红沙泉矿区位于西北荒漠化干旱地区，水资源较匮乏、降水稀少，随着采矿活动的加剧，势必会引起矿区环境发生变化。土壤湿度作为表征矿区地表环境的指标之一，对其时空变化规律及其影响因素进行探讨显得尤为重要。本文以红沙泉矿区及周边区域为研究对象，首先基于Landsat8 OLI影像利用基于植被信息的归一化植被指数NDVI（Normalized Difference Vegetation Index, NDVI）、比值植被指数RVI（Ratio Vegetation Index, RVI）、条件植被指数VCI（Vegetation Condition Index, VCI），基于植被、温度信息的温度植被干旱指数TVDI（Temperature Vegetation Drought Index, TVDI）、条件植被温度指数VTCI（Vegetation Temperature Condition Index, VTCI），基于热红外波段的表观热惯量（Apparent thermal inertial, ATI）和条件温度指数TCI（Temperature Condition Index, TCI）以及基于反射率特征空间的垂直干旱指数PDI（Perpendicular Drought Index, PDI）、改进型垂直干旱指数MPDI（Modified Perpendicular Drought Index, MPDI）、土壤湿度监测指数SMMI（Soil Moisture Monitoring Index, SMMI）、改进型土壤湿度监测指数MSMMI（Modified Soil Moisture Monitoring Index, MSMMI）反演荒漠化地区的土壤湿度，并利用实测土壤湿度数据验证分析上述传统指数监测不同地物类型下土壤湿度的精度，评估其适用性；其次，在传统土壤湿度监测方法的基础上结合荒漠化地区特殊的地表类型特点提出适用于研究区的新型修正土壤湿度监测指数NMSMI（New Modified Soil Moisture Index, NMSMI）；利用NMSMI反演不同时空分辨下的土壤湿度，并结合气象因子及实际统计资料（采矿面积、煤炭产量），基于皮尔逊相关分析、地理探测器分析了引起土壤湿度变化的驱动因素；最后，利用景观生态功能贡献率探讨了采矿对土壤湿度的影响范围。结果表明：

（1） NDVI、RVI以及VCI反映研究区的植被信息，与土壤湿度呈正比，与实测土壤湿度相关系数分别为0.445（P<0.01）、0.312和0.432（P<0.01），但由于研究区裸岩石砾地及裸地较多因而这三种植被不能单独作为监测研究区土壤湿度的指数因子；TCI与实测土壤湿度的相关性仅为0.131（P>0.01）；热惯量法、SMMI、MSMMI、PDI、MPDI与实测土壤湿度的相关性分别为0.231、-0.463（P<0.01）、-0.434（P<0.01）、-0.460（P<0.01）、和-0.458（P<0.01），但这五种指标显示裸岩石砾地土壤湿度最高，与实测结果不符；VTCI、TVDI与实测土壤湿度的相关性分别为0.004和-0.004，未通过95%的显著性检验，且其显示植被区域土壤湿度低于裸岩石砾地的土壤湿度，与实测结果不符。因此，单一传统土壤湿度监测指标均不能反映研究区的土壤湿度空间分布状况。

（2）鉴于传统土壤湿度监测指数不能单独正确反映裸岩石砾地土壤湿度状况，但研究区裸岩石砾地面积较大，因而，本研究考虑综合构建适用于研究区的土壤湿度遥感监测模型——新型修正土壤湿度监测指数NMSMI（New Modified Soil Moisture Index, NMSMI）。本研究首先构建裸岩石砾地指数NGLI（New Gravel Land Index, NGLI），用来有效的提取研究区的裸岩石砾地及露天采坑，该指数与实测土壤湿度呈负相关（R2=0.261, P<0.01），虽相关系数较低，但一定程度上能反映研究区的土壤湿度，其次，通过分析结合传统土壤湿度监测指数SMMI、NDVI，基于线性加权的方法利用SMMI、NDVI、NGLI三种指标综合构建NMSMI，经验证，该指数与实测土壤湿度呈负相关（R2=0.534, P<0.01），反演结果在空间分布上与实测土壤湿度相符。

（3）基于NMSMI指数对研究区的土壤湿度进行反演，得到NMSMI年均值呈波动递增趋势，增长率为0.0007/year。结合人为因子及自然因子，矿井尺度上经皮尔逊相关分析以及地理探测器分析，得出影响红沙泉矿井NMSMI变化的因子主要分两个阶段，2003年-2010年主要为地表温度（r=0.813, P<0.01）、气压（r=-0.750, P<0.05）和降水（r=-0.541, P<0.05）等自然因子；2011年采矿活动开始，NMSMI受到自然与人为因子的共同影响，其中，自然因子主要有地表温度（r=0.785, P<0.01）、气压（r=-0.736, P<0.01）及降水（r=-0.702, P<0.05）；人为因子主要受采矿面积（r=0.986, P<0.01）及煤炭产量（r=0.725, P<0.05）的影响。最后利用景观生态功能贡献率模型分别确定了红沙泉矿区采矿活动对2011年、2014年、2017年、2019年以及2020年的土壤湿度的影响范围，最终确定其影响范围为距离采坑2.5km处。

Hongshaquan mining area is located in a desertified and arid area in the northwest, which water resources and precipitation are scarce. With the intensification of mining activities, it is bound to cause environmental changes in the mining area. As one of the indicators that characterize the surface environment of mining areas, soil moisture is particularly important to discuss its temporal and spatial changes and its influencing factors. This paper takes the Hongshaquan mining area and surrounding areas as the research object. Firstly, based on the Landsat8 OLI image, use the NDVI(Normalized Difference Vegetation Index,NDVI), RVI(Ratio Vegetation Index ,RVI) and VCI(Conditional Vegetation Index,VCI), based on vegetation and temperature information such as TVDI(Temperature Vegetation Drought Index,TVDI) and VTCI(Conditional Vegetation Temperature Index,VTCI), based on thermal infrared bands such as ATI(Apparent thermal inertial,ATI) and TCI(Temperature Condition Index, TCI), as well as based on the reflectivity feature space such as PDI (Perpendicular Drought Index, PDI), MPDI(Modified Perpendicular Drought Index, MPDI), SMMI (Soil Moisture Monitoring Index, SMMI), MSMMI (Modified Soil Moisture Monitoring Index, MSMMI) to retrieves the soil moisture in desertified areas, and use the measured soil moisture data to verify and analyze the accuracy of the above-mentioned traditional index to monitor soil moisture under different types of ground features, and evaluate its applicability; secondly, on the basis of traditional soil moisture monitoring methods, combined with the characteristics of special land types in desertified areas, a New Modified Soil Monitoring Index(NMSMI) suitable for the study area is proposed; Using NMSMI to retrieve soil moisture under different temporal and spatial resolutions, combined with meteorological factors and actual statistical data (mining area, coal production), based on Pearson correlation analysis and geographic detectors to analyze the driving factors that cause soil moisture changes; Finally, use the contribution rate of landscape ecological function discusses the range of influence of mining on soil moisture. The result shows as follow:
（1）NDVI, RVI, and VCI reflect the vegetation information in the study area, which is directly proportional to the soil moisture, and the correlation coefficients with the measured soil moisture are 0.445 (P<0.01), 0.312 and 0.432 (P<0.01), but due to there are many bare rocky gravel and bare land in the study area, so these three types of vegetation cannot be used as a single factor to monitor the soil moisture in the study area; The correlation between TCI and the measured soil moisture is only 0.131 (P>0.01); The correlations between Thermal Inertia method, SMMI, MSMMI, PDI, MPDI and the measured soil moisture are 0.231, -0.463(P<0.01), -0.434(P<0.01), -0.460(P<0.01) and -0.458(P<0.01), but these five indicators show that the soil moisture of bare rock and gravel land is the highest, which is inconsistent with the measured results; The correlations between VTCI, TVDI and the measured soil moisture are 0.004 and -0.004, respectively, failing the 95% significance test, and they show that the soil moisture in the vegetation area is lower than that of the bare rock gravel, which is inconsistent with the measured results. Therefore, a single traditional soil moisture monitoring index cannot reflect the spatial distribution of soil moisture in the study area.
（2）In view of the fact that the traditional soil moisture monitoring index cannot accurately reflect the soil moisture status of bare rock and gravel land alone, but the area of bare rock and gravel land in the study area is large, this study considers the comprehensive construction of a soil moisture remote sensing monitoring model suitable for the study area—  —NMSMI (New Modified Soil Moisture Index, NMSMI). This study first constructed the New Gravel Land Index (NGLI), which is used to effectively extract the bare rock and gravel land and open pits in the study area. The index is negatively correlated with the measured soil moisture (R2=0.261, P<0.01). Although the correlation coefficient is low, it can reflect the soil moisture in the study area to a certain extent. Secondly, by analyzing and combining with the traditional soil moisture monitoring index SMMI, NDVI, based on the linear weighting method, using SMMI, NDVI and NGLI to comprehensively construct NMSMI, it is verified the index is negatively correlated with the measured soil moisture (R2=0.534, P<0.01), and the spatial distribution of the inversion result is consistent with the measured soil moisture.
（3）Based on the NMSMI index, the soil moisture in the study area was inverted, and the annual average value of NMSMI showed a fluctuating and increasing trend, with a growth rate of 0.0007/year. Combining man-made factors and natural factors, Pearson correlation analysis and geographic detector analysis on the mine scale, it is concluded that the factors affecting the changes of the Hongshaquan Mine’s NMSMI are mainly divided into two stages. From 2003 to 2010, it is mainly the surface temperature (r=0.813), P<0.01), air pressure (r=-0.750, P<0.05) and precipitation (r=-0.541, P<0.05); Mining activities started in 2011, NMSMI was affected by both natural and man-made factors. Among them, natural factors mainly   include surface temperature (r=0.785, P<0.01),air pressure (r=-0.736, P<0.01) and precipitation (r=-0.702, P<0.05); Anthropogenic factors are mainly affected by mining area (r= 0.986, P<0.01) and coal production (r=0.725, P<0.05). Finally, using the landscape ecological function contribution rate model to determine the impact of mining activities in the Hongshaquan mining area on the soil moisture in 2011, 2014, 2017, 2019 and 2020, and finally determine the impact range to be 2.5km from the pit Place.

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