西部黄土高原是我国煤炭资源主要产区之一，人居生态环境脆弱，多年来大规模地下采煤已引起大范围的地面塌陷和变形破坏，改变了矿区原有的地貌形态和土壤特性，对土壤水分造成了显著的扰动效应，成为驱动煤矿区生态环境演变的关键因素，但目前学术界对于采煤沉陷引起土壤水分变化的研究还很不充分。本文选择黄土高原大佛寺煤矿为研究区域，运用时序SAR（Synthetic Aperture Radar）和光学遥感影像反演矿区土壤含水量，结合实地采样和连续监测数据分析矿区土壤水分的时空变化特征及其影响因素；进一步针对采煤沉陷区采用无人机多光谱数据结合卫星影像数据，构建工作面尺度的土壤水分遥感反演模型。论文研究的主要内容及结果如下：

（1）基于时序Sentinel-1SAR数据和Sentinel-2光学影像数据，利用雷达信号变化探测算法构建了大佛寺矿区土壤水分时序变化的反演模型。运用水云模型消除植被对雷达后向散射系数影响，通过实地采样数据对该模型进行精度验证，获取了2022—2023年连续15期矿区土壤水分变化分布图。

（2）结合土壤水分遥感反演和现场监测数据，分析了大佛寺矿区土壤含水量变化的时空分布特征。矿区土壤含水量均值呈现季节性波动变化特征，总体上呈现微小增长趋势。对比分析了开采沉陷区与非沉陷区土壤水分变化的不同特征，发现30组对比数据中沉陷区土壤含水量均值较低的占18组，降雨后沉陷区土壤水分分布的变异系数显著增大，尤其在采动裂缝发育区更为明显，表明地下开采引起的地表沉陷变形对土壤水分造成了显著的扰动影响。

（3）利用遥感反演的土壤水分数据结合矿区DEM和植被覆盖度（FVC），分析了土壤含水量变化与主要影响因子之间的关系。通过提取研究区高程、坡度和坡向等地形因子，采用分布指数描述了不同地形因子等级所对应的土壤水分等级的分布特征；利用主成分分析法发现坡度因子对土壤水分影响最大，坡向次之，高程影响最小；对比分析发现，在干旱期植被覆盖度和土壤水分呈正相关趋势，在雨水期两者关系不稳定。

（4）基于无人机多光谱数据结合Sentinel-2光学影像，构建了适用于小范围采煤沉陷区的土壤水分协同反演模型。通过在大佛寺煤矿40201开采工作面地表采集多光谱影像，提取光谱反射率和敏感光谱指数，并结合实地采样数据，构建了基于BPNN、RBFNN、SVM和RF的土壤水分反演模型。对比验证发现随机森林（RF）模型的精度最优。进一步将无人机遥感数据通过尺度转换，构建了无人机—卫星遥感数据协同的土壤水分反演模型，提高了基于卫星遥感影像反演矿区土壤水分的实际精度，具有一定的应用价值。

The western Loess Plateau is one of the major coal resources producing areas in China, with fragile human ecological environment. Over the years, large-scale underground coal mining has caused widespread ground subsidence and deformation damage, which changed the original geomorphic morphology of the mining area and soil properties, and caused significant disturbance effects on soil moisture, which has become a key factor driving the evolution of the ecological environment in the coal mining area, but at present, academic research on the changes in soil moisture caused by coal mining subsidence is still very insufficient. However, the academic research on soil moisture changes caused by coal mining subsidence is still very insufficient. In this paper, we choose Dafosi Coal Mine in the Loess Plateau as the study area, and use Synthetic Aperture Radar (SAR) and optical remote sensing images to invert the soil moisture content in the mining area, and analyze the spatial and temporal characteristics of soil moisture and its influencing factors by combining with the field samples and continuous monitoring data; we further use the unmanned aerial vehicle (UAV) multispectral data combined with the satellite images to construct a working face scale soil moisture analysis of the coal mine subsidence area. In addition, a remote sensing inversion model of soil moisture at the working face scale was constructed for the coal mining subsidence area. The main contents and results of the thesis are as follows:

(1) Based on the time-series Sentinel-1 SAR data and Sentinel-2 optical image data, an inversion model of the time-series change of soil moisture in the Dafosi mine area was constructed using the radar signal change detection algorithm. The water cloud model was applied to eliminate the influence of vegetation on the radar backward scattering coefficient, and the accuracy of the model was verified by the field sampling data, and the distribution maps of soil moisture changes in the mining area in 15 consecutive periods in 2022-2023 were obtained.

(2) Combined with soil moisture remote sensing inversion and field monitoring data, the spatial and temporal distribution characteristics of soil moisture changes in the Dafosi mine area were analyzed. The average value of soil moisture content in the mining area shows seasonal fluctuation and change characteristics, and the overall trend is a slight increase. The different characteristics of soil moisture change in mining subsidence area and non-subsidence area were analyzed in comparison, and it was found that the average value of soil moisture content in subsidence area was lower in 18 groups out of 30 groups of comparative data, and the coefficient of variation of soil moisture distribution in subsidence area increased significantly after rainfall, which was especially more obvious in the area of mining fissure development, indicating that subsidence deformation of the ground surface caused by underground mining caused a significant perturbing effect on soil moisture.

(3) Using the soil moisture data from remote sensing inversion combined with DEM and vegetation cover (FVC) of the mining area, the relationship between soil moisture content changes and the main influencing factors was analyzed. By extracting topographic factors such as elevation, slope and slope direction in the study area, the distribution index was used to characterize the distribution of soil moisture classes corresponding to different topographic factor classes; using principal component analysis, it was found that the slope factor had the greatest influence on soil moisture, followed by slope direction, and elevation had the smallest influence; a comparative analysis found that vegetation cover and soil moisture showed a positive correlation trend in the drought period, and that the relationship between the two was unstable in the rainy period.

(4) Based on UAV multispectral data combined with Sentinel-2 optical images, a synergistic inversion model of soil moisture applicable to small-scale coal mining subsidence areas was constructed. A soil moisture inversion model based on BPNN, RBFNN, SVM and RF was constructed by collecting multispectral images on the surface of 40201 mining face in Dafosi coal mine, extracting spectral reflectance and sensitive spectral index, and combining with field sampling data. Comparative validation revealed that the accuracy of the random forest (RF) model was optimal. The soil moisture inversion model based on UAV remote sensing data was further constructed through scale conversion, which improves the actual accuracy of soil moisture inversion in the mining area based on satellite remote sensing images, and has certain application value.

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