文献类型：期刊文献

英文题名：Modeling and Predictive Mapping of Soil Organic Carbon Density in a Small-Scale Area Using Geographically Weighted Regression Kriging Approach

作者：Liu, Tao[1,2];Zhang, Huan[2];Shi, Tiezhu[3,4,5,6]

第一作者：Liu, Tao

通讯作者：Zhang, H[1]

机构：[1]Henan Univ Econ & Law, Coll Resources & Environm, Zhengzhou 450002, Peoples R China;[2]Henan Agr Univ, Key Lab New Mat & Facil Rural Renewable Energy MO, Zhengzhou 450002, Peoples R China;[3]Shenzhen Univ, MNR Key Lab Geoenvironm Monitoring Great Bay Area, Shenzhen 518060, Peoples R China;[4]Shenzhen Univ, Guangdong Key Lab Urban Informat, Shenzhen 518060, Peoples R China;[5]Shenzhen Univ, Shenzhen Key Lab Spatial Smart Sensing & Serv, Shenzhen 518060, Peoples R China;[6]Shenzhen Univ, Sch Architecture & Urban Planning, Shenzhen 518060, Peoples R China

第一机构：河南财经政法大学资源与环境学院

通讯机构：[1]corresponding author), Henan Agr Univ, Key Lab New Mat & Facil Rural Renewable Energy MO, Zhengzhou 450002, Peoples R China.

年份：2020

卷号：12

期号：22

起止页码：1-12

外文期刊名：SUSTAINABILITY

收录：;Scopus(收录号：2-s2.0-85095985027);WOS:【SSCI(收录号:WOS:000594621500001)，SCI-EXPANDED(收录号:WOS:000594621500001)】；

基金：This research was funded by National Natural Science Foundation of China (No. 41801376); China Postdoctoral Science Foundation (No. 2020M682293); the National Special Research Fund for Non-Profit Sector (Agriculture) (No. 201303099); the Basic Research Program of Shenzhen Science and Technology Innovation Committee (No. JCYJ20170302144323219); Open Research Fund of state key laboratory of information engineering in surveying, mapping and remote sensing, Wuhan University (18S03); and Key Research Projects of Henan Higher Education Institutions (19A420004).

语种：英文

外文关键词：soil organic carbon; kriging; geographical weighted regression; spatial heterogeneity; spatial variation

摘要：Different natural environmental variables affect the spatial distribution of soil organic carbon (SOC), which has strong spatial heterogeneity and non-stationarity. Additionally, the soil organic carbon density (SOCD) has strong spatial varying relationships with the environmental factors, and the residuals should keep independent. This is one hard and challenging study in digital soil mapping. This study was designed to explore the different impacts of natural environmental factors and construct spatial prediction models of SOC in the junction region (with an area of 2130.37 km(2)) between Enshi City and Yidu City, Hubei Province, China. Multiple spatial interpolation models, such as stepwise linear regression (STR), geographically weighted regression (GWR), regression kriging (RK), and geographically weighted regression kriging (GWRK), were built using different natural environmental variables (e.g., terrain, environmental, and human factors) as auxiliary variables. The goodness of fit (R-2), root mean square error, and improving accuracy were used to evaluate the predicted results of the spatial interpolation models. Results from Pearson correlation coefficient analysis and STR showed that SOCD was strongly correlated with elevation, topographic position index (TPI), topographic wetness index (TWI), slope, and normalized difference vegetation index (NDVI). GWRK had the highest simulation accuracy, followed by RK, whereas STR was the weakest. A theoretical scientific basis is, therefore, provided for exploring the relationship between SOCD and the corresponding environmental variables as well as for modeling and estimating the regional soil carbon pool.