Forest biomass estimation in rugged terrain: Sentinel-1 and ICEYE SAR insights

Forest biomass is a fundamental component of the global carbon cycle, essential for ecosystem protection and climate change mitigation. A variety of remote sensing data types, including optical, Synthetic Aperture Radar (SAR), have been explored for AGB and carbon quantification. Among these, multispectral and SAR image analyses are the most widely applied methods for large-scale biomass estimation. However, a significant challenge for both approaches is signal saturation, particularly in forested regions with complex topography. While various remote sensing techniques have been applied to estimate AGB, only one study has explored ICEYE’s capabilities in AGB estimation. The present study aims to investigate the potential of Sentinel-1 and ICEYE SAR data in reliably estimating above-ground biomass (AGB), which represents the total dry weight of the different tree components. A series of textures and indices were calculated on both datasets and subsequently included as features in the predictive models. Regarding the AGB modelling, a regression analysis was performed to develop the AGB models using the Random Forest (RF) algorithm. Consequently, two models were created (one for each sensor), and their predictive performance was assessed through the k-fold cross-validation method. The results demonstrated that Sentinel-1 can reliably estimate AGB (i.e. R²= 0.76, RMSE= 1616.66 kg, rRMSE= 23.97%, MAΕ= 1408.65 kg), while ICEYE data provided weaker performance (i.e. R²= 0.59, RMSE= 2157.55 kg, rRMSE= 33.82%, MAE= 2502.10 kg). Overall, this study highlights the capability of SAR data to reliably estimate AGB in forests with rugged terrain when combined with Vertical- Horizontal textural characteristics and the RF algorithm.