HYPERSPECTRAL REFLECTANCE MODEL TO ESTIMATE CHLOROPHYLL CONTENT IN SOYBEAN LEAVES

Good correlation between hyperspectral reflectance of plant leaf and chlorophyll content would make possible to estimate vegetation chlorophyll content by hyperspectral remote sensing.Scholars in the past often generated chlorophyll models on the basis of statistical regression,red edge parameters or neural network theories.Since leaf structure and biochemical compositions vary among different species,these models are not universally applicable.Parameters and methods must be modified to suite specific plants.Soybean was taken as an example,its leaf hyperspectral reflectance and chlorophyll content during vegetative period in soybean field were measured by ASD portable spectrometer and spectrophotometer.Sensitive wave bands,proper hyperspectral forms,red edge parameters were extracted and used to generate a model based on neural network theory.This model took advantages of all existing inversing methods and proved to have high accuracy.