Aboveground biomass estimation of tropical forest from Envisat advanced synthetic aperture radar data using modeling approach

Abstract

100 t/ha), remains a challenging task for the researchers worldwide. The retrieval of AGB over a tropical forest area in India using Envisat advanced synthetic aperture radar C-band backscatter, interferometric synthetic aperture radar (InSAR) coherence and semi-empirical models viz., water cloud model (WCM) and interferometric water cloud model (IWCM), is studied. In process, the model parameters, i.e., backscatter from vegetation and ground, two-way tree transmissivity, and coherence from vegetation and ground were retrieved. The model training procedure to retrieve the model parameters consisted of an iterative regression of WCM and IWCM. High AGB accuracy (R2=0.73) with low root mean square error (RMSE=53.76 t/ha) was achieved through multidate weighted averaging using RMSE-based weighting coefficients and WCM. Multidate data and InSAR coherence images showed better results (R2=0.90, RMSE=35.92 t/ha) compared to individual coherence images. The InSAR coherence was found to be better for AGB retrieval than SAR backscatter as the former did not saturate for high AGB values.