ESTIMATION OF LOW BIOMASS IN FOREST ECOSYSTEMS FOR LARGE-SCALE MAPPING USING SENTINEL-1 C-BAND SAR AND LIDAR DATA

Abstract

Accurate estimation of low biomass in forest ecosystems is crucial for understanding forest dynamics, biodiversity, and carbon cycles. This study explores the use of Sentinel-1 C-band Synthetic Aperture Radar (SAR) data to estimate above-ground biomass (AGB) in low biomass forests. By integrating field data, LiDAR-derived metrics, and SAR backscatter, we apply the Water Cloud Model (WCM) and a linear regression approach to characterize biomass distribution. Validation results highlight the strengths and limitations of both methods, emphasizing their applicability for large-scale biomass mapping. ©2025 IEEE.