Unveiling the explanatory power of environmental variables in soil organic carbon mapping: A global–local analysis framework

• DOI: 10.1016/j.geoderma.2024.117011
• Publication Date: 2024-08-26T21:16:28Z
• Authors: Yujiao Wei; Yiyun Chen; Jiaxue Wang; Bo Wang; Peiheng Yu; Yongsheng Hong; Liandong Zhu
• Keywords: Soil carbon; Explanatory power

Soil organic carbon (SOC) is a critical component that affects soil quality and global cycling. Current SOC mapping approaches are based on the spatial stationarity relationship of formation processes. Nevertheless, pattern consequence different soil-forming factors processes operate at scales. In this work, we hypothesized covariation environmental variables might differ spatially, proposed (whole area) local analysis framework aimed to enhance our comprehension explanatory scale variation. This primarily incorporates Geographically Weighted correlation Multi-scale Regression (MGWR) model. With 216 farmland topsoil samples collected from Qilu Lake watershed in Yunnan Province, China (area 354 km2), explored both relationships between verify feasibility framework. Results showed power variation scale-dependent. Our revealed certain variables, which may explain variations SOC, often overlooked due their insignificant with (p > 0.05). For example, case study, porosity two landscape metrics characterize anthropogenic land use patterns can effectively SOC. They improved model performance MGWR, but not significant. The highlights necessity investigating scale.