Twenty five years of remote sensing in precision agriculture: Key

Twenty five years of remote sensing in precision agriculture: Key
b i o s y s t e m s e n g i n e e r i n g 1 1 4 ( 2 0 1 3 ) 3 5 8 e3 7 1
Available online at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/issn/15375110
Special Issue: Sensing in Agriculture
Review
Twenty five years of remote sensing in precision agriculture:
Key advances and remaining knowledge gaps5
David J. Mulla
Dept. Soil, Water and Climate, University of Minnesota, USA
article info
Precision agriculture dates back to the middle of the 1980’s. Remote sensing applications in
Article history:
precision agriculture began with sensors for soil organic matter, and have quickly diver-
Received 16 November 2011
sified to include satellite, aerial, and hand held or tractor mounted sensors. Wavelengths of
Received in revised form
electromagnetic radiation initially focused on a few key visible or near infrared bands.
31 May 2012
Today, electromagnetic wavelengths in use range from the ultraviolet to microwave
Accepted 9 August 2012
portions of the spectrum, enabling advanced applications such as light detection and
Published online 13 September 2012
ranging (LiDAR), fluorescence spectroscopy, and thermal spectroscopy, along with more
traditional applications in the visible and near infrared portions of the spectrum. Spectral
bandwidth has decreased dramatically with the advent of hyperspectral remote sensing,
allowing improved analysis of specific compounds, molecular interactions, crop stress, and
crop biophysical or biochemical characteristics. A variety of spectral indices now exist for
various precision agriculture applications, rather than a focus on only normalised difference vegetation indices. Spatial resolution of aerial and satellite remote sensing imagery
has improved from 100’s of m to sub-metre accuracy, allowing evaluation of soil and crop
properties at fine spatial resolution at the expense of increased data storage and processing
requirements. Temporal frequency of remote sensing imagery has also improved
dramatically. At present there is considerable interest in collecting remote sensing data at
multiple times in order to conduct near real time soil, crop and pest management.
ª 2012 IAgrE. Published by Elsevier Ltd. All rights reserved.
1.
Introduction
Precision agriculture is one of the top ten revolutions in agriculture (Crookston, 2006), although it has only been practiced
commercially since the 1990’s. Over one-third of US Midwestern farmers already practice some form of precision
agriculture. Precision agriculture generally involves better
management of farm inputs such as fertilisers, herbicides,
seed, fuel (used during tillage, planting, spraying, etc.) by
doing the right management practice at the right place and
the right time. Whereas large farm fields under conventional
management receive uniform applications of fertilisers, irrigation, seed, etc., with precision agriculture, these fields can
be divided into management zones that each receives customised management inputs based on varying soil types,
landscape position, and management history. Precision
5
Developed from a presentation at AGRI-SENSING 2011 e International Symposium on Sensing in Agriculture in Memory of Dahlia
Greidinger, Haifa, Israel.
E-mail address: [email protected]
1537-5110/$ e see front matter ª 2012 IAgrE. Published by Elsevier Ltd. All rights reserved.
http://dx.doi.org/10.1016/j.biosystemseng.2012.08.009
b i o s y s t e m s e n g i n e e r i n g 1 1 4 ( 2 0 1 3 ) 3 5 8 e3 7 1
Nomenclature
ALI
AVIRIS
B
DVI
EO 1
EROS
EVI
G
GDVI
GNDVI
GOSAVI
GPS
GRVI
GSAVI
HyspIRI
INSEY
IRS
LAI
LED
LiDAR
MCARI
MSAVI
advanced land imager
airborne visible/infrared imaging spectrometer
blue
difference vegetation index
earth observing 1
earth resources observation and science
enhanced vegetation index
green
green difference vegetation index
green normalised difference vegetation index
green optimised soil adjusted vegetation index
global positioning system
green red vegetation index
green soil adjusted vegetation index
hyperspectral infrared imager
in season estimated yield
Indian remote sensing
leaf area index
light emitting diode
light detection and ranging
modified chlorophyll absorption in reflectance
index
modified soil adjusted vegetation index
agriculture offers to improve crop productivity and farm
profitability through improved management of farm inputs
(Larson & Robert, 1991; Zhang, Wang, & Wang, 2002), leading
to better environmental quality (Mulla, 1993; Mulla et al., 2002;
Mulla, Perillo, & Cogger, 1996; Tian, 2002). Other benefits
perceived by farmers include more precise hybrid selection,
rental agreements that are better aligned with actual soil
productivity, better matching of fertiliser applications to crop
yield potential, lower chemical bills and fuel costs, and
reduced compaction. Benefits to society include creation of
high technology jobs (computer hardware, computer software, machinery guidance, soil and crop sensors, information
management, decision support systems), and mitigation of
environmental pollution arising from over-application of
nitrogen and phosphorus fertiliser.
Precision agriculture uses intensive data and information
collection and processing in time and space to make more
efficient use of farm inputs, leading to improved crop
production and environmental quality (Harmon et al., 2005).
Precision agriculture is beginning to emphasise spatialtemporal data analysis and management rather than spatial
data analysis and management alone (Mamo, Malzer, Mulla,
Huggins, & Strock, 2003; Miao, Mulla, Randall, Vetsch, &
Vintila, 2009; Varvel, Wilhelm, Shanahan, & Schepers, 2007).
Crop yield and response to N fertiliser varies significantly
across years in response to changes in climate (Bakhsh,
Jaynes, Colvin, & Kanwar, 2000), just as it varies significantly
across the landscape in response to variations in soil type and
landscape features.
Precision agriculture involves both data collection/analysis
and information management, as well as technological
advances in computer processing, field positioning, yield
359
MSS
N
NASA
NDI
NDVI
NG
NGNDVI
multispectral scanning system
nitrogen
National Aeronautics and Space Administration
normalised difference index
normalised difference vegetation index
normalised green
normalised green normalized difference
vegetation index
NIR
near infrared
NR
normalised red
NSI
nitrogen sufficiency index
OMNBR optimal multiple narrow band reflectance
OSAVI optimised soil adjusted vegetation index
PNSI
plant nitrogen spectral index
PSSR
pigment specific simple ratio
R
red
RI
response index
RVI
ratio vegetation index
SPAD
soil plant analysis development
SPOT
système pour l’observation de la terre
SR8
simple ratio 8
TCARI transformed chlorophyll absorption in reflectance
index
TM
thematic mapper
monitoring, remote sensing, and sensor design (Mulla &
Schepers, 1997). More than 30% of the growth in US agribusiness (jobs, sales, exports, etc.) in the future is expected to
come from further adoption of precision agriculture by
farmers (Whipker & Akridge, 2006), including growth in
demand for both information management services and
technological advances such as global positioning system
(GPS) autosteer guidance (e.g. Real Time Kinetic technology),
variable rate irrigation, fertiliser and sprayer controllers,
robotics, and real time decision making based on sensor
networks and remote sensing. Adoption rates are also significant in Australia, Japan, Canada and Europe, specifically in
Germany, Sweden, France, Spain, Denmark and the UK.
Globally, there is little documented information about rates of
adoption for precision agriculture in the developing world.
Mondal and Basu (2009) state that countries such as
Argentina, Brazil, China, India and Malaysia have begun to
adopt precision agriculture. Precision agriculture is also
widely used in the vineyards of Chile.
The farms of the future are likely to be managed with much
greater spatial and temporal resolution than they are with
present approaches to precision agriculture. In Chilean vineyards, each grape vine receives an individually customised
fertiliser prescription that varies with soil type, landscape
position and hybrid. It is not unrealistic to expect that crops on
modern US farms of the future will be managed plant-byplant, a huge advance over farming by soil approaches of
the past. This approach will require massive data collection
and analysis on a scale not considered feasible today,
involving stationary or mobile sensors that can measure
characteristics of individual plants in real time. Sensors of the
future could be based on satellites (Bausch & Khosla, 2010),
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b i o s y s t e m s e n g i n e e r i n g 1 1 4 ( 2 0 1 3 ) 3 5 8 e3 7 1
airplanes (Goel et al., 2003; Haboudane, Miller, Pattey, ZarcoTejada, & Strachan, 2004; Haboudane, Miller, Tremblay,
Zarco-Tejada, & Dextraze, 2002; Miao, Mulla, Randall,
Vetsch, & Vintila, 2007), unmanned aerial vehicles (Berni,
Zarco-Tejada, Suárez, & Fereres, 2009; Herwitz et al., 2004),
tractors (Adamchuk, Hummel, Morgan, & Upadhyaya, 2004;
Long, Engel, & Siemens, 2008), or attached to mobile robots
(Astrand & Baerveldt, 2002) to record weed densities, crop
height, leaf reflectance, moisture status and other properties
important for decisions about fertilizer and pest management.
These sensors must be robust, run on renewable energy
sources, and be able to relay information using wireless
networks (O’Shaughnessy and Evett, 2010; Wang, Zhang, &
Wang, 2006) to computers that can perform data mining
procedures and make complex management recommendations. These recommendations can be transmitted to
computers and controllers in the field that are capable of
varying the rates of irrigation, fertilisers, and herbicides at fine
spatial resolution, if not plant-by-plant.
2.
Remote sensing applications in
agriculture
Remote sensing applications in agriculture are based on the
interaction of electromagnetic radiation with soil or plant
material. Typically, remote sensing involves the measurement
of reflected radiation, rather than transmitted or absorbed
radiation. Remote sensing refers to non-contact measurements of radiation reflected or emitted from agricultural fields.
The platforms for making these measurements include satellites, aircraft, tractors and hand-held sensors. Measurements
made with tractors and hand-held sensors are also known as
proximal sensing, especially if they do not involve measurements of reflected radiation. In addition to reflectance, transmittance and absorption, plant leaves can emit energy by
fluorescence (Apostol et al., 2003) or thermal emission (Cohen,
Alchanatis, Meron, Saranga, & Tsipris, 2005). Thermal remote
sensing for water stress in crops is based on emission of radiation in response to temperature of the leaf and canopy, which
varies with air temperature and the rate of evapotranspiration.
The amount of radiation reflected from plants is inversely
related to radiation absorbed by plant pigments, and varies
with the wavelength of incident radiation. Plant pigments
such as chlorophyll absorb radiation strongly in the visible
spectrum from 400 to 700 nm (Pinter et al., 2003), particularly
at wavelengths such as 430 (blue or B) and 660 (red or R) nm for
chlorophyll a and 450 (B) and 650 (R) nm for chlorophyll b.
Other plant pigments such as anthocyanins and carotenoids
are also important (Blackburn, 2007).
In contrast, plant reflectance is high in the near infrared
(NIR 700e1300 nm) region as a result of leaf density and
canopy structure effects. This sharp contrast in reflectance
behaviour between the red and NIR portions of the spectrum
is the motivation for development of spectral indices that are
based on ratios of reflectance values in the visible and NIR
regions (Sripada, Heiniger, White, & Weisz, 2006). These
spectral indices are often used to assess various attributes of
plant canopies, such as leaf area index (LAI), biomass, chlorophyll content or N content.
The amount of radiation reflected by bare soils is affected
primarily by soil moisture and organic matter content, but
also by clay minerals and calcium carbonate or iron oxides
(Thomasson, Sui, Cox, & AleRajehy, 2001; Viscarra Rossel,
Walvoort, McBratney, Janik, & Skjemstad, 2006). Each soil
constituent has a specific spectral region where reflectance is
strongest (Ben-Dor, 2010), and a specific spectral signature.
Bare soil and crop canopies are often both present in
a remotely sensed image, and the mixture of two spectral
signatures often confounds the interpretation of reflectance
data (Fig. 1). Spectral unmixing algorithms (Huete & Escadafal,
1991), derivative spectra (Demetriades-Shah, Steven, & Clark,
1990) or spectral indices that adjust for soil effects
(Haboudane et al., 2002, 2004) are often used to isolate information about plant characteristics when the reflectance is
affected by both sources.
Remote sensing applications in agriculture are typically
classified according to the type of platform for the sensor,
including satellite, aerial, and ground based platforms. These
platforms and their associated imaging systems can be
differentiated based on the altitude of the platform, the spatial
resolution of the image, and the minimum return frequency
for sequential imaging. Spatial resolution affects the area of
the smallest pixel that can be identified. As spatial resolution
improves, the area of the smallest pixel decreases, and the
homogeneity of soil or crop characteristics within that pixel
increases. Poor spatial resolution implies large pixels with
increased heterogeneity in soil or plant characteristics. Return
frequency is important for assessment of temporal patterns in
soil or plant characteristics. The availability of remote sensing
images from satellite and aerial platforms is often severely
limited by cloud cover (Moran, Inoue, & Barnes, 1997), whereas
ground based remote sensing is less affected by this
limitation.
Remote sensing applications in agriculture have focused
on a wide range of endeavours (Adamchuk et al., 2004; Moran
et al., 1997; Pinter et al., 2003). These include crop yield and
biomass (Shanahan et al., 2001; Yang, Everitt, Bradford, &
Escobar, 2000), crop nutrient and water stress (Bastiaanssen,
Fig. 1 e Reflectance signatures of dry or wet soil in
comparison to reflectance signatures of a Russet Burbank
potato canopy with low or high rates of N fertiliser
application.
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b i o s y s t e m s e n g i n e e r i n g 1 1 4 ( 2 0 1 3 ) 3 5 8 e3 7 1
Molden, & Makin, 2000; Clay, Kim, Chang, Clay, & Dalsted,
2006; Cohen et al., 2005; Moller et al., 2007; Tilling et al.,
2007), infestations of weeds (Lamb & Brown, 2001; Thorp &
Tian, 2004), insects and plant diseases (Seelan, Laguette,
Casady, & Seielstad, 2003), and soil properties such as
organic matter, moisture and clay content, and pH (Christy,
2008), or salinity (Corwin & Lesch, 2003).
2.1.
Soil sensing in precision agriculture
Precision agriculture started during the mid 1980’s with two
contrasting philosophies. The first was exemplified by the
“farming by soil” school (Larson & Robert, 1991). This school
promoted the use of soil sampling and customised management of farm inputs by soil mapping unit. The second was
exemplified by the “Soil Sampling Management Zone” school
(Mulla, 1991, 1993; Mulla & Bhatti, 1997; Mulla, Bhatti,
Hammond, & Benson, 1992), which later became known as
“site-specific crop management”. Management zones are
relatively homogeneous sub-units of farm fields that can each
be managed with a different, but uniform customised
management practice. This school became very popular when
field comparisons of uniform versus variable fertiliser applications showed that there was considerable variability at
scales finer than soil mapping units (Mulla et al., 1992). Bhatti,
Mulla, and Frazier (1991) were the first to demonstrate that
Landsat remote sensing had significant capabilities for estimating spatial patterns in soil organic matter, soil phosphorus
and crop yield potential for use in precision agriculture
applications.
A third approach to precision agriculture began to emerge
in the early 1990’s (Hummel, Gaultney, & Sudduth, 1996),
known as proximal soil sensing. This approach was based on
continuous real-time sensing of spatial variations in soil
properties using sensors mounted on tractors. The first
application of this approach was for soil organic matter
sensing (Shonk, Gaultney, Schulze, & Van Scoyoc, 1991) based
on reflectance from multiple light emitting diode (LED)
sensors emitting radiation at 660 nm. The sensor was very
accurate if calibrated for individual soil catenas, but was
affected by variations in soil moisture. Sudduth and Hummel
(1993) developed a portable NIR sensor which could simultaneously be used to estimate soil organic matter content and
soil moisture content. Other related technology was developed by Christy (2008), allowing simultaneous measurements
of soil organic matter content, soil moisture, soil pH, soil
carbon and soil phosphorus, potassium, and calcium.
A major breakthrough in precision agriculture occurred
when Carter, Rhoades, and Chesson (1993) introduced
continuous real-time, non-contact proximal sensing of soil
apparent electrical conductivity using non-invasive electromagnetic induction with the Geonics EM-38 (Geonics Ltd.,
Mississauga, Ontario, Canada). Soil electrical conductivity
measurements with the EM-38 have been used to map spatial
patterns in soil salinity (Corwin & Lesch, 2003), soil clay
content (Doolittle, Sudduth, Kitchen, & lndorante, 1994) and
soil moisture content (Sudduth et al., 2005). These patterns are
often used to define management zones.
2.2.
Satellite remote sensing
Satellites have been used for remote sensing imagery in
agriculture (Table 1) since the early 1970’s (Bauer & Cipra,
1973; Doraiswamy, Moulin, Cook, & Stern, 2003; Jewel, 1989)
when Landsat 1 (originally known as Earth Resources Technology Satellite 1) was launched in 1972. Multispectral
Scanner System (MSS) sensors on Landsat 1 collected imagery
in the green, red and two infrared bands at a spatial resolution
of 80 m and a return frequency of 18 days. Landsat 1 was
initially used by Bauer and Cipra (1973) to classify Midwestern
US agricultural landscapes into maize or soybean fields with
an overall accuracy of 83%. Landsat 5 was launched in 1984
Table 1 e Satellite remote sensing platforms and their spectral or spatial resolution, return frequency, and suitability for
precision agriculture (PA). P refers to purple, B to blue, G to green, R to red, IR to infrared, NIR to near infrared, MIR to mid
infrared, TIR to thermal infrared. Suitability class L refers to low, M to medium and H to high.
Satellite (year)
Landsat 1 (1972)
AVHRR (1978)
Landsat 5 TM (1984)
SPOT 1 (1986)
IRS 1A (1988)
ERS-1 (1991)
JERS-1 (1992)
LiDAR (1995)
RadarSAT (1995)
IKONOS (1999)
SRTM (2000)
Terra EOS ASTER (2000)
EO-1 Hyperion (2000)
QuickBird (2001)
EOS MODIS (2002)
RapidEye (2008)
GeoEye-1 (2008)
WorldView-2 (2009)
Spectral bands (spatial resolution)
Return frequency (d )
Suitability for PA
G, R, two IR (56 79 m)
R, NIR, two TIR (1090 m)
B, G, R, two NIR, MIR, TIR (30 m)
G, R, NIR (20 m)
B, G, R, NIR (72 m)
Ku band altimeter, IR (20 m)
L band radar (18 m)
VIS (vertical RMSE 10 cm)
C-band radar (30 m)
Panchromatic, B, G, R, NIR (1e4 m)
X-band radar (30 m)
G, R, NIR and 6 MIR, 5 TIR bands (15e90 m)
400e2500 nm, 10 nm bandwidth (30 m)
Panchromatic, B, G, R, NIR (0.61e2.4 m)
36 bands in VIS-IR (250e1000 m)
B, G, R, red edge, NIR (6.5 m)
Panchromatic, B, G, R, NIR1, NIR2 (1.6 m)
P, B, G, Y, R, red edge, NIR (0.5 m)
18
1
16
2e6
22
35
44
N/A
1e6
3
N/A
16
16
1e4
1e2
5.5
2e8
1.1
L
L
M
M
M
L
L
H
M
H
M
M
H
H
L
H
H
H
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and collected Thematic Mapper (TM) imagery at a spatial
resolution of 30 m in the blue, green, red, near infrared, and
three infrared (including thermal) bands. France launched
a comparable satellite (système pour l’observation de la terre
(SPOT) 1) in 1986, which collected 20 m imagery with a return
frequency of up to six days in the green, red and near infrared
frequencies. Jewel (1989) used four images collected between
February and September in East Anglia, UK to distinguish
cereal crops, field crops, grassland and forest land with an
accuracy of 88%. India launched the Indian Remote Sensing
(IRS-1A) satellite in 1988, with coverage in the blue, green, red
and NIR bands at a spatial resolution of 72 m. Panigrahy and
Sharma (1997) used reflectance in the red and NIR bands
collected on four dates between October and March to classify
agricultural landscapes in India into rice or riceepotato
cropping systems with 95% accuracy.
These applications of remote sensing in conventional
agriculture soon led to applications in precision agriculture.
The first application of remote sensing in precision agriculture
occurred when Bhatti et al. (1991) used Landsat imagery of
bare soil to estimate spatial patterns in soil organic matter
content, which were then used as auxiliary data along with
ground based measurements to estimate spatial patterns in
soil phosphorus and wheat grain yield (Mulla, 1997). The
spatial resolution of Landsat, SPOT and IRS satellites is fairly
coarse (20e30 m) for current applications in precision
agriculture.
Efforts were subsequently started to design satellite
imaging systems that had the higher spatial resolution and
quicker revisit cycles required for precision agriculture (Table
1). IKONOS was launched in 1999 by Satellite Imaging Corp,
Magnolia, TX, USA in partnership with Lockheed Martin,
Bethesda, MD, USA. IKONOS collected 4 m resolution imagery
in the blue, green, red and near infrared bands at a return
frequency of up to 5 days. Seelan et al. (2003) used IKONOS
images to identify N deficiencies in sugarbeet, fungicide
performance efficiency in wheat and field sites that had inadequate artificial drainage in wheat. In 2001, DigitalGlobe,
Longmont, CO, USA launched a satellite named QuickBird with
capabilities similar to IKONOS. QuickBird had a revisit
frequency of 1e3 days and collected imagery in the blue, green,
red and near infrared at a spatial resolution of 0.6e2.4 m.
Bausch and Khosla (2010) showed that QuickBird estimates of
normalised green normalised difference vegetation index
(NGNDVI) were strongly correlated with spatial patterns in
nitrogen sufficiency in irrigated maize. Garcı́a Torres, PeñaBarragán, López-Granados, Jurado-Expósito, and FernándezEscobar (2008) showed that QuickBird images of olive
orchards in Spain could be used to estimate areas of olive
plantations, numbers of trees, and spatial patterns in projected
area of tree canopies, and olive yields. These two satellites have
steadily gained a substantial base of commercial subscribers
interested in precision agriculture applications, in stark
contrast to older satellite technology such as Landsat or SPOT.
The next major breakthrough in satellite remote sensing
for precision agriculture was the five-satellite constellation
developed by the RapidEye, Brandenburg_an_der_Havel,
Germany in 2008 (Table 1). RapidEye satellites provide daily
coverage for any location on the globe, and collect data with
a 6.5 m spatial resolution. RapidEye is the first satellite to
provide imagery in the chlorophyll sensitive red-edge region
of the spectrum (690e730 nm), along with the more traditional
blue, green, red and near infrared reflectance. In 2008, GeoEye,
Herndon, VA, USA launched a commercial satellite designed
to provide services similar to RapidEye. The GeoEye 1 satellite
has a return visit frequency of less than three days, and
collects data at from 40 to 60 cm spatial resolution in the blue,
green, red and near infrared bands. One of the main uses for
GeoEye 1 imagery is providing Google Earth maps that are
available through the Internet. This has revolutionised the
ability to visualise land use patterns around the world. DigitalGlobe launched the WorldView 2 satellite in 2009, which
collects imagery at 50 cm resolution with a 1 day revisit cycle.
WorldView 2 is significantly more advanced than DigitalGlobe’s QuickBird satellite, as WorldView 2 collects imagery in
the standard blue, green, red, and near infrared bands, as well
as bands in the purple (450e480 nm), yellow, red-edge and
a second near infrared frequency range.
Several trends are apparent in satellite based remote
sensing (Table 1). Firstly, the spatial resolution of imaging
systems has improved from 80 m with Landsat to sub-metre
resolution with GeoEye and WorldView. Secondly, the return
visit frequency has improved from 18 days with Landsat to 1
day with WorldView. Thirdly, the number of spectral bands
available for analysis has improved from four bands (bandwidths greater than 60 nm) with Landsat to eight or more
bands (bandwidths greater than 40 nm) with WorldView.
Hyperspectral imaging systems such as Hyperion on the
National Aeronautics and Space Administration (NASA) earth
observing 1 (EO 1) satellite provided even greater spectral
resolution, with imaging from 400 to 2500 nm in 10 nm
increments.
As the spatial and spectral resolution of satellite imagery
has improved, the suitability of using reflectance data from
these platforms for precision agriculture applications has
increased (Table 1). The most appropriate spatial and spectral
resolution for precision agriculture applications depends on
factors such as crop management objectives, capacity of farm
equipment to vary farm inputs, and farm unit area. Estimation
of spatial patterns in crop biomass or yield requires better
spatial and spectral resolution (1e3 m) than variable rate
application of fertiliser (5e10 m). Accuracy of variable rate
application of fertiliser is often limited by fertiliser spreader
delay times (Chan, Schueller, Miller, Whitney, & Cornell,
2004). Variable rate spraying of herbicides for spot weed
control requires better spatial and spectral resolution
(0.5e1 m) than variable rate irrigation (5e10 m). Larger
commercial farms can often afford to pay for remote sensing
data with higher spatial and spectral resolution than smaller
farms in developing countries.
Satellite and/or aerial imagery is frequently used to estimate spatial patterns in crop biomass (Yang et al., 2000) and
potential crop yield (Doraiswamy et al., 2003) using the Normalised Difference Vegetation Index (NDVI). NDVI is calculated using reflectance ratios in the NIR and red portion of the
spectrum (Rouse, Hass, Schell, & Deering, 1973):
NDVI ¼ ðNIR RedÞ=ðNIR þ RedÞ
(1)
NDVI has several limitations, however, including potential
interference from soil reflectance at low canopy densities and
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insensitivity to changes in leaf chlorophyll content in mature
canopies with leaf area index values that exceed 2 or 3
(Thenkabail, Smith, & De Pauw, 2000). The advent of crop
combines equipped with yield monitors was a major advance
in precision agriculture (Schueller & Bae, 1987; Stafford,
Ambler, Lark, & Catt, 1996). Yield monitors provided fine
scale resolution yield measurements across large spatial areas
that could be used to improve the capacity of remote sensing
to predict crop structural characteristics such as LAI, biomass,
and yield.
Many broadband spectral indices (Table 2) other than NDVI
are available for use in precision agriculture (Miao et al., 2009;
Sripada et al., 2006; Sripada, Schmidt, Dellinger, & Beegle,
2008). These indices reflect two historical trends in remote
sensing for crop characteristics; namely, the prediction of
ratios of reflectance in the red (R) and NIR bands versus ratios
in the green (G) and NIR bands. The normalised red (NR) index
focuses on the portion of the spectrum where chlorophyll
strongly absorbs radiation. In contrast, the normalised green
(NG) index focuses on the portion of the spectrum where
pigments other than chlorophyll (carotenoids, anthocyanins,
xanthophylls) absorb radiation. Similarly, there are two forms
of the ratio vegetation index (RVI), one that consists of the ratio
of NIR to R reflectance, the other green red vegetation index
(GRVI) that consists of the ratio of NIR to G reflectance. Two
forms of the NDVI exist, one that involves NIR and R reflectance, the other green normalized difference vegetation index
(GNDVI) involves NIR and G reflectance. The difference vegetative index (DVI) was developed using the difference between
reflectance in the NIR and R bands to compensate for effects of
soil reflectance (Tucker, 1979). Sripada et al. (2006) found that
economically optimum N rate in corn was better correlated
with green difference vegetation index (GDVI) (NIR G) than
DVI (NIR R), and these indices that compensated for soil
effects performed better than NIR and R ratio indices such
as NDVI and RVI that did not compensate for soil effects.
A wide range of other indices have been developed to
compensate for soil effects, including soil adjusted vegetation index (SAVI), green soil adjusted vegetation index
(GSAVI), optimised soil adjusted vegetation index (OSAVI),
green optimised soil adjusted vegetation index (GOSAVI)
and modified soil adjusted vegetation index (MSAVI). A
comparison of NDVI and simple ratio (SR8) vegetation
indices for an irrigated commercial Minnesota potato
(Solanum tuberosum L.) farm is shown in Fig. 2. Spatial
patterns in N stress are identified more accurately with the
SR8 index than the NDVI index.
Moran et al. (1997) and Yao et al. (2010) summarised the
major challenges for using satellite remote sensing for precision agriculture. Satellite imagery in the visible and NIR bands
are limited to cloud free days, and are most usable when
irradiance is relatively consistent across time. Only radar
imagery collected using satellites or airplanes is unaffected by
cloud cover. Other challenges include calibrating raw digital
numbers to true surface reflectance, correcting imagery
for atmospheric interferences and/or off-nadir view angles,
and geo-rectifying pixels using GPS-based ground control
locations.
2.3.
Proximal remote sensing of crops for precision
agriculture
Given the limitations of satellite remote sensing for precision
agriculture, there has been significant interest in proximal
remote sensing techniques to assess crop growth and crop
stress (Table 3). Proximal remote sensing involves sensors
mounted on tractors, spreaders, sprayers or irrigation booms.
Proximal sensing allows real time site specific management of
fertiliser, pesticides or irrigation. The foundation for a transition from remote sensing to proximal sensing based assessment of crop status was established by Schepers, Francis,
Vigil, and Below (1992), who used a Minolta soil plant analysis development (SPAD) meter to measure leaf greenness
(chlorophyll) in maize crops at the silking stage under a range
of applied N fertiliser rates. SPAD meter readings of leaf
reflectance at 650 and 940 nm were found to be correlated with
applied rate of N fertiliser as well as independent measurements of leaf N concentration. Schepers et al. (1992) suggested
that proximal chlorophyll readings could be used to estimate
N stress in maize by referencing the SPAD readings for
stressed crops with the readings in a reference strip that
received adequate rates of N fertiliser. Chlorophyll content of
Table 2 e Multi-spectral broad-band vegetation indices available for use in precision agriculture. G refers to green
reflectance, NIR to near infrared, and R to red reflectance.
Index
NG
NR
RVI
GRVI
DVI
GDVI
NDVI
GNDVI
SAVI
GSAVI
OSAVI
GOSAVI
MSAVI2
Definition
Reference
G/(NIR þ R þ G)
R/(NIR þ R þ G)
NIR/R
NIR/G
NIR R
NIR G
(NIR R)/(NIR þ R)
(NIR G)/(NIR þ G)
1.5*[(NIR R)/(NIR þ R þ 0.5)]
1.5*[(NIR G)/(NIR þ G þ 0.5)]
(NIR R)/(NIR þ R þ 0.16)
(NIR G)/(NIR þ G þ 0.16)
0.5*[2*(NIR þ 1) SQRT((2*NIR þ 1)2 8*(NIR R))]
Sripada et al., 2006
Sripada et al., 2006
Jordan, 1969
Sripada et al., 2006
Tucker, 1979
Tucker, 1979
Rouse et al., 1973
Gitelson et al., 1996
Huete, 1988
Sripada et al., 2006
Rondeaux, Steven, & Baret, 1996
Sripada et al., 2006
Qi, Chehbouni, Huete, Keer, & Sorooshian, 1994
364
b i o s y s t e m s e n g i n e e r i n g 1 1 4 ( 2 0 1 3 ) 3 5 8 e3 7 1
Fig. 2 e Comparison between a) NDVI and b) SR8 spectral
indices in a commercial Russet Burbank potato field.
crop leaves is strongly affected by crop N status, but can also
be affected to a lesser degree by other factors, such as crop
variety and growth stage, pest infestations, water stress, and
nutrient deficiencies other than N.
Blackmer and Schepers (1995) introduced the concept of
a nitrogen sufficiency index (NSI) to assess the degree of N
stress in maize. The NSI was defined as the ratio of SPAD
meter greenness readings for crops in a given field location
relative to SPAD readings for the same crop in a well-fertilised
reference strip with no N deficiencies. NSI values less than
0.95 were used to indicate areas with N stress that required
additional N fertiliser. Varvel, Schepers, and Francis (1997)
showed that SPAD meters and NSI values could be used for
in-season correction of N deficiency in maize. Bausch and
Duke (1996) showed that the SPAD meter could be replaced
with a boom-mounted radiometer to estimate spatial patterns
in NIR/G ratio and NSI across an irrigated maize field based on
comparisons with a well-fertilised reference strip. They
observed that this approach could detect N deficiencies in the
V6 growth stage (Ritchie, Hanway, & Benson, 1993, p. 21), but
results were confounded by interference with reflectance
patterns from bare soil.
Stone et al. (1996) measured spectral radiance in the red
(671 nm) and NIR (780 nm) bands in wheat with a sensor
mounted on a mobile lawn tractor. They used these data to
estimate a spectral index known as the plant nitrogen spectral
index (PNSI), which was the absolute value of the inverse of
NDVI. Results showed that PNSI was strongly correlated with
crop N uptake. Sensor readings were used to vary N fertiliser
rates using an algorithm that increased exponentially with
PNSI values (Solie, Raun, Whitney, Stone, & Ringer, 1996). This
was the beginning of technology to variably apply N fertiliser
“on-the-go” in response to proximal crop sensing, and was the
basis for commercial development of the GreenSeeker NDVI
active sensor marketed by NTech Industries, Ukiah, CA, USA
in 2001. Raun et al. (2002) subsequently developed a seven step
response index (RI)-based algorithm to estimate crop N fertiliser needs for maize and wheat based on in-season sensing
of crop reflectance relative to check plots with no added fertiliser and reference plots with sufficient fertiliser. This algorithm accounted for both season-to-season temporal
variability in crop growth using the concept of in-season
estimated yield (INSEY) as well as within-field spatial variability in N supply. Algorithms for estimating potential crop
yield and N uptake are available for many crops and locations
around the world (Shanahan, Kitchen, Raun, & Schepers,
2008). The RI is estimated as the ratio of NDVI values in the
crop relative to those in a reference strip with sufficient
fertiliser.
Link, Panitzki, and Reusch (2002) and Reusch, Link, and
Lammel (2002) developed a tractor based passive sensor to
determine crop N status based on NDVI. This was originally
Table 3 e Innovations in remote and proximal leaf sensing in precision agriculture.
Year
Innovation
1992
1995
1996
SPAD meter (650, 940 nm) used to detect N deficiency in corn
Nitrogen sufficiency indices
Optical sensor (671, 780 nm) used for on-the-go detection of variability
in plant nitrogen stress
Yara N sensor
GreenSeeker (650, 770 nm)
Crop Circle (590, 880 nm or 670, 730, 780 nm)
CASI hyperspectral sensor based index measurements of chlorophyll
MSS remote sensing of ag fields with UAV
Fluorescence sensing for N deficiencies
2002
2002
2004
2002
2002
2003
Citation
Schepers et al., 1992
Blackmer & Schepers, 1995
Stone et al., 1996
Link et al., 2002, TopCon industries
Raun et al., 2002, NTech industries
Holland et al., 2004, Holland scientific
Haboudane et al., 2002, 2004
Herwitz et al., 2004
Apostol et al., 2003
b i o s y s t e m s e n g i n e e r i n g 1 1 4 ( 2 0 1 3 ) 3 5 8 e3 7 1
known as the Hydro-N sensor, but has since become known as
the Yara-N sensor (Yara, Olso, Norway) (Table 3). A version of
the Yara-N sensor is also available that uses active sensors
(Link & Reusch, 2006), which reduces the errors caused by
varying cloud cover, and allows the tractor operator to work at
night.
Holland, Schepers, Shanahan, and Horst (2004) developed
an active crop sensor known as Crop Circle (Table 3). This
sensor initially used reflectance in the green and NIR bands to
estimate crop N deficiencies. The rationale behind using green
rather than red reflectance with Crop Circle was that as crop
LAI increases beyond 2.0, the green NDVI is more sensitive to
changes in chlorophyll concentration and potential crop yield
than NDVI (Gitelson, Kaufmann, & Merzlyak, 1996; Shanahan
et al., 2001; Sripada et al., 2006, 2008). This overcomes the
limitation of using the GreenSeeker sensor at advanced crop
growth stages. Solari, Shanahan, Ferguson, Schepers, and
Gitelson (2008) used the Crop Circle sensor to show that N
deficiencies could be better predicted using a green chlorophyll index defined as (NIR880/VIS590) 1 in comparison with
the green NDVI. Sripada et al. (2008) showed that the performance of spectral indices could be improved using ratios with
the corresponding index values in reference strips receiving
sufficient N fertiliser. Kitchen et al. (2010) and Scharf et al.
(2011) showed that producers using the Crop Circle sensor
could reduce N fertiliser applications by making in-season
correction, while increasing crop yield and farm profitability.
One limitation of the chlorophyll meter, GreenSeeker, Yara
N and Crop Circle sensors, however, is that they cannot
directly estimate the amount of N fertiliser needed to overcome crop N stress (Samborski, Tremblay, & Fallon, 2009). To
overcome this, scientists have conducted comparisons of
sensor readings with readings in reference strips receiving
sufficient N fertiliser (Blackmer & Schepers, 1995; Kitchen
et al., 2010; Raun et al., 2002; Sripada et al., 2008). They have
used these data to develop N fertiliser response functions that
relate sensor readings to the amount of N fertiliser needed to
overcome crop N stress (Scharf et al., 2011). Even with this
approach, reference strips with adequate fertiliser have to be
strategically placed in representative soils and landscapes
because yield response to N fertiliser exhibits significant
spatial variability across production fields (Mamo et al., 2003).
Also, reference strips have limitations for other crops like
wheat (subject to lodging) and potato (subject to excessive
vine growth at the expense of tuber growth).
365
surface. This information is traditionally visualised using
a three-dimensional hyperspectral data cube, with two spatial
dimensions (x,y) and one spectral dimension (wavelength). An
example of a hyperspectral data cube is shown in Fig. 3 for
a commercial potato field in Minnesota, USA. The field shows
significant spatial and spectral variability in reflectance.
The first hyperspectral sensor was the airborne visible/
infrared imaging spectrometer (AVIRIS), launched in 1987.
This sensor provides continuous imagery from 380 to 2500 nm
in bands that have a spectral resolution of 10 nm and a spatial
resolution of 20 m. The satellite based Hyperion sensor,
launched aboard EO-1 by NASA in 2000, has hyperspectral
imaging capabilities similar to AVIRIS. Hyperspectral imagery
collected by EO-1 Hyperion via the advanced land imager (ALI)
sensor continues to be collected and is available for public use
through the US Geological Survey, Center for Earth Resources
Observation and Science (EROS). Datt, Jupp, McVicar, and Van
Niel (2003) showed that ALI hyperspectral data could be used
to more accurately predict spatial patterns in rice yield grown
in Australia with derivative indices and red edge position in
comparison with predictions based on NDVI. Miglani, Ray,
Pandey, and Parihar (2008) showed that 20 hyperspectral
bands from ALI were necessary for agricultural remote
sensing studies in the Meerut district of India. Wu, Wang, Niu,
Gao, and Wu (2010) showed that vegetative indices based on
red edge reflectance from hyperspectral ALI data could be
used to accurately estimate canopy chlorophyll content and
leaf area index for a broad range of agricultural crops in China.
An aerial hyperspectral imaging system, the compact airborne
spectrographic imager (CASI), has also been widely used
(Haboudane et al., 2002, 2004). There are also hand-held or
boom-mounted hyperspectral and multispectral imaging
systems, including the CropScan (CropScan Inc, Rochester,
MN, USA) sensor.
Hyperspectral imaging differs from multispectral imaging
in the continuity, range and spectral resolution of bands. In
theory, it offers the capability of sensing a wide variety of soil
and crop characteristics simultaneously, including moisture
status, organic matter, nutrients, chlorophyll, carotenoids,
cellulose, leaf area index and crop biomass (Goel et al., 2003;
Haboudane et al., 2002; Zarco-Tejada et al., 2005). Specific
2.4.
Hyperspectral remote sensing in precision
agriculture
Hyperspectral remote sensing collects reflectance data over
a wide spectral range at small spectral increments (typically
10 nm). It provides the ability to investigate spectral response
of soils and vegetated surfaces in narrow spectral bands
(10 nm wide) across a wide spectral range. This is not possible
with multispectral imaging that traditionally collects reflectance data in broadbands (greater than 40 nm wide) centred in
the B, G, R and NIR regions of the spectrum. When collected
across large spatial extents at fine spatial resolution, hyperspectral imaging provides powerful insight into the spatial
and spectral variability in reflectance for a bare or vegetated
Fig. 3 e Hyperspectral data cube for a commercial Russet
Burbank potato field in Minnesota. The face of the cube is
a redegreeneblue image of the same area shown in Fig. 2.
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b i o s y s t e m s e n g i n e e r i n g 1 1 4 ( 2 0 1 3 ) 3 5 8 e3 7 1
wavelengths are most sensitive to each type of soil or crop
characteristic. A red band centred at 687 nm is sensitive to
crop leaf area index and biomass, while a near infrared band
centred at 970 nm is sensitive to crop moisture status
(Thenkabail, Lyon, & Huete, 2010). Further examples of linking
specific soil and crop characteristics with reflectance are given
for 33 hyperspectral bands by Thenkabail et al. (2010). In
contrast, multispectral imaging is often limited to analysis of
single broadband combinations such as NDVI, which become
insensitive to chlorophyll and other plant characteristics at
LAI values exceeding 3.0 (Thenkabail et al., 2000), and are
strongly interfered with by bare soil reflectance at low LAI
values.
Thenkabail et al. (2000) showed that hyperspectral data can
be used to construct three general categories of predictive
spectral indices, including 1) optimal multiple narrow band
reflectance indices (OMNBR), 2) narrow band NDVI, and 3)
SAVI. Only two to four narrow bands were needed to describe
plant characteristics with OMNBR. The greatest information
about plant characteristics in OMNBR includes the longer red
wavelengths (650e700 nm), shorter green wavelengths
(500e550 nm), red-edge (720 nm), and two NIR (900e940 nm
and 982 nm) spectral bands. The information in these bands is
only available in narrow increments of 10e20 nm, and is easily
obscured in broad multispectral bands that are available with
older satellite imaging systems. The best combination of two
narrow bands in NDVI-like indices was centred in the red
(682 nm) and NIR (920 nm) wavelengths, but varied depending
on the type of crop (corn, soybean, cotton or potato) as well as
the plant characteristic of interest (LAI, biomass, etc.).
Advanced statistical methods for chemometric analysis of
reflectance spectra have been used to interpret hyperspectral
remote sensing data, including partial least squares (Lindgren,
Geladi, & Wold, 1994; Viscarra Rossel et al., 2006), principal
components analysis (Geladi, 2003), and pattern classification
and recognition techniques (Stuckens, Coppin, & Bauer, 2000),
including object oriented (Frohn, Reif, Lane, & Autrey, 2009)
and decision tree (Wright & Gallant, 2007) classification techniques. Partial least squares (PLS) regression is perhaps more
powerful than principal components analysis (PCA) in that
PLS (like PCA) not only identifies factors that describe spectral
variance, but also eliminates spectral bands that contain
redundant information (Alchanatis & Cohen, 2010).
Jain, Ray, Singh, and Panigrahy (2007) explored hyperspectral remote sensing for identification of N stress in potatoes grown in India. They used three techniques to identify
bands that were optimal for detection of N stress, namely; 1)
lambdaelambda plots, 2) principal component analysis, and 3)
discriminant analysis. Lambdaelambda plots involve calculating, for example, the coefficient of determination (r2) for
leaf N content at all hyperspectral reflectance bands. A graph
of r2 for all possible combinations of band 1 on the x-axis and
band 2 on the y-axis results in a lambdaelambda plot. The
lambdaelambda plot is useful for identifying which combinations of two bands contain redundant information about N
stress. Spectral bands or narrow band indices should be
selected with low r2 to eliminate redundancy and maximise
information about crop characteristics (such as N stress).
Principal component analysis was used by Jain et al. (2007)
to identify which combinations of bands account for
a majority of the variance in crop reflectance characteristics.
This technique is used to eliminate hyperspectral bands that
do not contain useful information about the crop characteristics of interest. Stepwise discriminant analysis uses a ratio
of treatment sums of squares to total sums of squares to find
spectral regions with distinctly different mean values of
reflectance.
A variety of narrow band hyperspectral indices (Table 4)
are available for use in precision agriculture (Haboudane et al.,
2002, 2004; Li et al., 2010; Miao et al., 2007, 2009). Many of these
have the same form as broadband spectral indices (Table 1),
but differ in that the reflectance bands for hyperspectral
indices are narrow (10e20 nm wide) bands centred around
a single specific wavelength. These indices variously respond
to canopy or leaf scale effects of leaf area index, chlorophyll,
specific pigments, or nitrogen stress. Simple ratios (SR) 1
through 7 and normalized difference indices (NDI) 1 through 3
typically respond to leaf level changes in chlorophyll. In
contrast, NDVI responds to canopy scale changes in leaf area
index and chlorophyll. GNDVI, modified chlorophyll absorption in reflectance index (MCARI), transformed chlorophyll
absorption in reflectance index (TCARI), MCARI2, OSAVI, and
MSAVI respond to canopy scale changes in chlorophyll, with
the latter two indices being designed to compensate for soil
reflectance effects. PSSRa and PSSRb were designed specifically to respond to changes in chlorophyll a and chlorophyll b,
respectively. New hyperspectral indices are continuously
being tested and developed (Li et al., 2010) using techniques
involving lambdaelambda plots where reflectance signatures
are compared for all possible combinations of two reflectance
bands.
Potential applications of hyperspectral remote sensing in
precision agriculture have recently been reviewed by Yao et al.
(2010). These applications include 1) bare soil imaging for
management zone delineation, 2) weed mapping, 3) crop N
stress detection, 4) crop yield mapping, and 5) pest and disease
detection. Perhaps of greatest interest for precision agriculture is using hyperspectral remote sensing for variable rate,
in-season management of nitrogen fertiliser based on spatial
patterns in chlorophyll content. Wu, Han, Niu, and Dong
(2010) used hyperspectral data from the Hyperion EO-1 satellite to study chlorophyll content in a variety of agricultural
canopy types in China, including flax, chestnuts, corn,
bamboo, potato, pine, saccharose and tea. Chlorophyll
content of leaves were estimated using a variety of vegetation
indices derived from red edge reflectance bands located at 705
and 750 nm. The MCARI/OSAVI705 index performed better
than all other vegetation indices evaluated, with an r2 value of
0.71. Wu, Wang et al. (2010) estimated chlorophyll content of
maize in China using Hyperion hyperspectral reflectance data.
The Enhanced Vegetation Index (EVI) was more accurate
(r2 ¼ 0.81) at predicting maize chlorophyll contents than
MSAVI or NDVI.
3.
Knowledge gaps for remote sensing in
precision agriculture
Rapid advances in remote sensing for precision agriculture
have occurred over the last twenty five years. Satellite imagery
367
b i o s y s t e m s e n g i n e e r i n g 1 1 4 ( 2 0 1 3 ) 3 5 8 e3 7 1
Table 4 e Hyperspectral narrow-band vegetation indices available for use in precision agriculture. R refers to reflectance at
the wavelength (nm) in subscript. NIR refers to near infrared reflectance.
Index
Greenness index (G)
SR1
SR2
SR3
SR4
SR5
SR6
SR7
DI1
NDVI
Green NDVI (GNDVI)
PSSRa
PSSRb
NDI1
NDI2
NDI3
MCARI
TCARI
OSAVI
TCARI/OSAVI
TVI
MCARI/OSAVI
RDVI
MSR
MSAVI
MTVI
MCARI2
Definition
Reference
R554/R677
NIR/red ¼ R801/R670
NIR/green ¼ R800/R550
R700/R670
R740/R720
R675/(R700*R650)
R672/(R550*R708)
R860/(R550*R708)
R800 R550
(R800 R680)/(R800 þ R680)
(R801 R550)/(R800 þ R550)
R800/R680
R800/R635
(R780 R710)/(R780 R680)
(R850 R710)/(R850 R680)
(R734 R747)/(R715 þ R726)
[(R700 R670) 0.2(R700 R550)](R700/R670)
3*[(R700 R670) 0.2*(R700 R550)(R700/R670)]
(1 þ 0.16)(R800 R670)/(R800 þ R670 þ 0.16)
0.5*[120*(R750 R550) 200*(R670 R550)]
(R800 R670)/SQRT(R800 þ R670)
(R800/R670 1)/SQRT(R800/R670 þ 1)
0.5[2R800 þ 1 SQRT((2R800 þ 1)2 8(R800 R670))]
1.2*[1.2*(R800 R550) 2.5*(R670 R550)]
1:5½2:5ðR800 R670 Þ 1:3ðR800 R550 Þ
ffi
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
pffiffiffiffiffiffiffiffiffiffi
ð2R800 þ 1Þ2 ð6R800 5 R670 Þ 0:5
has improved in spatial resolution, return visit frequency and
spectral resolution. Aerial hyperspectral imagery has revolutionised the ability to distinguish multiple crop characteristics, including nutrients, water, pests, diseases, weeds,
biomass and canopy structure. Ground-based sensors have
been developed for on-the-go monitoring of crop and soil
characteristics such as N stress, water stress, soil organic
matter and moisture content.
Precision farming has progressed through many stages. It
began with farming by soil and progressed to site-specific crop
management based on grid sampling and management zones.
More recently there has been increasing emphasis on realtime on-the-go monitoring with ground based sensors. The
challenge for the future is to develop precision farming
approaches that can provide customized management of farm
inputs for individual plants.
There is a significant potential in precision agriculture for
combining archived remote sensing data with real-time data
for improved agricultural management (Thenkabail, 2003).
Historical archives of satellite remote sensing data are available at many locations for Landsat, SPOT, IRS, IKONOS, and
QuickBird. These data typically include reflectance in the B, G,
R and NIR bands, at spatial resolutions of from 0.6 to 30 m
spatial resolution. Images at a fixed location could be analysed
across multiple crop growth stages, seasons and years in order
to identify relatively homogeneous sub-regions of fields that
differ from one another in leaf area index, NDVI, and potential
yield. Auxiliary data at these same sites, including crop yield
maps, digital elevation models and soil series maps could be
Smith, Adams, Stephens, & Hick, 1995
Daughtry, Walthall, Kim, de Colstoun, & McMurtrey, 2000
Buschman & Nagel, 1993
McMurtrey, Chappelle, Kim, Meisinger, & Corp, 1994
Vogelmann, Rock, & Moss, 1993
Chappelle at al., 1992
Datt, 1998
Datt, 1998
Buschman & Nagel, 1993
Lichtenthaler, Lang, Sowinska, Heisel, & Mieh, 1996
Daughtry et al., 2000
Blackburn, 1998
Blackburn, 1998
Datt, 1999
Datt, 1999
Vogelmann et al., 1993
Daughtry et al., 2000
Haboudane et al., 2002
Rondeaux et al., 1996
Haboudane et al., 2002
Broge & Leblanc, 2000
Zarco-Tejada, Miller, Morales, Berjón, & Agüera, 2004
Rougean & Breon, 1995
Chen, 1996
Qi et al., 1994
Haboudane et al., 2004
Haboudane et al., 2004
combined with historical remote sensing data to identify
potential management zones where precision agricultural
input operations can be implemented. Real time remote
sensing with high spatial and spectral resolution satellites
such as EO-1 Hyperion or the upcoming (2016) NASA Hyperspectral Infrared Imager (HyspIRI) satellite (or comparable
data collected with aerial platforms) could then be used for
real time precision agricultural decision making and to refine
the location of management zones identified using historical
imaging and auxiliary data.
With this in mind, there are several needs for future
research in precision farming. These include the following:
More emphasis is needed on chemometric or spectral
decomposition/derivative methods of analysis since spatial
and spectral resolution of hyperspectral sensing systems
are now adequate for most precision agriculture
applications
Sensors are needed for direct estimation of nutrient deficiencies without the use of reference strips
Spectral indices should continue to be developed that
simultaneously allow assessment of multiple crop characteristics (e.g. LAI, biomass, etc.) and stresses (e.g. water and
N; weeds and insects, etc.)
Historical archives of satellite remote sensing data at
moderate to high spatial resolution and traditional spectral
resolution should be integrated with real-time remote
sensing data at high spatial and spectral resolution for
improved decision making in precision agriculture.
368
b i o s y s t e m s e n g i n e e r i n g 1 1 4 ( 2 0 1 3 ) 3 5 8 e3 7 1
Acknowledgement
Support by the USDA-BARD fund through Research Grant
Award No. IS-4255-09 is acknowledged. The assistance of
Mr. Tyler Nigon in preparing figures is acknowledged.
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