Desertification prevention and control is the main task of desertification control, and Mu Us Sandy Land is one of the four major sandy lands in China, with frequent droughts and very fragile ecological environment. Climate change is the main factor leading to drought, and it may face more severe drought in the future. Normalized Difference Vegetation Index and Land Surface Temperature data were used to calculate the Temperature Vegetation Drought Index By using Sen+Mann- Kendall trend analysis, Hurst Index, partial correlation analysis and lag analysis methods, the spatial and temporal change characteristics, future change trends and time-delay effects of TVDI on climate factors in Mu Us Sandy Land from 2001 to 2020 have been explored. The results showed that: (1) the average TVDI was 0.6, and the drought gradually eased from west to east. In autumn (Z=1.99) 38.5% of the pixels showed a significant drying trend, and in the other three seasons (Z mean =2.95) and the whole year (Z=3.47), the trend was very significant drying trend. (2) The relationship between annual TVDI and precipitation is mainly not significant negative correlation, accounting for 63.4%, which is concentrated in the southern part of Shaanxi Province with higher altitude and the southeastern part of Yanchi County. (3) In summer and autumn, TVDI is mainly not significantly positively correlated with temperature and evapotranspiration, and mainly not significantly negatively correlated with precipitation.
Apart from being essential to the development of grasslands and crops, soil moisture has a significant impact on the water cycle and the global climate. Drawing from multispectral Landsat 8 OLI photos, Added to this study, soil moisture was simultaneously measured in the field based on topographic parameters and surface biophysical features. Soil moisture inversion models of multivariable linear regression (MLR) and random forest (RF) were constructed by empirical model method. And compared with inverse distance weighting (IDW). The findings demonstrated the dependence of surface soil moisture on surface biophysical characteristics was high, with the highest inversion accuracy of random forest, in which the R2 of RF model was above 0.8 in both months, which was at least 8% higher than that of MLR model, and at least 11% higher than that of IDW model, and the corresponding RMSE was the smallest in both months. In various months, the main variables influencing the soil moisture content were elevation and surface temperature. The investigation's area had low overall soil moisture content, with most of it having less than 20. High, steep slopes in the north and southeast of the area had higher soil moisture content, whereas the region's flat areas in the north-central region had lower soil moisture content.
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