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Modeling variations in soil salinity in the oasis of Junggar Basin, China

文献类型： 外文期刊

作者： Ma, Ligang 1 ; Yang, Shengtian 1 ; Simayi, Zibibula 1 ; Gu, Qing 4 ; Li, Jiadan 5 ; Yang, Xiaodong 1 ; Ding, Jianli 1 ;

作者机构： 1.Xinjiang Univ, Gen Coll Key Lab Smart City & Environm Modeling, Coll Resource & Environm Sci, Shengli Rd 666, Urumqi 830046, Xinjiang, Peoples R China

2.Xinjiang Univ, Minist Educ, Key Lab Oasis Ecol, Urumqi 830046, Xinjiang, Peoples R China

3.Beijing Normal Univ, Coll Water Sci, Beijing 100875, Peoples R China

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

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

关键词： control; coupling; empirical mode decomposition; remote sensing; scale

期刊名称：LAND DEGRADATION & DEVELOPMENT （ 影响因子：4.977； 五年影响因子：5.291 ）

ISSN： 1085-3278

年卷期： 2018 年 29 卷 3 期

页码：

收录情况： SCI

摘要： Soil salinization leads to a significant degradation for oasis land. Variations in soil salinity are controlled by geologic, geomorphic, climatic, and hydrologic factors that are scale dependent. Many factors characterize soil salinity and its variations. Empirical mode decomposition (EMD) and correlation analysis are usually applied to examine the variations of soil salinity at different scales. However, few researches have been conducted on the modeling of these variations among different scales that requires further development and refinement. This paper investigates the potential of assessing scale-specific variations in soil salinity via EMD using 2 modeling approaches (random forest and linear models). The remotely sensed environmental factors including land surface temperature, evapotranspiration, Tropical Rainfall Measuring Mission precipitation, and digital elevation model products were used as inputs for the models. The Junggar Basin in Xinjiang Province of China was selected as study area because the oasis was quite typical in the whole country and even in mid-Asia. Soil salinity data and environmental factors were first decomposed using the EMD algorithm. Then, the decomposed components of the remotely sensed environmental factors were evaluated, and the most important components of each factor were selected to model the salinity variations that were represented by the decomposed components of soil salinity. The salinity variations estimated from the environmental factors were favorably consistent with the decomposed components of the soil salinity data, with coefficients of determination (R-2) ranging from 0.24-0.52 and 0.73-0.98 for the linear and random forest models, respectively. In addition, land surface temperature and salinity were coupled well at the 326- and 334-km scale. Our results showed that reasonably accurate results can be obtained using the proposed approach.

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