Enhanced estimation of crop biomass and height using Sentinel-1 polarization texture indices and integration with optical remote sensing

Accurate estimation of above-ground biomass (AGB) and plant height is essential for precision crop management. However, traditional methods like synthetic aperture radar (SAR) data and optical vegetation indices (VIs) often face signal saturation at medium to high AGB levels. To address this, we proposed two polarization texture indices, i.e., Ratio SAR Texture Index (RSTI) and Normalized Difference SAR Texture Index (NDSTI), derived from Sentinel-1 (S-1) data to estimate crop AGB and height. We further investigated their integration with S-1 polarizations and Sentinel-2 (S-2) VIs using four machine learning algorithms to enhance retrieval performance. Results revealed that both RSTI and NDSTI outperformed individual polarizations, polarization texture features, and most of VIs in estimating crop AGB and height. Furthermore, the combination of these indices with S-2 VIs significantly improved the retrieval accuracy. The optimal models achieved R² values up to 0.75 and 0.80 for maize and soybean AGB, 0.89 and 0.94 for maize and soybean height, respectively. Validation with an independent dataset confirmed the robustness and transferability of the proposed models for estimating maize AGB and height. Overall, RSTI and NDSTI, along with their integration with optical VIs, provide an effective approach for improving crop AGB and height estimation for agricultural monitoring.