Multi-Source Remote Sensing for large-scale biomass estimation in mediterranean olive orchards using GEDI LiDAR and Machine Learning

Abstract. Accurate estimation of Above-Ground Biomass Density (AGBD) is essential for assessing carbon stocks and promoting sustainable agricultural practices. This study integrates multi-source remote sensing data, including GEDI LiDAR, optical, SAR, and topographic variables, to predict AGBD in Mediterranean olive orchards using a Random Forest regression model implemented on Google Earth Engine (GEE). The volumetric approach, based on GEDI L2A canopy height and dendrometric parameters, provided more accurate predictions than the GEDI L4A product, which is limited by its global stratification methodology. The model’s predictive performance varied depending on data combinations, with the fully multi-source configuration achieving the highest accuracy (R² = 0.62, RMSE = 5.95 Mg·ha⁻¹). NDWI, slope, and NDVI were identified as the most influential predictors. The spatial analysis revealed that Spain exhibited the highest total AGBD among the studied countries, followed by Italy and Greece, reflecting their dominance in olive production. The model effectively captured biomass variability across different regions, demonstrating its suitability for large-scale applications. This study highlights the potential of integrating LiDAR, optical, and SAR data for biomass estimation, offering a scalable and cost-effective approach for monitoring carbon stocks and optimizing agricultural resource management. By providing accurate AGBD predictions, this methodology supports climate-smart agriculture and facilitates data-driven decision-making for both farmers and policymakers, contributing to the advancement of sustainable agricultural systems in Mediterranean olive orchards.