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

作者: Wei, YJ (Wei, Yujiao); Chen, YY (Chen, Yiyun); Wang, JX (Wang, Jiaxue); Wang, B (Wang, Bo); Yu, PH (Yu, Peiheng); Hong, YS (Hong, Yongsheng); Zhu, LD (Zhu, Liandong)

来源出版物: GEODERMA  卷: 449  文献号: 117011  DOI: 10.1016/j.geoderma.2024.117011  Early Access Date: AUG 2024  Published Date: 2024 SEP  

摘要: Soil organic carbon (SOC) is a critical component that affects soil quality and global carbon cycling. Current SOC mapping approaches are based on the spatial stationarity relationship of SOC and soil formation processes. Nevertheless, the spatial pattern of SOC is the consequence of different soil-forming factors and processes that operate at different scales. In this work, we hypothesized that the covariation of environmental variables and SOC might differ spatially, and proposed a global (whole area) and local analysis framework that aimed to enhance our comprehension of the explanatory scale of environmental variables on SOC variation. This framework primarily incorporates Geographically Weighted correlation and the Multi-scale Geographically Weighted Regression (MGWR) model. With 216 farmland topsoil samples collected from the Qilu Lake watershed in Yunnan Province, China (area of 354 km(2)), we explored both global and local relationships between environmental variables and SOC to verify the feasibility of this framework. Results showed that the explanatory power of environmental variables on SOC variation is scale-dependent. Our analysis revealed that certain variables, which may explain local variations of SOC, are often overlooked due to their insignificant global correlation with SOC (p > 0.05). For example, in this case study, soil porosity and two landscape metrics that characterize the anthropogenic processes of land use patterns can effectively explain the local spatial variation of SOC. They improved the model performance of MGWR, but their global correlation with SOC is not significant. The proposed framework highlights the necessity of investigating the explanatory power of environmental variables on a global and local scale.

作者关键词: Soil organic carbon; Environmental variable; Geographically weighted correlation; Multi-scale geographically weighted regression; Global and local explanatory power

KeyWords Plus: LOESS PLATEAU; IMPACTS; HETEROGENEITY; PATTERNS; MOISTURE; STORAGE; WATER

地址: [Wei, Yujiao; Chen, Yiyun; Wang, Jiaxue; Wang, Bo; Zhu, Liandong] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

[Chen, Yiyun] Minist Nat Resources, Key Lab Digital Cartog & Land Informat Applicat En, Wuhan 430079, Peoples R China.

[Chen, Yiyun] Wuhan Univ, Hubei Luojia Lab, Wuhan 430079, Peoples R China.

[Yu, Peiheng] Hong Kong Polytech Univ, Res Inst Sustainable Urban Dev RISUD, Dept Bldg & Real Estate, Hong Kong 999077, Peoples R China.

[Yu, Peiheng] Zhejiang Univ, Coll Environm & Resource Sci, Hangzhou 310058, Peoples R China.

[Hong, Yongsheng] Chinese Acad Sci, Inst Soil Sci, State Key Lab Soil & Sustainable Agr, Nanjing 210008, Peoples R China.

[Hong, Yongsheng] Univ Chinese Acad Sci, Beijing 100049, Peoples R China.

通讯作者地址: Chen, YY (通讯作者)，Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

Chen, YY (通讯作者)，Minist Nat Resources, Key Lab Digital Cartog & Land Informat Applicat En, Wuhan 430079, Peoples R China.

Chen, YY (通讯作者)，Wuhan Univ, Hubei Luojia Lab, Wuhan 430079, Peoples R China.

电子邮件地址: chenyy@whu.edu.cn

影响因子：5.6