Spatial information of soil carbon storage at national and global level is essential for soil quality and environmental management. Improved knowledge on the amount and spatial distribution of the carbon stock in soils is crucial in estimating changes in the terrestrial carbon dynamics and management options for carbon-storing. A study was conducted to map the soil organic carbon stock (SOC) over 56,763 km² area of Western Ghats of south India using a digital soil mapping approach. Landsat data, terrain attributes, and bioclimatic variables were used as covariates. Equal-area quadratic splines were fitted to soil profile datasets to estimate soil organic carbon stock at six standard soil depths (0–5, 5–15, 15–30, 30–60, 60–100 and 100–200 cm) and Quantile Regression Forest (QRF) algorithm was used to predict the SOC stocks. Prediction of SOC stock was better for surface layer (R² = 31–43%) and the performance was decreasing with depth (R² = 7–21%). The modal performance was also compared with SoilGrids products. Although the spatial patterns were similar, the present predicted SOC maps outperformed SoilGrids products in terms of both R² and RMSE. The predicted total soil organic stock in the Western Ghats ranged from 7.1 kg m⁻² to 30.9 kg m⁻² and the total estimated SOC was 917 Tg. The present high resolution SOC maps help to assess and monitor the soil health and preparation of proper land use planning.

国家和全球层面的土壤碳储量空间信息对于土壤质量和环境管理至关重要。对土壤中碳储量的数量和空间分布的了解不断提高，对于估算陆地碳动态变化和储碳管理方案至关重要。进行了一项研究，以绘制56,763 km ²以上的土壤有机碳储量（SOC）的图印度南部西高止山脉地区使用数字土壤制图方法。Landsat数据，地形属性和生物气候变量被用作协变量。将等面积二次样条拟合到土壤剖面数据集，以估计六种标准土壤深度（0–5、5–15、15–30、30–60、60–100和100–200 cm）和分位数的土壤有机碳储量回归森林（QRF）算法用于预测SOC库存。对于表层，SOC储量的预测更好（R ²  = 31–43％），并且性能随深度而降低（R ²  = 7–21％）。模态性能也与SoilGrids产品进行了比较。尽管空间模式相似，但就R ²而言，目前的预测SOC映射均优于SoilGrids产品。和RMSE。西高止山脉的预测土壤总有机储量为7.1 kg m ^-2到30.9 kg m ^-2，总估计SOC为917 Tg。当前的高分辨率SOC地图有助于评估和监测土壤健康状况，并准备适当的土地使用计划。