Abstract
Non-destructive estimation of chlorophyll-a (Chl-a) and chlorophyll-b (Chl-b), is required for assessment and understanding of vegetation structural and functional dynamics. We have developed an empirical model for estimating Chl-a and Chl-b for different crops using Airborne Visible-Infrared Imaging Spectrometer Next Generation (AVIRIS-NG) data. Second derivative reflectance at 672 nm and 587 nm has shown maximum sensitivity towards Chl-a and Chl-b content, respectively. Separate linear models within a bootstrapped resampling framework were developed. During calibration, mean R2 of 0.46 and 0.51 were obtained for Chl-a and -b respectively. While during model validation, in both the cases mean R2 was found to be 0.3. This work shows the capability of airborne hyperspectral imaging data in segregating chlorophyll types using a simplistic modeling framework.
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