{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T01:33:37Z","timestamp":1779932017549,"version":"3.53.1"},"reference-count":49,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2023,10,30]],"date-time":"2023-10-30T00:00:00Z","timestamp":1698624000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2021YFC3200102"],"award-info":[{"award-number":["2021YFC3200102"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2019QZKK0207-02"],"award-info":[{"award-number":["2019QZKK0207-02"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["52179002"],"award-info":[{"award-number":["52179002"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Second Tibetan Plateau Scientific Expedition and Research Program (STEP)","award":["2021YFC3200102"],"award-info":[{"award-number":["2021YFC3200102"]}]},{"name":"Second Tibetan Plateau Scientific Expedition and Research Program (STEP)","award":["2019QZKK0207-02"],"award-info":[{"award-number":["2019QZKK0207-02"]}]},{"name":"Second Tibetan Plateau Scientific Expedition and Research Program (STEP)","award":["52179002"],"award-info":[{"award-number":["52179002"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2021YFC3200102"],"award-info":[{"award-number":["2021YFC3200102"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2019QZKK0207-02"],"award-info":[{"award-number":["2019QZKK0207-02"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52179002"],"award-info":[{"award-number":["52179002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>River water surface extent can be extracted from optical and radar satellite images; this is useful for estimating streamflow from space. The radiation characteristics of open water from the visible and microwave bands are different and provide independent information. In this study, for the purpose of improving streamflow estimation from space for data-sparse regions, a method that combines satellite optical and radar images data for streamflow estimation using a machine learning technique was proposed. The method was demonstratedthrough a case study in the river segment upstream of the Ganzi gauging station on the Yalong River, China. Utilizing the support vector regression (SVR) model, the feasibility of different combinations of water surface area derived from Sentinel-1 synthetic aperture radar images (AREA_SAR), modified normalized difference water index derived from Landsat 8 images (MNDWI), and reflectance ratios between NIR and SWIR channels derived from MODIS images (RNIR\/RSWIR) for streamflow estimation were evaluated through three experiments. In Experiment I, three models using AREA_SAR (Model 1), MNDWI (Model 2), and a combination of AREA_SAR and MNDWI (Model 3) were built; the mean relative error (MRE) and mean absolute error (MAE) of streamflow estimates corresponding to the SVR model using both AREA_SAR and MNDWI (Model 3) were 0.19 and 31.6 m3\/s for the testing dataset, respectively, and were lower than two models using AREA_SAR (Model 1) or MNDWI (Model 2) solely as inputs. In Experiment II, three models with AREA_SAR (Model 4), RNIR\/RSWIR (Model 5), and a combination of AREA_SAR and RNIR\/RSWIR (Model 6) as inputs were developed; the MRE and MAE for the model using AREA_SAR and RNIR\/RSWIR (Model 6) were 0.25 and 56.5 m3\/s, respectively, which outperformed the two models treating AREA_SAR (Model 4) or MNDWI (Model 5) as single types of inputs. In Experiment III, three models using AREA_SAR (Model 7), MNDWI, and RNIR\/RSWIR (Model 8) and the combination of AREA_SAR, MNDWI and RNIR\/RSWIR (Model 9) were built; combining all three types of satellite observations (Model 9) exhibited the highest accuracy, for which the MRE and MAE were 0.18 and 18.4 m3\/s, respectively. The results of all three experiments demonstrated that integrating optical and microwave observations could improve the accuracy of streamflow estimates using a data-driven model; the proposed method has great potential for near-real-time estimations of flood magnitude or to reconstruct past variations in streamflow using historical satellite images in data-sparse regions.<\/jats:p>","DOI":"10.3390\/rs15215184","type":"journal-article","created":{"date-parts":[[2023,10,30]],"date-time":"2023-10-30T13:20:07Z","timestamp":1698672007000},"page":"5184","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Combining Satellite Optical and Radar Image Data for Streamflow Estimation Using a Machine Learning Method"],"prefix":"10.3390","volume":"15","author":[{"given":"Xingcan","family":"Wang","sequence":"first","affiliation":[{"name":"Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, College of Water Sciences, Beijing Normal University, Xinjiekouwai Street 19, Beijing 100875, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenchao","family":"Sun","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, College of Water Sciences, Beijing Normal University, Xinjiekouwai Street 19, Beijing 100875, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fan","family":"Lu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Zuo","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, College of Water Sciences, Beijing Normal University, Xinjiekouwai Street 19, Beijing 100875, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.jhydrol.2012.09.035","article-title":"Calibration of satellite measurements of river discharge using a global hydrology model","volume":"475","author":"Cohen","year":"2012","journal-title":"J. Hydrol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1212","DOI":"10.1002\/2017WR021919","article-title":"Estimating the Natural Flow Regime of Rivers with Long-Standing Development: The Northern Branch of the Rio Grande","volume":"54","author":"Blythe","year":"2018","journal-title":"Water Resour. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/S0022-1694(03)00129-X","article-title":"Evaluating the potential for measuring river discharge from space","volume":"278","author":"Bjerklie","year":"2003","journal-title":"J. Hydrol."},{"key":"ref_4","first-page":"587","article-title":"Proof of Concept of an Altimeter-Based River Forecasting System for Transboundary Flow Inside Bangladesh","volume":"7","author":"Hossain","year":"2014","journal-title":"IEEE J.-Stars."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Biancamaria, S., Hossain, F., and Lettenmaier, D.P. (2011). Forecasting transboundary river water elevations from space. Geophys. Res. Lett., 38.","DOI":"10.1029\/2011GL047290"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1140","DOI":"10.1002\/2015WR017654","article-title":"Spatiotemporal densification of river water level time series by multimission satellite altimetry","volume":"52","author":"Tourian","year":"2016","journal-title":"Water Resour. Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"157","DOI":"10.5194\/hess-9-157-2005","article-title":"A comparison of regionalisation methods for catchment model parameters","volume":"9","author":"Parajka","year":"2005","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.advwatres.2015.02.007","article-title":"Inference of effective river properties from remotely sensed observations of water surface","volume":"79","author":"Garambois","year":"2015","journal-title":"Adv. Water Resour."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"9604","DOI":"10.1002\/2014WR016109","article-title":"Retrieval of river discharge solely from satellite imagery and at-many-stations hydraulic geometry: Sensitivity to river form and optimization parameters","volume":"50","author":"Gleason","year":"2014","journal-title":"Water Resour. Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.rse.2018.04.018","article-title":"An improved approach to monitoring Brahmaputra River water levels using retracked altimetry data","volume":"211","author":"Huang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.jhydrol.2010.04.013","article-title":"Integrating spatial altimetry data into the automatic calibration of hydrological models","volume":"387","author":"Getirana","year":"2010","journal-title":"J. Hydrol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1016\/j.jhydrol.2005.12.006","article-title":"Rating curves and estimation of average water depth at the upper Negro River based on satellite altimeter data and modeled discharges","volume":"328","author":"Leon","year":"2006","journal-title":"J. Hydrol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3524","DOI":"10.1002\/hyp.8429","article-title":"Calibration of hydrological models in ungauged basins based on satellite radar altimetry observations of river water level","volume":"26","author":"Sun","year":"2012","journal-title":"Hydrol. Process."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.rse.2004.07.007","article-title":"Ob\u2019 river discharge from TOPEX\/Poseidon satellite altimetry (1992\u20132002)","volume":"93","author":"Kouraev","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Papa, F., Durand, F., Rossow, W.B., Rahman, A., and Bala, S.K. (2010). Satellite altimeter-derived monthly discharge of the Ganga-Brahmaputra River and its seasonal to interannual variations from 1993 to 2008. J. Geophys. Res. Ocean., 115.","DOI":"10.1029\/2009JC006075"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e2020WR027309","DOI":"10.1029\/2020WR027309","article-title":"Daily Continuous River Discharge Estimation for Ungauged Basins Using a Hydrologic Model Calibrated by Satellite Altimetry: Implications for theSWOT Mission","volume":"56","author":"Huang","year":"2020","journal-title":"Water Resour. Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1109\/TGRS.2018.2854625","article-title":"Daily River Discharge Estimates by Merging Satellite Optical Sensors and Radar Altimetry Through Artificial Neural Network","volume":"57","author":"Tarpanelli","year":"2019","journal-title":"IEEE Trans. Geosci. Remote."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1029\/2018WR023743","article-title":"Assessing the Potential of the Surface Water and Ocean Topography Mission for Reservoir Monitoring in the Mekong River Basin","volume":"55","author":"Bonnema","year":"2019","journal-title":"Water Resour. Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.14358\/PERS.72.10.1171","article-title":"Landsat: Yesterday, today, and tomorrow","volume":"72","author":"Williams","year":"2006","journal-title":"Photogramm. Eng. Rem. S."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"54021","DOI":"10.1088\/1748-9326\/ab82cb","article-title":"Estimation of water volume in ungauged, dynamic floodplain lakes","volume":"15","author":"Tan","year":"2020","journal-title":"Environ. Res. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1325","DOI":"10.1029\/95WR00145","article-title":"Estimation of discharge from braided glacial rivers using ERS 1 synthetic aperture radar: First results","volume":"31","author":"Smith","year":"1995","journal-title":"Water Resour. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.rse.2018.10.008","article-title":"Discharge estimation in high-mountain regions with improved methods using multisource remote sensing: A case study of the Upper Brahmaputra River","volume":"219","author":"Huang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"e2019WR025326","DOI":"10.1029\/2019WR025326","article-title":"A Rainfall-Runoff Model with LSTM-Based Sequence-to-Sequence Learning","volume":"56","author":"Xiang","year":"2020","journal-title":"Water Resour. Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"124371","DOI":"10.1016\/j.jhydrol.2019.124371","article-title":"Least square support vector machine and multivariate adaptive regression splines for streamflow prediction in mountainous basin using hydro-meteorological data as inputs","volume":"586","author":"Adnan","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Band, S.S., Janizadeh, S., Chandra Pal, S., Saha, A., Chakrabortty, R., Melesse, A.M., and Mosavi, A. (2020). Flash Flood Susceptibility Modeling Using New Approaches of Hybrid and Ensemble Tree-Based Machine Learning Algorithms. Remote Sens., 12.","DOI":"10.3390\/rs12213568"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3429","DOI":"10.1016\/j.asej.2021.03.014","article-title":"An assessment of sedimentation in Terengganu River, Malaysia using satellite imagery","volume":"12","author":"Aziz","year":"2021","journal-title":"Ain Shams Eng. J."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"116121","DOI":"10.1016\/j.jenvman.2022.116121","article-title":"Integrated remote sensing and machine learning tools for estimating ecological flow regimes in tropical river reaches","volume":"322","author":"Sahoo","year":"2022","journal-title":"J. Environ. Manage."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"902","DOI":"10.1109\/TGRS.2018.2862640","article-title":"Applying Upstream Satellite Signals and a 2-D Error Minimization Algorithm to Advance Early Warning and Management of Flood Water Levels and River Discharge","volume":"57","author":"Zaji","year":"2019","journal-title":"IEEE Trans. Geosci. Remote."},{"key":"ref_29","unstructured":"Google Earth Engine (2019). Sentinel-1 SAR GRD: C-band Synthetic Aperture Radar Ground Range Detected, ESA."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2363","DOI":"10.1109\/36.789635","article-title":"Polarimetric SAR speckle filtering and its implication for classification","volume":"37","author":"Lee","year":"1999","journal-title":"IEEE Trans. Geosci. Remote."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3025","DOI":"10.1080\/01431160600589179","article-title":"Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery","volume":"27","author":"Xu","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1852","DOI":"10.1109\/TGRS.2012.2208466","article-title":"A New Short-Wave Infrared (SWIR) Method for Quantitative Water Fraction Derivation and Evaluation with EOS\/MODIS and Landsat\/TM Data","volume":"51","author":"Li","year":"2013","journal-title":"IEEE Trans. Geosci. Remote."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A Threshold Selection Method from Gray-Level Histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1623\/hysj.51.4.599","article-title":"Using support vector machines for long-term discharge prediction","volume":"51","author":"Lin","year":"2006","journal-title":"Hydrol. Sci. J."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"130141","DOI":"10.1016\/j.jhydrol.2023.130141","article-title":"A review of hybrid deep learning applications for streamflow forecasting","volume":"625","author":"Ng","year":"2023","journal-title":"J. Hydrol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1016\/j.jhydrol.2006.04.030","article-title":"Downscaling of precipitation for climate change scenarios: A support vector machine approach","volume":"330","author":"Tripathi","year":"2006","journal-title":"J. Hydrol."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Vapnik, V.N. (1995). The Nature of Statistical Learning Theory, Springer.","DOI":"10.1007\/978-1-4757-2440-0"},{"key":"ref_38","unstructured":"Kenedy, J., and Eberheart, R. (December, January 27). Particle Swarm Optimization. Proceedings of the IEEE International Conference on Neural Networks, Perth, WA, Australia."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"10751","DOI":"10.1007\/s00521-022-07009-7","article-title":"Groundwater level prediction using machine learning algorithms in a drought-prone area","volume":"34","author":"Pham","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"138563","DOI":"10.1016\/j.jclepro.2023.138563","article-title":"Integrating remote sensing derived indices and machine learning algorithms for precise extraction of small surface water bodies in the lower Thoubal river watershed, India","volume":"422","author":"Rahaman","year":"2023","journal-title":"J. Clean. Prod."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2233","DOI":"10.1080\/02626667.2020.1792473","article-title":"Suitability of satellite-based hydro-climate variables and machine learning for streamflow modeling at various scale watersheds","volume":"65","author":"Seyoum","year":"2020","journal-title":"Hydrol. Sci. J."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"124296","DOI":"10.1016\/j.jhydrol.2019.124296","article-title":"Streamflow and rainfall forecasting by two long short-term memory-based models","volume":"583","author":"Ni","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"630","DOI":"10.1016\/j.jhydrol.2016.10.037","article-title":"Improving daily streamflow forecasts in mountainous Upper Euphrates basin by multi-layer perceptron model with satellite snow products","volume":"543","author":"Uysal","year":"2016","journal-title":"J. Hydrol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1675","DOI":"10.1007\/s11069-022-05363-2","article-title":"Deep insight into daily runoff forecasting based on a CNN-LSTM model","volume":"113","author":"Deng","year":"2022","journal-title":"Nat. Hazards."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"127297","DOI":"10.1016\/j.jhydrol.2021.127297","article-title":"Improving streamflow prediction in the WRF-Hydro model with LSTM networks","volume":"605","author":"Cho","year":"2022","journal-title":"J. Hydrol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Xiong, J., Guo, S., and Yin, J. (2021). Discharge Estimation Using Integrated Satellite Data and Hybrid Model in the Midstream Yangtze River. Remote Sens., 13.","DOI":"10.3390\/rs13122272"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"706","DOI":"10.1016\/j.jhydrol.2015.01.084","article-title":"Satellite-supported flood forecasting in river networks: A real case study","volume":"523","author":"Mason","year":"2015","journal-title":"J. Hydrol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"e2020WR027794","DOI":"10.1029\/2020WR027794","article-title":"Combining Optical Remote Sensing, McFLI Discharge Estimation, Global Hydrologic Modeling, and Data Assimilation to Improve Daily Discharge Estimates Across an Entire Large Watershed","volume":"57","author":"Ishitsuka","year":"2021","journal-title":"Water Resour. Res."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/21\/5184\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:14:33Z","timestamp":1760130873000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/21\/5184"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,30]]},"references-count":49,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2023,11]]}},"alternative-id":["rs15215184"],"URL":"https:\/\/doi.org\/10.3390\/rs15215184","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,30]]}}}