Forest Biomass Estimation using Multi-Polarization SAR Data Coupled with Optical Data

This study was carried out to estimate biomass extraction from multi-frequency and multi-polarization of Synthetic Aperture Radar (SAR) data coupled with optical data. Further, the estimated biomass was validated with field-observed data. ALOS-2/PALSAR was utilized for retrieval of forest above-ground biomass (AGB) biophysical parameters. Subsequently, Sentinel- 2 optical data and 90 m TanDEM were used to identify the bare ground area for calculating pseudo height. Ground-truth data were utilized for estimation and validation of the modelled biomass from radar data. In this study, five allometric models were used. Multivariate regression models were trained using backscatter from the same acquisition (date) on 10 randomly selected samples from 21 field plots. The validation was carried out on the remaining 11 field plots. Co-validation method was used to validate these models. Biomass was estimated from radar data using regression models. Since the objective of the study was to present generalized biomass estimation models using backscatter information and AGB, the AGB value range 100–400 tonne/ha was estimated/mapped. Combined backscatter and height inputs were better than backscatter models. In the estimation of AGB, polarimetric information content and backscatter information played a significant role.