Abstract: Satellite remote sensing can identify the irrigation information, because of its rapid and wide-area observation. However, only a single source is often extracted from remote sensing data. Spatial and temporal resolution cannot fully meet the requirement for the high-accuracy and dynamic identification of irrigation information at the regional scale, especially for the strong spatial heterogeneity of agricultural activities in the complex terrain. In this study, a remote sensing framework was developed to identify the irrigation information using drought index analysis and spatiotemporal fusion. The Guanzhong Region was also taken as the study area. The temperature vegetation dryness index (TVDI) was selected as the identification index after correlation analysis between drought indices and soil moisture. Elevation correction with fusion optimization was introduced to characterize the variation in the soil moisture. Its spatiotemporal fusion accuracy was also enhanced under complex terrain conditions. Subsequently, the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) was used to fuse high-spatial-resolution Landsat imagery and high-temporal-resolution MODIS data for the high-spatiotemporal-resolution TVDI time series. Spring irrigation information in the Guanzhong Region in 2024 was identified using the threshold method with precipitation data. The results showed that the correlations between the remote sensing drought indices and soil moisture at the 10-20 cm depth were generally higher than those with soil moisture at the 0-10 cm depth. Elevation topographic correction effectively reduced the influence of terrain on land surface temperature. There was a strong correlation between TVDI and soil moisture. Furthermore, the elevation-corrected TVDI showed a strong negative correlation with soil moisture at the 10-20 cm depth, with the maximum correlation coefficient of -0.77 during the spring crop growth period. Normalized difference vegetation index (NDVI) and land surface temperature (LST) were fused for the higher accuracy of TVDI than the strategy of first calculation and then fusion. R²and RMSE values of 0.76 and 0.07 for the former, whereas 0.44 and 0.13 for the latter, respectively. The validation showed that the overall accuracy was 90.8% for the identification in the Donglei Phase II irrigation district, with a Kappa coefficient of 0.80. The mean error was 15.1% and 14.3%, respectively, for accumulated and actual irrigated areas in the irrigation districts. Regional identification results indicated that the spring irrigation was mainly concentrated from March to April, with the irrigation frequency ranging from one to two times. Irrigated areas were distributed in the relatively flat Weihe Plain, with a spatial pattern characterized by broader irrigation extent and higher irrigation frequency in the eastern and western parts. While the central part exhibited relatively lower irrigation intensity. The spatial distribution and irrigation frequency of spring irrigation were dominated by regional topography, water supply, and cropping structure. The finding can provide a strong reference to identify the regional-scale irrigation information and water resources under complex terrains.