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Remote Sensing for Natural Resources    2021, Vol. 33 Issue (4) : 235-242     DOI: 10.6046/zrzyyg.2020418
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The remote sensing-based estimation and spatial-temporal dynamic analysis of SOM in coal mining
GAO Wenlong1(), ZHANG Shengwei1,2,3(), LIN Xi1, LUO Meng1, REN Zhaoyi1
1. College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
2. Key Laboratory of Protection and Utilization of Water Resources of Inner Mongolia Atuonomous Region, Hohhot 010018, China
3. Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry, Hohhot 010018, China
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Abstract  

Soil is the largest potential reservoir of carbon, and the content of soil organic matter (SOM) is the key influencing factor of soil carbon storage. Therefore, SOM is an important index in the analysis of the changes in soil carbon storage. This paper aims to understand the optimal response bands in spectra to the SOM content in the process of coal mining and the changes in the temporal-spatial dynamic patterns of the SOM in a whole coal mining area. Based on the linear regression analysis of measured SOM, near-earth hyperspectral reflectance, and satellite multispectral reflectance, the SOM changes in the study area on June 1, July 4, and September 21, 2019 were quantitatively analyzed, and the SOM changes in underground coal mines (named Dahaize, Balasu, Nalinhe 2, and Yingpanhao) and their surrounding river basins were monitored. The SOM inversion results obtained using the first-order differential transformation of the near-earth hyperspectral reflectance were the closest to the measured SOM. A regression inversion model was established based on the extracted hyperspectral and multispectral characteristic bands and their correlation with the SOM. As indicated by the precision verification results, the correlation between the values predicted through SOM reversion and measured SOM values reached 0.90. Meanwhile, the SOM content in the study area was high in the east and low in the west and it gradually decreased along the upper, middle, and lower reaches of rivers and estuaries. The SOM content obtained through pre-mining simulation was 5% higher than that acquired via remote sensing-based estimation, indicating that coal mining affects the SOM content to a certain extent. It is also proven that the linear regression model of SOM inversion has the prospect of wide application. The above results will provide bases for quantitative research, management, and sustainable development of soil resources and ecological environment in the study area.
Keywords hyperspectral images      soil organic matter(SOM)      coal mine      soil moisture content      hyperspectral remote sensing     
ZTFLH:  TP79S15  
Corresponding Authors: ZHANG Shengwei     E-mail: gao19950723@126.com;zsw@imau.edu.cn
Issue Date: 23 December 2021
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Wenlong GAO
Shengwei ZHANG
Xi LIN
Meng LUO
Zhaoyi REN
Cite this article:   
Wenlong GAO,Shengwei ZHANG,Xi LIN, et al. The remote sensing-based estimation and spatial-temporal dynamic analysis of SOM in coal mining[J]. Remote Sensing for Natural Resources, 2021, 33(4): 235-242.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2020418     OR     https://www.gtzyyg.com/EN/Y2021/V33/I4/235
Fig.1  Overview of study area and sampling point location
Fig.2  Correlation between SOM and Spectrum
样本
类型		 样本
数/个		 最小值
(Min)/
(g·kg-1)		 最大值
(Max)/
(g·kg-1)		 均值
(Mean)/
(g·kg-1)		 标准差
(Sd)/
(g·kg-1)		 变异
系数
(CV)/%
建模样本 30 0.14 4.66 1.96 1.29 0.66
验证样本 15 0.72 3.99 2.12 1.05 0.50
总体样本 45 0.14 4.66 2.01 1.21 0.60
Tab.1  Basic statistic characteristic values of SOM content
波段组合 回归方程 R2 RMSE
绿 Y=2.27 X绿+3.14 0.32 5.31
红 Y=1.42 X红+4.72 0.46 4.39
近红 Y=1.38 X近红-4.83 0.70 4.24
绿+红+
近红		 Y=0.08 X绿+0.19 X红+0.18 X近红-1.9 0.82 2.03
Tab.2  Linear regression comparison
Fig.3  Spatial distribution of SOM in different times
Fig.4  Influence of SWC to SOM
Fig.5  SOM inversion before mining
Fig.6  Accuracy evaluation of SOM inversion
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