Research Papers

Modeling shallow groundwater levels in Horqin Sandy Land, North China, using satellite-based remote sensing images

[+] Author Affiliations
Yan Yan

State Key Laboratory of Forest and Soil Ecology, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110164, China

Liaoning Key Laboratory for Management of Noncommercial Forests, Shenyang 110016, China

University of Chinese Academy of Sciences, Beijing 100049, China

Jiaojun Zhu

State Key Laboratory of Forest and Soil Ecology, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110164, China

Liaoning Key Laboratory for Management of Noncommercial Forests, Shenyang 110016, China

Qiaoling Yan

State Key Laboratory of Forest and Soil Ecology, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110164, China

Liaoning Key Laboratory for Management of Noncommercial Forests, Shenyang 110016, China

Xiao Zheng

State Key Laboratory of Forest and Soil Ecology, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110164, China

Liaoning Key Laboratory for Management of Noncommercial Forests, Shenyang 110016, China

Lining Song

State Key Laboratory of Forest and Soil Ecology, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110164, China

Liaoning Key Laboratory for Management of Noncommercial Forests, Shenyang 110016, China

J. Appl. Remote Sens. 8(1), 083647 (Apr 10, 2014). doi:10.1117/1.JRS.8.083647
History: Received October 10, 2013; Revised February 21, 2014; Accepted March 17, 2014
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Abstract.  The objective of this study is to establish a method using remote sensing and inverse modeling techniques to rapidly determine the groundwater levels in Horqin Sandy Land, North China. First, the tasseled cap wetness (TCW) data derived from Landsat images with the corresponding soil water content (SWC) via field investigations were processed, and their statistical relationships were established. The determination coefficient of the linear regression was 0.72, indicating a good agreement between the TCW and SWC data. Second, the principles of how groundwater affected the near-surface soil moisture are discussed. The critical condition that the groundwater could seep upward through capillaries to the near-surface was applied to the relationship between the SWC and the groundwater levels. Finally, the relationship between the TCW and the groundwater levels was established and an empirical inverse model was developed. The results were verified using 82 groundwater level samples obtained by observation wells and vertical electrical sounding methods. The determination coefficient between the groundwater levels derived from the empirical model and the field measurements was 0.80, demonstrating that the inverse model closely reflected the actual groundwater levels. The established method could be used to rapidly determine the shallow groundwater levels of the study area with reliable results and may be applicable to aeolian desert areas with low vegetation cover.

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Citation

Yan Yan ; Jiaojun Zhu ; Qiaoling Yan ; Xiao Zheng and Lining Song
"Modeling shallow groundwater levels in Horqin Sandy Land, North China, using satellite-based remote sensing images", J. Appl. Remote Sens. 8(1), 083647 (Apr 10, 2014). ; http://dx.doi.org/10.1117/1.JRS.8.083647


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