A multi-factor weighted regression approach for estimating the spatial distribution of soil organic carbon in grasslands

Abstract

Soil organic carbon (SOC) plays an important role in global carbon cycling and is increasingly important to the ecosystem. An accurate SOC content map would significantly contribute to the proper application of ecological modeling. Therefore, there is a need to accurately estimate and map SOC content in grasslands. We evaluated various methods for estimating the SOC content of grasslands using field soil sampling data and auxiliary data in the pastoral area. The results showed that (1) SOC is affected by various factors, including geographic location, soil, topography, and climate. Single-variable SOC models account for 2%–72% of the variations in the grassland SOC. (2) Based on the correlation of environmental variables of SOC, normalized difference vegetation index, annual precipitation, annual average temperature, elevation, and moisture index were explored as critical auxiliary data to predict SOC content. We established multi-factor weighted regression model (MWRM). (3) We compared three spatial estimation methods, including inverse distance weighting, regression kriging, and MWRM, to determine a suitable method for SOC mapping. Our results indicate that among the three spatial estimation methods, MWRM provides the lowest prediction error (RMSE = 4.85 g/kg; MAE = 3.47 g/kg; MRE = 24.04%) and highest R2 (0.89) and Lin's concordance (0.94) values in the spatial estimation at a 0–10 cm soil layer. (4) Therefore, we applied MWRM to predict SOC content at various layers, and its SOC content prediction in the topsoil (0–20 cm) is better than that in the subsurface (20–30 cm) and subsoil (30–40 cm). The SOC content spatial distribution demonstrated a similar pattern for each soil layer and the SOC content gradually decreased with increasing soil depth.