Province-Scale Mapping of Cropland Plough Layer Thickness Using Crop Spectral Response Metrics and Multi-Source Environmental Covariates

Accurate prediction of plough layer thickness (PLT) in cropland is essential for soil quality assessment and sustainable land management, yet regional-scale PLT mapping remains challenging because PLT is a subsurface structural attribute that cannot be directly retrieved from surface spectral signals. This study developed an interpretable framework for province-scale mapping of cropland plough layer thickness (PLT) in Hubei Province, China, by integrating multi-source remote sensing observations, including Landsat 8 optical spectral bands, Sentinel-1 SAR backscatter, and crop dynamic spectral response metrics derived from multi-year Enhanced Vegetation Index (EVI) time series, together with topographic, climatic, soil physicochemical, and land-use variables. A total of 1926 cropland soil samples were used to train and validate random forest (RF), extreme gradient boosting (XGBoost), and Cubist models, while prediction uncertainty was quantified using 90% prediction intervals. The relative contributions of different environmental variable groups were assessed, and Shapley Additive Explanations (SHAP) were used to interpret key predictors. The all-variable scenario achieved the best overall performance, with RF showing the highest accuracy (R2 = 0.46; RMSE = 3.12 cm) and the narrowest prediction interval. Climatic and topographic factors dominated PLT spatial variability, whereas other variable groups provided complementary predictive information. These findings demonstrate the potential of integrating multi-source environmental data and interpretable machine learning for regional PLT mapping, and the mapped distribution of cropland PLT provides a spatial basis for cropland quality assessment and targeted soil management, although further improvements will require spatially explicit agricultural management information and more direct PLT-related predictors.