Oki, T. & Kanae, S. Global hydrological cycles and world water resources. Science 313, 1068–1072 (2006).
Feng, D. & Gleason, C. J. More flow upstream and less flow downstream: the changing form and function of global rivers. Science 386, 1305–1311 (2024).
Best, J. Anthropogenic stresses on the world’s big rivers. Nat. Geosci. 12, 7–21 (2019).
Smith, L. C. Rivers of Power: How a Natural Force Raised Kingdoms, Destroyed Civilizations, and Shapes Our World (Penguin, 2020).
Gleason, C. J. & Brown, C. M. The once and future hydrology is Whole Earth Hydrology. Perspect. Earth Space Sci. 6, e2024CN000267 (2025).
Allen, G. H. & Pavelsky, T. M. Global extent of rivers and streams. Science 361, 585–588 (2018).
Dethier, E. N., Renshaw, C. E. & Magilligan, F. J. Rapid changes to global river suspended sediment flux by humans. Science 376, 1447–1452 (2022).
Langhorst, T. & Pavelsky, T. Global observations of riverbank erosion and accretion from Landsat imagery. J. Geophys. Res. Earth Surf. 128, e2022JF006774 (2023).
Nyberg, B., Henstra, G., Gawthorpe, R. L., Ravnås, R. & Ahokas, J. Global scale analysis on the extent of river channel belts. Nat. Commun. 14, 2163 (2023).
Wu, Q. et al. Satellites reveal hotspots of global river extent change. Nat. Commun. 14, 1587 (2023).
Yang, X. et al. Mapping flow-obstructing structures on global rivers. Water Resour. Res. 58, e2021WR030386 (2022).
Durand, M. et al. Achieving breakthroughs in global hydrologic science by unlocking the power of multisensor, multidisciplinary Earth observations. AGU Adv. 2, e2021AV000455 (2021).
Gorelick, N. et al. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).
Sheffield, J. et al. Satellite remote sensing for water resources management: potential for supporting sustainable development in data-poor regions. Water Resour. Res. 54, 9724–9758 (2018).
Brinkerhoff, C. B., Gleason, C. J., Zappa, C. J., Raymond, P. A. & Harlan, M. E. Remotely sensing river greenhouse gas exchange velocity using the SWOT satellite. Global Biogeochem. Cycles 36, e2022GB007419 (2022).
Battin, T. J. et al. River ecosystem metabolism and carbon biogeochemistry in a changing world. Nature 613, 449–459 (2023).
Liu, S. et al. The importance of hydrology in routing terrestrial carbon to the atmosphere via global streams and rivers. Proc. Natl Acad. Sci. USA 119, e2106322119 (2022).
Rocher-Ros, G. et al. Global methane emissions from rivers and streams. Nature 621, 530–535 (2023).
Grill, G. et al. Mapping the world’s free-flowing rivers. Nature 569, 215–221 (2019).
Knox, R. L., Morrison, R. R. & Wohl, E. E. Identification of artificial levees in the contiguous United States. Water Resour. Res. 58, e2021WR031308 (2022).
Lehner, B. et al. The Global Dam Watch database of river barrier and reservoir information for large-scale applications. Sci. Data 11, 1069 (2024).
Cawse-Nicholson, K. et al. NASA’s surface biology and geology designated observable: a perspective on surface imaging algorithms. Remote Sens. Environ. 257, 112349 (2021).
Sullivan, E. et al. In situ correlation between microplastic and suspended particulate matter concentrations in river-estuary systems support proxies for satellite-derived estimates of microplastic flux. Mar. Pollut. Bull. 196, 115529 (2023).
Yamazaki, D., Kanae, S., Kim, H. & Oki, T. A physically based description of floodplain inundation dynamics in a global river routing model. Water Resour. Res. https://doi.org/10.1029/2010WR009726 (2011).
Sutanudjaja, E. H. et al. PCR-GLOBWB 2: a 5 arcmin global hydrological and water resources model. Geosci. Model Dev. 11, 2429–2453 (2018).
Sampson, C. C. et al. A high-resolution global flood hazard model. Water Resour. Res. 51, 7358–7381 (2015).
Wing, O. E. J. et al. A 30 m global flood inundation model for any climate scenario. Water Resour. Res. 60, e2023WR036460 (2024).
Smith, L. C. Satellite remote sensing of river inundation area, stage, and discharge: a review. Hydrol. Process. 11, 1427–1439 (1997).
Rango, A. & Anderson, A. T. Flood hazard studies in the Mississippi River basin using remote sensing. J. Am. Water Resour. Assoc. 10, 1060–1081 (1974).
Kritikos, H., Yorinks, L. & Smith, H. Suspended solids analysis using ERTS-A data. Remote Sens. Environ. 3, 69–78 (1974).
Muller, E., Décamps, H. & Dobson, M. K. Contribution of space remote sensing to river studies. Freshw. Biol. 29, 301–312 (1993).
Rahn, P. H. Erosion below main stem dams on the Missouri River. Environ. Eng. Geosci. xiv, 157–181 (1977).
Ritchie, J. C., Schiebe, F. R. & McHenry, J. R. Remote sensing of suspended sediments in surface waters. Photogramm. Eng. Remote Sens. 42, 1539–1545 (1976).
Lawler, D. M. The measurement of river bank erosion and lateral channel change: a review. Earth Surf. Process. Landf. 18, 777–821 (1993).
HYDRO1K: A Global Hydrologic Database Derived From 1996 GTOPO30 Data (USGS, 2000).
Lehner, B., Verdin, K. & Jarvis, A. New global hydrography derived from spaceborne elevation data. Eos 89, 93–94 (2008).
Yamazaki, D. et al. MERIT Hydro: a high-resolution global hydrography map based on latest topography dataset. Water Resour. Res. 55, 5053–5073 (2019).
Lin, P. et al. Global reconstruction of naturalized river flows at 2.94 million reaches. Water Resour. Res. 55, 6499–6516 (2019).
Hirabayashi, Y. et al. Global flood risk under climate change. Nat. Clim. Change 3, 816–821 (2013).
Marzadri, A. et al. Global riverine nitrous oxide emissions: the role of small streams and large rivers. Sci. Total Environ. 776, 145148 (2021).
Raymond, P. A. et al. Global carbon dioxide emissions from inland waters. Nature 503, 355–359 (2013).
Matthes, G. H. River surveys in unmapped territory. Trans. Am. Soc. Civ. Eng. 121, 739–752 (1956).
Gannett, H. Profiles of Rivers in the United States Water Supply Paper 44 (USGS, 1901); https://doi.org/10.3133/wsp44
Horton, R. E. Erosional development of streams and their drainage basins: hydrophysical approach to quantitative morphology. Geol. Soc. Am. Bull. 56, 275–370 (1945).
Leopold, L. B. Rivers. Am. Sci. 50, 511–537 (1962).
Boggs, S. W. The international map of the world. Mil. Eng. 21, 112–114 (1929).
Gesch, D. B., Verdin, K. L. & Greenlee, S. K. New land surface digital elevation model covers the Earth. Eos 80, 69–70 (1999).
Farr, T. G. et al. The Shuttle Radar Topography Mission. Rev. Geophys. https://doi.org/10.1029/2005RG000183 (2007).
Potapov, P. et al. Mapping global forest canopy height through integration of GEDI and Landsat data. Remote Sens. Environ. 253, 112165 (2021).
Simard, M., Pinto, N., Fisher, J. B. & Baccini, A. Mapping forest canopy height globally with spaceborne lidar. J. Geophys. Res. Biogeosci. https://doi.org/10.1029/2011JG001708 (2011).
Wortmann, M. et al. Global River Topology (GRIT): a bifurcating river hydrography. Water Resour. Res. 61, e2024WR038308 (2025).
Yamazaki, D. et al. A high-accuracy map of global terrain elevations. Geophys. Res. Lett. 44, 5844–5853 (2017).
Yamazaki, D. et al. Development of the Global Width Database for Large Rivers. Water Resour. Res. 50, 3467–3480 (2014).
Carlson, K. A. et al. TDX-Hydro: global high-resolution hydrography derived from TanDEM-X. Preprint at https://doi.org/10.22541/essoar.171629686.65893579/v1 (2024).
Zink, M. et al. TanDEM-X: 10 years of formation flying bistatic SAR interferometry. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 14, 3546–3565 (2021).
NASA JPL. NASA Shuttle Radar Topography Mission global 1 arc second number [data set]. NASA Land Processes Distributed Active Archive Center https://doi.org/10.5067/MEaSUREs/SRTM/SRTMGL1N.003 (2013).
Crippen, R. et al. NASADEM global elevation model: methods and progress. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. XLI-B4, 125–128 (2016).
Uhe, P. et al. FathomDEM: an improved global terrain map using a hybrid vision transformer model. Environ. Res. Lett. 20, 034002 (2025).
Lehner, B. & Grill, G. Global river hydrography and network routing: baseline data and new approaches to study the world’s large river systems. Hydrol. Process. 27, 2171–2186 (2013).
Lin, P., Pan, M., Wood, E. F., Yamazaki, D. & Allen, G. H. A new vector-based global river network dataset accounting for variable drainage density. Sci. Data 8, 28 (2021).
Amatulli, G. et al. Hydrography90m: a new high-resolution global hydrographic dataset. Earth Syst. Sci. Data 14, 4525–4550 (2022).
Mizukami, N. et al. A vector-based river routing model for Earth System Models: parallelization and global applications. J. Adv. Model. Earth Syst. 13, e2020MS002434 (2021).
Altenau, E. H. et al. The Surface Water and Ocean Topography (SWOT) mission River Database (SWORD): a global river network for satellite data products. Water Resour. Res. 57, e2021WR030054 (2021).
Sikder, M. S. et al. Lake-TopoCat: a global lake drainage topology and catchment database. Earth Syst. Sci. Data 15, 3483–3511 (2023).
Passalacqua, P. The Delta Connectome: a network-based framework for studying connectivity in river deltas. Geomorphology 277, 50–62 (2017).
Feng, D., Gleason, C. J., Yang, X., Allen, G. H. & Pavelsky, T. M. How have global river widths changed over time? Water Resour. Res. 58, e2021WR031712 (2022).
Leopold, L. B. & Maddock, T. Jr The Hydraulic Geometry of Stream Channels and Some Physiographic Implications Professional Paper 252 (USGS, 1953); https://doi.org/10.3133/pp252
Hooke, J. M. Magnitude and distribution of rates of river bank erosion. Earth Surf. Process. 5, 143–157 (1980).
Ferguson, R. I. Hydraulics and hydraulic geometry. Prog. Phys. Geogr. Earth Environ. 10, 1–31 (1986).
Gleason, C. J. Hydraulic geometry of natural rivers: a review and future directions. Prog. Phys. Geogr. Earth Environ. 39, 337–360 (2015).
Morel, M., Booker, D. J., Gob, F. & Lamouroux, N. Intercontinental predictions of river hydraulic geometry from catchment physical characteristics. J. Hydrol. 582, 124292 (2020).
Gao, S. et al. Spatiotemporal variability of global river extent and the natural driving factors revealed by decades of Landsat observations, GRACE gravimetry observations, and land surface model simulations. Remote Sens. Environ. 267, 112725 (2021).
Nyberg, B., Sayre, R. & Luijendijk, E. Increasing seasonal variation in the extent of rivers and lakes from 1984 to 2022. Hydrol. Earth Syst. Sci. 28, 1653–1663 (2024).
Carbonneau, P. E. & Bizzi, S. Global mapping of river sediment bars. Earth Surf. Process. Landf. 49, 15–23 (2024).
Wang, B. et al. Remote sensing of broad-scale controls on large river anabranching. Remote Sens. Environ. 281, 113243 (2022).
de Moraes Frasson, R. P. et al. Global relationships between river width, slope, catchment area, meander wavelength, sinuosity, and discharge. Geophys. Res. Lett. 46, 3252–3262 (2019).
Peng, Y., Hao, Z., Fang, S. & Ling, F. Estimating small river widths from remote-sensing images using a fraction image-based deep learning model. Remote Sens. Lett. 16, 1280–1290 (2025).
Li, Y. et al. Global classification of river morphology based on inland water dynamics characterization and digital elevation data. Sci. Rep. 15, 14258 (2025).
Luo, Q. et al. Global alluvial channel patterns. Nat. Commun. https://doi.org/10.1038/s41467-026-68569-z (2026).
Scherer, D., Schwatke, C., Dettmering, D. & Seitz, F. ICESat-2 river surface slope (IRIS): a global reach-scale water surface slope dataset. Sci. Data 10, 359 (2023).
Brooke, S. et al. Where rivers jump course. Science 376, 987–990 (2022).
Pekel, J.-F., Cottam, A., Gorelick, N. & Belward, A. S. High-resolution mapping of global surface water and its long-term changes. Nature 540, 418–422 (2016).
Finotello, A. et al. Vegetation enhances curvature-driven dynamics in meandering rivers. Nat. Commun. 15, 1968 (2024).
Greenberg, E. & Ganti, V. The pace of global river meandering influenced by fluvial sediment supply. Earth Planet. Sci. Lett. 634, 118674 (2024).
Valman, S. J., Boyd, D. S., Carbonneau, P. E., Johnson, M. F. & Dugdale, S. J. An AI approach to operationalise global daily PlanetScope satellite imagery for river water masking. Remote Sens. Environ. 301, 113932 (2024).
Freihardt, J. & Frey, O. Assessing riverbank erosion in Bangladesh using time series of Sentinel-1 radar imagery in the Google Earth Engine. Nat. Hazards Earth Syst. Sci. 23, 751–770 (2023).
Stroud, M., Allen, G. H., Minear, J. T., Cisneros, J. & Smith, L. C. SWOT satellite: a new tool for fluvial geomorphology. GSA Today 35, 4–9 (2025).
Gleason, C. J. & Durand, M. T. Remote sensing of river discharge: a review and a framing for the discipline. Remote Sens. 12, 1107 (2020).
Durand, M. et al. An intercomparison of remote sensing river discharge estimation algorithms from measurements of river height, width, and slope. Water Resour. Res. 52, 4527–4549 (2016).
Van Dijk, A. I. J. M. et al. River gauging at global scale using optical and passive microwave remote sensing. Water Resour. Res. 52, 6404–6418 (2016).
Brinkerhoff, C. B., Gleason, C. J., Feng, D. & Lin, P. Constraining remote river discharge estimation using reach-scale geomorphology. Water Resour. Res. 56, e2020WR027949 (2020).
Gleason, C. J. & Smith, L. C. Toward global mapping of river discharge using satellite images and at-many-stations hydraulic geometry. Proc. Natl Acad. Sci. USA 111, 4788–4791 (2014).
Hagemann, M. W., Gleason, C. J. & Durand, M. T. BAM: Bayesian AMHG-Manning inference of discharge using remotely sensed stream width, slope, and height. Water Resour. Res. 53, 9692–9707 (2017).
Hou, J., van Dijk, A. I. J. M. & Beck, H. E. Global satellite-based river gauging and the influence of river morphology on its application. Remote Sens. Environ. 239, 111629 (2020).
Filippucci, P., Sahoo, D. P. & Tarpanelli, A. Two decades of river discharge from multi-mission multispectral data. Remote Sens. Environ. 329, 114919 (2025).
Brakenridge, G. R., Nghiem, S. V., Anderson, E. & Mic, R. Orbital microwave measurement of river discharge and ice status. Water Resour. Res. https://doi.org/10.1029/2006WR005238 (2007).
Saemian, P. et al. Satellite Altimetry-based Extension of global-scale in situ river discharge Measurements (SAEM). Earth Syst. Sci. Data 17, 2063–2085 (2025).
Elmi, O., Tourian, M. J., Saemian, P. & Sneeuw, N. Remote sensing-based extension of GRDC discharge time series – a monthly product with uncertainty estimates. Sci. Data 11, 240 (2024).
Smith, L. C., Isacks, B. L., Forster, R. R., Bloom, A. L. & Preuss, I. Estimation of discharge from braided glacial rivers using ERS 1 synthetic aperture radar: first results. Water Resour. Res. 31, 1325–1329 (1995).
Durand, M. et al. A framework for estimating global river discharge from the Surface Water and Ocean Topography satellite mission. Water Resour. Res. 59, e2021WR031614 (2023).
Lin, P. et al. Inversion of river discharge from remotely sensed river widths: a critical assessment at three-thousand global river gauges. Remote Sens. Environ. 287, 113489 (2023).
Andreadis, K. M. et al. A first look at river discharge estimation from SWOT satellite observations. Geophys. Res. Lett. 52, e2024GL114185 (2025).
de Moraes Frasson, R. P. et al. Exploring the factors controlling the error characteristics of the Surface Water and Ocean Topography mission discharge estimates. Water Resour. Res. 57, e2020WR028519 (2021).
Canova, M., Fulton, J. W. & Bjerklie, D. M. USGS HYDRoacoustic dataset in support of the Surface Water Oceanographic Topography satellite mission (HYDRoSWOT). USGS https://doi.org/10.5066/F7D798H6 (2016).
Abolafia-Rosenzweig, R., Pan, M., Zeng, J. L. & Livneh, B. Remotely sensed ensembles of the terrestrial water budget over major global river basins: an assessment of three closure techniques. Remote Sens. Environ. 252, 112191 (2021).
Ellis, E. A. et al. Bridging the divide between inland water quantity and quality with satellite remote sensing: an interdisciplinary review. WIREs Water 11, e1725 (2024).
Topp, S. N., Pavelsky, T. M., Jensen, D., Simard, M. & Ross, M. R. V. Research trends in the use of remote sensing for inland water quality science: moving towards multidisciplinary applications. Water 12, 169 (2020).
Hou, X., Xie, D., Feng, L., Shen, F. & Nienhuis, J. H. Sustained increase in suspended sediments near global river deltas over the past two decades. Nat. Commun. 15, 3319 (2024).
Dethier, E. N. et al. A global rise in alluvial mining increases sediment load in tropical rivers. Nature 620, 787–793 (2023).
Sun, X. et al. Changes in global fluvial sediment concentrations and fluxes between 1985 and 2020. Nat. Sustain. https://doi.org/10.1038/s41893-024-01476-7 (2025).
Kuhn, C. et al. Performance of Landsat-8 and Sentinel-2 surface reflectance products for river remote sensing retrievals of chlorophyll-a and turbidity. Remote Sens. Environ. 224, 104–118 (2019).
Herrault, P.-A. et al. Using high spatio-temporal optical remote sensing to monitor dissolved organic carbon in the Arctic river Yenisei. Remote Sens. 8, 803 (2016).
Tian, S. et al. A novel framework for river organic carbon retrieval through satellite data and machine learning. ISPRS J. Photogramm. Remote Sens. 221, 109–123 (2025).
Ramtel, P., Feng, D. & Gardner, J. Toward large-scale riverine phosphorus estimation using remote sensing and machine learning. J. Geophys. Res. Biogeosci. 129, e2024JG008121 (2024).
Ramtel, P. & Feng, D. Long-term spatio-temporal changes in total phosphorus concentration in CONUS rivers. Environ. Res. Commun. 8, 031007 (2026).
Yang, X., Pavelsky, T. M. & Allen, G. H. The past and future of global river ice. Nature 577, 69–73 (2020).
Wang, X. & Feng, L. Patterns and trends in Northern Hemisphere river ice phenology from 2000 to 2021. Remote Sens. Environ. 313, 114346 (2024).
Philippus, D., Sytsma, A., Rust, A. & Hogue, T. S. A machine learning model for estimating the temperature of small rivers using satellite-based spatial data. Remote Sens. Environ. 311, 114271 (2024).
Nilsson, C. & Berggren, K. Alterations of riparian ecosystems caused by river regulation: dam operations have caused global-scale ecological changes in riparian ecosystems. How to protect river environments and human needs of rivers remains one of the most important questions of our time. BioScience 50, 783–792 (2000).
Sun, J. et al. Convolutional neural networks facilitate river barrier detection and evidence severe habitat fragmentation in the Mekong River biodiversity hotspot. Water Resour. Res. 60, e2022WR034375 (2024).
Li, H. et al. Leveraging OpenStreetMap and multimodal remote sensing data with joint deep learning for wastewater treatment plants detection. Int. J. Appl. Earth Obs. Geoinf. 110, 102804 (2022).
Wu, J. et al. An annotated satellite imagery dataset for automated river barrier object detection. Sci. Data 12, 237 (2025).
Gu, H., Gao, Y., Fei, Y., Sun, Y. & Tian, Y. Deep learning and hydrological feature constraint strategies for dam detection: global application to Sentinel-2 remote sensing imagery. Remote Sens. 17, 1194 (2025).
Donchyts, G. et al. High-resolution surface water dynamics in Earth’s small and medium-sized reservoirs. Sci. Rep. 12, 13776 (2022).
Fan, C. et al. Emerging global reservoirs in the new millennium: abundance, hotspots, and total water storage. Sci. Bull. 69, 2179–2182 (2024).
Mulligan, M., van Soesbergen, A. & Sáenz, L. GOODD, a global dataset of more than 38,000 georeferenced dams. Sci. Data 7, 31 (2020).
Lehner, B. et al. High-resolution mapping of the world’s reservoirs and dams for sustainable river-flow management. Front. Ecol. Environ. 9, 494–502 (2011).
Minocha, S. et al. Reservoir Assessment Tool version 3.0: a scalable and user-friendly software platform to mobilize the global water management community. Geosci. Model Dev. 17, 3137–3156 (2024).
Moragoda, N. et al. Modeling and analysis of sediment trapping efficiency of large dams using remote sensing. Water Resour. Res. 59, e2022WR033296 (2023).
Andreadis, K. M. et al. Urbanizing the floodplain: global changes of imperviousness in flood-prone areas. Environ. Res. Lett. 17, 104024 (2022).
Tellman, B. et al. Satellite imaging reveals increased proportion of population exposed to floods. Nature 596, 80–86 (2021).
Mård, J., Di Baldassarre, G. & Mazzoleni, M. Nighttime light data reveal how flood protection shapes human proximity to rivers. Sci. Adv. 4, eaar5779 (2018).
Ceola, S., Laio, F. & Montanari, A. Satellite nighttime lights reveal increasing human exposure to floods worldwide. Geophys. Res. Lett. 41, 7184–7190 (2014).
Benstead, J. P. & Leigh, D. S. An expanded role for river networks. Nat. Geosci. 5, 678–679 (2012).
Allen, G. H. et al. Similarity of stream width distributions across headwater systems. Nat. Commun. 9, 610 (2018).
Godsey, S. E. & Kirchner, J. W. Dynamic, discontinuous stream networks: hydrologically driven variations in active drainage density, flowing channels and stream order. Hydrol. Process. 28, 5791–5803 (2014).
Huylenbroeck, L. et al. Using remote sensing to characterize riparian vegetation: a review of available tools and perspectives for managers. J. Environ. Manage. 267, 110652 (2020).
Brinkerhoff, C. B., Gleason, C. J., Kotchen, M. J., Kysar, D. A. & Raymond, P. A. Ephemeral stream water contributions to United States drainage networks. Science 384, 1476–1482 (2024).
Richardson, J. S. Biological diversity in headwater streams. Water 11, 366 (2019).
Wohl, E. The significance of small streams. Front. Earth Sci. 11, 447–456 (2017).
Christensen, J. R. et al. Headwater streams and inland wetlands: status and advancements of geospatial datasets and maps across the United States. Earth Sci. Rev. 235, 104230 (2022).
Vanderhoof, M. K. & Burt, C. Applying high-resolution imagery to evaluate restoration-induced changes in stream condition, Missouri River Headwaters Basin, Montana. Remote Sens. 10, 913 (2018).
Feng, D., Gleason, C. J., Yang, X. & Pavelsky, T. M. Comparing discharge estimates made via the BAM algorithm in high-order Arctic rivers derived solely from optical CubeSat, Landsat, and Sentinel-2 Data. Water Resour. Res. 55, 7753–7771 (2019).
James, L. A., Watson, D. G. & Hansen, W. F. Using LiDAR data to map gullies and headwater streams under forest canopy: South Carolina, USA. Catena 71, 132–144 (2007).
Lu, X. et al. Small Arctic rivers mapped from Sentinel-2 satellite imagery and ArcticDEM. J. Hydrol. 584, 124689 (2020).
Wang, Z. et al. Basin-scale high-resolution extraction of drainage networks using 10-m Sentinel-2 imagery. Remote Sens. Environ. 255, 112281 (2021).
Chen, Q., Mudd, S. M., Attal, M. & Hancock, S. Extracting an accurate river network: stream burning re-revisited. Remote Sens. Environ. 312, 114333 (2024).
Tarboton, D. G., Bras, R. L. & Rodriguez-Iturbe, I. On the extraction of channel networks from digital elevation data. Hydrol. Process. 5, 81–100 (1991).
Messager, M. L. et al. Global prevalence of non-perennial rivers and streams. Nature 594, 391–397 (2021).
Schwenk, J., Piliouras, A. & Rowland, J. C. Determining flow directions in river channel networks using planform morphology and topology. Earth Surf. Dyn. 8, 87–102 (2020).
Yamazaki, D., Sato, T., Kanae, S., Hirabayashi, Y. & Bates, P. D. Regional flood dynamics in a bifurcating mega delta simulated in a global river model. Geophys. Res. Lett. 41, 3127–3135 (2014).
Trigg, M. A., Bates, P. D., Wilson, M. D., Schumann, G. & Baugh, C. Floodplain channel morphology and networks of the middle Amazon River. Water Resour. Res. https://doi.org/10.1029/2012WR011888 (2012).
Wohl, E. et al. Connectivity as an emergent property of geomorphic systems. Earth Surf. Process. Landf. 44, 4–26 (2019).
Lehner, B. et al. Mapping the world’s inland surface waters: an upgrade to the Global Lakes and Wetlands Database (GLWD v2). Earth Syst. Sci. Data 17, 2277–2329 (2025).
Garambois, P.-A. & Monnier, J. Inference of effective river properties from remotely sensed observations of water surface. Adv. Water Resour. 79, 103–120 (2015).
Yoon, Y. et al. Improved error estimates of a discharge algorithm for remotely sensed river measurements: test cases on Sacramento and Garonne Rivers. Water Resour. Res. 52, 278–294 (2016).
Cerbelaud, A. et al. Satellite requirements to capture water propagation in Earth’s rivers. Rev. Geophys. 63, e2024RG000871 (2025).
Garambois, P.-A. et al. Variational estimation of effective channel and ungauged anabranching river discharge from multi-satellite water heights of different spatial sparsity. J. Hydrol. 581, 124409 (2020).
Nearing, G. et al. Global prediction of extreme floods in ungauged watersheds. Nature 627, 559–563 (2024).
Larnier, K. & Monnier, J. Hybrid neural network – variational data assimilation algorithm to infer river discharges from SWOT-like data. Comput. Geosci. 27, 853–877 (2023).
Paiva, R. C. D., Durand, M. T. & Hossain, F. Spatiotemporal interpolation of discharge across a river network by using synthetic SWOT satellite data. Water Resour. Res. 51, 430–449 (2015).
Revel, M., Zhou, X., Yamazaki, D. & Kanae, S. Assimilation of transformed water surface elevation to improve river discharge estimation in a continental-scale river. Hydrol. Earth Syst. Sci. 27, 647–671 (2023).
Langhorst, T. et al. Estimating daily suspended sediment flux from multiple data sources using deep learning. J. Geophys. Res. Earth Surf. 130, e2024JF008212 (2025).
Fichot, C. G., Tzortziou, M. & Mannino, A. Remote sensing of dissolved organic carbon (DOC) stocks, fluxes and transformations along the land-ocean aquatic continuum: advances, challenges, and opportunities. Earth Sci. Rev. 242, 104446 (2023).
Griffin, C. G., McClelland, J. W., Frey, K. E., Fiske, G. & Holmes, R. M. Quantifying CDOM and DOC in major Arctic rivers during ice-free conditions using Landsat TM and ETM+ data. Remote Sens. Environ. 209, 395–409 (2018).
Swain, R. & Sahoo, B. Mapping of heavy metal pollution in river water at daily time-scale using spatio-temporal fusion of MODIS-aqua and Landsat satellite imageries. J. Environ. Manage. 192, 1–14 (2017).
Dierssen, H. M. et al. Living up to the hype of hyperspectral aquatic remote sensing: science, resources and outlook. Front. Environ. Sci. 9, 649528 (2021).
Balasubramanian, S. V. et al. Robust algorithm for estimating total suspended solids (TSS) in inland and nearshore coastal waters. Remote Sens. Environ. 246, 111768 (2020).
Dethier, E. N., Renshaw, C. E. & Magilligan, F. J. Toward improved accuracy of remote sensing approaches for quantifying suspended sediment: implications for suspended-sediment monitoring. J. Geophys. Res. Earth Surf. 125, e2019JF005033 (2020).
Langhorst, T., Andreadis, K. M. & Allen, G. H. Global cloud biases in optical satellite remote sensing of rivers. Geophys. Res. Lett. 51, e2024GL110085 (2024).
Legleiter, C. J. & Fosness, R. L. Defining the limits of spectrally based bathymetric mapping on a large river. Remote Sens. 11, 665 (2019).
Overstreet, B. T. & Legleiter, C. J. Removing sun glint from optical remote sensing images of shallow rivers. Earth Surf. Process. Landf. 42, 318–333 (2017).
Meister, G. et al. The Ocean Color Instrument (OCI) on the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission: system design and prelaunch radiometric performance. IEEE Trans. Geosci. Remote Sens. 62, 1–18 (2024).
Hartmann, J., Lauerwald, R. & Moosdorf, N. A brief overview of the GLObal RIver Chemistry database, GLORICH. Procedia Earth Planet. Sci. 10, 23–27 (2014).
Avouris, D. M. et al. Advancements in satellite observations of inland and coastal waters: building towards a global validation network. Remote Sens. 17, 4008 (2025).
Harrigan, S. et al. GloFAS-ERA5 operational global river discharge reanalysis 1979–present. Earth Syst. Sci. Data 12, 2043–2060 (2020).
Tarpanelli, A. et al. The potential of EO data for enhanced flood monitoring and forecasting: a consortium assessment. Surv. Geophys. https://doi.org/10.1007/s10712-026-09935-w (2026).
Bates, P. D., Horritt, M. S. & Fewtrell, T. J. A simple inertial formulation of the shallow water equations for efficient two-dimensional flood inundation modelling. J. Hydrol. 387, 33–45 (2010).
Neal, J. et al. How much physical complexity is needed to model flood inundation? Hydrol. Process. 26, 2264–2282 (2012).
Sanders, B. F. & Schubert, J. E. PRIMo: Parallel Raster Inundation Model. Adv. Water Resour. 126, 79–95 (2019).
Wagner, W. et al. The fully-automatic Sentinel-1 Global Flood Monitoring service: scientific challenges and future directions. Remote Sens. Environ. 333, 115108 (2026).
Vousdoukas, M. I., Mentaschi, L., Voukouvalas, E., Verlaan, M. & Feyen, L. Extreme sea levels on the rise along Europe’s coasts. Earths Future 5, 304–323 (2017).
Wing, O. E. J. et al. Estimates of present and future flood risk in the conterminous United States. Environ. Res. Lett. 13, 034023 (2018).
Wing, O. E. J. et al. Inequitable patterns of US flood risk in the Anthropocene. Nat. Clim. Change 12, 156–162 (2022).
Winsemius, H. C. et al. Global drivers of future river flood risk. Nat. Clim. Change 6, 381–385 (2016).
Zhao, G., Bates, P., Neal, J. & Pang, B. Design flood estimation for global river networks based on machine learning models. Hydrol. Earth Syst. Sci. 25, 5981–5999 (2021).
Schneider, R., Tarpanelli, A., Nielsen, K., Madsen, H. & Bauer-Gottwein, P. Evaluation of multi-mode CryoSat-2 altimetry data over the Po River against in situ data and a hydrodynamic model. Adv. Water Resour. 112, 17–26 (2018).
Biggin, D. S. & Blyth, K. A comparison of ERS-1 satellite radar and aerial photography for river flood mapping. Water Environ. J. 10, 59–64 (1996).
Biancamaria, S. et al. Satellite radar altimetry water elevations performance over a 200 m wide river: Evaluation over the Garonne River. Adv. Space Res. 59, 128–146 (2017).
Fu, L.-L. et al. The Surface Water and Ocean Topography mission: a breakthrough in radar remote sensing of the ocean and land surface water. Geophys. Res. Lett. 51, e2023GL107652 (2024).
Yoon, Y. et al. Estimating river bathymetry from data assimilation of synthetic SWOT measurements. J. Hydrol. 464–465, 363–375 (2012).
Larnier, K., Monnier, J., Garambois, P.-A. & Verley, J. River discharge and bathymetry estimation from SWOT altimetry measurements. Inverse Probl. Sci. Eng. 29, 759–789 (2021).
Thurman, H. R., Allen, G. H., Williams, B. A., Cerbelaud, A. & David, C. H. SWOT captures hydrologic waves traveling down rivers. Geophys. Res. Lett. https://doi.org/10.1029/2024GL113875 (2025).
Wood, M., de Jong, S. M. & Straatsma, M. W. Locating flood embankments using SAR time series: a proof of concept. Int. J. Appl. Earth Obs. Geoinf. 70, 72–83 (2018).
Laipelt, L. et al. SWOT Reveals how the 2024 disastrous flood in south Brazil was intensified by increased water slope and wind forcing. Geophys. Res. Lett. 52, e2024GL111287 (2025).
Simoes-Sousa, I. T. et al. The May 2024 flood disaster in southern Brazil: causes, impacts, and SWOT-based volume estimation. Geophys. Res. Lett. 52, e2024GL112442 (2025).
Yague-Martinez, N., Leach, N. R., Dasgupta, A., Tellman, E. & Brown, J. S. Towards frequent flood mapping with the Capella SAR system. The 2021 eastern Australia floods case. In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS 6174–6177 (IEEE, 2021); https://doi.org/10.1109/IGARSS47720.2021.9554825
Ardila, J., Laurila, P., Kourkouli, P. & Strong, S. Persistent monitoring and mapping of floods globally based on the ICEYE SAR imaging constellation. In 2022 IEEE International Geoscience and Remote Sensing Symposium IGRASS 6296–6299 (IEEE, 2022); https://doi.org/10.1109/IGARSS46834.2022.9883587
Benveniste, J. Altimetric nanosatellite constellation for daily monitoring of continental watersurfaces. In 45th COSPAR Scientific Assembly 29 (2024); https://ui.adsabs.harvard.edu/abs/2024cosp…45…29B/abstract
Lopez, T., Al Bitar, A., Biancamaria, S., Güntner, A. & Jäggi, A. On the use of satellite remote sensing to detect floods and droughts at large scales. Surv. Geophys. 41, 1461–1487 (2020).
AghaKouchak, A. et al. Remote sensing of drought: progress, challenges and opportunities. Rev. Geophys. 53, 452–480 (2015).
Li, X., Tao, R. & Zhang, K. Drought monitoring based on remote sensing. In Remote Sensing of Water-Related Hazards (eds Zhang, K. et al.) 149–168 (Americal Geophysical Union, 2022); https://doi.org/10.1002/9781119159131.ch9
Rad, A. M., AghaKouchak, A., Navari, M. & Sadegh, M. Progress, challenges, and opportunities in remote sensing of drought. In Global Drought and Flood: Observation, Modeling, and Prediction (eds Wu, H. et al.) 1–28 (American Geophysical Union, 2021); https://doi.org/10.1002/9781119427339.ch1
Fang, C. et al. Satellite altimetry reveals intensifying global river water level variability. Nat. Commun. 17, 958 (2025).
Coss, S. et al. Global River Radar Altimetry Time Series (GRRATS): new river elevation earth science data records for the hydrologic community. Earth Syst. Sci. Data 12, 137–150 (2020).
Gao, H., Zhao, G., Li, Y. & Zhang, S. Drought monitoring using reservoir data collected via satellite remote sensing. In Global Drought and Flood: Observation, Modeling, and Prediction (eds Wu, H. et al.) 47–59 (American Geophysical Union, 2021); https://doi.org/10.1002/9781119427339.ch3
Sun, J. et al. Towards a comprehensive river barrier mapping solution to support environmental management. Nat. Water 3, 38–48 (2025).
International Commission on Large Dams (ICOLD) https://www.icold-cigb.org/
Camalan, S. et al. Change detection of Amazonian alluvial gold mining using deep learning and Sentinel-2 imagery. Remote Sens. 14, 1746 (2022).
Smigaj, M. et al. Monitoring riverine traffic from space: the untapped potential of remote sensing for measuring human footprint on inland waterways. Sci. Total Environ. 860, 160363 (2023).
Riggs, R. M., Allen, G. H., Brinkerhoff, C. B., Sikder, M. S. & Wang, J. Turning lakes into river gauges using the LakeFlow algorithm. Geophys. Res. Lett. 50, e2023GL103924 (2023).
Guzkowska, M. et al. Developments in Inland Water and Land Altimetry: Final Report (University College London, 1990).
Brakenridge, R. & Anderson, E. MODIS-based flood detection, mapping and measurement: the potential for operational hydrological applications. In Transboundary Floods: Reducing Risks Through Flood Management (eds Marsalek, J. et al.) 1–12 (Springer, 2006).
Linke, S. et al. Global hydro-environmental sub-basin and river reach characteristics at high spatial resolution. Sci. Data 6, 283 (2019).
McCormack, K. A. et al. Validation of TDX-Hydro; a global, TanDEM-X derived, 12m resolution hydrographic data suite. In AGU Fall Meeting Abstracts H43B-06 (American Geophysical Union, 2022); https://ui.adsabs.harvard.edu/abs/2022AGUFM.H43B..06M/abstract
Coss, S. et al. Channel water storage anomaly: a new remotely sensed quantity for global river analysis. Geophys. Res. Lett. 50, e2022GL100185 (2023).
Sentinel CHIME-A satellite mission summary. Committee on Earth Observation Satellites Earth Observation Handbook https://database.eohandbook.com/database/missionsummary.aspx?missionID=1047&utm_source=eoportal&utm_content=chime-copernicus (2024).
Surface biology and geology (SBG). NASA JPL https://sbg.jpl.nasa.gov/ (2019)
Landsat Next. NASA https://svs.gsfc.nasa.gov/5112/ (12 July 2023).
Daras, I. et al. Mass-change And Geosciences International Constellation (MAGIC) expected impact on science and applications. Geophys. J. Int. 236, 1288–1308 (2024).
MAGIC/MCDO satellite mission summary. Committee on Earth Observation Satellites Earth Observation Handbook https://database.eohandbook.com/database/missionsummary.aspx?missionID=1387&utm_source=eoportal&utm_content=magic (2024).
Vuilleumier, P. & Egido, A. S3NG-TOPO Mission Status (ESA, 2023); https://ostst.aviso.altimetry.fr/fileadmin/user_upload/OSTST2023/Presentations/OPE2023-Overview_and_Status_of_the_Copernicus_Sentinel-3_Next_Generation_Topography__S3NG-T__Mission.pdf
Donnellan, A. et al. Observing Earth’s Changing Surface Topography and Vegetation Structure: A Framework for the Decade (NASA, 2021); https://science.nasa.gov/wp-content/uploads/2023/06/STV_Study_Report_20210622.pdf