Random Forest Outperforms SVM and SGB in Estimating Shorea robusta Biomass Using Sentinel‐2 Indices in the Terai Region of Nepal

Accurate estimation of above‐ground tree biomass (AGTB) is vital for sustainable forest management and climate change mitigation. This study evaluates the performance of three machine learning algorithms (MLAs): support vector machine (SVM), random forest (RF), and stochastic gradient boosting (SGB) for predicting AGTB in Shorea robusta (Sal) forests of Nepal’s Terai region. Using field data from 198 sample plots and 11 Sentinel‐2 derived vegetation indices, models were calibrated and validated. RF outperformed SVM and SGB, achieving the lowest root mean square error (RMSE = 68.36 t ha −1 ), highest correlation coefficient ( r = 0.61), and coefficient of determination ( R 2 = 0.38). Predicted AGTB ranged from 33.93 to 567.03 t ha −1 , reflecting the forest’s heterogeneity. Variable importance analysis revealed normalized difference vegetation index (NDVI) as critical for SVM/SGB, while Modified Chlorophyll Absorption Ratio Index (MCARI) was key for RF. This study establishes RF as the most robust MLA for AGTB mapping in Sal forests, providing a scalable framework for enhancing Nepal’s forest carbon monitoring and supporting Reducing Emissions from Deforestation and Forest Degradation (REDD+) initiatives. Although the predictive accuracy was moderate, this study demonstrates the challenges of biomass estimation in Nepal’s highly heterogeneous Sal forests and provides a replicable framework under data‐limited conditions.