Variation in Soil Organic Carbon Along an Altitudinal Gradient Across Different Aspects in the Timberline Zone of the Western Himalaya, India

The timberline region of the Western Himalaya is a crucial ecological transition area highly sensitive to climate changes, which significantly influence vegetation patterns, soil formation, and carbon dynamics. This study aimed to investigate the spatial and altitudinal changes in soil organic carbon (SOC) across different topographic orientations and to evaluate machine-learning models for spatial SOC prediction in the timberline ecotone (2100–3300 m) of the Kedarnath Wildlife Sanctuary. Through systematic random sampling across 100 m elevation bands, composite soil samples were collected from three aspects (North-East, South-West, and North-West) at two depths (0–15 cm and 15–30 cm) and analyzed alongside topographic, spectral, and climatic covariates. Results indicated that SOC trends varied significantly by aspect; the North-East aspect exhibited a considerable increase in SOC with elevation, while the North-West and South-West sides responded differently. Furthermore, Digital Soil Mapping using the Random Forest (RF) model outperformed Support Vector Machine and XGBoost, explaining 62% of surface and 74% of subsurface SOC variability. In conclusion, aspect-induced microclimatic gradients play a major role in controlling soil characteristics near the Himalayan timberline, and machine-learning models like RF can successfully capture these complex spatial patterns. Consequently, it is recommended that the established baseline SOC maps be utilized as reference points for future climate-change monitoring, carbon accounting, and directing conservation strategies in high-altitude forests.