Estimating soil organic carbon across a large-scale region: A state-space modeling approach

Soil organic carbon (SOC) plays a dynamic role in the global carbon cycle and is important in sustaining soil fertility and ecosystem productivity. Information about the spatial distribution of SOC across large-scale areas and its relationships with pertinent environmental factors is limited although required. In our study, a total of 283 sampling sites were investigated to estimate the spatial variation of SOC across the entire Loess Plateau (620,000 km) of China. Two strategies, state-space modeling and classical linear regression, were used to quantify the relationships between SOC and selected soil properties (bulk density, soil pH, and clay and silt contents) and climatic (precipitation and temperature) and topographic (elevation) variables. The best state-space models explained more than 80% of the variation of SOC, whereas the best linear regression model explained less than 45% of the variation of SOC. The results showed that all state-space models described spatial variation of SOC much better than the equivalent linear-regression models. Soil-based properties were more important than climatic and topographic variables in identifying localized variation of SOC; the best bivariate and multivariate state-space models included bulk density, silt content, and soil pH. The state-space models performed even better when only 50% of the SOC data were used. However, when using only 25% of the data, the state-space models marginally yielded good estimates of SOC. State-space modeling is recommended as a useful tool for quantifying the spatial relationships between SOC and other environmental factors in large-scale regions.