In 1981, [PERSON] et al. [13] introduced the crop water stress index (CWSI), which needs to be estimated based on actual transpiration. Due to the complexity and limitations of the estimation method of actual transpiration (the Penman-Monteith formula remains only useful in humid conditions), [PERSON] et al. [14] created a non-parametric method (NP) based on imagery from Landsat 8 and CLDAS materials to calculate realistic evapotranspiration. The Penman-Monteith (P-M) method was used to compute potential evapotranspiration, and the results indicate that CWSI-NM has a stronger relationship with measured soil moisture levels than standard CWSI, making it a dependable method in persistent drought assessment. However, this method depends on specific meteorological conditions. The climatic characteristics of MUSL are low precipitation, high evaporation, and strong wind speed. These extreme and complex environmental conditions affect the accuracy and applicability of CWSI. [PERSON] et al. [15], based on the perpendicular drought index (PDI) and the modified perpendicular drought index (MPDI), assessed the soil humid conditions throughout the crop growing season. Results demonstrate that PDI and MPDI have high applicability in the inversion of soil moisture, but when the vegetation coverage is very high, especially close to 1, it is easy to produce negative values. [PERSON] et al. [16] established a comprehensive drought index (CDI) using spatial principal component analysis (SPCA) and verified it by standardized precipitation evapotranspiration index (SPEI), gross primary productivity (GPP), vegetation condition index (VCI), and soil moisture (SM). The results show that CDI is a reliable drought index in short-term drought monitoring. However, building CDI in northern desert regions and areas with low vegetation cover is challenging due to the lack of ET and PET information. In summary, the applicability of different drought indices is affected by various factors. Therefore, the selection of drought indices in this study area needs to consider many aspects, including climatic conditions, vegetation coverage, and data coverage. Because VSWI and MPDI are suitable for high vegetation coverage areas, CWSI is suitable for regions with partial vegetation coverage, and PDI is appropriate for monitoring drought in areas with low vegetation coverage or bare surfaces. VTCI is limited to drought research at specific time scales [17; 18]. [PERSON] et al. [19] mentioned a temperature vegetation drought index (TVDI) using a simplified NDVI-LIST feature space. This index combines information from visible, near-infrared, and thermal infrared bands to observe soil moisture. It is particularly suitable for monitoring arid areas with variable climates and significant changes in vegetation cover. The land cover of the MUSL ranges from bare land to areas with partial vegetation coverage and extends to regions with high vegetation coverage in the southern cultivated lands. This distribution aligns well with the vegetation distribution characteristics observed in the LST-NDVI feature space. Many researchers have used TVDI for drought monitoring and compared the monitoring capabilities of other drought indices and achieved good results. For example, [PERSON] et al. [20] employed TVDI to track the features of the drought in Shandong Province between 2011 and 2020. They realized that the east-central region of the province had a severe drought and that TVDI and GLDAS 0-10 cm had a strong correlation. They also found that TVDI had a certain lag on precipitation. [PERSON] et al. [21] constructed TVDI, the vegetation supply water index (VSWI), VCI, and TCI based on MODIS data. Through correlation analysis using observed soil moisture within the 0-20 cm depth range, they found that TVDI exhibited a stronger correlation than the other three remote-sensing drought indices.