Explainable Ensemble Learning Framework for Surface Soil Moisture Retrieval in Sunflower Fields Using Sentinel-1 SAR and Sentinel-2 Fusion
2026 IEEE Mediterranean and Middle-East Geoscience and Remote Sensing Symposium, M2GARSS 2026, Hybrid, Marrakech, Fas, 22 - 24 Nisan 2026, ss.194-198, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1109/m2garss67833.2026.11582619
- Basıldığı Şehir: Hybrid, Marrakech
- Basıldığı Ülke: Fas
- Sayfa Sayıları: ss.194-198
- Anahtar Kelimeler: Machine Learning, Remote Sensing, SAR, Soil Moisture, Sunflower
- Yıldız Teknik Üniversitesi Adresli: Evet
Özet
Accurate surface soil moisture retrieval is essential for precision agriculture, yet challenging in broad-leaved crops like sunflowers due to canopy attenuation. This study proposes an explainable stacked ensemble learning framework using Sentinel-1 SAR and Sentinel-2 optical data for surface SM retrieval in Zile, Türkiye. To address the common issue of spatial data leakage in agricultural machine learning, a strict parcel-based GroupKFold cross-validation strategy was implemented. An ensemble of Random Forest, LightGBM and XGBoost, optimized via Optuna, achieved the best performance (R2=0.711, RMSE =8.447%). Crucially, SHAP interpretability analysis revealed that temporal features acted as the dominant predictors over SAR backscatter, reflecting the strong continental drying cycle. These findings emphasize the importance of rigorous spatial validation and interpretability when applying machine learning to regional crop monitoring.