Terrain attributes and seasonal Sentinel-2 covariates for machine learning-based digital mapping of soil organic carbon fractions in a Himalayan watershed

Accurate spatial prediction of soil organic carbon (SOC) fractions is crucial for carbon accounting, mitigating climate change and sustainable land management of montane ecosystems. This study used four machine-learning (ML) models viz., Cubist, Random Forest (RF), boosted regression trees (BRT) and weighted k-nearest neighbours (wkNN) as part of a digital soil-mapping (DSM) system in the prediction of SOC, particulate organic carbon (POC), and dissolved organic carbon (DOC) across the Chattergul watershed, Ganderbal district, Kashmir. 157 surface soil samples at a depth of 0–15 cm with three covariate ensembles were studied, the terrain attributes based on SRTM DEM (DEM only), the terrain attributes based on DEM + summer Sentinel-2 spectral indices (DEM + SUM) and DEM + autumn Sentinel-2 indices (DEM + AUT). The values of the SOC were between 1.0% and 4.74% (mean 2.59%, SD 0.96%), and the mean values of POC and DOC were 1,421.21 mg kg − 1 and 20.49 mg kg − 1 respectively. Elevation, topographic position index (TPI), and valley depth were the dominant terrain predictors across all models. Cubist with DEM-only covariates achieved the best SOC prediction (RMSE = 0.73%; rRMSE = 28.2%; R² = 0.58). wkNN produced the most accurate POC predictions across all covariate sets (RMSE = 422 mg kg − 1 ; rRMSE = 29.7%; R 2 = 0.12) and DOC (RMSE = 1.29 mg kg − 1 ; rRMSE = 6.3%; R² = 0.58) across all covariate configurations. Moran’s I tests confirmed no significant spatial autocorrelation of model residuals ( p > 0.05; n = 157). These results demonstrate that terrain covariates alone suffice for SOC fraction mapping in this forest-dominated Himalayan catchment, while multi-temporal satellite imagery complements prediction of soil physico-chemical and biological properties, providing a reproducible and scalable multi-temporal framework for SOC monitoring and carbon-sequestration policy in humid Himalayan catchments.