Genetic Algorithm based Inversion Modelling of PROSAIL for Retrieval of Wheat Biophysical Parameters from Bi-directional Reflectance Data

Abstract

Canopy reflectance model inversions are widely used for estimation of vegetation properties from remote sensing, but the inversion accuracy needs significant improvements. In recent years, applications of the genetic algorithms (GA) to a variety of optimization problems in remote sensing have been successfully demonstrated. The present study is focused on the GA based inversion approach of radiative transfer model ‘PROSAIL’, for the retrieval of LAI, leaf chlorophyll content (Cab), canopy chlorophyll content (CCC) and leaf wetness (Cw) of wheat from field-measured spectral reflectance in the whole spectral range (400-2500nm) and in broad bands corresponding to MODIS, TM and IRS LISS-3sensors. Performance of inversion was evaluated for both broadbands and the whole spectral range (400-2500 nm). The estimation accuracy was in the order of LAI>CCC>Cab> Cw. The retrieval accuracy of Cw was poor, although the estimate from whole spectra reflectance was better than that from broadband reflectance.