The water cycle or hydrological cycle involves the continuous movement of water on, above, and below the surface of the Earth. In general, hydrological cycle components (e.g., precipitation, evaporation, water storage, and runoff) are characterized by large temporal and spatial variability. Accurate monitoring of various hydrological cycle components and developing hydrological models are important for improving our understanding of hydrological processes. With significant development of sensor technology and sharply growing platforms in past decades, remote sensing offers enhanced capability to monitor various hydrological cycle components at different temporal and spatial scales to complement conventional in situ measurements. Considerable efforts have been made to explore the potentials of remotely sensed data from a vast range of different platforms (e.g., satellite, airborne, drone, ground-based radar) and sensors (e.g., optical, infrared, microwave) in advancing hydrology research, particularly in poorly gauged and ungauged regions. The application of remote sensing in hydrology is expected to increase with enhanced recognition of its potential and continuous development of advanced sensors (e.g., new satellite missions) and retrieval methods (e.g., innovative machine learning, data assimilation and data merging techniques).

The session aims to present and discuss recent advances in the remote sensing of hydrological cycle components as well as the application of remote sensing in hydrological modeling. We encourage studies to investigate the performance of remotely sensed data in multi-variable calibration and spatial evaluation of hydrological models. The added-value of spatially downscaling remotely sensed data in improving hydrological modelling is also particularly welcome.