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《Geographical Research》 2001-06
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Spatial prediction of soil moisture content using multiple-linear regressions in a gully catchment of the Loess Plateau, China

QIU Yang 1,2, FU Bo-jie 2, WANG Jun 3, CHEN Li-ding 1 (1.Department of Systems Ecology, Research Center for Eco-Environmental Sciences, CAS, Beijing 100085, China; 2.Department of Resource and Environmental Sciences, Beijing Normal Univer  
The multiple-linear regression models with more readily observed environmental variables (land use and topography) were developed to spatially predict soil moisture content using six methods and their performances and cost-benefit were evaluated using 13 indices in Danangou catchment (3.5 km 2) in the loess area of China. Soil moisture measurements were performed biweekly at five depths in soil profile (0~5 cm, 10~15 cm, 20~25 cm, 40~45 cm and 70~75 cm) on 81 plots from May to September 1999 using time domain reflectometry (TDR). It is indicated that the 13 measured indices almost exhibit the similar conclusions. In terms of fitness, optimum, precision, outlier and cost-benefit, the with-attributes group models, including generalized multiple-linear regression models with environmental attributes (GMLRMs) and stepwise multiple-linear regression models with environmental variables (SMLRMs), were shown to be superior to those multiple-linear regressions models with linear transformation on environmental attributes by principal component analysis (PCA-based group models) and those regression models with nonlinear transformation by detrended correspondence analysis (DCA-based group models). Within each group models, the models using generalized-method or enter-method are better than those using stepwise-method are. However, such within-group differences are not so evident as that of inter-group. Among the six methods, the GMLRMs are the best in terms of fitness, optimum, precision and outlier based on the 11 performance indices, while the SMLRMs are most effective and economical according to the Akaike information criterion (AIC) and Schwarz or Bayesian information criterion (SIC) that can evaluate the cost-benefit of models.
【Fund】: 中国科学院知识创新工程项目 (KZCX2 - 40 5 );; 国家杰出青年科学基金资助项目 (4 972 5 10 1)
【CateGory Index】: F301.24
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