Remote Sensing and AI-Based Monitoring of Soil Properties for Tier-3 MRV Framework of Complex Mediterranean Agroforestry Systems

Résumé fourni par la source

Soil organic carbon (SOC) plays a critical role in climate regulation, soil fertility, and ecosystem resilience, making its accurate spatial quantification essential for sustainable land management and greenhouse gas (GHG) reporting. However, mapping SOC in heterogeneous agroforestry systems remains challenging due to vegetation cover and landscape complexity. In this study, we develop and evaluate a hybrid bare soil modelling- Digital Soil Mapping supported by ML framework to generate high-resolution soil properties predictions in Mediterranean agroforestry systems (Extremadura, Spain). A dual modelling approach was implemented, combining (i) Bare Soil modelling using Sentinel-2 multi-temporal reflectance composites and (ii) Digital Soil Mapping (DSM) supported by environmental covariates (climate, terrain, vegetation) following the SCORPAN framework. Machine learning models, namely Quantile Regression Forests (QRF) and Extreme Gradient Boosting (XGBoost), were applied and optimised using automated hyperparameter tuning (FLAML). A total of 107 LUCAS topsoil samples and 36 complementary points from the Forest ICP Level I were used for calibration and validation, with a 70/30 train–test split. Results show that Sentinel-2-based modelling can effectively capture SOC spatial variability in bare soil conditions, while DSM improves predictions in vegetated areas. Model performance reached R2 values up to 0.76 (QRF, pH) and RMSE as low as 0.03 (XGBoost, N), with uncertainty quantified using the Prediction Interval Ratio (PIR) and performance further supported by RPIQ values up to 3.15. However, prediction accuracy remains sensitive to vegetation structure and sample density. The proposed framework provides a scalable and uncertainty-aware approach for SOC mapping, supporting Tier-3 GHG inventories and emerging Monitoring, Reporting, and Verification (MRV) systems. The results highlight the importance of integrating multi-source datasets and hybrid modelling strategies for reliable SOC estimation in complex landscapes.

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