文献类型： 外文期刊

作者： Ma, Ligang ¹ ; Ma, Fenglan ³ ; Li, Jiadan ⁴ ; Gu, Qing ⁵ ; Yang, Shengtian ¹ ; Wu, Di ⁶ ; Feng, Juan ¹ ; Ding, Jianli ¹ ;

作者机构： 1.Xinjiang Univ, Coll Resource & Environm Sci, Shengli Rd 666, Urumqi 830046, Peoples R China

2.Minist Educ, Lab Oasis Ecosyst, Urumqi 830046, Peoples R China

3.Chinese Acad Sci, State Key Lab Desert & Oasis Ecol, Xinjiang Inst Ecol & Geog, Urumqi 830011, Peoples R China

4.Ningbo Acad Agr Sci, Inst Rural Dev & Informat, Ningbo 315040, Zhejiang, Peoples R China

5.Zhejiang Acad Agr Sci, Inst Digital Agr, Hangzhou 310021, Zhejiang, Peoples R China

6.Second Surveying & Mapping Inst Zhejiang Prov, Hangzhou 310012, Zhejiang, Peoples R China

关键词： Empirical mode decomposition;Remote sensing;Correlation;Coupling;Random Forest

期刊名称：GEODERMA （ 影响因子：6.114； 五年影响因子：6.183 ）

ISSN：

年卷期：

页码：

收录情况： SCI

摘要： Soil spatial variations are scale dependent and can be controlled by many environmental factors. Numerous factors have been related to variations of soil salinity, some of them combined empirical mode decomposition (EMD) method and correlation analysis. However, environmental factors that essentially affect soil-water balance are not given enough attention. In addition, further analysis is needed in exploring how well the environmental factors can interpret the variations in soil salinity at different scales, especially in arid oasis areas and at large scales. This paper explores the potential of modeling variations in soil salinity via the EMD and Random Forest modeling of remote sensing based environmental factors. A case study is presented for Tarim basin, Xinjiang, China, using land surface temperature (LST), evapotranspiration (ET), TRMM precipitation (TRM) and digital elevation model (DEM) products. Soil salinity and its decompositions were first correlated with environmental factors for feature selection. Then, those selected environmental factors and their decompositions were correlated and coupled with their counterparts of soil salinity to evaluate their synchronization. Finally, those IMF components of environmental factors that had high correlation coefficients and were coupled well with corresponding IMF components of soil salinity were identified and divided into different feature sets for modeling. Mean absolute error and mean bias error were adopted for accuracy assessment of the models. Our results indicate that soil salinity series can be separated into eight scales ranging from 170 km to 480 km. IMF components 5-7 account for most of the variation and can be modeled using the corresponding IMF components of different combinations of DEM, ET, LST and TRM. IMF components 6-7 are well coupled with LST and ET at approximately 475 km scale. Overall, regional-scale modeling of variations in soil salinity based on remote sensing products is possible. Reasonably accurate results can be obtained in arid oasis areas where researchers and policy makers must focus on preventing the loss of agricultural productivity and ecosystem health.