The growing number, size, and frequency of coastal hypoxia increasingly threaten marine ecosystem health and essential ecosystem services for human well-being. It is therefore urgent to use continuous and consistent observation and develop advanced tools to characterize and track the spatial and temporal change of coastal hypoxia. Satellite imagery with fine spatiotemporal resolution and global coverage has shown great potential for monitoring environmental changes, yet has rarely been applied to hypoxia mapping. To advance the understanding, we synthesized satellite-derived ocean color variables and dissolved oxygen measurements collected during 2014, and used random forest regression, lagged linear regression, and functional data analysis to estimate the spatiotemporal change of the hypoxia zone in the Gulf of Mexico. The three models achieved similar predictive accuracy (±1.2–1.4 mg/L dissolved oxygen), but the random forest regression performed the best in estimating the bottom dissolved oxygen from satellite-derived variables. Our models also revealed time lags of roughly 0–5 and 16–19 days between the surface water process (e.g., algae bloom and ocean warming) and bottom water hypoxia, which was rarely considered in previous hypoxia studies using satellite data. Finally, our models showed that the area of Gulf hypoxia increased gradually from May and reached a peak during mid-July and mid-August in 2014, and the hypoxia zone occurred in the estuary of the Mississippi River and Suwannee River during roughly 25% of summer days. In addition to predicting the size of hypoxic zones, our study provides additional information on where, when, and how long hypoxic zones persist with greater spatial details and enables modeling hypoxic zones at near-real-time (e.g., days) temporal scales. More importantly, we demonstrate the great potential of applying satellite remote sensing for spatially explicit hypoxia mapping, which could promote more cost-effective coastal hypoxia monitoring and assessment practices.