Page 1 of 2 Analysis of reflectance spectra for identification of vegetation substance, A Lecture Note. Redmond Ramin Shamshiri, Ph.D. https://florida.academia.edu/RaminShamshiri

A lecture note on Analysis of reflectance spectra for identification of vegetation substance

Redmond Ramin Shamshiri, PhD

ramin.sh@ufl.edu Reflectance spectroscopy provides non-destructive, rapid,

reliable and precise analysis approach for plant’s health condition assessment. Currently there are three main remote sensing platforms for this purpose, including close-range platforms such as ground based or handheld sensors, middle-range platforms such as drone-based sensors or imaging devices mounted on autonomous unmanned aerial vehicles (UAVs), and far-range platforms such as piloted air planes or satellite-based sensors. Multi-spectral images contain monochrome images taken from sensors with four or more channels, each representing reflectance values for the objects in the image corresponding to a specific wavelength. Hyperspectral images can be acquired by ground-based, airborne or space borne hyperspectral cameras, with each image pixel containing hundreds or thousands wavelength bands (i.e., from 350 to 2500 nm). The spectral data of a pixel in a hyperspectral image is a combination of the reflected light from the entire features (i.e., moisture content and chemical elements) that exist in that pixel area. For example, a typical hyperspectral image in the size of 256 by 320 pixels that is acquired with a 118-channels hyperspectral camera will have 81920 spectra (or reflectance signal), each with a length of 118. The main difference between hyperspectral imaging and spectroradiometer devices is that data from hyperspectral camera includes spectral and spatial information, whereas a spectroradiometer only provides reflectance spectra. In the other words, a pixel in a hyperspectral image includes reflected radiation for the piece of object in that pixel over hundreds of wavelength bands. Indeed, the spectral data for that pixel has a similar shape as data produced by a spectroradiometer. An image from hyperspectral camera contains spatial axis, corresponding to a line of pixels, and spectral axis, corresponding to reflectance data distribution.

The idea behind analyzing of leaf reflectance spectra is to find a

correlation between the reflectance values from different leaves at a specific wavelength, and the amount of nutrient concentration obtained from laboratory results. Leaf reflectance spectra can be collected in field using portable devices such as FieldSpec4 Hi-Res Spectroradiometer (ASD, Inc., Boulder, Colorado), or PSR-3500 High Resolution Full Range (350-2500nm) Portable Spectroradiometer (Spectral evolution, Lawrence, MA) as shown in Figure 1. Spectral data can be analyzed with different techniques such as wavelet transformation for early detection of plants disease, water stress and nutrient deficiencies, before visible symptoms can be identified.

Figure 1. Left: FieldSpec4 and right: PSR-3500, high resolution

Full Range (350-2500nm) Portable Spectroradiometer To illustrate the concept of spectral analysis and how it can be

used for classification of a random agricultural sample based on a specific physical or chemical property (such as color or nutrient content), reflectance data of different samples that are generated using full range PSR-3500 Spectroradiometer device are plotted versus wavelength in Figure 2 A to D. To demonstrate the reflectance differences between colors of a similar type material in the visible range (visible wavelength lies between 380-700 nm, where different colors are distinct for human eyes), reflectance spectra of four paper

samples with different colors (black, white, yellow and green) are shown in Figure 2.A. An immediate observation is the distinct differences between the black and white samples through the entire wavelength band (350-2500 nm). This different is due to the fact that black color paper sample absorbed almost the entire portion of light, while white color paper sample reflected most of the intercepted light, specifically in the visible range. On the same plot, the green paper sample shows a pick value between 500-550 nm, which is because of the wavelength range of green color that lies on 546 nm. For lighter color paper samples, i.e., white and yellow, a sharp increase in reflectance values between wavelengths of 350-500 nm can be observed, followed by a decreasing trend that begins at around 1000 nm. Except for the black sample, reflectance values of other three samples converge to 30% at 2500 nm.

Other than color properties, moisture content, thickness, and cellular structure of leaflets are among other factors that influence leaf reflectance spectra. Water stress and chlorophyll concentration can be estimated directly from reflectance data. The source of vegetation color in plants is due to the existence of chlorophyll that absorbs blue and red wavelengths. That is the main reason that healthy plants appear green in the visible range. To illustrate this, spectral reflectance of a dark green citrus leaf and two sandy soil samples, one wet and the other dry are provided in Figure 2.B. It can be seen that the wet sand samples has a closer spectral shape to the citrus leaf in the water band due to its moisture content, however the dry sand sample has an entirely different spectral shape due to its deficiency of moisture content. The difference between colors of sand samples and citrus leaves samples with different green color due to differences in their vegetation, as shown in Figure 2.C, can be referred in visible wavelength range, where sand samples absorbed lesser portion of light and reflected more because of having color lighter than green. In the near infrared (wavelength range between 750-900 nm), the dry sand sample has significant deviation from the other two samples. It can also be observed that none of the sand samples have red edge (680-730 nm) when compared with the vegetation samples. Water absorption band is approximately on 1400 and 1950 nm wavelength and can be used to distinguish between real vegetation and artificial vegetation (i.e., plastic leaf or green paper). Hence, plant’s leaves suffering from water and nitrogen deficiency can be distinguished with higher ratio of blue over red reflectance. It can be clearly observed from Figure 2.B and Figure 2.C that beyond 1350 nm, as wavelength increases, reflectance decreases, except in 1400 and 1900 nm (water absorption bands).

A more in-depth demonstration of water absorption band is

provided by plotting spectral signature for a green color artificial plastic leaf, a green paper sample, and three real citrus leaves in Figure 2.D. While the plastic leaf sample could be hardly distinguished from the real leaves in visible range, they can be easily separated in the NIR range (700-2500nm), specifically after 1300 nm. Plant’s species type, age and its growth stage as well as possible stress and deficiency affect reflectance measurements, especially in the NIR range. Leaf reflectance is altered by stress more consistently at visible wavelengths than in the reminder of the spectrum (730-2500 nm). The difference in the spectral of the actual leaves sample is due to the different levels of Chlorophyll concentration. This difference can also be detected in the range of 400-700nm wavelength, or by referring to the Nitrogen band which is approximately 550 nm. This is shown by plotting spectral signature of five citrus leaves sample in Figure 3.A. A closer view is provided in the range of 400-700nm in Figure 3.B showing Nitrogen band and the differences between the five samples. It can be seen that the first sample has the lowest level of Nitrogen and has reflected more light in the Nitrogen band range. As the

Page 2 of 2 Analysis of reflectance spectra for identification of vegetation substance, A Lecture Note. Redmond Ramin Shamshiri, Ph.D. https://florida.academia.edu/RaminShamshiri

amount of nitrogen increases in the leaves samples, the chlorophyll content also increase in proportion and reflectance decreases. In fact, a substantial amount of Nitrogen concentration is because of the chlorophyll content. Nitrogen band is therefore used as an accurate representation of plant vegetation status. Available commercial cholorophyll meters that measures green color of leaflet sample are not accurate due to their inconsistent readings caused by variability in light source (shade, cloud, sunlight intensity). Other lower cost instruments such as SPAD-502Plus uses red and NIR regions to measure leaf absorbance, where absorbance is equal to log(1/reflectance). SPAD calculates Ratio Vegetation Index (NIR/R), a numerical value using R and NIR absorbance that is proportional to the level of chlorophyll content in the leaf, and can be used for assessing plant’s health condition. Another approach to distinguish the differences between Chlorophyll concentration in samples is the Normalized Difference Vegetation Index (NDVI), defined as . Here, NIR is the near-infrared band (740-760 nm) reflectance and R is the red band (640-620 nm) reflectance. It can be implied from this index that higher NIR reflectance and lower red reflectance values are resulted from yellow or brown leaves. Therefore NDVI is a good indication for determine growth stage (age), leaf area index and to some extend health state of the plant. An example of such application includes identification of infected and healthy wheat plots using NDVI. Other optical properties of leaves around 700 nm and associations with stress detection, chlorophyll content estimation, and physiological conditions of the plant in blue-green (400-630 nm) and red to far red (630-800 nm) can be addressed from correlating reflectance spectra with ground-truth laboratory results.

Figure 2. Reflectance spectra of different substances, A: color paper, B:

sand and citrus leaf, C: different color citrus leaves, D: plastic leaf vs. actual citrus leaves and a green paper

Figure 3. Differences in reflectance spectra of citrus leaves due to

different levels of Nitrogen

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