Abstract
Abstract. Accurate mapping of soil carbon in low relief areas is of great challenge because of the defect of conventional “soil-landscape” model. Efforts have been made to integrate the land use information in the modelling and mapping of soil organic carbon (SOC), in which the spatial context was ignored. With 256 topsoil samples collected from Jianghan Plain, we aim to (i) explore the land-use dependency of SOC via one-way ANOVA; (ii) investigate the “spillover effect” of land use on SOC content; (iii) examine the feasibility of land use types and percentages (obtained with a 200-meter buffer) for soil mapping via regression Kriging (RK) models. Results showed that the SOC of paddy fields was higher than that of woodlands and irrigated lands. The land use type could explain 20.5 % variation of the SOC, and the value increased to 24.7 % when the land use percentages were considered. SOC was positively correlated with the percentage of water area and irrigation canals. Further research indicated that SOC of irrigated lands was significantly correlated with the percentage of water area and irrigation canals, while paddy fields and woodlands did not show similar trends. RK model that combined land use types and percentages outperformed the other models with the lowest values of RMSEC (5.644 g/kg) and RMSEP (6.229 g/kg), and the highest R2C (0.193) and R2P (0.197). In conclusions, land use types and percentages serve as efficient indicators for the SOC mapping in plain areas. Additionally, irrigation facilities contributed to the farmland SOC sequestration especially in irrigated lands.
Highlights
Soil organic carbon (SOC) content is an essential indicator of soil productivity, adequate SOC content contributes to plant growth and water and soil conservation (Rasool et al, 2008)
The results indicated that the percentage of irrigated lands was negatively correlated with SOC when only considering land use percentages
There was no contradiction between types and percentages: when combined with the land use types and percentages, the R2 of regression was further improved to 24.7%
Summary
Soil organic carbon (SOC) content is an essential indicator of soil productivity, adequate SOC content contributes to plant growth and water and soil conservation (Rasool et al, 2008). Disturbed by the complex process of soil formation and the intensive human activities, the distribution of SOC content exists spatial heterogeneity, which brings serious challenges for SOC mapping. Ordinary Kriging (OK) method has been widely used for spatial prediction of soil properties in those areas with similar landscape patterns (Robinson and Metternicht, 2006; Shahbeik et al, 2014). The prediction accuracy of OK method decreases as the soil properties are strongly interfered by complex terrain and human activities (Liu et al, 2006). In low relief areas, conventional soil-landscape model, in which topography derived factors play an important role, performed poorly due to the low relief amplitude (Zhao et al, 2014; Zhu et al, 2010). Finding feasible explanatory variables becomes an important direction of current research
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