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Soil moisture assessment through the SSMMI and GSSIM algorithm based on SPOT, WorldView-2, and Sentinel-2 images in the Daliuta Coal Mining Area, China

Environmental Monitoring and Assessment volume 192, Article number: 237 (2020) Cite this article

Abstract

A set of indicators that focus only on numerical values is constructed based on remotely sensed images to assess soil moisture conditions. The quantitative evaluation of soil moisture variation in two periods is rarely referred to in the current literature. In this study, a scaled soil moisture monitoring index (SSMMI) was established to monitor the soil moisture status during 2010–2018 in the Daliuta Coal Mining Area (DCMA), China, based on SPOT-5, SPOT-6, and Sentinel-2 images. We also employed a gradient-based structural similarity (GSSIM) algorithm to quantitatively analyze the characteristics of the spatial distribution of the soil moisture in the DCMA. The optimal scale for exploring the spatial heterogeneity of the soil moisture was determined by local variance and semivariance methods. The results showed that the soil moisture decreased at a rate of 0.0213/a from 2010 to 2018. The areas with the extremely dry and dry levels, which were mainly located in the northwest, some regions of the central area, and the southeast of the DCMA, decreased from 14.48% in 2010 to 13.66% in 2018. The proportion of the no dry level was improved by 14.62%, while the area of the extremely wet and wet levels decreased by 13.79%. The mean value of the soil moisture in the unmined area was greater than that in the DCMA, which was larger than that in the mined area. The result of the GSSIM analysis indicated that the area of dramatic change, where the soil moisture changed substantially, was chiefly distributed in the north, west, some central regions, and some parts of the south and east of the DCMA. The region where the substantial change occurred was surrounded by a moderate-change area, which was encompassed by a low-change area. The area with dramatic and moderate decreases in the soil moisture accounted for 64.52% of the region, which was greater than that with incremental soil moisture changes, which accounted for 5.85% of the region. The area also showed decreased soil moisture from 2010 to 2018. Soil moisture changes are closely related to variations in land cover. For instance, vegetative cover over an open-pit mining area can cause a dramatic reduction in soil moisture. Ninety-three meters was the optimal scale used for monitoring the soil moisture in the DCMA, which indicates that we can adopt the SPOT-5, SPOT-6, and Sentinel-2 images to evaluate the soil moisture conditions in the DCMA.

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Funding

This work was jointly supported by the National Natural Science Foundation of China (41401496); Xi’an University of Science and Technology (2019YQ3-04); Key Laboratory of Mine Geological Hazards Mechanism and Control (6000180096 and KF2018-04).

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Affiliations

  1. College of Geomatics, Xi’an University of Science and Technology, Xi’an, 710054, People’s Republic of China

    Hui Yue, Ying Liu & Jiaxin Qian

  2. Key Laboratory of Mine Geological Hazards Mechanism and Control, Xi’an, 710054, China

    Hui Yue & Ying Liu

Authors
  1. Hui Yue
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  2. Ying Liu
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  3. Jiaxin Qian
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Contributions

Hui Yue and Ying Liu conceived the idea and wrote the paper; Jiaxin Qian helped with the algorithm.

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Correspondence to Ying Liu.

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Yue, H., Liu, Y. & Qian, J. Soil moisture assessment through the SSMMI and GSSIM algorithm based on SPOT, WorldView-2, and Sentinel-2 images in the Daliuta Coal Mining Area, China. Environ Monit Assess 192, 237 (2020). https://doi.org/10.1007/s10661-020-8174-9

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  • DOI: https://doi.org/10.1007/s10661-020-8174-9

Keywords

  • Soil moisture
  • Scaled soil moisture monitoring index
  • Gradient-based structural similarity
  • Optimal scale
  • Daliuta coal mining area