Agricultural Remote Sensing Basics
AE-1262, April 2004
John Nowatzki, Geospatial Technology Specialist, NDSU Extension Service
Robert Andres, Professor, UND Department of Space Studies
Karry Kyllo, Graduate Student, UND Department of Space Studies
Click here for an Adobe Acrobat PDF file suitable for
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Remote Sensing . . . How You
Can Use It On Your Farm
The Electromagnetic Spectrum
Electromagnetic Energy and Plants
How Does Remote Sensing Work?
Remote Sensing: The Complete Process
References
When farmers or ranchers observe their fields or pastures to assess their condition
without physically touching them, it is a form of remote sensing. Observing
the colors of leaves or the overall appearances of plants can determine the
plant's condition. Remotely sensed images taken from satellites and aircraft
provide a means to assess field conditions without physically touching them
from a point of view high above the field.
Most remote sensors see the same visible wavelengths of light that are seen
by the human eye, although in most cases remote sensors can also detect energy
from wavelengths that are undetectable to the human eye. The remote view of
the sensor and the ability to store, analyze, and display the sensed data on
field maps are what make remote sensing a potentially important tool for agricultural
producers. Agricultural remote sensing is not new and dates back to the 1950s,
but recent technological advances have made the benefits of remote sensing accessible
to most agricultural producers.
Remote Sensing . . . How You Can Use It On Your Farm
Remotely sensed images can be used to identify nutrient
deficiencies, diseases, water deficiency or surplus, weed infestations, insect
damage, hail damage, wind damage, herbicide damage, and plant populations.
Information from remote sensing can be used as base maps
in variable rate applications of fertilizers and pesticides. Information from
remotely sensed images allows farmers to treat only affected areas of a field.
Problems within a field may be identified remotely before they can be visually
identified.
Ranchers use remote sensing to identify prime grazing
areas, overgrazed areas or areas of weed infestations. Lending institutions
use remote sensing data to evaluate the relative values of land by comparing
archived images with those of surrounding fields.
The Electromagnetic Spectrum
The basic principles of remote sensing with satellites
and aircraft are similar to visual observations. Energy in the form of light
waves travels from the sun to Earth. Light waves travel similarly to waves traveling
across a lake. The distance from the peak of one wave to the peak of the next
wave is the wavelength. Energy from sunlight is called the electromagnetic spectrum.
The wavelengths used in most agricultural remote sensing
applications cover only a small region of the electromagnetic spectrum. Wavelengths
are measured in micrometers (µm) or nanometers (nm). One um is about .00003937
inch and 1 µm equals 1,000 nm. The visible region of the electromagnetic
spectrum is from about 400 nm to about 700 nm. The green color associated with
plant vigor has a wavelength that centers near 500 nm (Figure 1).
Figure 1. The visible region of the spectrum ranges
from about 0.4 µm to 0.7 µm (Kyllo, 2004).
(Click here for an 18KB color illustration.)
Wavelengths longer than those in the visible region and
up to about 25 µm are in the infrared region. The infrared region nearest
to that of the visible region is the near infrared (NIR) region. Both the visible
and infrared regions are used in agricultural remote sensing.
Electromagnetic Energy and Plants
When electromagnetic energy from the sun strikes plants,
three things can happen. Depending upon the wavelength of the energy and characteristics
of individual plants, the energy will be reflected, absorbed, or transmitted.
Reflected energy bounces off leaves and is readily identified by human eyes
as the green color of plants. A plant looks green because the chlorophyll in
the leaves absorbs much of the energy in the visible wavelengths and the green
color is reflected. Sunlight that is not reflected or absorbed is transmitted
through the leaves to the ground.
Interactions between reflected, absorbed, and transmitted
energy can be detected by remote sensing. The differences in leaf colors, textures,
shapes or even how the leaves are attached to plants, determine how much energy
will be reflected, absorbed or transmitted. The relationship between reflected,
absorbed and transmitted energy is used to determine spectral signatures of
individual plants. Spectral signatures are unique to plant species.
Remote sensing is used to identify stressed areas in
fields by first establishing the spectral signatures of healthy plants. The
spectral signatures of stressed plants appear altered from those of healthy
plants. Figure 3 compares the spectral signatures of healthy and stressed sugarbeets.
Figure 2. Spectral signatures of crops and soil
(Kyllo, 2003). (Click
here for a 19KB color graph.)
Figure 3. Spectral signatures of healthy and stressed
sugarbeets (Kyllo, 2003). (Click
here for an 11KB color graph.)
Stressed sugarbeets have a higher reflectance value in
the visible region of the spectrum from 400-700 nm. This pattern is reversed
for stressed sugarbeets in the nonvisible range from about 750-1200 nm. The
visible pattern is repeated in the higher reflectance range from about 1300-2400
nm. Interpreting the reflectance values at various wavelengths of energy can
be used to assess crop health.
The comparison of the reflectance values at different
wavelengths, called a vegetative index, is commonly used to determine plant
vigor. The most common vegetative index is the normalized difference vegetative
index (NDVI). NDVI compares the reflectance values of the red and NIR regions
of the electromagnetic spectrum. The NDVI value of each area on an image helps
identify areas of varying levels of plant vigor within fields.
How Does Remote Sensing Work?
There are several types of remote sensing systems used
in agriculture but the most common is a passive system that senses the electromagnetic
energy reflected from plants. The sun is the most common source of energy for
passive systems. Passive system sensors can be mounted on satellites, manned
or unmanned aircraft, or directly on farm equipment.
There are several factors to consider when choosing
a remote sensing system for a particular application, including spatial resolution,
spectral resolution, radiometric resolution, and temporal resolution.
Spatial resolution refers to the size of the smallest
object that can be detected in an image. The basic unit in an image is called
a pixel. One-meter spatial resolution means each pixel image represents an area
of one square meter. The smaller an area represented by one pixel, the higher
the resolution of the image.
Spectral resolution refers to the number of bands
and the wavelength width of each band. A band is a narrow portion of the electromagnetic
spectrum. Shorter wavelength widths can be distinguished in higher spectral
resolution images. Multi-spectral imagery can measure several wavelength bands
such as visible green or NIR. Landsat, Quickbird and Spot satellites use multi-spectral
sensors. Hyperspectral imagery measures energy in narrower and more numerous
bands than multi-spectral imagery. The narrow bands of hyperspectral imagery
are more sensitive to variations in energy wavelengths and therefore have a
greater potential to detect crop stress than multi-spectral imagery. Multi-spectral
and hyperspectral imagery are used together to provide a more complete picture
of crop conditions.
Radiometric resolution refers to the sensitivity
of a remote sensor to variations in the reflectance levels. The higher the radiometric
resolution of a remote sensor, the more sensitive it is to detecting small differences
in reflectance values. Higher radiometric resolution allows a remote sensor
to provide a more precise picture of a specific portion of the electromagnetic
spectrum.
Temporal resolution refers to how often a remote
sensing platform can provide coverage of an area. Geo-stationary satellites
can provide continuous sensing while normal orbiting satellites can only provide
data each time they pass over an area. Remote sensing taken from cameras mounted
on airplanes is often used to provide data for applications requiring more frequent
sensing. Cloud cover can interfere with the data from a scheduled remotely sensed
data system. Remote sensors located in fields or attached to agricultural equipment
can provide the most frequent temporal resolution.
Remote Sensing: The Complete Process
Figure 4 illustrates a satellite remote sensing process
as applied to agricultural monitoring processes. The sun (A) emits electromagnetic
energy (B) to plants (C). A portion of the electromagnetic energy is transmitted
through the leaves. The sensor on the satellite detects the reflected energy
(D). The data is then transmitted to the ground station (E). The data is analyzed
(F) and displayed on field maps (G).
Figure 4.The remote sensing process. (Click
here for a 17KB diagram.)
References
Adamchuk, V.I., Perk, R.L., & Schepers, J.S. (2003).
Applications of Remote Sensing in Site-Specific Management. University
of Nebraska Cooperative Extension Publication EC 03-702.
Bauer, M.E. (1985). Spectral inputs to crop identification
and condition assessment. Proceedings of the IEEE, 73, No. 6, 1985, 1081.
Hatfield, J.L. & Pinter, P.J.Jr. (1993). Remote
sensing for crop protection (Publication No. 0261-2194/93/06/0414-09). Ames,
IA: USDA - Agricultural Research Service.
Jackson, R.D., & Huete, A.R. (1991). Interpreting
vegetative indices. Preventative Veterinary Medicine, 11, 185-200.
Kyllo, K. P. (2003). NASA funded research on agricultural
remote sensing, Department of Space Studies, University of North Dakota.
Moran, M.S., Inoue, Y., & Barnes, E.M. (1997). Opportunities
and limitations for image-based remote sensing in precision crop management.
Remote Sensing of Environment, 61, 319-346.
National Academy of Sciences. (1997) Precision Agriculture
in the 21st Century.
For more information on this and other topics,
see: www.ag.ndsu.nodak.edu
AE-1262, April 2004
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