TEMPERATURE, MOISTURE PROPERTIES SOILS Carol

TEMPERATURE, MOISTURE PROPERTIES SOILS Carol
TEMPERATURE, MOISTURE AND ALBEDO PROPERTIES
OF ARIZONA SOILS
by
Carol Dawn Franks
A Thesis Submitted to the Faculty of the
DEPARTMENT OF SOILS, WATER AND ENGINEERING
In Partial Fulfillment of the Requirements
For the Degree of
MASTER OF SCIENCE
WITH A MAJOR IN SOIL AND WATER SCIENCE
In the Graduate College
THE UNIVERSITY OF ARIZONA
1985
STATEMENT BY AUTHOR
This thesis has been submitted in partial
fulfillment of requirements for a Masters Degree at the
University of Arizona and is deposited in the University
Library to be made available to borrowers under rules of the
Library.
Brief quotations from this thesis are allowable
without special permission, provided that accurate
acknowledgement of source is made. Requests for permission
for extended quotation from or reproduction of this
manuscript in whole or in part may be granted by the head of
the major department of the Dean of the Graduate College
when in his or her judgement the proposed use of the
material is in the interests of scholarship. In all other
instances, however, permission must be obtained from the
author.
SIGNED
121
APPROVAL BY THESIS DIRECTOR
a
This thesis has been approved on the date shown below:
ltk, /c- zr-0,6r-92
Donald F. Post
Professor of Soil and
Water Sciences
ate /4
/9l6'
ACKNOWLEDGEMENTS
The author wishes to thank D.F. Post for his help,
guidance and support; and committee members, A.W. Warrick,
A. Huete and A.D. Matthias for their invaluable advice.
Sheri Musil was of great assistance in statistical
analysis, computer assistance and preparation.
Stephen Franks was invaluable in his assistance in
soil preparation and review of manuscript composition. Lou
Stevens was most helpful in preparing the boxes and helping
with the data collection.
Thanks to Sharon Cunningham for her timely typing
and preparation of the manuscript.
Thanks also to the members of the faculty and staff
of the Department of Soils, Water and Engineering for their
advice and assistance.
TABLE OF CONTENTS
Page
LIST OF TABLES
vi
LIST OF ILLUSTRATIONS
xii
ABSTRACT
xiii
1.
INTRODUCTION
1
2.
LITERATURE REVIEW
3
General Current and Future Applications
Radiometers Used as a Tool to Determine
Reflectance Factor of Soils
Pyranometers Used as a Tool to Determine
the Albedo of Soils
Using Temperature Measurement to Assess Soil
Conditions and Plant Stress
Soil Moisture and Its Influence on Remotely
Sensed Data
3.
EXPERIMENTAL PROCEDURES
4.
RESULTS AND DISCUSSION
3
7
12
14
19
Introduction
Albedo Analysis on Dry Test Soils
Maximum Minus Minimum Surface Temperature
Data for Dry Soils
Maximum Minus Minimum Surface Temperature
(Simple Regression Analysis for Drying
Soils)
Maximum Soil Surface Temperature Minus
Maximum Air Temperature (Simple Regression Analysis for Drying Soils)
Simple Regression Analysis of Evaporation
Versus Daily Maximum Soil Surface Temperature Minus Daily Minimum Soil Surface Temperature for Drying Soils
Simple Regression Analysis of Surface
Moisture Content Versus Albedo for
Drying Soils
iv
27
36
36
44
50
57
67
78
85
TABLE OF CONTENTS--Continued
Page
SUMMARY AND CONCLUSIONS
89
APPENDIX I - WEATHER RELATED DATA
92
5.
APPENDIX II - SIMPLE REGRESSION ANALYSES
SELECTED BIBLIOGRAPHY
98
127
LIST OF TABLES
Table
1.
Page
Physical and chemical characteristics of
the 20 test soils
33
Dry and wet reflectance data for the 20
test soils 34
3.
Classification of the 20 test soils
35
4.
List of simple linear regression analyses
included in Appendix II
40
5.
Analysis of variance - "dry" albedo
47
6.
One tailed student t-tests-"dry" albedo
48
7.
Soil groups from one tailed student t-tests
compared to Munsell Color Value and percent
sand for "dry" albedo
49
Analysis of variance - "dry" maximum minus
minimum soil surface temperature ( C)
53
2.
8.
9.
°
One tailed student t-tests - "dry" maximum
minus minimum soil surface temperature ( C)
°
54
10.
Soil groups from one tailed student t-tests
compared to Munsell Color Value and percent
sand for "dry" Delta I (Maximum minus
minimum soil surface temperature
C)
°
55
11.
12.
Comparison of "dry" albedo groups and Delta
T Groups
56
Simple Regression Analysis - Soil surfaye
(0-1 cm depth) moisture content (gg )
versus maximum soil surface temperature
( C) minus minimum soil surface temperature
( C) for Replication 1
58
-
°
°
vi
vii
LIST OF TABLES--Continued
Page
13.
14.
15.
16.
17.
18.
19.
Simple Regression Analysis - Combined
regression lines for illustration 2 showing
soil groups 60
Simple Regression Analysis - Total soil
moisture content (kg kg) versus maximum
soil surface temperature ( ° C) minus minimum
soil surface temperature ( ° C)
64
Simple Regression Analysis - Combined
regression lines for illustration 3 showing
soil groups 66
Simple Regression Analysis - Soil surfaie
(0-1 cm depth) moisture content (gg -i )
versus maximum soil surface temperature
( ° C) minus maximum ambient air temperature
(o c ) 69
Simple Regression Analysis - Combined
regression lines for illustration 4 showing
soil groups 71
Simple Regression Analysis - Total soil
moisture content (kg kg) versus maximum
soil surface temperature ( ° C) minus maximum
ambient air temperature ( ° C)
75
Simple Regression Analysis - Combined
regression lines for illustration 5 showing
soil groups
77
20.
Simple Regression Analysis - Daily
evaporation (cm) versus maximum soil
surface temperature ( ° C) minus minimum soil
81
surface temperature ( ° C)
21.
Simple Regression Analysis - Daily
evaporation (cm)/pan evaporation (cm)
surface temperature ( ° C) minus minimum soil
surface temperature ( ° C)
82
22.
Simple Regression Analysis - Combined
regression lines for illustration 6 showing
soil groups
84
viii
LIST OF TABLES-Continued
*
Page
23.
24.
25.
26.
27.
28.
29.
Simple Regression Analysis - Soil surfaie
(0-1 cm depth) moisture content (gg ')
versus albedo at 1000 hours 86
Simple Regression Analysis - Combined
regression lines for illustration 7 showing
soil groups
88
Wind speed for January, February and March,
1982
93
-
°
Maximum and minimum air temperature ( C)
for January, February and March, 1982
94
Twenty-four hour evaporation (cm) for
January, February and March, 1982
95
Relative humidity, February 18 through
March 11, 1982
96
Simple Linear Regression Analysis - Soil
surface (0-1 cm depth) moisture content
( g ) versus maximum soil surface
temperature ( C) minus minimum soil surface
temperature ( C)*
97
°
30.
°
Simple Linear Regression Analysis - Total
soil moisture content (kg kg) ver sus
maximum soil surface temperature ( C) minus
minimum soil surface temperature ( C)*
°
°
99
31.
Simple Linear Regression Analysis - Soil
surface (0-1 cm depth) moisture content
versus maximum soil surface
(gg-1
temperature ( C) minus maximum ambient air
temperature ( C)*
)
32.
° °
100
Simple Linear Regression Analysis - Total
soil moisture content (kg kg) versus
maximum soil surface temperature ( C) minus
maximum ambient air temperature ( C)*
°
°
101
*replicate 2
ix
TABLE OF CONTENTS--Continued
Page
33.
Simple Linear Regression Analysis - Soil
surfa ce (0-1 cm depth) moisture content
(gg ') versus maximum soil surface
temperature ( C) minus air temperature (°C)
over box
102
Simple Linear Regression Analysis - Soil
surface (0-1 cm depth) moisture content
) versus maximum soil surface
temperature ( C) minus air temperature ( C
over
box*
103
Simple Linear Regression Analysis - Total
soil moisture content (kg kg) versus
maximum soil surface temperature ( C) minus
air temperature ( C) over box
104
Simple Linear Regression Analysis - Total
soil moisture content (kg kg) versus
maximum soil surface temperature ( C) minus
air temperature ( C) over box*
105
-
34.
i
(gg-1
35.
°
°
°
°
°
36.
°
°
37.
Simple Linear Regression Analysis - Daily
evaporation (cm) versus maximum soil
surface temperature ( C) minus minimum soil
surface temperature ( C)*
106
°
38.
°
Simple Linear Regression Analysis - Daily
evaporation (cm)/Pan evaporation (cm)
versus maximum soil surface temperature
( C) minus
minimum soil surface temperature
( C)*
°
°
39.
Simple Linear Regression Analysis - Daily
evaporation (cm) versus maximum soil
surface temperature ( C) minus maximum
ambient air temperature ( C)
108
°
40.
107
°
Simple Linear Regression Analysis - Daily
evaporation (cm) versus maximum soil
surface temperature ( C) minus maximum
ambient air temperature ( C)*
109
°
°
LIST OF TABLES--Continued
Page
41.
Simple Linear Regression Analysis - Daily
evaporation (cm)/Pan evaporation (cm)
versus maximum soil surface temperature
( C) minus maximum ambient air temperature
(o c ) 110
Simple Linear Regression Analysis - Daily
evaporation (cm)/Pan evaporation (cm)
versus maximum soil surface temperature
( C) minus maximum ambient air temperature
(o c ) *
111
Simple Linear Regression Analysis - Daily
evaporation (cm) versus maximum soil
C) minus air
surface temperature (
temperature ( C) over box
112
Simple Linear Regression Analysis - Daily
evaporation (cm) versus maximum soil
C) minus air
surface temperature (
box*
C)
over
temperature (
113
Simple Linear Regression Analysis - Daily
evaporation (cm)/Pan evaporation (cm)
versus maximum soil surface temperature
( C) minus air temperature ( c) over box ..
114
Simple Linear Regression Analysis - Daily
evaporation (cm)/Pan evaporation (cm)
versus maximum soil surface temperature
( C) minus air temperature ( C) over box*..
115
Simple Linear Regression Analysis - Soil
surface (0-1 cm depth) moisture content
-I.
(gg ) versus albedo at 1200 hours 116
Simple Linear Regression Analysis - Soil
surfAce (0-1 cm depth) moisture content
-I.
(gg ) versus albedo at 1400 hours 117
Simple Linear Regression Analysis - Total
soil moisture content (kg kg) versus albedo
at 1000 hours 118
Simple Linear Regression Analysis - Total
soil moisture content (kg kg) versus albedo
at 1200 hours 119
°
42.
°
43.
°
44.
°
45.
°
46.
°
47.
48.
49.
50.
°
°
°
°
xi
LIST OF TABLES--Continued
Page
51.
52.
53.
54.
55.
56.
57.
Simple Linear Regression Analysis - Total
soil moisture content (kg kg) versus albedo
at 1400 hours
120
Simple Linear Regression Analysis - Daily
evaporation (cm) versus albedo at 1000
hours
121
Simple Linear Regression Analysis - Daily
evaporation (cm) versus albedo at 1200
hours
122
Simple Linear Regression Analysis - Daily
evaporation (cm) versus albedo at 1400
hours
123
Simple Linear Regression Analysis - Daily
evaporation (cm)/Pan evaporation (cm)
versus albedo at 1000 hours
124
Simple Linear Regression Analysis - Daily
evaporation (cm)/Pan evaporation (cm)
versus albedo at 1200 hours
125
Simple Linear Regression Analysis - Daily
evaporation (cm)/Pan evaporation (cm)
versus albedo at 1400 hours
126
LIST OF ILLUSTRATIONS
Figure
1.
2.
3.
4.
5.
6.
7.
Page
Comparison of behavior of selected in situ
soils and box samples of the same soil ....
Simple Regression Analysis - Combined
regression lines. Soil surface (0-1 cm
depth) moisture content (gg -1 ) versus
maximum soil surface temperature ( ° C) minus
minimum soil surface temperature (
43
59
Simple Regression Analysis - Combined
regression lines. Total soil moisture
content (kg kg) versus maximum soil surface
temperature ( ° C) minus minimum soil surface
temperature ( ° C)
65
Simple Regression Analysis - Combined
regression lines. Soil surface (0-1 cm
depth) moisture content (gg -1 ) versus
maximum soil surface temperature ( ° C) minus
maximum ambient air temperature ( ° C)
70
Simple Regression Analysis - Combined
regression lines. Total soil moisture
content (kg kg) versus maximum soil surface
temperature ( ° C) minus maximum ambient air
temperature ( ° C)
76
Simple Regression Analysis - Combined
regression lines. Daily evaporation
(cm)/Pan evaporation (cm) versus maximum
soil surface temperature ( ° C) minus minimum
soil surface temperature ( ° C)
83
Simple Regression Analysis - Combined
regression lines. Soil surface (0-1 cm
depth) moisture content (gg -1 ) versus
albedo 1000 hours
87
xii
ABSTRACT
The objective of this research was to correlate
daily soil surface temperature changes and soil water
evaporation losses to specific soil properties. Twenty test
soils exhibiting a broad range of physical and chemical
properties were placed in 50 x 50 x 28 cm boxes and
monitored under dry and wet conditions from January to
March, 1982. Soil moisture content, soil texture, soil
albedo, and Munsell color were identified as the most
important soil properties affecting daily changes in soil
temperature and evaporation. The soils were placed into
behavior groups based on these properties. Under dry
conditions soil albedo, Munsell color value and sand content
were the most important soil properties, whereas soil
moisture was most important under wet soil conditions.
Tables presenting tests for significance and simple linear
regression relationship for each of the test soils plus the
mean for all soils are included in the thesis. Equations in
this thesis can be used to predict relationships between
soil characteristics and the temperature-evaporation
properties of Arizona soils.
CHAPTER 1
INTRODUCTION
Remote sensing is defined as the science of acquiring
information about distant objects from measurements made
without coming into contact with the objects. Such data may
be acquired from aerial photography, radar, thermal scanners
and optical scanners.
Remote sensing found some of its earliest
applications in agriculture as a tool for studying soils and
in soil surveys. Black and white aerial photographs were
first introduced in soil survey work in 1929 in Indiana
(Stoner and Baumgardner, 1980). Such aerial photographs are
used today as the base map for soil mapping completed by the
Soil Conservation Service.
During the 1960s optical-mechanical sensors came
into civilian use. These sensors could detect visible,
reflective and thermal infrared radiation. At about the
same time computer techniques became available that could
sort multispectral scanner data and recognize patterns which
indicated that soil surface conditions could be mapped using
the scanner data. At this point soil scientists began
research in earnest to determine what physical and chemical
1
2
factors of soils were most responsible for differences in
remotely sensed data. In addition, problems confounding the
use of multispectral scanners were identified (Stoner and
Baumgardner, 1980).
Soil factors which were found to influence remotely
sensed data include clay content, organic matter content,
type of organic matter present, moisture content, surface
crusting, surface roughness, soil structure, soil particle
size and shape, and parent material (Stoner and Baumgardner,
1980).
The objectives of this research were to study
selected Arizona soils using remotely sensed data collected
with hand-held instruments. Soil temperature data was
collected with an infrared (IR) thermometer, and soil albedo
with pyranometers. These data were then evaluated and
related to soil properties such as moisture content, soil
color, soil texture and an array of other soil parameters.
CHAPTER 2
LITERATURE REVIEW
General Current and Future Applications
Remote sensing is currently being used as a tool for
investigation and problem solving in industry, agriculture
and land use planning. Current applications include soil
survey research work, crop forecasting, predicting insect
pest outbreaks, oil and mineral exploration, wetlands
inventories, detecting illegal land development, the effects
of strip mining, soil moisture evaluation, oceanography and
meteorology.
Industrial applications of remote sensing are rapidly
increasing. Earth Satellite Corporation is investigating
the use of meteorological satellite data in predicting
insect pest outbreaks based on their known life-cycle and
its relationship to sunshine, rainfall and soil moisture.
In addition, they are investigating the presence of "hazy
anomalies" found in earth resources satellite imagery of
areas with producing oil fields, known oil reserves, and
landforms generally associated with oil deposits. Martin
Marietta's sister operation, the Aggregates Division has
3
4
been using National Aeronautics and Space Administration
(NASA) data to find new sources of limestone and other
construction materials for real estate development. NASA
data is further being used by mining companies searching for
new copper and molybdenum deposits in Colorado and Arizona.
Agricultural applications of remote sensing are
growing at an astounding rate. Satellite data has been used
at the University of Kansas to estimate crop yields as
accurately as the Department of Agriculture, and it did so
before final harvest. They also report that remote sensing
data, aerial photographs and satellite imagery, have been
used by states such as New Jersey for wetlands inventories
and detection of illegal land development within the
wetlands.
Remote sensing is used in soil survey mapping by the
USDA Soil Conservation Service. Aerial photographs and
satellite images are used as map bases for their soil
surveys. Work by Matthews, Cunningham, Cipra and West
(1973) shows that computer analysis of multispectral imagery
from aircraft can be used to increase the accuracy of and
reduce the time required for developing soil survey maps.
Henderson (1974) feels that remote sensing will be a
more valuable tool to agriculture as world population
increases. He feels remotely sensed data can be used to
forecast crop conditions in different parts of the world
fast enough for other areas to respond by increasing or
5
reducing the production of particular crops and eliminate
the current shortages and excessive production of particular
crops. He feels that combining knowledge of irrigation
practices with imagery could be used to explain within and
between field variations, weed infestations and land use.
Sewell, Allen and Pile (1971) found that much research has
been conducted to determine the usefulness of infrared
remote sensing in areas as diverse as crop species
. identification, plant disease detection, detection of
salinity problems as well as determining soil moisture
levels. They also found that the most widely used infrared
line sensors can increase the wavelength range detected from
0.7 to 0.9 um to up to 14 um. This increases the wavelength
range that can be sensed into the area of thermal emissions.
Remote sensing has expanded into the use of microwave
radiometers. Schmugge et al. (1974) found that it could be
used successfully in the detection of soil moisture. Schmer
and Werner (1974) felt that thermal infrared radiation may
be the most valuable remote sensing tool for moisture status
because it is least affected by vegetation and soil
differences. Stoner and Baumgardner (1980) did extensive
work to characterize soils using their bidirectional
reflectance factors to associate them with the known
physical factors of soils. Stoner et al. (1980) later took
this work out of the laboratory and found similar
relationships existed in field studies.
6
Future application of this type of research could
include predicting crop yields (Idso, Jackson and Reginato,
1975). Currently the government does this by assessing the
crop condition at the time of the estimate, in conjunction
wit the previous two months rainfall and the predicted
rainfall for the next two months. Rainfall is clearly
assumed to be a principal factor determining yield. If soil
moisture is substituted for rainfall, predictions could be
improved for the following reasons: Soil moisture is more
directly related to crop development than rainfall; not all
rainfall is available for crop use; rainfall can only be
measured effectively in situ whereas moisture content can be
evaluated for much greater area by remote sensing; and
rainfall, at times, can actually be excessive and lead to
crop damage and reduce yields. Soil moisture evaluation
would take these aspects into account.
Predicting pest outbreaks is another future
application. The moisture content of the soil is the most
basic ecological factor in the development of insects that
spend part of their life cycle in the soil. Moisture
content influences the fertility; rate of development; and
the survival rates at various stages of the pest's growth
cycle. Outbreaks of the desert locust in Africa and Asia
are dependent on the soil moisture content, because eggs
must absorb their weight in water in order to hatch.
Breeding areas are largely uninhabited and normally arid.
7
Therefore the outbreak is unobserved until large swarms have
hatched out and entered farmers field sometimes as far as
2,500 km from the breeding grounds. Currently attempts are
made to determine when this pest will be danger through the
use of meteorological data and the condition of vegetative
growth in the breeding grounds and the initial observance by
the native population of the pest. If soil moisture could
be assessed by remote sensing, this situation might be
prevented by determining if the critical amount of moisture
is present in the soil for th extremely high hatch rate to
occur before a single locust is actually observed.
Another potential use relates to predicting plant
diseases. Soil water content is one of the most important
aspects of the soil environment influencing the growth and
survival of soil—borne plant pathogens. Management of
rangelands could also be improved, if it is known when the
moisture is available.
Radiometers Used as a Tool to Determine the Reflectance
Factor of Soils
One of the most widely used tools in remote sensing
is the radiometer. Jackson, Pinter, Reginato and Idso
(1980) define radiometers as "small instruments that measure
radiation that has been reflected or emitted from a target."
These radiometers have band pass regions similar to those of
scanners aboard satellites now in orbit, and they are
8
particularly useful for obtaining frequent spectral and
thermal data from small plots receiving different
treatments, such as irrigation or fertilization. These
kinds of experiments allow for the development of
relationships needed for improved interpretation of
satellite data and their applications to agriculture.
Radiometers lend themselves to studies of natural
surface and have been useful tools in determining the energy
balance of the earth's surface. Coulson and Reynolds (1971)
employed them for this purpose, and they took measurements
in six spectral bands ranging from the ultraviolet to the
near-infrared.
Coulson and Reynolds found that:
1. The reflectance of mineral surfaces increases
with increasing wavelength throughout the (0.320-0.795 um)
region.
2. The reflectance of green vegetation exhibit very
low values in the ultraviolet, blue and red spectral
regions, a strong increase in the near-infrared region and
less pronounced increase in the green region. These low
values corresponded to regions of strong absorption by plant
pigments.
However, penetration of plant tissues and
reflection at cell walls is thought to be responsible for
high value in the infrared region.
9
3. Most surfaces are most highly reflective at sun
elevations of 10 to 20 degrees, probably caused by the fact
that most light at this time is diffuse.
4. Surfaces of a complex nature (roughness) show a
decrease in reflectance with increasing sun elevation
probably caused by the ability of a surface with many
interstices to act as a radiation trap.
Stoner and Baumgardner (1980) did extensive research
using radiometers to determine the bidirectional reflectance
factors of soils collected from around the world. This
research was conducted to determine which physical and
chemical factors of soils affected the bidirectional
reflectance factors. All soils studied were measured
initially at a uniform moisture tension for the <2mm soil
fraction. Physical and chemical factors measured included
organic matter content, particle size distribution, cation
exchange capacity and iron oxide content.
Stoner and Baumgardner found five general types of
soil reflectance curves could be identified based on the
probable presence or absence of ferric iron absorption in
the bands of 0.7 and 0.9 um. They also found that these
five curves were also influenced by organic matter content
and soil drainage characteristics. They found that soils
with the same Munsell color notation often showed quite
different reflectance curves. The apparent role of organic
matter content was examined and found to effect the form of
10
the curve and the magnitude of the reflectance values. As
particle size decreased, soil reflectance of sandy texture
soils increased. The opposite was found to be true for
medium or finer textured soils. In addition, they found
soil temperature was higher and rainfall lower (climatic
factors), which probably caused their resulting low organic
matter content.
Statistical correlation of the soil characteristics
measured with reflectance found a negative correlation with
organic matter content. Reflectance in the middle infrared
band was negatively correlated with moisture content, cation
exchange capacity and iron oxide content. A positive
correlation was found with fine and medium sand contents.
Regression equations were developed using the
reflectance values of the ten wavebands as the independent
variables and these equations showed a high degree of
accuracy in predicting organic matter content, moisture
content, particle size class, iron oxide content and cation
exchange capacity when inferences are drawn among soils from
specific climatic zones.
Later Stoner, Baumgardner, Weismiller, Biehl and
Robinson (1980) took their research outside of the
laboratory in an attempt to relate previous work to field
conditions. They found that Chalmers and Fincastle soils
showed exactly the same spectral response in the field as
that observed in the lab in the 0.6 to 0.8 um band
11
regardless of field condition or laboratory preparation.
The only difference observed for the highly organic
glaciated Chalmers soils, over the range 0.5-1.3 um band,
was caused by the presence of crop residues. The Chalmers
soils showed a typical curve for Mollisols (concave) and
could easily be distinguished from one another and showed
the same curves regardless of field condition except when
plant residue was present.
Arnfield (1975) found that latitudinal and seasonal
variation in the surface reflectance coefficient could
effect measurements enough to significantly alter results
and should be incorporated in models, especially when an
experimenter wishes to apply them to widely separated
locations. He found that a simple exponential function
could be used to account for the diurnal dependence of the
reflectance coefficient on the solar zenith angle.
Gausman, Gerbermann, Wiegand, Learner, Rodriquez and
Noriega (1975) studied differences in reflectance factors of
fields of bare soil and ones with crop residues. They found
that soils littered with crop residue had lower reflectance
values than bare soil. However, they found that they could
not distinguish between amounts of crop residue present.
Horvath (1981) used a multi-spectral radiometer to
study the reflectance of Arizona soils. He was able to
develop charts converting Munsell color to reflectance
readings. Eighty percent of the variability for reflectance
12
measurements were explained by Munsell chroma and value,
organic carbon and carbonates. He also found that the <2 mm
fraction of the soils had significantly different
reflectance values than coarser fragments from the same
surface.
From the research described above it is apparent that
radiometers can be highly useful tools for soil research and
have merit in unravelling the problems encountered in
relating field to laboratory work.
Pyranometers Used as a Tool to Determine the Albedo of Soils
Idso, Jackson, Reginato, Kimball and Nakayama (1975)
define albedo "as the ratio of reflected to incoming solar
radiation." Studying the albedo of soils can lead to
information about soil moisture content, the phases of soil
drying and irrigation.
Often instruments such as the Eppley pyranometer or
LI-COR, LI-200S Pyranometer are employed in taking albedo
measurements. Fritschen and Gay (1971) state that the
Eppley pyranometer measures radiation over the range 0.35 to
2.5 um. The LI-200s pyranometer sensor instruction manual
states that it measures radiation over the range 0.4 to 1.2
um. One of the primary advantages of using this type of
instrument to evaluate albedo is cost. They are far less
expensive than radiometers, which break down the radiation
measured into small bands.
13
Nkemdirim (1972) evaluated the nature of problems
encountered in albedo measurements.
The areas studied
included: the relationship between albedo and zenith angle
as effected by cloudiness; the relationship between albedo
and the time of day, morning and afternoon; and the
influence of added moisture on albedo. He found that the
albedo decreases with increases in cloud cover, zenith angle
increases the variation in the average albedo values and
that irrigation (over a potato crop) increases albedo values
in the morning but had no significant effect on afternoon
values.
Idso, Jackson, Reginato, Kimball and Nakayama (1975)
found that albedo measurements were a good indicator of the
moisture status of soil. Correlated albedo values
(normalized to remove solar zenith angle effects) were a
linear function of the moisture content of the soil surface.
In addition, for the upper 2 cm of the soil the results were
independent of season. However, they found that one of the
primary problems encountered in using albedo techniques to
assess the moisture status of soil comes from the need to
know the soil type involved, which so far has limited its
usefulness.
Idso et al. (1975) further found that correlated
albedo values could be used to determine the three stages of
soil drying. During stage one evaporation proceeds at the
potential rate determined by climatic conditions. The
14
second stage is characterized by a rapid decline in the
evaporation rate due to a decrease in the hydraulic
conductivity which prevents the soil from providing moisture
to the surface to evaporate at the stage one potential rate.
During stage three the evaporation rate tapers off and
approaches zero over a long period of time.
Using Temperature Measurements to Assess Soil Conditions and
Plant Stress
Temperature measurements can be used to determine a
wide variety of soil properties and conditions which are
important to agriculture. Such properties and conditions
include thermophysical indices, thermal regimes, soil
moisture, evaporation rates and soil temperatures.
Serova (1971) attempted to map the thermophysical
indices of soil, which are basically the thermal diffusivity
and volume specific heat of soils. These two factors are
influenced by the chemical and mineralogical nature of the
soil, soil structure, mechanical composition, density and
temperature. The most important of these factors are
mechanical composition, density and moisture content.
Serova used data available in the Agroclimatology handbooks
published by the Hydrometeorological Service of the USSR to
make the initial calculations. The volume specific heat
(cp) = ps(cs + w) where (ps) is the volume weight of the dry
soil; the specific heat of the soil (cs) is known for sand,
clay, sandy loam and loam; and the moisture content (0 is
15
calculated from the known reserves of predictive moisture
and the wilting coefficient. The thermal conductivity (A)
is obtained from a graph in previously noted source material
and the diffusivity (k) = A/cp. From these thermophysical
indices Serova was able to develop maps of large areas of
the USSR which can be used to assist in determining planting
time, time of germination and other soil dependent
agriculturally important time factors.
Konstantinov and Popovich (1971) developed a method
for calculating the thermal regimes of soils at various
depths based on air temperature and humidity measured at
meteorological stations. They found an empirical
relationship between air temperature and humidity (at 2
meters above the soil) and soil temperatures at 5, 10 and 20
centimeters. From this relationship it was possible to
predict soil temperature from air temperature and humidity.
It was first necessary to determine the time lag of surface
temperature influences reaching a given depth which are
related to soil moisture, and to correct for the difference
in the amplitude of diurnal curves as observed at different
depths. Using a physicostatistical method developed by Ukr
Nigmi, a simplified relationship describing the above
predictions could be made. This also enabled them to avoid
using the thermal conductivity equation using Fourier series
and harmonics.
16
One of the more difficult problems to overcome in
using temperature measurements to determine moisture content
of a soil has been removing the data scatter caused by
environmental variability. Idso, Jackson and Reginato
(1976) developed a procedure for removing this data scatter,
making the "thermal inertia approach" in determining soil
moisture more useful. The "thermal inertia approach" uses
the magnitude of the daily maximum and minimum temperature
differences of bare soil of crop canopies to determine
moisture content. They used National Weather Service data
gathered near the experimental site to normalize surface
temperature measurements. The magnitude of the temperature
measurements are a function of internal and external
factors. The internal factors are thermal conductivity,
density and specific heat. The external factors are solar
radiation, air temperature, atmospheric precipitable water
content, cloudiness, wind, aerosol concentration, etc.,
generally referred to as the surface heat flux. Air
temperature was selected as the factor to represent surface
heat flux since a linear relationship was found between
surface heat flux and change in air temperature. Idso found
this relationship held true for only shallow soil depths and
was useful only in predicting moisture content values for
the soil he used. However, if moisture content is converted
to soil water potential the predictive value is more
universal.
17
Reginato, Idso, Vedder, Jackson, Blanchard and
Goettelman (1976) found that such measurements could also
predict evaporation rates. The temperature measurements
could be taken from ground based instruments or from thermal
scanners in an aircraft.
Hasfurther and Burman (1974) found that modeling,
using air temperature as a driving mechanism, could be used
to predict soil temperature within three degrees Fahrenheit,
to depths of 72 inches. This method is primarily limited by
the accurate prediction of air temperature in advance. The
assumed model at one inch depth was ST(t) = C AT(t); where
ST(t) is the soil temperature as a function of time, AT(t)
is the air temperature as a function of time and C is the
convolution of AT(t). Using a series of Fourier
transformations the following equation is found:
T(z,t) = i + n . I TA n exp(-zn 1/2 /D) sin (nwt + TOn- zn1/2/D)
This can be used to predict soil temperatures to depths of
72 inches.
Determining plant stress before wilting occurs is
very important to agriculture. Temperature measurements of
crop canopies could be used to time irrigation applications
which would increase yields and consume less water. For
these reasons it would also be beneficial to use thermal
data from satellites and aircraft to make such
determinations.
18
Thermal imagery can be used to differentiate between
water stressed and nonstressed fields, evaluate uniformity
of irrigation and evaluate moisture conditions in the soil
surface (Bartholic, Namken and Wiegand, 1972). Bartholic et
al. found that thermal scanners mounted in an airplane could
be used to make this determination. The resultant data
showed a variation in temperature of up to six degrees
centigrade between cotton plots of the least stressed and
most stressed plants. Also, the irradiance of soils varied
greatly as a function of time after irrigation and tillage,
and they found that ignoring variations in emissivity from
plants and fields caused an error of no more than two
degrees centigrade.
In another study, Pinter, Stanghellini, Reginato,
Idso, Jenkins and Jackson (1979) found that an infrared
thermometer could be used to determine temperature
differences in the leaves of sugar beets and cotton. Plants
under water stress exhibited significantly higher leaf
temperatures than nonstressed plants and leaf temperatures
were also higher in plants infected with soil-born root
rotting fungi.
On a larger scale, satellite borne thermal scanners
have been used to determine temperatures of terrestrial
surfaces, although the measurements must first be corrected
for emissivity of the surface. Agricultural areas were
largely ignored because they are a complex pattern of soil
19
and plant cover which confounds the determination of
emissivity of the surface. However, Sutherland and
Bartholic (1977) examined the influence of variation in
emissivity from agricultural and non-agricultural surfaces.
They assumed that areas between crop rows were infinitely
long diffuse cavities and calculated geometrical view
factors to account for internal reflected radiations and
background (sky) radiation. Then they averaged all of the
components influencing the emissivity, soil, crop and
background. Further, Sutherland et al. found that if height
to spacing ratios of row crops were greater than one, the
resulting composite emissivity was high and caused less
error in the remotely sensed temperatures. They felt that
it was reasonable to assume that the emissivity of
agricultural regions falls between .98 and 1.0, resulting in
an error of two degrees centigrade or less in temperature
measurements.
Soil Moisture and Its Influence on Remotely Sensed
Data
Soil moisture content has long been studied because
of its obvious importance to agriculture. A great deal of
research has focused on finding a means of predicting soil
moisture content, and the simpler the means of accurate
prediction the greater its potential usefulness.
Gardner (1959) sought a means of finding a numerical
solution of the flow equation for drying porous media, such
20
as soil. He found that by assuming an exponential
relationship between diffusivity and water content, a
numerical solution could be found. The predicted values
agreed closely with laboratory determinations.
Later Black, Gardner and Thurtell (1969) developed a
simple method of solving the flow equation for evaporation
of a drying soil. First, soil capillary conductivity,
diffusivity and moisture retention must be known. Second,
they determined evaporation predictions from the soil water
diffusivity characteristics of the surface layer and
rainfall data. Drainage was shown to be a function of soil
water conductivity below 25 cm depth with the drainage rate
a function of water storage. Cumulative evaporation was
proportional to the square root of time after a heavy rain.
In an effort to better understand soil water
relations, Jackson, Kimball, Reginato and Nakayama (1973)
studied time, depth and flux patterns for diurnal soil
evaporation. They soon showed that soil water flux is
dynamic in nature due to diurnal variations and
environmental conditions. The surface flux (evaporation)
was dominant through day 3. The flux decreased as the soil
dried over time, after day 3. The flux at greater depths
became more dominant with time. A period of downward flux
occurred during part of the day, below 3 cm. The data
showed that soil water movement was bidirectional during the
morning. Above 1 cm movement was upward whereas below this
21
depth movement was downward. A period of downward flux also
occurred at night.
Jackson (1973) examined the diurnal changes in soil
moisture content. The soil surface dried during the day and
rewets at night which effects the evaporation rate. It was
previously established, by Hillel under laboratory
conditions, that a soil subjected to conditions causing high
evaporation forms a dry layer at the surface which reduces
cumulative evaporation over the long run to a level less
than that observed for a soil subjected to initially low
evaporation conditions. Jackson found that under field
conditions this phenomena might not occur due to the diurnal
nature of drying and rewetting. He also found that the
three stages of soil drying could not readily be observed
due to the dynamic nature of soil water movement in field
conditions.
Using the water transport model, Cameron (1979) was
able to predict soil water content. He measured saturated
hydraulic conductivity and soil water characteristics
curves. He derived conductivity and diffusivity using the
Millington-Quirk method. However, they did not give an
exact fit to the field data. He was forced to make
adjustments to K(0.) and 1)(19) to make more accurate
predictions. Cameron feels that further study of the means
of adjusting these values is needed if the unsaturated flow
22
equation is to be used for field soil water content
predictions.
Research has been conducted to assess the effect of
surface conditions on soil water content. Selim (1970)
studied the effect of surface cracks in a drying soil. He
found that cracks decreased the water content by as much as
5 to 10%. He also found that surface cracks caused the soil
temperature to be lower than in uncracked soil when a wind
was present. Temperatures were higher for cracked soil
under radiation only. Bond and Willis (1970, 1971) looked
at the influence of surface residue on evaporation. They
found that the evaporation rate decreased with increasing
residue for first stage drying. They also found that
residue decreased evaporation rate through 60 days of
drying.
Remote sensing would seem to be an ideal means of
assessing soil moisture content, evapotranspiration and
evaporation. If they could be effectively determined
remotely, it would eliminate many costly trips into the
field for samples as well as a great deal of laboratory time
in preparation and for drying.
Bowers and Hanks (1965) examined the influence of
moisture content, particle size and organic matter on
reflectance. They found that reflectance of soils could be
used to determine surface soil moisture contents if the soil
is known, using a spectrophotometer because each soil has
23
its own moisture-reflectance curve. High organic matter
content causes soils to be darker, and it was found that
such darker soils did absorb more energy although energy
absorption decreased after oxidation of the soil. Bowers
and Hanks were able to show that reflectance increased
exponentially with decreasing particle size for particles
less than 400 um in size. Larger particles had little
influence. This was also though to be a function of surface
roughness (larger particles, rougher surface).
Schmugge, Gloersen, Wilheit and Geiger (1974)
examined the viability of using microwave radiometers to
determine soil moisture content. They found, by comparison
with ground truth data, that soil moisture could be
monitored by aircraft mounted microwave radiometers. They
used the Fresnel equations to estimate emissivity which
proved to be adequate. The 0.8 cm and 1.55 cm wavelength
radiometers were sensitive to surface moisture only. The
1.55 cm radiometer gave brightness temperatures from which
moisture contents greater than 15% (by weight) could be
predicted. There was a linear decrease in the emission with
increasing soil moisture content. For the 21 cm radiometer,
a similar linear increase in soil moisture content with
decreasing emission was observed for moisture contents
between 0 and 35%.
Schmer and Werner (1974) wished to determine which
bands of the electromagnetic spectrum were most useful for
24
soil moisture determinations. They found that the blue
spectral band was best for assessing soil moisture early in
the season when little crop cover was present. As time
passed and sorghum grew, the red spectral band became the
most useful as an indicator of soil moisture. Near infrared
reflectance related to the most severe conditions of soil
water stress. They also found that thermal infrared
radiation may be the best indicator of soil moisture status
because it was influenced less by vegetation and soil
surface variability. Data varied with season, weather and
applications.
Using color and black-and-white infrared film,
Sewell, Allen and Pile (1971) found that differences in bare
soil moisture could be detected. They found that color
infrared film was the best indicator.
Idso, Jackson and Reginato (1975) found that albedo
values could be used to determine moisture content if the
soil is known. They were also able to use albedo values to
delineate the three classical stages of soil drying.
Thermal radiation measurements could be used to determine
soil moisture. They feel that microwave radiation
measurements could also be used to evaluate soil moisture.
Jackson, Hatfield, Reginato, Idso and Pinter (1983)
determined that daily evapotranspiration could be estimated
from remotely sensed surface temperature measurements and
latitude, day of year and time-of-day. Their method
25
requires that the temperature measurements be taken about
two hours after solar noon, and night time
evapotranspiration should be considered when working in arid
regions. This method was applied to bare soil and crops of
various stages and generally agreed with on-ground 24-hour
direct measurements.
Ben-Asher, Matthias and Warrick (1983) found that a
simple theoretical model could be used to estimate daily
soil water evaporation from a uniform drying bare soil. The
only measurements required are wind speeds, midday infrared
thermometer measurement of surface temperature of the drying
soil and of a nearby dry reference soil. This model assumes
that evaporation is a linear function of the difference
between temperatures of the dry and drying soils (y) and
wind speed (x). The advantage of this particular method of
estimating evaporation of a bare soil is found in the fact
that it is easily adapted to remotely sensed data and
requires only one set of measurements on a particular day;
wind speed, dry reference soil temperature and drying soil
temperature.
Vauclin, Vieira, Bernard and Hatfield (1982) studied
spatial variability in surface temperature in a sprinkler
irrigated field soil. They found a strong correlation
between spatial variability in surface temperature and the
observed lack of uniformity in irrigation water application.
Where irrigation water application was relatively uniform
26
they found surface temperature measurements were
interdependent. This indicates that autocorrelation and
semivariograms used with transects where data is collected
at specific intervals are better indicators of what is
actually happening in a given field than random sampling.
CHAPTER 3
EXPERIMENTAL PROCEDURES
This research was conducted at the University of
Arizona Campus Agricultural Center, Tucson, Arizona. "Dry"
data was collected January 13 through January 18, 1982.
"Wet" data was collected February 18 through March 11, 1982.
Eighteen Arizona soils were collected for study because they
exhibited a wide variety of characteristics ranging from
sand to silty clay in texture, and from very pale brown to
reddish brown and very dark grayish brown in color. These
are representative Arizona soils, and included a range of
climates from desert to mountain. The soil series used
included: Agua, Gila, Hayhook, Laveen, Whitehouse, Comoro,
Grabe, Avondale, Pinaleno, Vint, Contine, Brazito, Mohave,
Pimer, Pima, Holtville, Cloversprings and Superstition.
Number 20 silica sand was included for comparative purposes.
For all soils the A or Ap surface horizon was collected. In
addition, the B horizon of the Whitehouse soil was also
collected. In this thesis these twenty (20) soil samples
will be referred to as the "twenty test soils."
Each soil was air-dried and passed through a 19
millimeter sieve to remove large coarse fragments and roots.
27
28
Forty wooden boxes 50 cm square and 28 cm deep were built to
contain the soils. This size was selected to closely
monitor to a depth of 12 cm. A field in-situ site of the
Gila soil was also used for comparative purposes. Each box
was weighed and a 3 cm layer of gravel was placed in the
bottom of the box and covered with a wire screen wrapped in
muslin or cheesecloth to facilitate drainage without soil
loss. Each box was then reweighed to determine the weight
of the gravel. The nineteen soils and No. 20 silica sand
were randomly added to a pair of boxes, and then reweighed
to determine the weight of the soil. The boxes were caulked
to prevent water loss through the seams and channeling in
soils during drainage. Records were kept of all weights.
Two pore volumes of water were added to each box of
soil the day before the wet phase of the research was
conducted. The soils were allowed to drain freely for
approximately 12 hours. The morning the wet phase of the
experiment began, approximately 1 liter of water was added
to each soil to insure that the surface of all soils were
uniformly wet. Measurement were begun when there was no
free water on the surface of any soil. The field soil was
irrigated the day before the experiment began and no free
water was present when measurements were taken.
The boxes were positioned in a row, on a support
frame and leveled, near the Campus Agricultural Center
weather station. When it rained the boxes were covered with
29
a sheet of heavy plastic to prevent the addition of an
unknown amount of moisture. Reflectance readings for both
wet and dry soils were obtained from research reported by
Huete, Post and Jackson (1984).
The albedo measurements for both dry and wet phases
of the research were taken with an "albedo meter". The
albedo meter consisted of an LI 200S pyranometer mounted on
a metal arm facing upward to measure incoming radiation and
an Eppley pyrheliometer mounted facing downward to measure
reflected radiation. Both instruments were leveled to 90
degrees to take measurements. The electrodes were attached
to a CR 21 micrologger which converted direct measurements
to millivolts thus allowing almost instantaneous
measurements of albedo. Albedo was calculated as the ratio
of incoming radiation to reflected radiation. The LI-COR
pyranometer (LI 200S) measures solar radiation over the
wavelengths 0.4 micrometers to 1.2 micrometers with a
maximum response at 0.95 micrometers (LI-COR 1980). the
Eppley (180 degree) Pyrheliometer measures radiation over
the wavelengths 0.35 to 2.5 micrometers (Fritschen and Gay,
1979).
Albedo measurements were taken and recorded three
times per day at approximately 1000, 1200 and 1400 hours.
The selected times represent the lowest portion of the curve
for radiation measurements because the zenith angle is near
30
90 degrees, and these three measurements could therefore be
used as replications.
Soil temperature measurements were taken on an hourly
basis (except when other measurements such as albedo were
taken) from sunrise until near sunset, or when shadows
crossed the boxes. Surface temperature measurements were
taken with a portable Everest Interscience Infrared
Thermometer Model 110(3 degree field of view and + 0.5
degree accuracy). The emissivity was set at .98 throughout
the research (Everest Interscience 1981). The infrared
thermometer was manually held at a 45 degree angle of
incidence at a height above the soil surface to yield a
diameter of the target area near the center of the box of 6
C M.
Subsurface temperature measurements were taken on an
hourly basis at depths below the surface of 2 cm, 7 cm and
12 cm. Thermometers were inserted into drilled holes in the
side of one box of soil per soil pair. Thermometers were a
direct read type. Thermocouples were installed at the same
depths in the Gila field soil and hourly temperature
measurements were recorded on an automatic continuous
recording device. Copper-constantin thermocouples were used
which read directly in degrees. The second references was a
black body from which surface temperature measurements were
taken during the drying phase of the experiment.
31
One box from each pair of soils was weighed on a
daily basis to calculate moisture content and evaporation
losses throughout the drying phase of the research. A
sample of the surface 1 centimeter of each soil was also
taken on a daily basis, oven-dried, and weighed to determine
the moisture content of the surface centimeter of each soil
throughout the experiment. The surface cm sampler and
methodology for sampling is described by Reginato (1975).
The soils used in this research have been analyzed
both in the field and laboratory to determine a variety of
physical and chemical factors for each soil, and these are
presented in Tables 1 and 2. The soil colors were
determined by 36 field soil scientists, and the mean color
notation is given. The taxonomic classification of each
soil series is listed in Table 3. Regression coefficients
were determined for the drying process of each soil making
it possible to determine which soil characteristic
influenced the evaporation process and temperature change
the most. The regression coefficients, R, R 2 and standard
error of the estimate are included in Tables 29 to 57,
located in Appendix 2 of this thesis for regression analysis
not discussed in detail in Chapter 4.
Weather data recorded at the Campus Agricultural
Center are presented in Appendix I of this thesis (Tables
25, 26, 27 and 28).
32
Statistical analysis of the dry data included
analysis of variance and one—tailed student t—tests.
Analysis of variance using a completely randomized design
was calculated using the method described by McClave and
Dietrich, 1982. One—tailed student t—tests were calculated
using the method described by McClave and Dietrich, 1982.
This test determines if one sample mean is larger than
another which allowed us to group soils exhibiting similar
behavior.
Statistical analysis of the "wet" data included
simple linear regressions for each soil and combining
regression lines for soils that were similar for selected
data groups. Simple linear regression lines were calculated
for each soil using the University of Arizona computer and
the Statistical Package for the Social Sciences (SPSS).
When regression lines were combined an F—test to compare
regression models was performed using the method described
by Neter and Wasserman, 1974. This allowed us to group
soils exhibiting similar behavior.
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34
Dry and wet reflectance data (%) for the twenty
test soils (from Huete et al., 1984).
TABLE 2.
% Dry Reflectance
% Wet Reflectance
Soil
Bl*
B2*
B3*
B4*
Aqua
19.3
24.7
30.8
34.1 108.9
6.6
Avondale
15.6
22.1
27.3
29.7
94.7
Brazito
19.6
25.3
31.5
5.2
6.7
Comoro
10.3
Contine
B3
B4
9.4
13.5
15.9
45.4
6.1
9.9
13.5
15.4
44.9
35.1 111.5
7.3
10.4
14.4
17.2
49.3
9.2
11.4
32.5
2.1
2.6
3.6
5.0
13.3
13.3
18.1
21.6
63.3
4.2
6.1
8.9
11.5
30.7
17.3
26.3
32.9
34.4 110.9
8.5
14.5
18.8
19.8
61.6
Gila
22.0
28.5
34.9
38.3 123.7
7.3
10.7
15.5
18.0
51.5
Crabe
17.2
21.7
27.6
30.8
97.3
5.3
7.8
11.1
13.8
38.0
Hayhook
19.0
27.5
34.8
37.2 118.5
8.6
13.1
17.2
19.1
58.0
Holtville
13.5
17.7
21.2
23.1
75.5
5.5
8.1
10.5
12.0
36.1
Laveen
18.6
26.1
32.3
34.7 111.7
7.0
11.1
15.1
16.9
50.1
bbhave
18.4
27.8
35.4
37.6 119.2
8.8
14.8
19.6
21.5
64.7
Pima
19.5
25.2
31.0
34.3 110.0
6.0
8.9
12.5
15.4
42.8
Pimer
20.2
26.5
32.1
34.7 113.5
7.9
11.4
14.4
16.6
50.3
Pinaleno
16.4
24.1
30.5
33.4 104.4
5.9
9.9
13.9
16.1
45.8
Superstition
24.7
32.9
38.0
39.7 135.3
13.9
20.1
23.6
25.0
82.6
Vint
18.6
25.4
30.9
32.7 107.6
7.0
10.7
13.8
14.8
46.3
Whitehouse (A)
12.2
20.1
26.8
29.9
89.0
5.5
10.5
15.0
17.4
48.4
Whitehouse (B)
9.1
17.3
25.2
7.1
78.7
5.3
10.6
15.4
16.4
47.7
39.2
43.0
49.1
54.2 185.5
22.7
25.6
30.2
34.3 112.8
Cloversprings
Silica Sand
Bi
*B1 = .5-.6 pra; B2 = .6-.7 ixa; B3 .7-.8 un; and B4 = .8-1.1 pm.
B2
35
TABLE 3. Classification of the 20 test soils.
Soil Name
Family
Aqua
Coarse-loamy over sandy or sandy-skeletal, mixed
(calcareous), thermic Typic Torrifluvent.
Avondale
Fine-loamy, mixed (calcareous), hyperthermic Typic
Torrifluvents.
Brazito
Mixed, thermic Typic Torripsamments.
Cloversprings
Fine-silty, mixed Cumulic Cryoborolls.
Comoro
Coarse-loamy, mixed (calcareous), thermic Typic
Torrifluvents.
Contine
Fine, mixed, hyperthermic Typic Haplargids.
Gila
Coarse-loamy, mixed (calcareous), thermic Typic
Torrifluvents.
Grabe
Coarse-loamy, mixed (calcareous), thermic Typic
Torrifluvents.
Hayhook
Coarse-loamy, mixed, non-acid, thermic Typic
Torriorthents.
Holtville
Clayey over loamy, montmorillonitic (calcareous),
hyperthermic Typic Torrifluvents.
Laveen
Coarse-loamy, mixed, hyperthermic Typic Calciorthids.
Mohave
Fine-loamy, mixed, thermic Typic Haplargids.
Pima
Fine-silty, mixed (calcareous), thermic Typic
Torrifluvents.
Pimer
Fine-silty, mixed (calcareous), hyperthermic Typic
Torrifluvents.
Pinaleno
Loamy-skeletal, mixed, thermic Typic Haplargids.
Silica Sand
Not classified.
Superstition
Sandy, mixed, hyperthermic Typic Claciorthids
Vint
Sandy, mixed, hyperthermic Typic Torrifluvents.
Whitehouse
Fine, mixed, thermic Ustollic Haplargids.
CHAPTER 4
RESULTS AND DISCUSSION
Introduction
This research was conducted to examine the
relationship between soil surface temperature, soil albedo,
soil moisture and other soil characteristics such as color
and texture. Soil moisture influences both maximum and
minimum soil surface temperature and albedo. The rate at
which soil moisture leaves the soil is also influenced by a
variety of physical and chemical factors possessed by the
soil. In my research I was interested in researching
several questions noted below.
In 1976 Reginato, Idso, Vedder, Jackson, Blanchard
and Goettelman reported on research they had conducted to
examine the relationship between soil surface temperature
and moisture content. They found, as expected, an inverse
relationship between moisture content or evaporation and
maximum minus minimum soil surface temperature or maximum
soil surface temperature minus maximum ambient air
temperature. One of the questions they posed was whether or
not this close a relationship could be found for soils other
than the Avondale loam they studied.
36
37
A second question raised in 1975 by Idso, Jackson,
Reginato, Kimball and Nakayama related to soil albedo. They
showed albedo values to be a linear function of soil surface
moisture content. This technique had predictive value for
determining surface moisture content and had the advantage
of being independent of season. They found the primary
problem limiting its usefulness came from the need to know
what soil type was involved. The question then pertains to
predicting soil behavior where little information is
available about soil types.
I also wanted to see if distinct soil behavior
groups could be identified for each relationship described
and compare them to what happened to these soils in the dry
state. I also wanted to quantify the relationship between
the drying cycle and inherent physical characteristics of
soils.
Both soil surface temperature and albedo (or
reflectance) data can be obtained from some distance from
the soil through the use of pyranometers, infrared
thermometers, radiometers and a wide variety of scanners
mounted on either aircraft or satellites. If we can somehow
combine and evaluate the mass of data that can be gathered
through remote sensing of soil in the dry state and over a
drying cycle, can we then learn something about physical
factors of the soil by comparing them to soils we know a
great deal about, such as the twenty test soils studied
38
here?
Is it then possible to predict soil behavior in an
area where little or no data is available about soil type?
I attempted to examine the relationship between soil
surface temperature, soil albedo, soil moisture and other
soil characteristics such as color and texture. As we learn
more about these relationships through remote sensing we can
come a little closer to making predictions about soil
behavior, especially if this data can be combined with
meteorological data.
The body of data collected during the research was
quite extensive. Simple linear regressions were performed
on all data related to soil surface temperature, albedo,
soil surface moisture content and total soil moisture
content. Tables of this information are included in
Appendix II.
Because of the extensive number of simple linear
regressions only certain data groups were subjected to
further analysis and broken into soil behavior groups.
These groups were then compared to color and texture data.
Only those subjected to further analysis are discussed in
detail in this chapter. It should be noted that generally
coefficients of correlation were high for all simple linear
regressions. A list of all simple linear regressions of
drying soil data are included in Table 4. Results of the
simple linear regression analysis are found in Appendix II.
39
In April and May of 1982, extensive data was
collected to determine if the soil in the boxes used for the
research did simulate soil behavior in actual field
conditions for bare soil.
Three field plots were selected. They consisted of
three of the soils included in the boxes used for this
research. The soil sample placed in the boxes was taken
from these three fields.
The three graphs appearing on Illustration 1 show
the relationship of surface moisture over time for the two
box samples and the field soil. Except for the first three
days there was good agreement between the field soils and
the box samples. The higher moisture contents found in the
boxes for the first three days were caused by the fact that
there was essentially no drainage from the boxes and only
evaporation caused moisture loss. The field soils were
still being subjected to deeper drainage at that time. This
indicates that data collected from the boxes might better be
applied to field conditions after a few days are allowed for
drainage in the fields.
40
TABLE 4.
List of simple linear regression analyses
included in Appendix II.
29.
Simple Linear Regression Analysis - Soil surface (01 cm depth) moisture content (gg -1 ) versus maximum
soil surface temperature ( ° C) minus minimum soil
surface temperature ( ° C).*
30.
Simple Linear Regression Analysis - Total soil
moisture content (kg kg) versus maximum soil surface
t emperature ( ° C) minus minimum soil surface
temperature ( ° C)*
31.
Simple Linear Regression Analysis - Soil surface (01 cm depth) moisture content (gg ) versus maximum
soil surface temperature ( C) minus maximum ambient
air temperature ( C)*
-1
°
°
32.
Simple Linear Regression Analysis - Total soil
moisture content (kg kg) versus maximum soil surface
t emperature ( ° C) minus maximum ambient air
temperature ( ° C)*
33.
Simple Linear Regression Analysis - Soil surface (01 cm depth) moisture content (gg -1 ) versus maximum
soil surface temperature ( ° C) minus air temperature
( ° C) over box
34.
Simple Linear Regression Analysis - Soil surface (01 cm depth) moisture content (gg -1 ) versus maximum
soil surface temperature ( ° C) minus air temperature
( ° C) over box*
35.
Simple Linear Regression Analysis - Total soil
moisture content (kg kg) versus maximum soil surface
temperature ( ° C) minus air temperature ( ° C) over box
36.
Simple Linear Regression Analysis - Total soil
moisture content (kg kg) versus maximum soil surface
temperature ( ° C) minus air temperature ( ° C) over
box*
37.
Simple Linear Regression Analysis - Daily
evaporation (cm) versus maximum soil surface
t emperature ( C) minus minimum soil surface
temperature ( C)*
°
38.
°
Simple Linear Regression Analysis - Daily
evaporation (cm)/Pan evaporation (cm) versus maximum
soil surface temperature ( C) minus minimum soil
surface temperature ( C)*
°
* Replicate 2
°
41
List of simple linear regression analyses
included in Appendix II (continued)
TABLE 4.
39.
Simple Linear R egress ion Analysis - Daily
evaporation (cm) versus maximum soil surface
temperature ( 0) minus maximum ambient air
temperature ( 0)
°
°
40.
Simple Linear Regress ion Analysis - Daily
evaporation (cm) versus maximum soil surface
temperature ( C) minus maximum ambient air
temperature ( (D C)*
°
41.
Simple Linear Regression Analysis - Daily
evaporation (cm)/Pan evaporation (cm) versus maximum
soil surface temperature ( C) minus maximum ambient
air temperature ( C)
°
42.
Daily
Simple Linear Regression Analysis
evaporation (cm)/Pan evaporation (cm) versus maximum
soil surface temperature ( C) minus maximum ambient
air temperature ( 0)*
°
43.
°
°
Simple Linear Regression Analysis - Daily
evaporation (cm) versus maximum soil surface
temperature ( C) minus air temperature ( C) over box
°
44.
°
Simple Linear Regression Analysis - Daily
evaporation (cm) versus maximum soil surface
temperature ( C) minus air temperature ( C) over
box*
°
45.
Simple Linear Regression Analysis - Daily
evaporation (cm)/Pan evaporation (cm) versus maximum
soil surface temperature ( C) minus air temperature
( c) over box
°
46.
°
Simple Linear Regression Analysis - Daily
evaporation (cm)/Pan evaporation (cm) versus maximum
soil surface temperature ( C) minus air temperature
( C) over box*
°
47.
°
°
Simple Linear Regression Analysis - Soil surface (0) versus albedo at
1 cm depth) moisture content (gg
1200 hours
-1
48.
Simple Linear Regression Analysis 1 - Soil surface (01 cm depth) moisture content (gg
) versus albedo at
1400 hours
49.
Simple Linear Regression Analysis - Total soil
moisture content (kg kg) versus albedo at 1000 hours
-1
42
TABLE 4.
List of simple linear regression analyses
included in Appendix II (continued)
50.
Simple Linear Regression Analysis - Total soil
moisture content (kg kg) versus albedo at 1200 hours
51.
Simple Linear Regression Analysis - Total soil
moisture content (kg kg) versus albedo at 1400 hours
52.
Simple Linear Regression Analysis - Daily
evaporation (cm) versus albedo at 1000 hours
53.
Simple Linear Regression Analysis - Daily
evaporation (cm) versus albedo at 1200 hours
54.
Simple Linear Regression Analysis - Daily
evaporation (cm) versus albedo at 1400 hours
55.
Simple Linear Regression Analysis Daily
evaporation (cm)/Pan evaporation (cm) versus albedo
at 1000 hours
56.
Simple Linear Regression Analysis - Daily
evaporation (cm)/Pan evaporation (cm) versus albedo
at 1200 hours
57.
Simple Linear Regression Analysis - Daily
evaporation (cm)/Pan evaporation (cm) versus albedo
at 1400 hours
43
Agua
• ... 7";
Field Soil
Box Sample A
Box Sample B
...
.........
.........
• •
• • • • • • .....
-
•••
...........
.
20
15
10
5
0
4/30 5/3
5/ 1 2
5/17
5/21
6/4
Sampling Date 1982
Figure 1. Comparison of behavior of selected In Situ soils
and box samples of the same soil.
44
Albedo Analysis on Dry Test Soils
Albedo data, the ratio of incoming to reflected
radiation, was collected for a four-day period in January
1982. Simultaneous measurements of incoming and reflected
radiation were collected at about 1000, 1200 and 1400 hours.
all 20 test soils remained dry during this phase of the
research. This data was used to determine two things:
1.
Were the soils exhibiting a wide variety of
physical and chemical characteristics,
behaving in different ways which could be
measured with albedo data?
2.
If so, which soils behaved the same way and
which were different?
In order to answer the first question an analysis of
variance was performed using a completely randomized design.
This statistical analysis established that at least two of
the soils studied were in fact behaving significantly
differently at the 95% confidence level (Table 5).
It then became pertinent to address the second
question: which soils were behaving differently? This was
accomplished by performing a 1-tailed student t-test at the
95% confidence level on the mean for each soil against the
soil that most closely resembled the first soil (Table 6).
Several of the soils were different but many were similar.
It then became possible to divide the soils into similar
behavior groups (Table 7). There were six of these groups.
45
Those that were definitely different were: consisting of silica sand alone;
Superstition sand alone;
Group A
Group B consisting
and Group F consisting of the
black Cloversprings alone. The fact that the silica sand
behaved like no other soil is best explained by its physical
-
characteristics.
Its Munsell color value is 8, and it
consisted of 100% sand.
It was also the most reflective of
the 20 test soils.
Superstition sand was the second most reflective of
the soils.
It has the next lightest Munsell color value of
6.5 and consists of 96% sand.
Since it resembled silica
sand most, but was not statistically the same, this
difference is most easily explained by the color difference.
It was also lighter in color than any of the other 18 test
soils.
Cloversprings was the least reflective of the 20
test soils. This is most easily explained by the fact that
it is quite a bit darker, Munsell color value of 2.8, than
any other soil studied. Also, the albedos of the soils
discussed above, each behaving differently in the dry
condition, could easily be explained by Munsell color value
alone.
Group C consisted of Hayhook, Pimer, Brazito, Vint,
Gila and Pima. These soils tended to be lighter in color
than soils in groups D and E. The average Munsell color
value for each soil in the group ranged from 5.2 to 5.8. It
46
should also be noted that all but two of these soils
contained more than 50% sand. Color value and % sand
largely explained the behavior of this group.
Group D consisted of Pinaleno, Agua and Mohave.
These soils have similar color values (5.1 to 5.4) and
surprisingly similar sand content (59 to 71%). Even though
they range in values into groups C and E they are
surprisingly similar in characteristics. Two are sandy
barns and one is a light-colored sandy clay loam.
Group E consisted of Contine, Avondale, Laveen,
Whitehouse A, Grabe, Comoro, Holtville and Whitehouse B.
The Munsell color values in this group ranged from 3.9 to
5.4. It should be noted that the Holtville, throughout the
entire research, behaved differently than the other soils.
This is because it is a silty clay and is subject to
extensive, deep cracking when drying. For these reasons it
could reasonably be excluded from statistical analysis.
However, it probably is typical of soils in this uncommon
textural class that crack and could be applicable to
Vertisols as well. If Holtville is excluded, this group
ranged from 3.9 to 5.3 in Munsell color value and all but
three of these soils contained less than 50% sand and only
two contained appreciably more. There is surprisingly
little overlap in the color and sand content for groups C
and E and group D, as expected, seems to fall midway between
the two although overlapping both.
47
TABLE 5.
Analysis of variance — "Dry" Albedo.
AN OVA
Source
df
SS
MS
Soils
19
.3566
.01877
Error
60
.06459
.00107
Total
79
.42119
a = .05
Ha:
17.44 > 1.75
At least two soil
means differ.
F
17.44
48
TABLE 6.
One-tailed student t-tests - "Dry" Albedo.
Soil Series (a)
Calculated
Tabular
Ho:(a-b) = 0
Ha:(a-b) > 0
Soil Series (h)
Silica sand
Superstition
2.908
1.943
a >b
Superstition
Hayhook
3.728
1.943
a >b
Hayhook
Pimer
.099
1.943
a=b
Pimer
Brazito
.135
1.943
a=b
Brazito
Vint
.228
1.943
a=b
Vint
Gila
.151
1.943
a=b
Gila
Pima
.382
1.943
a =b
Pima
Pinaleno
2.011
1.943
a >b
Pinaleno
Agua
.138
1.943
a=b
Agua
Mohave
.117
1.943 •
a =b
Mohave
Contine
3.613
1.943
a >b
Contine
Avondale
.092
1.943
a =b
Avondale
Laveen
.097
1.943
a = b
Laveen
Whitehouse A
.041
1.943
a = b
Whitehouse A
Grabe
1.117
1.943
a=b
Grabe
Comoro
.914
1.943
a =b
Comoro
Holtville
.102
1.943
a=b
Holtville
Whitehouse B
1.424
1.943
a = b
Whitehouse B
Cloversprings
6.025
1.943
a >b
49
TABLE 7.
Soil groups from one-tailed student t-tests compared to
Munsell Color Value and Percent Sand for "Dry" Albdeo.
Soil Name
Average
Albedo
Albedo
Group
Munsell
Value
%
Sand
% Sand
and Color*
Silica Sand
.48175
A
8.0
100
2
Superstition
.38658
B
6.5
96
4
Hayhook
.35335
C
5.5
70
11
Pimer
.35073
C
5.5
21
23
Brazito
.34428
C
5.2
68
15
Vint
.33500
C
5.3
82
10
Gila
.33383
C
5.8
54
14
Pima
.32925
C
5.2
36
25
Pinaleno
.32468
D
5.1
71
14
Agua
.31520
D
5.4
60
14
Mohave
.31408
D
5.2
59
17
Contine
.28520
E
5.3
52
18
Avondale
.28450
E
5.3
40
22
Laveen
.28245
E
5.2
33
24
Whitehouse A
.28133
E
4.8
79
15
Grabe
.25365
E
4.9
41
23
Comoro
.23468
E
3.9
84
15
Holtville
.23328
E
5.4
9
25
Whitehouse B
.21950
E
4.1
49
24
Cloversprings
.16250
F
2.8
40
29
*ranked and combined
50
Maximum Minus Minimum Surface Temperature
Data for Dry Soils
Minimum and maximum temperature data were collected
for a three-day period in January 1982. Minimum surface
temperatures occurred at about sunrise and maximum surface
temperatures occurred between 1300 and 1400 hours. All 20
test soils remained dry during this phase of the research.
As in the albedo analysis, this data was used to determine
two things:
1.
Were the soils exhibiting a wide variety of
physical and chemical characteristics, being
in different ways, which could be measured
with maximum minus minimum temperature data?
2.
If so, which soils behaved the same way and
which were different?
An analysis of variance was performed using a
completely randomized design to answer the first question,
and this analysis established that at least two soils were
behaving significantly differently at the 95% confidence
level (Table 8). Once again, it became possible to address
the second question about which soils were similar and which
were different. This was accomplished in the same manner as
with the albedo data, with a one-tailed student t-test
(Table 9).
The comparison of the means for the three-day study
period were used to establish four distinct soil behavior
groups (Table 10). Groups 1 and 4 each consisted of a
51
single soil. Group 1 contained silica sand. It had the
lowest temperature differential (maximum minus minimum soil
surface temperature) or delta T of 25.6
° C.
This can most
easily be explained by the fact that it had the lightest
Munsell color value of 8 and consisted of 100% sand. Group
4 consisted of the dark Cloversprings soil, and it had the
highest delta T of 42.3
° C.
This can be explained by its
Munsell color value of 2.8, which is significantly darker
than any other soil.
Group 2 consisted of Superstition, Contine, Hayhook,
Brazito, Pimer and Mohave. These soils tended to be lighter
in color with Munsell values ranging from 5.2 to 6.5. They
also had high sand contents with all but one soil containing
more than 50% sand. The break between groups 2 and 3 was
not immediately apparent by comparing means until the
extremes of each group were compared. This probably was
caused by the small number of observations (3) anda
particularly large standard deviation associated with one of
the soils at the break.
Group 3 consisted of Pinaleno, Agua, Avondale, Pima,
Whitehouse A, Vint, Gila, Laveen, Grabe, Whitehouse B,
Comoro and Holtville. Their Munsell color values ranged
from 5.8 to 3.9. This range shows considerable overlap with
group 2. Their sand contents showed considerable
variability as well, ranging from 9% (Holtville) to 84%
(Comoro). Group 3 showed no apparent relationship between
52
color or sand content and soil group although these soils
did tend to be darker than Group 2. Groups 1, 2, and 4
could easily be explained by Munsell color vale.
It should be noted that the temperature groups and
albedo groups showed some similarities (Tables 7 and 10).
When albedo and delta T groups are combined there are nine
distinct groups (Table 11). Albedo group C is similar to
temperature group 2. These two groups showed the strongest
correlation between both Munsell color value and sand
content. Albedo group A (silica sand) was the same as delta
T group 1. Albedo group F (Cloversprings) and delta T group
4 were the same. Albedo group E consisted almost
exclusively (Except Contine) of members of temperature group
3. Albedo group D (Pinaleno, Mohave and Agua) ranked close
together in temperature analysis even though binding groups
2 and 3.
Dry albedo behavior of soils can be largely
explained by Munsell color value. Dry maximum minus minimum
soil surface temperature behavior shows some relationship
between both Munsell color values and the texture component
of % sand. However, these two characteristics do not
completely explain soil behavior in the dry state. It is
also interesting to note that with the distinct remotely
sensed measurements (albedo and delta T) that nine distinct
soil groups were observed, making it possible in part to
distinguish soil series in this research from one another.
53
TABLE 8.
Analysis of variance - "Dry" Maximum minus
Minimum Soil Surface Temperature ( ° C).
ANOVA
df
SS
Soils
19
569
30.07
Error
40
122
3.05
Total
59
691
Source
a = .05
Ha:
9.85 > 1.84
At least two soil
means differ.
MS
F
9.85
54
TABLE 9.
One-tailed student t-tests - "Dry" maximum minus minimum soil
surface temperature ( O).
°
Calculated
Soil Series (a)
Tabular
Soil Series (h)
Ho:(a-b) = 0
Ha:(a-b) < 0
6.136
2.132
a <b
Contine
.678
2.132
a=b
Continue
Hayhook
.510
2.132
a = b
Hayhook
Brazito
.139
2.132
a=b
Brazito
Pimer
.052
2.132
a=b
Pimer
Mohave
.164
2.132
a=b
Mohave
Pinaleno
.041
2.132
a =b
Pinaleno
Agua
1.385
2.132
a =b
Agua
Avondale
.017
2.132
a = b
Avondale
Pima
.136
2.132
a =b
Pima
Whitehouse A
.197
2.132
a = b
Whitehouse A
Vint
.127
2.132
a=b
Vint
Gila
.243
2.132
a =b
Gila
Laveen
.224
2.132
a =b
Laveen
Grabe
.214
2.132
a=b
Grabe
Whitehouse B
.246
2.132
a =b
Whitehouse B
Comoro
.027
2.132
a = b
Comoro
Holtville
.113
2.132
a =b
Holtville
Clover springs
2.401
2.132
a <b
Silica sand
Contine
5.258
2.132
a <b
Superstition
Pinaleno
2.766
2.132
a <b
Superstition
Agua
4.635
2.132
a <b
Pinaleno
Holtville
2.139
2.132
a <b
Silica sand
Superstition
Superstition
55
TABLE 10. Soil groups from one-tailed student t-tests compared to
Munsell Color Value and Percent Sand for "Dry" Delta T
(maximum minus minimum soil surface temperature C).
°
Soil Name
Average
TemperaDelta T( C) ture Group
°
Munsell
Value
Sand
% Sand
and Color*
Silica sand
25.57
1
8.0
100
3
Superstition
31.53
2
6.5
96
6
Contine
32.22
2
5.3
52
30
Hayhook
32.90
2
5.5
70
18
Brazito
33.08
2
5.2
68
23
Pimer
33.15
2
5.5
21
42
Mohave
33.40
2
5.2
59
33
Pinaleno
33.45
3
5.1
71
20
Agua
34.75
3
5.4
60
23
Avondale
34.77
3
5.3
40
38
Pima
34.97
3
5.2
36
43
Whitehouse A
35.32
3
4.8
79
20
Vint
35.53
3
5.3
82
14
Gila
35.83
3
5.8
54
23
Laveen
36.08
3
5.2
33
41
Grabe
36.40
3
4.9
41
37
Whitehouse B
36.75
3
4.1
49
33
Comoro
36.78
3
3.9
84
18
Holtville
36.98
3
5.4
9
45
Clover springs
42.28
4
2.8
40
43
*ranked and combined
56
TABLE 11.
Comparison of "dry" albedo groups and Delta T groups.
Soil name
Albedo Group
Delta T Group
Munsell
Value
%
Sand
Silica sand
A
1
8.0
100
Superstition
B
2
6.5
96
Hayhook
C
2
5.5
70
Pimer
C
2
5.5
21
Brazito
C
2
5.2
68
Vint
C
3
5.3
82
Gila
C
3
5.8
54
Pima
C
3
5.2
36
Pinaleno
D
3
5.1
71
Agua
D
3
5.4
60
Mohave
D
2
5.2
59
Contine
E
2
5.3
52
Avondale
E
3
5.3
40
Lave en
E
3
5.2
33
Whitehouse A
E
3
4.8
79
Grabe
E
3
4.9
41
Comoro
E
3
3.9
84
Holtville
E
3
5.4
9
Whitehouse B
E
3
4.1
49
Cloversprings
F
4
2.8
40
57
Maximum Minus Minimum Surface Temperature Simple
Regression Analysis for Drying Soil
Maximum and minimum soil surface temperature (delta
T) measurements were taken for a three-week period in
February and March, 1982. Moisture content samples of.the
surface one centimeter of soil and for the total soil were
collected on a daily basis. These measurements were used to
determine what influence moisture contents were exerting
over maximum minus minimum soil surface temperatures.
Using a simple linear regression analysis,
regression lines were developed for each soil from the
simple formula y = a + bx, where y=1 cm moisture content (by
weight) and x = delta T (Table 12). The coefficients of
correlation were quite high in most instances.
This
indicates that most of the differences in delta T observed
for the soils can be explained by their surface moisture
content. It was then possible to combine regression lines
that were statistically the same and break the 20 test soils
into behavior groups (Illustration 2 and Table 13). possible to identify four distinct groups.
consisted of silica sand (r = -.30).
It was
Group I
This is most easily
explained because its wet and dry Munsell color values are
the same and there was very little change in delta T
throughout the drying cycle. The surface centimeter of
silica sand is dry in a matter of hours, not days.
Group II consisted of Whitehouse A, Hayhook,
Whitehouse B, Comoro and Superstition.
This group is most
58
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b
Maximum-Minimum soil surface temperature
Figure
2.
°
3b
( C)
Simple regression analysis, combined regression lines,
for soil surface (0-1 cm depth) moisture versus maximum soil surface temperature minus minimum soil surface temperature ( C).
60
TABLE 13. Simple Regression Analysis - Combined regression
lines for Illustration 2 showing soil groups.
Soil Name
r
r2
Soil Group
Regression Line
y=0.002-0.00005x
Silica sand
-.30
.09
I
Whitehouse A
-.73
.54
II
y=0.133-0.0038x
-.67
.45
III
y=0.261-0.0075x
-.73
.53
IV
Hayhook
Whitehouse B
Comoro
Superstition
Pimer
Vint
Contine
Pima
Agua
Brazito
Avondale
Clover springs
Pinaleno
Holtville
Gila (Field)
Mohave
Gila
Lave en
Grabe
r 2 =
.51
y=0.399-0.114x
61
easily explained by the sand content of the soils which
ranged from 49% (Whitehouse B) to 96% (Superstition). Their
behavior during drying showed no apparent relationship
between dry Munsell color value, dry albedo groups and dry
soil temperature groups.
Group III consisted of Pimer, Vint, Continue, Pima,
Agua, Brazito, Avondale, Pinaleno, Holtville and field soil
(Gila). There was no apparent relationship between this
group and dry albedo groups, sand content and dry
temperature group. They do however have similar dry Munsell
color values (5.1 to 5.5) except for the field soil (Gila)
whose value was 5.8. The field soil could be removed from
analysis because it rained on this soil causing it to be
treated differently from the box soils.
Group IV consisted of Mohave, Gila, Laveen, Grabe
and Cloversprings. These soils have a midrange of sand
contents (33 to 59%) but seem to have no relationship to dry
albedo groups, Munsell color value and dry temperature
groups.
Using the same type of regression analysis, as
above, regression lines were developed in the same manner
except total moisture content = x, instead of 1 cm moisture
content (Table 14). Once again the coefficients of
correlation were quite high which indicates that differences
in delta T observed for the 20 test soils can be explained by
their total moisture content.
62
It was possible to combine regression lines that
were statistically the same and break the soils into six
different behavior groups (Illustration 3 and Table 15).
Group I consisted of Superstition only. This soil contains
96% sand and has the lightest dry Munsell color value of all
the soils (except silica sand which was excluded from this
analysis for reasons previously explained). Group IV
consisted of Holtville. This can be explained by deep wide
cracks in its surface during drying which effectively
increased the surface area considerably. Group VI consisted
of Cloversprings which had the darkest dry Munsell color
value of the soils studied.
Group II consisted of Hayhook, Whitehouse A and
Comoro. The soils in this group each contained 70% or more
sand. There appears to be no relationship between dry
temperature groups, dry Munsell color value and dry albedo
groups.
Group III consisted of Whitehouse B, Mohave, Agua,
Avondale, Pinaleno, Contine and Brazito. This group
contains a moderately high sand content ranging from 40 to
68%. With the exception of Whitehouse B (Munsell color
value 4.1) these soils were relatively light in dry color
value ranging from (5.1 to 5.4).
Group V consisted of Gila, Laveen, Pimer, Vint, Pima
and Grabe. With two exceptions, this group contained less
than 41% sand. The two exceptions were Gila (54%) and Vint
63
(82%).
The sand found in these two soils is almost
exclusively fine or very fine. This seems to indicate that
the size of the sand particles is important.
Generally speaking moisture content has the single
most important influenceover delta T measurements. Other
factors exerting lesser influence would include dry Munsell
color value for particularly dark soils and sand content.
The size of the sand grains seems to be important too, with
fine and very fine sand behaving more like smaller soil
particles.
64
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65
soil mo8sture content (kg kg) versus maximum soil surface
temperature ( C) minus minimum soil surface temperature ( C).
Total
.4
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—
0
I
0
I
1
30
20
Maximum - Minimum soil surface temperature ( C)
10
ngure :1:: Simple
-
Lines.
°
Regression Analysis - Combined Regression
66
TABLE 15.
Simple Regression Analysis - Combined regression
lines for Illustration 3 showing soil groups.
Soil Name
r
r2
Soil Group
Regression Line
Superstition
-.76
.57
I
y=0.168-0.0047x
Hayhook
-.64
.41
II
y=0.149-0.0032x
-.57
.32
III
y=0.192-0.0032x
Holtville
-.32
.10
IV
y=0.230-0.0025x
Gila
-.65
.42
V
y=0.257-0.0052x
-.71
.50
VI
y=0.358-0.0051x
Whitehouse A
Comoro
Whitehouse B
Mohave
Agua
Avondale
Pinaleno
Contine
Brazito
Laveen
Pimer
Vint
Pima
Grabe
Clover springs
67
Maximum Soil Surface Temperature Minus
Maximum Air TemRerature SimRie Regression Analysis for
Drying Soils
Maximum soil surface temperature measurements were
taken for a three-week period in February and March 1982.
Maximum ambient air temperature measurements were taken for
the same period at a weather station adjacent to the
research site.
Surface 1 cm and total moisture content
measurements were taken as previously described.
These
measurements were used to determine what influence moisture
content was exerting over maximum soil surface temperature
minus maximum ambient air temperature (delta TA)
Using a simple linear regression analysis,
regression lines were developed for each soil from the
simple formula y=a+bx where x=delta TA and y=1 cm moisture
content (by weight) (Table 16).
the coefficients of
correlation were quite high in most instances.
This
indicates that most of the difference in delta TA observed
for the 20 test soils can be explained by their surface
moisture content.
It was then possible to combine regression lines
that were statistically the same and break the 20 test soils
into behavior groups (Illustration 4 and Table 17).
possible to identify seven distinct groups.
It was
Group I
consisted of silica sand. Once again this is most easily
explained by no difference in wet and dry Munsell color
68
values, and little change in delta TA throughout the drying
cycle as previously described. Group V consisted of
Holtville. It could not be expected to behave like any
other soil due to the presence of large surface cracks.
Group VII consisted of Cloversprings. It had the darkest
-
Munsell color value (2.8) and a relatively low sand content
(40%).
Group II consisted of Hayhook, Superstition and
Comoro. These three soils contained from 70 to 96% sand.
Their dry Munsell color values varied widely (6.5 to 3.9).
Two of these soils belong to dry soil temperature group 2.
There appears to be no relationship between this group and
dry albedo groups. Sand content seems to have the greatest
influence on this group.
Group III consisted of Whitehouse A and field soil
(Gila). Whitehouse A contains 79% sand and has a dry
Munsell color value of 4.8. Gila contains 54% sand and has
a dry Munsell color value of 5.8. This field soil probably
should have been excluded from analysis for the reasons
previously described.
Group IV consisted of Avondale, Pima, Agua, Brazito,
Vint, Pinaleno and Whitehouse B. They contain 36 to 82%
sand. There is no apparent relationship with dry albedo
groups. Their dry Munsell color values ranged from 5.1 to
5.4. This group seems to be largely influenced by color and
69
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MaXimum soil surface temperature - maximum air temperature ( C)
FigUte
4. Simple Regression Analysis - Combined Regression lines.
Soil surface (0-1 cm depth) moistuse content (gg I ) versus
maximum soil surface temperature ( C) minus maximum ambient
air temperature ( C).
71
TABLE 17.
Simple Regression Analysis - Combined regression
lines for Illustration 4 showing soil groups.
Soil Name
r
r2
Soil Group
Regression Line
y=0.0005+0.00004x
Silica sand
-.20
.04
I
Hayhook
-.75
.56
II
y=0.046-0.0044x
-.81
.66
VI
y=0.129-0.0160x
-.78
.61
IV
y=0.100-0.0099x
Holtville
-.63
.40
V
Whitehouse A
-.79
.62
III
y=0.07-0.0072x
-.85
.73
VII
y=0.261-0.0185x
Superstition
Comoro
Grabe
Gila (Field Soil)
Avondale
Pima
Agua
Brazito
Vint
Pinaleno
Whitehouse B
y-0.1812-0.0105x
Contine
Pimer
Mohave
Gila
Laveen
Clover springs
72
not by sand content. With the exception of Brazito (Group
2) all of these soils belong to dry soil temperature group
3.
Group VI consisted of Grabe, Contine, Pimer, Mohave,
Gila and Laveen. Their sand contents range from 21 to 59%.
Their dry Munsell color values ranged from 4.9 to 5.8.
There seems to be no relationship between this group and dry
albedo groups or dry temperature groups.
Using the same type of regression analysis,
regression lines were developed in the same manner except
total moisture content (by weight) = x instead of 1 cm
moisture content (Table 18). Once again the coefficients of
correlation were quite high indicating that differences in
delta TA observed for the 20 test soils can be explained by
their total moisture content.
It was possible to combine regression lines that
were statistically the same and break the soils into 8
different behavior groups (Illustration 5 and Table 19).
Group I consisted of silica sand as expected from previous
analysis. Group III consisted of Superstition. It contained
96% sand and has the lightest dry Munsell color value of 6.5.
It falls in dry temperature group 2 and is the only soil in
dry albedo group B. This indicates a relationship between
this group and all four factors. Group VI consisted of
Holtville. It is hardly surprising it behaved differently.
Group VII consisted of Grabe. It contains 41% sand and has a
73
dry Munsell color value of 4.9. Group VIII consisted of
Cloversprings. It has the darkest dry Munsell color value of
2.8 and is the only soil in dry albedo group F and dry
temperature group 4.
Group II consisted of Whitehouse A, Comoro and
Hayhook. These soils contain 70 to 84% sand. They have dry
Munsell color values of 3.9 to 5.5. Two of these soils fall
in dry temperature group 3. Two of them are in dry albedo
group E. Sand content seems to have the greatest influence
on this group.
Group IV consisted of Pima, Avondale, Mohave,
Contine, Whitehouse B, Pinaleno and Agua. These soils have
a mid-range of sand contents (36 to 71%). Their dry Munsell
color values range from 5.1 to 5.4 (except Whitehouse B,
4.1). There seems to be no relationship between this group
and dry albedo or dry temperature groups. Dry Munsell color
value seems to have the greatest influence.
Group B consisted of Pimer, Brazito, Gila, Laveen
and Vint. Their sand contents ranged from 21 to 82%. Their
dry Munsell color values range from 5.1 to 5.8. There
appears to be no relationship between this group and dry
temperature groups. However, all but one soil was in dry
albedo group C (Laveen in Group E).
Once again moisture content has the single most
important influence on delta TA measurements. Other factors
74
exerting lesser influence are dry Munsell color value, sand
content, dry temperature groups and dry albedo groups.
The regression analysis of delta T and Delta TA also
seems to indicate that one time of day measurements (about
1300 to 1400 hours) may be more useful than maximum and
minimum soil surface temperature measurements. Since
maximum ambient air temperature and maximum soil surface
temperature occur at about the same time, this would require
only one trip into the field. The fact that more distinct
soil behavior groups were identified this way may have been
caused by the lower standard deviations associated with the
one ambient air temperature measurement.
75
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Maximum soil surface temperature - maximum air temperature
Figure 5. Simple Regression Analysis - Combined Regression Lines. Total soil moisture content
(kg kg) versus maximum soil surface temperature
(%) minus maximum ambient air temperature ( C).
77
TABLE 19.
Simple Regression Analysis - Combined regression
lines for Illustration 5 showing soil groups.
Soil Name
r
r2
Soil Group
Regression Line
1
y=0.104-0.0004x
.55
II
y=0.188-0.0044x
-.86
.74
III
y=0.066-0.0061x
-.71
.51
IV
y=0.135-0.0050x
-.76
.58
V
y=0.137-0.0083x
Holtville
-.52
.27
VI
y=0.199-0.0044x
Grabe
-.78
.61
VII
y=0.186-0.0088x
Clover springs
-.76
.58
VIII
y=0.267-0.0074x
Silica sand
-.08
.006
Whitehouse A
-.74
Superstition
Pima
Comoro
Hayhook
Avondale
Mohave
Contine
Whitehouse B
Pinaleno
Agua
Pimer
Brazito
Gila
Laveen
Vint
78
Simple Regression Analysis of Evaporation versus
Daily Maximum Soil Surface Temperature minus
Daily Minimum Soil Surface Temperature (Drying Soil)
Daily evaporation was calculated (in cm) from total
moisture content for a three-week period in February and
March 1982. (Day one was removed from analysis since
drainage was not complete.) this values was then divided by
pan evaporation recorded on a daily basis. This ratio ties
raw evaporation data to prevailing weather conditions which
would include incoming radiation influenced by cloud cover,
wind speed, etc. These measurements were used to determine
what influence evaporation was exerting over maximum soil
surface temperature minus minimum soil surface temperature
(delta T).
Using a simple linear regression analysis,
regression lines were developed for each soil as previously
described where y = delta T and x = evaporation or
evaporation/pan evaporation (Tables 20 and 21). Once again,
coefficients of correlation were quite high in most
instances. This indicates that most of the differences in
delta T observed for the 20 soils can be explained by their
evaporation rates. By adding the weather component
represented by pan evaporation coefficients of correlation
were improved in most instances.
It was then possible to combine regression lines
that were statistically the same and break the 20 soils into
behavior groups (Illustration 6 and Table 22). It was
79
possible to identify six distinct groups.
Group VI
consisted of silica sand for the reasons previously
explained.
Group I consisted of Crabe and Cloversprings. They
contained 40 to 41% sand. No relationship was apparent
between this group and dry albedo groups, dry soil
temperature groups or dry Munsell color value.
Group II consisted of Superstition, Hayhook, Laveen
and Vint. Except for Laveen (33% sand) these soils had sand
contents ranging from 70 to 96%. Their dry Munsell color
values ranged from 6.5 to 5.2. There s no apparent
relationship between this group and dry soil temperature
groups and dry albedo groups. Sand content seems to
influence this group the most.
Group III consisted of Pinaleno, Gila, Pimer, Agua
and Pima. Their dry Munsell color values ranged from 5.1 to
5.5 Sand contents ranged from 71 to 21%. They all belong
to dry albedo groups C and D. All of the soils except Pimer
(group 2) belong to dry soil temperature group 3. Dry soil
temperature group seems to have the greatest influence on
this group.
Group IV consisted of Mohave, Whitehouse A,
Avondale, Whitehouse B and Brazito. All but Brazito (group
2) belong to dry soil temperature group 3. they have a midrange of sand contents (40 to 79%). Their dry Munsell color
values ranged from 4.1 to 5.3. Dry soil temperature group
80
and sand content seem to have the greatest influence on this
group.
Group V consisted of Contine, Holtville and Comoro.
Holtville had a larger evaporating surface than any other
soil due to large surface cracks. The other two had 52 to
84% sand. All three soils belong to dry albedo group E.
Brazito and Contine belong to dry soil temperature group 2,
Holtville to group 3. Sand content and dry albedo group
seem to influence this group the most.
As before, evaporation rates have the greatest
influence on delta T. Sand content, dry soil temperature
group and dry albedo group seem to have a lesser but
significant influence.
4
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Maximum - minimuw soil surface temperature
( u C)
Figure 6. Simple Regression Analysis. Daily evaporation
(cm)/pan evaporation cm) versus maximum soil
surface temperature ('C) minus minimum soil
surface temperature ( ° C).
84
TABLE 22.
Soil Name
Simple Regression Analysis - Combined regression
lines for Illustration 6 showing soil groups.
Regression Line
r2
Soil Group
-.78
.60
-.86
.75
-.89
Pinaleno
.80
Crabe
y-3.069-0.081x
Cloversprings
Superstition
II
y=2.886-0.084x
Hayhook
Laveen
Vint
III
y=2.760-0.0799x
Gila
Pimer
Agua
Pima
all1Mn
-.87
Mohave
.76
IV
y=2.556-0.071x
V
y=2.159-0.056x
VI
y=1.111-0.030x
Whitehouse A
Avondale
Whitehouse B
Brazito
-.74
Contine
.55
Holtville
Comoro
-.45
Silica sand .21
85
Simple Regression Analysis of Surface Moisture Content
versus Albedo (Drying Soil)
Albedo measurement were taken at 1000, 1200 and 1400
hours on a daily basis in February and March 1982. Surface
1 cm moisture content samples were taken on a daily basis as
previously described. These measurements were used to
determine what influence surface moisture content was
exerting over albedo measurements. (Previous research by
Jackson et al. (1975) indicates this is primarily a surface
phenomenon.)
Using a simple linear regression analysis,
regression lines were developed for each soil as previously
described where y = surface 1 cm moisture content and x =
albedo at 1000 hours. Coefficients of correlation were
still fairly high in most cases (Table 23). However, when
statistically similar regression lines were combined
(Illustration 7 and Table 24) variances were found to be
unequal. This indicates a probable non-normal (nonlinear)
data distribution. This was caused by the fact that most
soils were exhibiting essentially dry colors and
corresponding albedo values in about 2 weeks instead of 3.
This analysis is included because it does indicate
the potential for describing soil behavior groups through
albedo measurements.
86
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87
Figure 7. Simple Regression Analysis - Combined Regression Lines. Soil surface (0-1 cm) moisture
-1
content (gg ) versus albedo at 1000 hours.
88
TABLE 24.
Simple Regression Analysis - Combined regression
lines for Illustration 7 showing soil groups.
Soil Name
r
r2
Soil Group
Regression Line
-.85
.72
I
y=0.55-1.53x
-.83
.70
II
y=0.44-1.09x
-.83
.60
III
y=0.44-1.31x
-.75
.57
IV
y=0.36-0.93x
-.60
.36
V
y=0.20-0.47x
-.60
.37
VI
y=0.21-0.62x
Comoro
-.69
.48
VII
y=0.15-0.57x
Cloversprings
-.82
.67
VIII
y=0.64-2.77x
Silica sand
-.07
.005
Ix
Grabe
Mohave
Contine
Gila
Pimer
Holtville
Vint
Avondale
Brazito
Lave en
Agua
Pinaleno
Pima
Field (Gila)
Hayhook
Superstition
Whitehouse B
Whitehouse A
y=-0.00009+0.00137
CHAPTER 5
SUMMARY AND CONCLUSIONS
This research was conducted to examine the
relationship between soil surface temperature, bare soil
albedo, soil moisture content and other characteristics of
the soil such as Munsell color and texture. As we learn
more about these relationships through remote sensing, we
can come a little closer to making accurate predictions
about soil behavior when combined with meteorological data.
Twenty test soils were selected for study because of
the broad range they exhibit in physical and chemical
characteristics. Data was collected for each soil in both
the dry and wet states under natural weather conditions.
Data collected for the dry state was subjected to
analysis of variance. It was found that the soils were
behaving significantly differently for both albedo and
maximum minus minimum soil surface temperature (delta T).
Using a one-tailed student t-test soil behavior groups were
established. For albedo, dry Munsell color value had the
greatest influence on soil behavior with a lesser influence
on soil behavior being exerted by sand content. For delta
89
90
T, both dry Munsell color value and sand content had a great
deal of influence on soil behavior.
Data collected for the drying soils was subjected to
simple linear regression analysis, and from this I concluded
that soil surface moisture content exhibited the most
significant influence over albedo measurements. For delta T,
simple linear regression analysis indicated that the most
significant factor influencing soil surface temperature was
soil moisture content. By combining regression lines that
were similar soil behavior groups were found. These behavior
groups could largely be explained by their Munsell color
values and textural components such as total sand content and
fine and very find sand contents.
A similar relationship was found for maximum soil
surface temperature minus maximum ambient air temperature
(delta TA). However, more distinct soil behavior groups
were found which could be explained more easily. This
indicates that one-time-of-day measurements could be the
best indicator of these relationships especially if a
researcher wished to predict moisture content.
When we examined the relationship between daily
evaporation/pan evaporation versus delta T we found, once
again, that evaporation exerted the greatest influence over
delta T as indicated by the simple linear regression lines.
When similar regression lines were combined and soils were
broken into behavior groups we found that Munsell color
91
value and sand content were exerting the most influence over
delta T. Evaporation probably has the greatest potential
for prediction since it can be normalized by other weather
data such as pan evaporation and consequently is not
seasonally dependent nor is it tied to a particular
location.
Through the use of remote sensing equipment such as
the albedo meter and infrared thermometer, it is possible to
study the relationship between bare soil albedo, soil
surface temperature, soil moisture, color and texture.
Distinct soil behavior groups can be found for both dry and
drying states. It should be possible to develop predictive
models of soil behavior that can be applied in field
situations when combined with meteorological data.
APPENDIX I
WEATHER RELATED DATA
92
93
TABLE 25.
Date
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
Wind speed for January, February and
March 1982.
January 1982
24 h rovement (mi)
31.0
31.0
31.0
31.0
18.7
47.5
22.8
16.8
16.8
16.8
16.8
27.2
28.3
21.7
11.6
18.4
18.4
18.4
24.1
42.0
36.7
83.4
16.2
16.2
16.2
18.1
21.3
17.5
34.9
18.0
18.0
February 1982
24 h =vault (mi)
18.0
29.6
20.1
35.4
35.7
17.8
17.8
17.8
19.5
23.0
38.1
31.0
28.6
28.6
28.6
27.2
35.5
23.5
20.1
35.9
35.9
35.9
11.4
58.4
20.6
24.5
28.0
28.0
March 1982
24 h movement (mi)
28.0
25,4
23.2
28.0
28.0
24.5
24.5
24.5
211
23.7
21.7
41.0
51.1
51.1
51.1
31.4
30.8
94
TABLE 26. Maximum and minimum air temperature ( C) for January, February
and March 1982.
°
Date
January 1982
minimm
maximum
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
22.0
18.0
16.5
17.0
13.0
-3.0
-2.0
6.5
5.0
0.0
26.0
22.0
13.0
14.0
16.0
21.0
0.0
9.0
0.0
-1.5
-1.5
2.50
23.5
24.0
17.5
22.0
19.0
1.0
3.0
4.0
4.0
-8.0
21.0
29.0
26.0
24.0
22.0
-6.0
3.0
-2.0
3.0
2.0
February 1982
March 1982
udnimon
maxima
minimum
ulaximam
18.0
14.5
16.0
17.0
22.0
0.0
-3.0
-2.0
0.0
-1.0
27.0
30.0
24.0
22.5
23.0
22.0
4.0
9.0
7.0
6.0
2.0
2.0
21.0
12.0
19.0
20.5
18.0
2.0
-1.0
1.0
7.5
1.0
26.5
27.0
28.0
30.0
30.0
-0.5
4.0
7.0
9.0
14.0
25.0
24.0
25.0
25.0
16.0
27.5
3.0
5.0
6.0
6.0
2.5
7.5
20.5
19.0
23.0
3.0
9.5
5.0
30.0
31.0
28.0
19.0
21.0
23.0
6.0
7.5
9.0
10.5
5.0
6.5
95
TABLE 27. Twenty-four hour pan evaporation data for
January, February and March 1982.
Date
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
January 1982
24 h prec.
February 1982
24 h prec.
rain
1.18
0.22
0.05
0.11
0.56
0.50
0.34
0.152
0.252
0.235
0.235
0.126
0.126
0.126
0.086
+1.340
0.060
0.289
0.229
0.229
0.229
0.246
0.226
0.402
0.121
0.121
0.121
0.121
0.450
0.362
0.286
0.003
0.003
0.003
0.03
0.01
0.15
0.003
0.346
0.156
0.248
0.369
0.204
0.204
0.204
0.362
0.256
0.243
0.061
0.323
0.323
0.323
0.480
0.390
0.338
0.140
0.460
0.460
0.460
0.504
0.428
+0.322
0.337
0.354
0.354
March 1982
24 h prec.
0.01
rain
0.82
0.354
0.408
0.339
0.475
0.475
0.410
0.410
0.410
0.376
0.516
0.528
0.540
+0.494
+0.494
+0.494
0.344
0.508
96
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