
Bill
GHCC
Bill.Crosson@msfc.nasa.gov
Archer
Frank
GHCC
frank.archer@msfc.nasa.gov
Limaye
Ashutosh
GHCC
Wood
Eric
Princeton
efwood@h2o.Princeton.EDU
Gao
Huilin
Princeton
huiling@Princeton.EDU
Pan
Ming
Princeton
Goteti
Gopi
Princeton
Drusch
Mattias
U Bonn
mdrusch@uni-bonn.de
O'Neill
Peggy
NASA
peggy@hsb.gsfc.nasa.gov
Kim
Ed
NASA
Edward.J.Kim.1@gsfc.nasa.gov
Peters-Lidard
Christa
NASA
cpeters@hsb.gsfc.nasa.gov
Zhan
Xiwu
NASA
xzhan@hsb.gsfc.nasa.gov
Koike
Toshio
U Tokyo
tkoike@hydra.t.u-tokyo.ac.jp
Pan
Zaito
ISU
panz@iastate.edu
Prada
Laura
UC Berkley
lparada@hydro.CE.Berkeley.EDU
Wen
Jun
Wageningen
J.Wen@Alterra.wag-ur.nl
Manu
Andrew
ISU
akmanu@iastate.edu
Lobl
Elena
UAH/MSFC
elena.lobl@msfc.nasa.gov
Dinardo
Steve
JPL PALS
steven.j.dinardo@jpl.jpl.nasa.gov
Njoku
Eni
JPL PALS
eni.g.njoku@jpl.nasa.gov
Yeuh
Simon
JPL PALS
simon@pals.jpl.nasa.gov
Wilson
Bill
JPL PALS
william.j.wilson@jpl.nasa.gov
Masters
Dallas
CU GPS
Dallas.Masters@Colorado.EDU
Cusak
John
NCAR C-130
Eagan
Kip
NCAR C-130
Genzinger
Lowell
NCAR C-130
Kidd
Bret
NCAR C-130
Nicoll
George
NCAR C-130
Zrubek
Kurt
NCAR C-130
Boynton
Henry
NCAR C-130
Easmunt
Dave
NASA P-3B
Dykes
William
NASA P-3B
White
Larry
NASA P-3B
Yates
Brian
NASA P-3B
Doyle
John
NASA P-3B
Chase
Larry
NASA P-3B
Church
Lee
NOAA PSR
Leuskiy
Vladimir
NOAA PSR
deasmunt@pop800.gsfc.nasa.gov
183
Gasiewski
Al
NOAA PSR
al.gasiewski@noaa.gov
Klein
Marian
NOAA PSR
marian.klein@noaa.gov
Yevgrafov
Aleksandre
NOAA PSR
Zamora
Bob
NOAA PSR
Zavorotny
Valery
NOAA PSR
Le Vine
David
NASA ESTAR
Gosselin
Brian
NASA ESTAR
dmlevine@meneg.gsfc.nasa.gov
Miller
Chris
NASA DC-8
chris.miller@dfrc.nasa.gov
Imel
Dave
JPL AIRSAR
imel@jpl.nasa.gov
Kustas
Bill
HRSL
bkustas@hydrolab.arsusda.gov
Russ
Andy
HRSL
aruss@hydrolab.arsusda.gov
Prueger
John
NSTL
prueger@nstl.gov
Hart
Tim
NSTL
hart@nstl.gov
cneale@cc.usu.edu
Neale
Chris
USU
Chavez
Jose
USU
Akasheh
Osama
USU
Hernandez
Jairo
USU
MacPherson
Ian
NRC-CA
Aitken
John
NRC-CA
Taylor
Chuck
NRC-CA
Depper
Don
NRC-CA
Desjardins
Ray
Agri-CA
Riznek
Richard
Agri-CA
Dow
Dave
Agri-CA
Albertson
John
Duke U.
jdalbertson@virginia.edu
Williams
Christopher
Duke U.
caw4r@virginia.edu
Emanuel
Ryan
Duke U.
Ian.Macpherson@nrc.ca
desjardins@EM.AGR.CA
Cahill
Tony
TAMU
tcahill@civilmail.tamu.edu
Hipps
Larry
USU
larry@claret.agsci.usu.edu
Brunsell
Nate
USU
nate@gis.usu.edu
Eichinger
Bill
U. Iowa
william-eichinger@uiowa.edu
Nichol
Jennifer
U. Iowa
Laszczak
Steve
U. Iowa
Cooper
Dan
LANL
dccoper@lanl.gov
Archuleta
John
LANL
jaa@lanl.gov
Fernandez
Al
LANL
Everett
Dan
LANL
Vigil
Kevin
LANL
Norman
John
U. Wisc.
norman@calshp.cals.wisc.edu
Anderson
Martha
U. Wisc.
mcanders@facstaff.wisc.edu
Diak
George
U. Wisc.
george.diak@ssec.wisc.edu
184
15
LOGISTICS
15.1
Security
Access to fields
Do not enter any field that you do not have permission to enter. Prior to the experiment all
requests for field access are to be directed to Tom Jackson. Do not assume that you can use a
field without permission. Requests for installations and unplanned sampling made during the
experiment will not be easy to satisfy. Tracking down a landowner and getting permission can
take up to a half-day of time by our most valuable people. These people will be extremely busy
during the experiment. Therefore, if you think you will have specific needs that have not been
addressed, you did not spend enough time planning…so learn for the next time.
•
•
•
•
Access to field headquarters: Open 7 am until 6 pm everyday
Access to the NSTL: Restricted to USDA employees and others by prior arrangements
Access to C-130 at Des Moines airport: Restricted to aircraft personnel and others ONLY by
prior arrangement and approval
Access to the P-3B at Des Moines Airport: Restricted to aircraft personnel and others ONLY
by prior arrangement and approval
15.2
Safety
Field Hazards
There are a number of potential hazards in doing field work. The following page has some good
suggestions. Common sense can avoid most problems. Remember to:
•
•
•
•
•
•
Work in teams of two
Carry a phone
Know where you are. All roads have street signs. Make a note of your closest intersection.
Dress correctly; long pants, long sleeves, boots, hat
Contact with corn leaves can cause a skin irritation
Use sunscreen and bring fluids
For medical emergencies call 911 or go to:
Mary Greeley Medical Center
1111 Duff Avenue, Ames, IA 50010
Phone: 515-239-2011
For non-emergency medical problems:
Adult Medicine in the McFarland Clinic
1215 Duff Avenue, Ames IA, 50010
Phone: 515-239-4431
185
186
Ticks
Ticks are flat, gray or brownish and about an eighth of an inch long. When they are filled with
their victim's blood they can grow to be about a quarter of an inch around. If a tick bites you, you
won't feel any pain. In fact you probably won't even know it until you find the tick clamped on
tightly to your body. There may be some redness around the area, and in the case of a deer tick
bite, the kind that carries Lyme Disease, a red "bulls-eye" may develop around the area. This
pattern could spread over several inches of your body.
When you find a tick on you body, soak a cotton ball with alcohol and swab the tick. This will
make it loosen its grip and fall off. Be patient, and don't try to pull the tick off. If you pull it off
and it leaves its mouth-parts in you, you might develop an irritation around these remaining
pieces of tick. You can also kill ticks on you by swabbing them with a drop of hot wax (ouch!) or
fingernail polish. After you've removed the tick, wash the area with soap and water and swab it
with an antiseptic such as iodine.
Ticks are very common outdoors during warm weather. When you are outdoors in fields and in
the woods, wear long pants and boots. Also spray yourself before you go out with insect
repellent containing DEET.
(Source:<http://kidshealth.org/cgi-bin/print_hit_bold.pl/kid/games/tick.html?ticks#first _hit>)
Drying Ovens
The temperature used for the soil drying ovens is 105oC. Touching the metal sample cans or the
inside of the oven may result in burns. Use the safety gloves provided when placing cans in or
removing cans from a hot oven. Vegetation drying is conducted at lower temperatures that pose
no hazard.
187
15.3
Hotels
Ames, IA
The following two are suggested. More info can be obtained at
http://www.amescvb.com/lodging/index.asp
Comfort Suites
2609 Elwood Drive
Ames, IA 50010
(515) 268-8808
Fax (515) 268-8858
Information provided by Jennifer
One person Mini-suite 7 or more days
One person less than 7 days
Additional person
$50/day plus tax
$55/day plus tax (also the govt. per diem rate)
Add $5 per day
These rooms have a refrigerator, microwave (some rooms), breakfast, pool, exercise room.
http://www4.choicehotels.com/ires/en-US/html/HotelInfo?sid=OdVj.23qbiFKan.4&hotel=IA070
Howard Johnson Express Inn
Hwy. 69 & Hwy. 30
Ames, IA 50010
515-232-8363
1- 800-798-8363
FAX 515-232-7751
Information from Sandy
One person 7 or more days
One person less than 7 days
Two people 7 or more days
$43/day plus tax
$50/day plus tax
$47/day plus tax
http://www.hojo-ames.com
Breakfast, pool, within walking distance of a wide range of restaurants, etc.
Des Moines, IA
Embassy Suites Hotel Des Moines-On The River
101 East Locust Street
Des Moines, IA 50309
515-244-1700 Fax: 1-515-244-2537
NASA Group Rate for Extended Stay
$67/day plus tax (govt. per diem rate)
http://www.embassysuites.com/en/es/hotels/index.jhtml?ctyhocn=DSMDNES
188
Four Points by Sheraton Des Moines Airport
1810 Army Post Road
Des Moines, Iowa 50325
(515) 287-6464
Fax:(515) 287-5818
One person standard room
$60/night plus tax (govt. rate)
http://www.starwood.com/fourpoints/index.html
15.4
Shipping Addresses
Tim Hart
USDA-ARS
National Soil Tilth Laboratory
2150 Pammel Drive
Ames, IA 50011
189
15.5
Directions
The following map indicates one way to get to Ames from Des Moines. If coming from the
airport it is more efficient to head east on Army Post Rd. to Rt. 69 North. This intersects I-235
(East) and becomes I-35 N. Maps are available at the airport near baggage claim.
190
This map shows general features of the City of Ames. All hotels have an excellent street map of
Ames available for free. When coming from Des Moines, get off I-35 at Rt. 30 West and then
either head North on Duff for the Ramada or South on Elwood for the Comfort Suites.
191
The location of the Field Headquarters for SMEX02 is an ARS building located on 240th St near
W Ave. A photo is also shown. The All Hands meeting on June 24th will be held here.
SMEX02 Field Headquarters
15.6
Local Contacts
USDA/ARS National Soil Tilth Laboratory
2150 Pammel Drive
Ames, IA 50011-4420
Jerry Hatfield
(515) 294-5723
hatfield@nstl.gov
John Prueger
(515) 294-7694
prueger@nstl.gov
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