The application of satellite and airborne sensors to observe and quantify components of the water cycle across spatial scales. Remote sensing provides critical data for hydrological modeling, water management, and flood forecasting in data-scarce regions.
Remote sensing in hydrology encompasses the use of satellite, airborne, and ground-based sensors to observe water cycle components including precipitation, evapotranspiration, soil moisture, snow cover and water equivalent, surface water extent and level, groundwater storage changes, and water quality parameters. Key satellite missions include GRACE/GRACE-FO for total water storage, SMAP for soil moisture, GPM for precipitation, Landsat and Sentinel for surface water mapping, and SWOT for river discharge estimation. Remote sensing addresses a fundamental challenge in hydrology: the spatial heterogeneity of water cycle processes that cannot be captured by sparse point measurements from gauging networks. Data assimilation techniques integrate satellite observations into hydrological models to improve streamflow predictions and flood forecasts. The increasing availability of free, analysis-ready satellite data combined with cloud computing platforms like Google Earth Engine has democratized access to remote sensing for water resource applications. Challenges remain in scaling satellite observations to match the spatial and temporal resolution needed for local water management decisions, and in maintaining calibration/validation against declining in-situ monitoring networks.