Effective agricultural drought monitoring requires data with both high spatial and temporal resolution to capture rapid vegetation dynamics at the field scale, while current satellite systems rarely provide both simultaneously. This study addresses this limitation by applying a spatiotemporal data fusion and developing a robust agricultural drought index (ADI). The spatial-temporal image fusion model (STI-FM) was used to fuse moderate resolution imaging spectroradiometer (MODIS) and Landsat-8 data over Iranshahr County, southeastern Iran, producing high-temporal-resolution synthetic Landsat-8 imagery. A novel ADI was then developed by integrating uncorrelated drought-related indicators (normalized difference water index (NDWI), visible and shortwave infrared drought index (VSDI), temperature vegetation dryness index (TVDI), and land surface temperature (LST)), with TVDI included to enhance sensitivity to soil moisture and thermal stress in semi-arid fields. The fused images showed strong agreement with observed Landsat data, confirming reliable reconstruction of spectral and thermal patterns in agricultural areas. ADI-based drought assessment revealed persistent drought conditions during the 2014-2018 growing seasons (April-June). Comparisons with meteorological and hydrological drought indices showed consistent drought patterns, confirming the robustness and applicability of the proposed framework.