Vegetative photosynthesis is highly sensitive to water and heat stress, and the indirect monitoring of vegetative photosynthesis through Sun-induced chlorophyll fluorescence (SIF) has significant potential in global drought monitoring. However, substantial knowledge gaps remain regarding effective methods for assessing vegetation drought stress using remotely sensed SIF data. In this study, we employ GOCI geostationary satellite observations and OCO-3 SIF retrieval to drive a machine learning model for the purpose of monitoring SIF in typical drylands in China at high spatial resolution (500 m). Additionally, we investigated the spatial response patterns and quantitative metrics of drought by SIF and its decoupled components. The data-driven SIF reconstruction products successfully captured the afternoon decrease in photosynthesis in both space and time, particularly evident during the 2020 summer drought-heatwave composite event. It was observed that the disparity in photosynthetic intensity between the morning and afternoon periods was markedly diminished with the advent of drought conditions. The difference-type index, based on these observations, showed statistically significant correlation with both the soil drought anomaly indicator (SMZ; Pearson r: 0.53; P < 0.05) and the Standardized Precipitation Evapotranspiration Index (SPEI; Pearson r: 0.71; P < 0.01). Furthermore, it exhibited superior performance compared to the SIF and SIF yields derived from a single time observation. This study demonstrates the application of SIF for drought monitoring in drylands vegetation at a fine spatial scale, emphasizing the importance of multi-temporal remote sensing monitoring of vegetation photosynthesis for drought tracking.