Aboveground Biomass Estimation in Pure Calabrian Pine Stands of Southern Türkiye Using Multi-Seasonal Sentinel-2 Imagery and Topographic Variables: A Multi-Seasonal Comparison

Aboveground biomass (AGB) is considered a key variable in surveilling the sustainability of forest management and the carbon cycle. This study examined the effects of image acquisition season and topographic variables on the performance of AGB estimates using Sentinel-2 imagery from three seasons. The study was conducted in 224 sample plots in pure Calabrian pine (Pinus brutia) stands in southern Türkiye. Random forest models were trained on 26 datasets derived from rainy, arid, and semi-arid season imagery, including single-season, two-season, and three-season combinations with and without topographic variables. Models trained using satellite images from rainy and semi-arid seasons performed better than models trained using data from the more arid season. All two-season combinations outperformed the corresponding single-season models, but none reached the accuracy of the three-season model. Including topographic variables reduced RMSE for every season and season combination. The best results were obtained using all three seasons together with topographic variables (R2 = 0.74, RMSE = 13.97 Mg ha−1), and removing topography from this model increased RMSE to 15.69 Mg ha−1. According to the SHAP analysis, elevation was the second-most influential predictor after Aug_SAVI (mean SHAP value = 1.65 Mg ha−1) and the only topographic variable that notably contributed to predictive performance. These findings indicate that, for the pure Calabrian pine stands and conditions examined in this study, combining multi-seasonal Sentinel-2 imagery with topographic variables improves AGB estimation accuracy compared with single-season models and spectral data alone.