Spatially explicit measurement of Above Ground Biomass (AGB) is crucial for the quantification of forest carbon stock and fluxes. To achieve this, an integration of Optical and Synthetic Aperture Radar (SAR) satellite datasets could provide an accurate estimation of forest biomass. This will also help in removing the uncertainties associated with the single sensor-based estimation approaches. Therefore, the present study attempts to integrate Sentinel-2 optical data with Sentinel-1 SAR dataset to estimate AGB in the Shoolpaneshwar Wildlife Sanctuary (SWS), Gujarat, India. In this study, two non-parametric machine learning algorithms viz Support Vector Machines (SVMs) with different kernel functions—linear, sigmoidal, radial and polynomial and Random Forest (RF) were employed for the prediction of AGB using different combinations of VV, VH, Normalized Difference Vegetation Index (NDVI) and Incidence Angle (IA). Ground based AGB was estimated through allometric equation at 35 sampling sites with the help of tree height and Diameter at Breast’s Height (DBH). Standalone collinearity analysis among different parameters resulted in poor correlation of AGB with VH (r = 0.05) and IA (r = 0.015), whereas a significantly good correlation with NDVI (r = 0.80) and VV (r = 0.74) were observed. Inclusion of NDVI with VV and VH together also resulted in a better correlation (r = 0.85) than other combinations. The SVM with linear kernel utilizing parametric the combinations of VV + VH + NDVI and VV + VH + NDVI + IA were found to be best performing on the basis of evaluation metrics. The outcome of this study highlighted the significance of machine learning techniques and synergistic use of different remote sensing data for an improved AGB quantification in tropical forests.

地上生物量 (AGB) 的空间显式测量对于森林碳储量和通量的量化至关重要。为了实现这一目标，光学和合成孔径雷达 (SAR) 卫星数据集的集成可以提供对森林生物量的准确估计。这也将有助于消除与基于单一传感器的估计方法相关的不确定性。因此，本研究试图将 Sentinel-2 光学数据与 Sentinel-1 SAR 数据集相结合，以估计印度古吉拉特邦 Shoolpaneshwar 野生动物保护区 (SWS) 的 AGB。在这项研究中，使用两种非参数机器学习算法，即具有不同核函数的支持向量机 (SVM)——线性、S 型、径向和多项式以及随机森林 (RF)，使用 VV、VH 的不同组合来预测 AGB , 归一化差异植被指数 (NDVI) 和入射角 (IA)。借助树高和胸径 (DBH)，通过异速生长方程在 35 个采样点估算基于地面的 AGB。不同参数之间的独立共线性分析导致 AGB 与 VH (r = 0.05) 和 IA (r = 0.015) 的相关性较差，而观察到与 NDVI (r = 0.80) 和 VV (r = 0.74) 的相关性显着良好。将 NDVI 与 VV 和 VH 一起包含也导致比其他组合更好的相关性 (r = 0.85)。在评估指标的基础上，发现使用参数化 VV + VH + NDVI 和 VV + VH + NDVI + IA 组合的具有线性核的 SVM 表现最佳。