Surface water dynamics play a critical role in Earth’s climate system, influencing ecosystems, agriculture, disaster resilience, and sustainable development. Yet monitoring rivers and surface water at fine spatial and temporal scales remains challenging---especially for narrow or sediment-rich rivers that are poorly captured by low-resolution satellite data. To address this, we introduce RiverScope, a high-resolution dataset developed through collaboration between computer science and hydrology experts. RiverScope comprises 1,145 high-resolution images (covering 2,577 square kilometers) with expert-labeled river and surface water masks, requiring over 100 hours of manual annotation. Each image is co-registered with Sentinel-2, SWOT, and the SWOT River Database (SWORD), enabling the evaluation of cost-accuracy trade-offs across sensors---a key consideration for operational water monitoring. We also establish the first global, high-resolution benchmark for river width estimation, achieving a median error of 7.2 meters---significantly outperforming existing satellite-derived methods. We extensively evaluate deep networks across multiple architectures (e.g., CNNs and transformers), pretraining strategies (e.g., supervised and self-supervised), and training datasets (e.g., ImageNet and satellite imagery). Our best-performing models combine the benefits of transfer learning with the use of all the multispectral PlanetScope channels via learned adaptors. RiverScope provides a valuable resource for fine-scale and multi-sensor hydrological modeling, supporting climate adaptation and sustainable water management.

Remote sensing of hydrology provides the key to one of the longest-standing problems in hydrology: ungaged basins. The global network of river discharge measurements available publicly accurately captures overall streamflow into global ocean, but coverage for smaller tributaries upstream, is vanishingly small. The forthcoming Surface Water and Ocean Topography (SWOT) mission will vastly expand measurements of global rivers. However, converting the SWOT measurements of height, width and slope into river discharge is non-trivial. SWOT discharge algorithms will provide discharge for all SWOT-observed rivers, but at lower accuracy than what is possible at a gage. In this paper, we answer the question of where and how SWOT discharge will be valuable. We show that median river discharge accuracy will be on the order of 50%, in terms of normalized root mean squared error (nRMSE), whereas unbiased nRMSE will be on the order of 10-20%. We discuss how SWOT discharge will contribute to global hydrologic science in the coming decade.