The Standardized Precipitation Evapotranspiration Index (SPEI) [5] is derived from precipitation and temperature data. It has the advantage of incorporating multi-scalar features and considering the effect of temperature changes on drought assessment. [PERSON] et al. [6] confirmed the effectiveness of the SPEI index in evaluating the impact of drought on crop yield by studying the drought situation during the maize growing season in Yunnan Province. Although the drought monitoring method based on traditional station data has shown significant progress after nearly a century of development and is widely accepted in practice, it is influenced by local topography, land use, and vegetation cover. This makes the monitoring results susceptible to local geomorphology and environment, making it challenging to accurately represent the drought situation across the entire region. Drought monitoring research based on remote sensing data is different from traditional methods. It integrates perspectives from meteorology, hydrology, vegetation, and other disciplines, providing new technical means for drought monitoring. This not only supports an in-depth understanding of drought formation mechanisms and influencing factors but also offers a more comprehensive and effective monitoring tool [7]. Vegetation plays a crucial role on the Earth's surface, being one of the biomes with the highest land coverage, and its impact on the ecological environment is significant. Studies indicate that vegetation has high absorption of red light in the visible band and high reflectance in the near-infrared band. Therefore, the vegetation index can effectively reflect the growth status and coverage degree of vegetation by comparing the reflectance of these two bands, thus enabling drought monitoring. [PERSON] and [PERSON] [8] introduced the ratio vegetation index (RVI) for monitoring vegetation growth status in the development of the vegetation drought index, enhancing the radiative contrast between surface and vegetation information. [PERSON] et al. [9] proposed the normalized vegetation index (NDVI), one of the most widely used remote sensing vegetation drought indices. To address spatial and temporal differences, soil type, vegetation cover, and other factors affecting drought monitoring results, researchers have developed other vegetation indices based on the original definition of NDVI, such as the Standard Vegetation Index (SVI) [10] and the Vegetation Condition Index (VCI) [11]. Studies have shown that vegetation indices based on short-wave infrared bands are more sensitive to moisture changes in the vegetation canopy than those based on red and near-infrared bands [12; 13]. Building on this, vegetation indices like the normalized vegetation water index (NDWI) [14] and the short-wave infrared vertical water loss index (SPSI) [15] have been proposed to reflect changes in vegetation canopy water. Considering the properties of vegetation for visible light, the vegetation drought index is more suitable for drought monitoring in areas with high vegetation cover. The integrated remote sensing drought monitoring index refers to the method of combining multi-band remote sensing information for drought calculation. Studies have shown that the integrated remote sensing drought detection model has wider applicability, especially in assessing agricultural and vegetation drought conditions [16]. Integrating NDVI index and land surface temperature (LST) can provide more complete drought information, based on which research scholars have proposed many drought indices constructed from NDVI index and LST. The most representative one is the temperature vegetation drought index (TVDI) [17], which has been used by many scholars at home and abroad for drought monitoring at different spatial and temporal scales. [PERSON] et al.