标题：Comparing geospatial techniques to predict SOC stocks作者：Liu, Yaolin; Guo, Long; Jiang, Qinghu; Zhang, Haitao; Chen, Yiyun
来源出版物：SOIL & TILLAGE RESEARCH 卷：148 页：46-58 DOI：10.1016/j.still.2014.12.002 出版年：MAY 2015
摘要：Soil organic carbon density (SOCD) has strong spatial variability and dependency, and its impact factors vary with changes in scale and geographic location. With 272 topsoil samples (0-30 cm) collected from Chahe Town in the Jianghan Plain, China, we (i) investigated the impacts of environmental variables and land cover types on the spatial distribution of SOCD; (ii) estimated the spatial distribution of SOCD by using global and local spatial interpolation models, including geographically weighted regression kriging (GWRK), regression kriging, geographically weighted regression (GWR), multiple linear regression (MLR), and ordinary kriging; and (iii) used mean absolute estimation error (MAEE), mean relative error (MRE), root mean square error (RMSE) and Pearson's correlation coefficients (r) to evaluate the performance of these models. SOCD was significantly correlated with elevation, normalized difference moisture index (NDMI), and the nearest distance to road (TRD) and residential area (p入藏号：WOS:000349585300006
文献类型：Article
语种：English
作者关键词：Soil organic carbon density, Spatial variation, Spatial dependency, Geographically weighted regression, Regression kriging, Geographically weighted regression kriging
扩展关键词：SOIL ORGANIC-CARBON; GEOGRAPHICALLY WEIGHTED REGRESSION; URBAN HEAT-ISLAND; SPATIAL INTERPOLATION; REGIONAL-SCALE; SW POLAND; CHINA; MATTER; MODEL; VARIABILITY
通讯作者地址：Chen, Yiyun; 129 Luoyu Rd, Wuhan 430079, Peoples R China.
电子邮件地址：kellypcyy@126.com
地址：
[Liu, Yaolin; Guo, Long; Jiang, Qinghu; Chen, Yiyun] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.
[Liu, Yaolin] Wuhan Univ, Minist Educ, Key Lab Geog Informat Syst, Wuhan 430079, Peoples R China.
[Zhang, Haitao] Huazhong Agr Univ, Coll Resources & Environm, Wuhan 430070, Peoples R China.
[Chen, Yiyun] Wuhan Univ, Suzhou Inst, Suzhou 215123, Peoples R China.
研究方向：Agriculture
ISSN：0167-1987
eISSN:1879-3444
