| Soil moisture is of great significance to the ecological construction of mining areas.Jinchang City,Gansu Province,in the desert area of northwest China,has serious soil erosion,the area is mostly bare soil and low vegetation area,and frequent droughts make the local ecological environment more fragile.Thus,this paper takes Jinchuan copper-nickel mine as the study area,applies remote sensing time series analysis method to soil moisture monitoring in the mine area,and uses Landsat TM/OLI image data in summer(June to September)from 2000 to 2021 to carry out soil moisture inversion,spatial and temporal variation and soil moisture perturbation study in the mine area during 21 years,so as to provide drought monitoring,ecological construction and related policies for the mine area.This study will provide a basis for drought monitoring,ecological construction and related policies.Firstly,based on previous studies,we selected Enhanced Vegetation Index(EVI),Modified Soil-adjusted Vegetation Index(MSAVI)and Normalized Difference Vegetation Index(NDVI).Difference Vegetation Index(NDVI)and Land Surface Temperature(LST)in the study area to construct a temperature vegetation drought index model,and evaluate the dry and wet side equations based on the three characteristic spaces to determine the soil moisture characteristic space of MSAVI-T＿s suitable for this paper.Then,based on this model,the inversion of soil moisture in the study area was realized,and the Theil-Sen(Sen)trend degree,Mann-Kendall(M-K)test and Coefficient of Variation(CV)were used to analyze the spatial and temporal distribution characteristics and variation patterns of soil moisture during the study period.Finally,the dynamic time warping(DTW)algorithm and Cart decision classification algorithm are combined to cluster the soil moisture trajectories in the study area and explore the spatial and temporal distribution characteristics of the image elements in terms of disturbance type and disturbance year,respectively.The most important research contents and findings of the paper are as follows:(1)By constructing a temperature vegetation drought index model through different vegetation indices and combining the magnitude of correlation coefficients of dry and wet side equations,it is found that the feature space constructed by MSAVI and LST is more suitable for soil moisture research in mining areas.The TVDI calculated based on the MSAVI-Ts feature space has a significant negative correlation with the soil backscatter coefficient obtained from the inversion of the water cloud model in the mine area;indicating that the TVDI constructed by MSAVI can be used for the inversion of soil moisture in this mine area.(2)The total annual mean value of soil moisture represented by TVDI in the study area from 2000 to 2021 is 0.61.The positive values of Sen trend of TVDI are mainly distributed near the urban center in the central part of the study area,and the smaller negative values appear near the Jinshui Lake Garden,and overall the soil moisture in the study area is increasing;the fluctuation of soil moisture data in the mine area is in the state of mild fluctuation,indicating that in the past 20 years TVDI is relatively stable.And the change of vegetation cover in the same period was analyzed in comparison.(3)During the period from 2000 to 2021,the number of disturbed pixels in the study area was 101236,with an area of about 91.11 km~2,accounting for 55.61%of the total area of the study area,of which the years of disturbance were mainly concentrated in 2003～2009,and the cumulative number of disturbed pixels was 84,449,83.4%of all disturbed pixels,and the disturbed pixels in the whole study area showed a The trend of gradually decreasing in the whole study area indicates that the ecological environment is recovering in a good direction.Finally,the overall recognition accuracy of the disturbance types verified by the confusion matrix is 83.9%,which is ideal for recognition. |
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