Integrating moisture-stratified correction with multi-source remote sensing for regional maize residue cover mapping

Accurate estimation of maize residue cover (MRC) at field to regional scales is essential for evaluating conservation tillage practices and quantifying soil carbon sequestration potential. However, optical remote sensing-based MRC retrieval is strongly affected by surface moisture variability, which alters spectral responses and exacerbates the spectral confusion between maize residues and bare soil, while single-source data lack sufficient discriminatory power to resolve this complexity. To address these limitations, we propose an integrated MRC estimation framework that combines moisture-stratified correction, multi-source feature fusion, and explainable ensemble learning. First, surface moisture heterogeneity is characterized using fuzzy C-means (FCM) clustering, enabling the partitioning of the landscape into moisture-consistent zones. Within each zone, the Kubelka-Munk (KM) radiative transfer model is employed to explicitly correct moisture-induced absorption effects in Sentinel-2 reflectance, thereby restoring the intrinsic residue spectral properties. Second, Sentinel-2 reflectance, Sentinel-1 SAR backscatter, and texture features are jointly integrated to enhance residue-soil separability. Finally, a Stacking ensemble model is constructed to fuse multi-source features, and SHAP analysis is used to interpret variable contributions and reveal the mechanisms underpinning model performance. Results demonstrate that the proposed FCM+KM framework substantially improves MRC estimation accuracy across heterogeneous moisture conditions, increasing R 2 from 0.46 to 0.83 and reducing RMSE from 0.14 to 0.08. The proposed framework provides a physics-informed and interpretable pathway for improving large-scale residue monitoring, particularly in regions where moisture variability constrains optical-based retrievals.