Comparison of GIS-based interpolation methods for spatial distribution of soil organic carbon (SOC)

TLDR

Accurate interpolation of soil organic carbon (SOC) spatial patterns is recognized for its ecological, economic, and agricultural benefits. This study compares GIS-based interpolation techniques for estimating SOC variation at three soil depths in Medinipur Block, West Bengal, India. The authors sampled 98 soils from diverse land‑use sites, recorded GPS coordinates, applied five GIS interpolation methods (IDW, LPI, RBF, OK, EBK), and evaluated accuracy using cross‑validation metrics (R² and RMSE). SOC is highest in forest land and lowest in bare land, and ordinary kriging outperforms the other methods with the lowest RMSE and highest R².

Abstract

The ecological, economical, and agricultural benefits of accurate interpolation of spatial distribution patterns of soil organic carbon (SOC) are well recognized. In the present study, different interpolation techniques in a geographical information system (GIS) environment are analyzed and compared for estimating the spatial variation of SOC at three different soil depths (0–20 cm, 20–40 cm and 40–100 cm) in Medinipur Block, West Bengal, India. Stratified random samples of total 98 soils were collected from different landuse sites including agriculture, scrubland, forest, grassland, and fallow land of the study area. A portable global positioning system (GPS) was used to collect coordinates of each sample site. Five interpolation methods such as inverse distance weighting (IDW), local polynomial interpolation (LPI), radial basis function (RBF), ordinary kriging (OK) and Empirical Bayes kriging (EBK) are used to generate spatial distribution of SOC. SOC is concentrated in forest land and less SOC is observed in bare land. The cross validation is applied to evaluate the accuracy of interpolation methods through coefficient of determination (R2) and root mean square error (RMSE). The results indicate that OK is superior method with the least RMSE and highest R2 value for interpolation of SOC spatial distribution.