The First Steps to Understanding Agriculture Remote Sensing
By: Mark Servilla

Commercial remote sensing has vastly improved its capabilities since its introduction in the early 1970s. Advanced sensors on airborne and spaceborne platforms are now able to obtain quality digital imagery in a time-critical and cost-effective fashion that is necessary for the agriculture industry. Today, many producers are beginning to use remotely sensed digital imagery as a tool to assess the state-of-health of their crops. For digital imagery to be an effective tool, the fundamental principles behind remote sensing should be understood to evaluate the best approach for the use of this new tool in the daily process of crop management.

Solar Energy-Where It All BeginsÉ
Most agriculture remote sensing platforms rely on passive sensors. Passive sensors measure energy that originates from the Earth's surface, generally in the form of reflected solar energy, much in the same way photographic film records reflected light intensity of an object being photographed. In contrast, active sensors utilize an energy source on the remote sensing platform that transmits and receives its own signal, such as radio detecting and ranging (RADAR) systems.
    The energy that we receive from the sun, which is characterized by wave theory in physics, covers a broad region of the electromagnetic (EM) spectrum. A specific region within the EM spectrum is defined by a unique waveform. Each waveform of the same region is characterized by its wavelength, the distance between two successive peaks (or troughs) of the waveform, and is measured in units of micrometers (m). As a producer, it is important to know what wavelength region a sensor can measure to determine whether the sensor is useful as an agriculture tool.
    To better understand how remote sensing works, it is helpful to understand a natural passive sensor that we are all familiar with: The human eye. Our eyes are sensitive to only the visible region of the EM spectrum. This is why we are able to visualize the primary colors blue, green, and red, and combinations of these colors. The visible region of natural sunlight contains all colors in equal intensity. When the primary colors are combined in equal intensity, the result is "white" light. By varying the absorption and reflection of the primary colors, we are able to see different shades of color. A red car, for example, looks "red" to us because pigments within the paint absorb the blue and green light and reflect the red light. Man-made passive sensors, on the other hand, have the ability to detect regions of the EM spectrum that our eyes cannot see. This is why they can provide us with much greater information.

Spectral Characteristics of Vegetation
Because the interaction of solar energy with vegetation is fairly well understood, the amount of reflected solar energy from vegetation can tell us quite a lot about the vegetation's physical state (health), and this is what you, as a grower, should understand when using an agriculture remote sensing tool.
    The two primary components of solar energy that interact with vegetation are visible and infrared energy. Most of this interaction takes place within the leafy material of the vegetation. The chlorophyll rich tissue in the leaf, known as the mesophyll tissue, consists of two structurally different layers, the palisade layer and the spongy layer. Because the palisade layer is near the surface of the leaf, the chlorophyll in this layer absorbs most of the incoming visible energy for use during photosynthesis. The amount of visible energy absorbed by chlorophyll is not equal across the visible spectrum. Chlorophyll absorbs red and blue energy more readily than green energy. For this reason, more green energy (or light) is reflected from the leaf, thereby making the leaf appear green to our eyes.
    Infrared energy, on the other hand, is not affected by chlorophyll. Instead, the longer wavelengths of infrared energy interact directly with the cell structure of the tissue. The vertical alignment of cells within the palisade layer allows the infrared energy to pass through to the spongy layer without change. In the spongy layer, though, the open structure of cells results in about half of the infrared energy being reflected up from the leaf, while the other half continues on through the leaf. In general, a healthy spongy layer will reflect more infrared energy than an unhealthy layer in the same vegetation species.
    The direct relationship between the near-infrared reflectance and the spongy layer cell structure is still not fully understood by researchers. It is important to know that the amount of infrared energy that is reflected from vegetation is much greater than the amount of visible energy reflected at the same location. For this reason, passive sensors that measure near-infrared energy are much more sensitive to subtle changes in vegetation health than sensors which measure only visible energy, such as our eyes. When considering remotely sensed data for interpreting vegetation health, imagery that includes measurements of the near-infrared reflected energy provides the best results.
    Mid-infrared and thermal-infrared energy is not affected by either the chlorophyll or the cell structure, but instead, is influenced by the water content within the leaf. Since water absorbs the mid-infrared and thermal-infrared energy, the amount that this energy is absorbed can be correlated to the degree of hydration of the plant tissue. Research is just now beginning to correlate the degree of moisture stress in vegetation to the amount of mid-infrared and thermal-infrared energy that is absorbed by the vegetation. This will be yet another important tool for the grower in the very near future.

Measuring "Stress" in Vegetation
There are a variety of ways that you can assess the health of vegetation from remotely sensed digital imagery. The most common method is to combine the reflected visible and near-infrared energy into a composite image that highlights areas of stress. The choice of these two regions is simple (as described earlier): The intensity of solar energy reflected by vegetation is dependent on the chlorophyll's ability to absorb the red and blue energy and the spongy layer to reflect the near-infrared energy. As the vegetation's health declines, greater reflection of the visible energy (decreased absorption by chlorophyll) and less reflection of near-infrared energy occurs. Such conditions can occur during normal vegetation "senescence" and during the onset of vegetation disease and stress. The "yellowing" of vegetation that we commonly see when plants under go senescence or severe stress is simply the result of increased reflectance of the green and red energy, the combination of which creates the color yellow.
    There are two presentations that are commonly used for digital images, gray scale and color. Gray scale images utilize varying shades of gray to represent intensity changes of reflectance, or another measurement, across the image scene. Gray scale images are useful for interpreting measurements of a single region of the EM spectrum, such as the near-infrared. Color images use three different intensity measurements from the same location and translates them to one of the three primary colors, blue, green, and red, for display. Depending on the intensity of each measurement, color images can contain all shades of color. Color images are useful for interpreting measurements of three regions of the EM spectrum in a single image.
    The effectiveness of passive sensor digital imagery is illustrated by a case in which three images were taken of the same center-pivot cornfield in south central Nebraska. This particular field was imaged in August of 1997, about a month prior to harvest, using an airborne sensor that measures the blue, green, red, and near-infrared energy. The "true" color composite image was generated by combining the blue, green, and red energy measurements from the sensor into a single color image. The image provided a close representation of what you would see if you were flying above the field. Except for the "yellowing" in the west side of the pivot, there is very little visible stress in this particular image.
    In comparison, the "color-infrared" or "false color" composite image was generated by combining the green, red, and near-infrared energy measurements. In this case, the near-infrared energy was translated to the color "red", which resulted in the red hue of vegetation. Annotations were placed on this image based on field scouting interpretations made by an agronomist. The "yellowed" area remained evident in this color-infrared image, as did the distinct difference of vegetation in the narrow strip of field where the planter failed to apply corn seed. The area of "sloped ground", however, showed no apparent variation in the image, and, therefore, aroused little suspicion with the producer when interpreting this image.
    Images providing a Normalized Difference Vegetation Index (NDVI), are derived from a mathematical normalization technique that utilizes only the red and near-infrared energy measurements. The calculation of an NDVI results in a value between negative and positive, one for each location in the field that is measured by the sensor. This measurement takes into account the amount of red energy that is absorbed by chlorophyll and the amount of near-infrared energy that is reflected by the cellular structure of the leaf (because the red and near-infrared measurements are normalized in an indirect measure of vegetation health).
    Most NDVI images are presented in a grayscale format and are divided into distinct colors that represent relative stress over all fields within the image area. Since each crop type (including hybrids) within the image will reflect different energy signals, interpretation of vegetation health must be made on a field by field basis.
    Interpretation of the center-pivot cornfield (see "Vegetation index map," this page) quickly reveals two areas of "stressed" vegetation: the area where poor drainage is affecting corn health (as seen in earlier images) and the area where sloped ground is resulting in poor moisture retention in the soils. It is evident from this NDVI color image that preseason surface work would be required in two regions within this center-pivot field for uniform production of corn. Using similar techniques on other fields, digital imagery has revealed areas of stress resulting from herbicide/pesticide damage, fertilizer variations, compacted soils, insect damage, and poor irrigation practices.
    Today, technology and science are able to measure the relative stress in vegetation through passive sensor remote sensing. New applications involving the use of mid-infrared and thermal-infrared sensors to identify moisture stress are now being developed. Similar to the vegetation index images, moisture stress images will show areas in the field where vegetation is dehydrated and lacking sufficient irrigation. The future promises to bring quantitative measurements of stress, in addition to determining the actual cause of stress to vegetation. Growers who stay informed will benefit from these developments with new opportunities to maximize their efficiency.

About the author:
Mark Servilla, Ph.D. is a research engineer with Photo Research Associates, Inc., he has 2.5 years applied research experience. His research focuses on applications in agriculture remote sensing. He has also worked on developing data warehousing and distribution software for remote sensing imagery.

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