Low Aboveground Biomass Estimation Using Global Sentinel-1 Backscatter Dataset Over a Sub-Tropical Indian Forest

This study explores the use of Global Sentinel-1 Backscatter Dataset for estimating Above-Ground Biomass (AGB) in low biomass regions like grasslands, shrubland and dry forests which cover almost half of the India forest cover. Traditionally, L-band radar has been used for AGB estimation in forests, but its effectiveness in low-biomass areas is limited due to subtle backscatter differences. Sentinel-1’s shorter wavelength provides a promising alternative, as shown by our analysis using the Water Cloud Model (WCM). With a Root Mean Square Error (RMSE) of $15.4 \pm 0.9 \mathrm{Mg} / \mathrm{ha}$ over 30 iterations calculated for AGB’s upto $55 \mathrm{Mg} / \mathrm{ha}$, our results indicate that Sentinel-1 C-band is effective for AGB estimation in these regions. The study highlights the potential of Sentinel-1 for monitoring biomass dynamics in low-biomass areas. Including more field data and Lidar GEDI data may improve the estimate accuracies.