Digital soil mapping of soil organic carbon stocks in Western Ghats, South India

• Published in: Geoderma Regional Vol. 25; p. e00387
• Main Authors: Dharumarajan, S., Kalaiselvi, B., Suputhra, Amar, Lalitha, M., Vasundhara, R., Kumar, K.S. Anil, Nair, K.M., Hegde, Rajendra, Singh, S.K., Lagacherie, Philippe
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.06.2021; Elsevier
• Subjects: Agricultural sciences; algorithms; carbon sequestration; carbon sinks; Cross validation; data collection; Digital soil mapping; India; Landsat; landscapes; Life Sciences; Multiple soil classes; prediction; Quantile Regression Forest; regression analysis; SOC stock; soil organic carbon; soil profiles; soil quality; Soil study; spatial data; Western Ghats; India; Quantile Regression Forest; SOC stock; Western Ghats; Cross validation; Multiple soil classes; Digital soil mapping
• DOI: 10.1016/j.geodrs.2021.e00387
• ISSN: 2352-0094

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. •SOC stock of Western Ghats was mapped using digital soil mapping approach.•Quantile Regression Forest algorithm was used to predict the SOC stocks and uncertainty.•Prediction performance was better for surface (R2 = 31–43%) than the sub-surface layer.•The predicted total SOC stock in the Western Ghats ranged from 7.1 kg m-2 to 30.9 kg m-2.