{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T18:13:01Z","timestamp":1770833581129,"version":"3.50.1"},"reference-count":94,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,6,10]],"date-time":"2021-06-10T00:00:00Z","timestamp":1623283200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U20A20317, 52009091"],"award-info":[{"award-number":["U20A20317, 52009091"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003819","name":"Natural Science Foundation of Hubei Province","doi-asserted-by":"publisher","award":["2020CFB239"],"award-info":[{"award-number":["2020CFB239"]}],"id":[{"id":"10.13039\/501100003819","id-type":"DOI","asserted-by":"publisher"}]},{"name":"\u201c111 Project\u201d Fund of China","award":["B18037"],"award-info":[{"award-number":["B18037"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Remotely sensing data have advantages in filling spatiotemporal gaps of in situ observation networks, showing potential application for monitoring floods in data-sparse regions. By using the water level retrievals of Jason-2\/3 altimetry satellites, this study estimates discharge at a 10-day timescale for the virtual station (VS) 012 and 077 across the midstream Yangtze River Basin during 2009\u20132016 based on the developed Manning formula. Moreover, we calibrate a hybrid model combined with Gravity Recovery and Climate Experiment (GRACE) data, by coupling the GR6J hydrological model with a machine learning model to simulate discharge. To physically capture the flood processes, the random forest (RF) model is employed to downscale the 10-day discharge into a daily scale. The results show that: (1) discharge estimates from the developed Manning formula show good accuracy for the VS012 and VS077 based on the improved Multi-subwaveform Multi-weight Threshold Retracker; (2) the combination of the GR6J and the LSTM models substantially improves the performance of the discharge estimates solely from either the GR6J or LSTM models; (3) RF-downscaled daily discharge demonstrates a general consistency with in situ data, where NSE\/KGE between them are as high as 0.69\/0.83. Our approach, based on multi-source remotely sensing data and machine learning techniques, may benefit flood monitoring in poorly gauged areas.<\/jats:p>","DOI":"10.3390\/rs13122272","type":"journal-article","created":{"date-parts":[[2021,6,10]],"date-time":"2021-06-10T21:34:38Z","timestamp":1623360878000},"page":"2272","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Discharge Estimation Using Integrated Satellite Data and Hybrid Model in the Midstream Yangtze River"],"prefix":"10.3390","volume":"13","author":[{"given":"Jinghua","family":"Xiong","sequence":"first","affiliation":[{"name":"State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8594-4988","authenticated-orcid":false,"given":"Shenglian","family":"Guo","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2305-8729","authenticated-orcid":false,"given":"Jiabo","family":"Yin","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1007\/s11069-006-9065-2","article-title":"Major flood disasters in Europe: 1950\u20132005","volume":"42","author":"Barredo","year":"2007","journal-title":"Nat. Hazards"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1038\/415514a","article-title":"Increasing risk of great floods in a changing climate","volume":"415","author":"Milly","year":"2002","journal-title":"Nature"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-018-06765-2","article-title":"Large increase in global storm runoff extremes driven by climate and anthropogenic changes","volume":"9","author":"Yin","year":"2018","journal-title":"Nat. Commun."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"111338","DOI":"10.1016\/j.rse.2019.111338","article-title":"Remote sensing monitoring of multi-scale watersheds impermeability for urban hydrological evaluation","volume":"232","author":"Shao","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1016\/j.scitotenv.2016.10.122","article-title":"Eurasian beaver activity increases water storage, attenuates flow and mitigates diffuse pollution from intensively-managed grasslands","volume":"576","author":"Puttock","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4419","DOI":"10.1523\/JNEUROSCI.5714-09.2010","article-title":"Longitudinal Magnetic Resonance Imaging Study of Cortical Development through Early Childhood in Autism","volume":"30","author":"Schumann","year":"2010","journal-title":"J. Neurosci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1016\/j.jhydrol.2017.06.047","article-title":"Calculating e-flow using UAV and ground monitoring","volume":"552","author":"Zhao","year":"2017","journal-title":"J. Hydrol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10712-020-09618-0","article-title":"On the Use of Satellite Remote Sensing to Detect Floods and Droughts at Large Scales","volume":"41","author":"Lopez","year":"2020","journal-title":"Surv. Geophys."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"W04405","DOI":"10.1029\/2006WR005238","article-title":"Orbital microwave measurement of river discharge and ice status","volume":"43","author":"Brakenridge","year":"2007","journal-title":"Water Resour. Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4467","DOI":"10.5194\/hess-18-4467-2014","article-title":"Evaluation of the satellite-based Global Flood Detection System for measuring river discharge: Influence of local factors","volume":"18","author":"Revillaromero","year":"2014","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2623","DOI":"10.1002\/hyp.8020","article-title":"A multi-temporal analysis of AMSR-E data for flood and discharge monitoring during the 2008 flood in Iowa","volume":"25","author":"Temimi","year":"2011","journal-title":"Hydrol. Process"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2393","DOI":"10.3390\/rs6032393","article-title":"Multi-Sensor Imaging and Space-Ground Cross-Validation for 2010 Flood along Indus River, Pakistan","volume":"6","author":"Khan","year":"2014","journal-title":"Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.rse.2013.04.010","article-title":"Toward the estimation of river discharge variations using MODIS data in ungauged basins","volume":"136","author":"Tarpanelli","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.rse.2017.04.015","article-title":"Discharge estimation and forecasting by MODIS and altimetry data in Niger-Benue River","volume":"195","author":"Tarpanelli","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"8404","DOI":"10.1029\/2018WR023808","article-title":"Extending the ability of near-infrared images to monitor small river discharge on the northeastern Tibetan Plateau","volume":"55","author":"Li","year":"2019","journal-title":"Water Resour. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.rse.2018.09.002","article-title":"The Harmonized Landsat and Sentinel-2 surface reflectance data set","volume":"219","author":"Claverie","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"599030","DOI":"10.3389\/fenvs.2020.599030","article-title":"Analysis and Classification of Stormwater and Wastewater Runoff from the Tijuana River Using Remote Sensing Imagery","volume":"8","author":"Ayad","year":"2020","journal-title":"Front. Environ. Sci."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"2021","DOI":"10.1029\/96WR00752","article-title":"Estimation of discharge from three braided Rivers using synthetic aperture radar satellite imagery: Potential application to ungauged basins","volume":"32","author":"Smith","year":"1996","journal-title":"Water Resour. Res."},{"key":"ref_20","unstructured":"Brakenridge, R., and Anderson, E. (2006). MODIS-based flood detection, mapping and measurement: The potential for operational hydrological applications. Transboundary Floods: Reducing Risks Through Flood Management, Springer."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"W03427","DOI":"10.1029\/2007WR006133","article-title":"Estimation of river discharge, propagation speed, and hydraulic geometry from space: Lena River, Siberia","volume":"44","author":"Smith","year":"2008","journal-title":"Water Resour. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"6404","DOI":"10.1002\/2015WR018545","article-title":"River gauging at global scale using optical and passive microwave remote sensing","volume":"52","author":"Brakenridge","year":"2016","journal-title":"Water Resour. Res."},{"key":"ref_23","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. Space Phys., 115.","DOI":"10.1029\/2009JC006075"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2160","DOI":"10.1016\/j.rse.2010.04.020","article-title":"Water levels in the Amazon basin derived from the ERS 2 and ENVISAT radar altimetry missions","volume":"114","author":"Calmant","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.rse.2005.10.027","article-title":"Preliminary results of ENVISAT RA-2-derived water levels validation over the Amazon basin","volume":"100","author":"Frappart","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"614","DOI":"10.1002\/wrcr.20077","article-title":"Automatic parameterization of a flow routing scheme driven by radar altimetry data: Evaluation in the Amazon basin","volume":"49","author":"Getirana","year":"2013","journal-title":"Water Resour. Res."},{"key":"ref_27","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_28","doi-asserted-by":"crossref","first-page":"341","DOI":"10.5194\/hess-19-341-2015","article-title":"Satellite radar altimetry for monitoring small rivers and lakes in Indonesia","volume":"19","author":"Sulistioadi","year":"2015","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_29","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_30","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.rse.2016.03.019","article-title":"Estimating continental river basin discharges using multiple remote sensing data sets","volume":"179","author":"Sichangi","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3787","DOI":"10.1002\/2014WR016618","article-title":"Stage-discharge rating curves based on satellite altimetry and modeled discharge in the Amazon basin","volume":"52","author":"Paris","year":"2016","journal-title":"Water Resour. Res."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1016\/j.rse.2018.12.010","article-title":"Ensemble learning regression for estimating river discharges using satellite altimetry data: Central Congo River as a Test-bed","volume":"221","author":"Kim","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1760","DOI":"10.1007\/s11431-019-9535-3","article-title":"Validation and application of water levels derived from Sentinel-3A for the Brahmaputra River","volume":"62","author":"Huang","year":"2019","journal-title":"Sci. China Technol. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"333","DOI":"10.5194\/hess-25-333-2021","article-title":"Sentinel-3 radar altimetry for river monitoring\u2014A catchment-scale evaluation of satellite water surface elevation from Sentinel-3A and Sentinel-3B","volume":"25","author":"Kittel","year":"2021","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"124561","DOI":"10.1016\/j.jhydrol.2020.124561","article-title":"River discharge estimation from radar altimetry: Assessment of satellite performance, river scales and methods","volume":"583","author":"Zakharova","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1007\/s10712-015-9346-y","article-title":"The SWOT mission and its capabilities for land hydrology","volume":"37","author":"Biancamaria","year":"2016","journal-title":"Surv. Geophys."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"638","DOI":"10.1016\/j.jhydrol.2018.02.004","article-title":"River discharge estimation from synthetic SWOT-type observations using variational data assimilation and the full Saint-Venant hydraulic model","volume":"559","author":"Oubanas","year":"2018","journal-title":"J. Hydrol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1851","DOI":"10.1175\/JHM-D-18-0206.1","article-title":"River discharge estimation based on satellite water extent and topography: An application over the Amazon","volume":"20","author":"Dinh","year":"2019","journal-title":"J. Hydrometeorol."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Chen, Y., Fok, H.S., Ma, Z., and Tenzer, R. (2019). Improved remotely sensed total basin discharge and its seasonal error characterization in the Yangtze River Basin. Sensors, 19.","DOI":"10.3390\/s19153386"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"7617","DOI":"10.1029\/2018JD030025","article-title":"Total basin discharge from GRACE and water balance method for the Yarlung Tsangpo River basin, Southwestern China","volume":"124","author":"Xie","year":"2019","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"4807","DOI":"10.1002\/wrcr.20345","article-title":"Assimilation of radar altimetry to a routing model of the Brahmaputra River","volume":"49","author":"Michailovsky","year":"2013","journal-title":"Water Resour. Res."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3331","DOI":"10.5194\/hess-24-3331-2020","article-title":"Using altimetry observations combined with GRACE to select parameter sets of a hydrological model in a data-scarce region","volume":"24","author":"Hulsman","year":"2020","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"125473","DOI":"10.1016\/j.jhydrol.2020.125473","article-title":"Assimilation of future SWOT-based river elevations, surface extent observations and discharge estimations into uncertain global hydrological models","volume":"590","author":"Sly","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Brinkerhoff, C.B., Gleason, C.J., Feng, D., and Lin, P. (2020). Constraining Remote River Discharge Estimation Using Reach-Scale Geomorphology. Water Resour. Res., 56.","DOI":"10.1029\/2020WR027949"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"041101","DOI":"10.1063\/1.5028373","article-title":"Hybrid forecasting of chaotic processes: Using machine learning in conjunction with a knowledge-based model","volume":"28","author":"Pathak","year":"2018","journal-title":"Chaos"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1007\/s11600-020-00522-0","article-title":"Machine learning for predicting discharge fluctuation of a karst spring in North China","volume":"69","author":"Cheng","year":"2021","journal-title":"Acta Geophys."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2045","DOI":"10.5194\/hess-25-2045-2021","article-title":"Rainfall\u2013runoff prediction at multiple timescales with a single Long Short-Term Memory network","volume":"25","author":"Gauch","year":"2021","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"5089","DOI":"10.5194\/hess-23-5089-2019","article-title":"Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets","volume":"23","author":"Kratzert","year":"2019","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1038\/s41586-019-0912-1","article-title":"Prabhat Deep learning and process understanding for data-driven Earth system science","volume":"566","author":"Reichstein","year":"2019","journal-title":"Nature"},{"key":"ref_50","unstructured":"Karpatne, A., Watkins, W., Read, J., and Kumar, V. (2017). Physicsguided Neural Networks (PGNN): An application in lake temperature modeling. arXiv."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"e2020WR028491","DOI":"10.1029\/2020WR028491","article-title":"Does the hook structure constrain future flood intensification under anthropogenic climate warming?","volume":"57","author":"Yin","year":"2021","journal-title":"Water Resour. Res."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"139008","DOI":"10.1016\/j.scitotenv.2020.139008","article-title":"Altimetry-derived surface water data assimilation over the Nile Basin","volume":"735","author":"Mehdi","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"129411","DOI":"10.1016\/j.chemosphere.2020.129411","article-title":"Temporal-spatial dynamics of anthropogenic nitrogen inputs and hotspots in a large river basin","volume":"269","author":"Cui","year":"2021","journal-title":"Chemosphere"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"111546","DOI":"10.1016\/j.rse.2019.111546","article-title":"Evaluation of Sentinel-3 SRAL SAR altimetry over Chinese rivers","volume":"237","author":"Jiang","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"04020036","DOI":"10.1061\/(ASCE)NH.1527-6996.0000394","article-title":"Multiscale Variability of Historical Meteorological Droughts and Floods in the Middle Yangtze River Basin, China","volume":"21","author":"Chen","year":"2020","journal-title":"Nat. Hazards Rev."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Wu, S., Cao, S., Wang, Z., Qu, X., Li, S., and Zhao, W. (2019). Spatiotemporal Variations in Agricultural Flooding In Middle and Lower Reaches of Yangtze River From 1970 to 2018. Sustainability, 11.","DOI":"10.3390\/su11236613"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Sun, Z., Zhu, X., Pan, Y., and Zhang, J. (2017). Assessing Terrestrial Water Storage and Flood Potential Using GRACE Data in the Yangtze River Basin, China. Remote Sens., 9.","DOI":"10.3390\/rs9101011"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Noel","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1995","DOI":"10.1029\/94GL01730","article-title":"ERS-1 altimeter fast delivery data quality flagging over land surfaces","volume":"21","author":"Strawbridge","year":"1994","journal-title":"Geophys. Res. Lett."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1080\/2150704X.2016.1278309","article-title":"Monitoring water level changes from retracked Jason-2 altimetry data: A case study in the Yangtze River, China","volume":"8","author":"Yuan","year":"2017","journal-title":"Remote Sens. Lett."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1007\/s00190-006-0052-x","article-title":"Coastal Gravity Anomalies from Retracked Geosat\/GM Altimetry: Improvement, Limitation and the Role of Airborne Gravity Data","volume":"80","author":"Hwang","year":"2006","journal-title":"J. Geodesy"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Liu, Z., Yao, Z., and Wang, R. (2019). Evaluation and Validation of CryoSat-2-Derived Water Levels Using In Situ Lake Data from China. Remote Sens., 11.","DOI":"10.3390\/rs11080899"},{"key":"ref_63","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_64","doi-asserted-by":"crossref","first-page":"2829","DOI":"10.5194\/hess-18-2829-2014","article-title":"Benchmarking hydrological models for low-flow simulation and forecasting on French catchments","volume":"18","author":"Nicolle","year":"2014","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1080\/02626667.2019.1659509","article-title":"Lessons learnt from checking the quality of openly accessible river flow data worldwide","volume":"65","author":"Crochemore","year":"2020","journal-title":"Hydrol. Sci. J."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Afzaal, H., Farooque, A.A., Abbas, F., Acharya, B., and Esau, T. (2020). Computation of evapotranspiration with artificial intelligence for precision water resource management. Appl. Sci., 10.","DOI":"10.3390\/app10051621"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"103622","DOI":"10.1016\/j.advwatres.2020.103622","article-title":"A long Short-Term memory cyclic model with mutual information for hydrology forecasting: A Case study in the xixian basin","volume":"141","author":"Lv","year":"2020","journal-title":"Adv. Water Resour."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"114027","DOI":"10.1088\/1748-9326\/ab4d5e","article-title":"Evaluation and machine learning improvement of global hydrological model-based flood simulations","volume":"14","author":"Yang","year":"2019","journal-title":"Environ. Res. Lett."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.jhydrol.2011.09.034","article-title":"A downward structural sensitivity analysis of hydrological models to improve low-flow simulation","volume":"411","author":"Pushpalatha","year":"2011","journal-title":"J. Hydrol."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.rse.2018.04.034","article-title":"Deriving daily water levels from satellite altimetry and land surface temperature for sparsely gauged catchments: A case study for the Mekong River","volume":"212","author":"Hung","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1023\/A:1017934522171","article-title":"Using iterated bagging to debias regressions","volume":"45","author":"Breiman","year":"2021","journal-title":"Mach. Learn."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"2225","DOI":"10.1016\/j.patrec.2010.03.014","article-title":"Variable selection using random forests","volume":"31","author":"Genuer","year":"2010","journal-title":"Pattern Recognit. Lett."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Chen, L., He, Q., Liu, K., Li, J., and Jing, C. (2019). Downscaling of GRACE-Derived Groundwater Storage Based on the Random Forest Model. Remote Sens., 11.","DOI":"10.3390\/rs11242979"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/0022-1694(70)90255-6","article-title":"River flow forecasting through conceptual models part I\u2014A discussion of principles","volume":"10","author":"Nash","year":"1970","journal-title":"J. Hydrol."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.jhydrol.2009.08.003","article-title":"Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling","volume":"377","author":"Gupta","year":"2009","journal-title":"J. Hydrol."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"7441","DOI":"10.1029\/2018WR024010","article-title":"Assimilation of Satellite Altimetry Data for Effective River Bathymetry","volume":"55","author":"Paiva","year":"2019","journal-title":"Water Resour. Res."},{"key":"ref_77","unstructured":"Bergmann-Wolf, I., Forootan, E., Klemann, V., Kusche, J., and Dobslaw, H. (2015). Updating ESA\u2019s Earth System Model for Gravity Mission Simulation Studies: 3. A Realistically Perturbed Nontidal Atosphere and Ocan Dealiasing Model, (Scientific Technical Report; 14\/09), Potsdam: Deutsches Geo Forschungs Zentrum, GFZ."},{"key":"ref_78","unstructured":"G\u00fcrr, M., Behzadpur, S., Ellmer, M., Kvas, A., Klinger, B., Strasser, S., and Zehentner, N. (2018). ITSG-Grace2018-monthly, daily and static gravity field solutions from GRACE. GFZ Data Serv."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"9332","DOI":"10.1029\/2019JB017415","article-title":"ITSG-Grace2018: Overview and evaluation of a new GRACE-only gravity field time series","volume":"124","author":"Kvas","year":"2019","journal-title":"J. Geophys. Res. Solid Earth"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1175\/BAMS-D-13-00164.1","article-title":"The global precipitation measurement mission","volume":"95","author":"Hou","year":"2014","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1175\/JHM-D-15-0059.1","article-title":"Statistical and Hydrological Comparisons between TRMM and GPM Level-3 Products over a Midlatitude Basin: Is Day-1 IMERG a Good Successor for TMPA 3B42V7?","volume":"17","author":"Tang","year":"2016","journal-title":"J. Hydrometeorol."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"1927","DOI":"10.1002\/hyp.1458","article-title":"Estimating river discharge from very high-resolution satellite data: A case study in the Yangtze River, China","volume":"18","author":"Xu","year":"2004","journal-title":"Hydrol. Process."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1007\/s10661-006-5233-9","article-title":"Measuring Water Storage Fluctuations in Lake Dongting, China, by Topex\/Poseidon Satellite Altimetry","volume":"115","author":"Zhang","year":"2006","journal-title":"Environ. Monit. Assess."},{"key":"ref_84","unstructured":"Berrisford, P., Dee, D.P., Poli, P., Brugge, R., Fielding, M., Fuentes, M., Kallberg, P.W., Kobayashi, S., Uppala, S., and Simmons, A. (2011). The ERA-Interim Archive Version 2.0, ECMWF. Available online: https:\/\/www.ecmwf.int\/node\/8174."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Sichangi, A.W., Wang, L., and Hu, Z. (2018). Estimation of River Discharge Solely from Remote-Sensing Derived Data: An Initial Study Over the Yangtze River. Remote Sens., 10.","DOI":"10.3390\/rs10091385"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"125878","DOI":"10.1016\/j.jhydrol.2020.125878","article-title":"Blending multi-satellite, atmospheric reanalysis and gauge precipitation products to facilitate hydrological modelling","volume":"593","author":"Yin","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"1207","DOI":"10.1007\/s10584-020-02929-6","article-title":"Comprehensive evaluation of hydrological models for climate change impact assessment in the Upper Yangtze River Basin, China","volume":"163","author":"Wen","year":"2020","journal-title":"Clim. Chang."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Wu, Y., Luo, G., Chen, C., Duan, Z., and Gao, C. (2021). Using Integrated Hydrological Models to Assess the Impacts of Climate Change on Discharges and Extreme Flood Events in the Upper Yangtze River Basin. Water, 13.","DOI":"10.3390\/w13030299"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"1358","DOI":"10.2166\/nh.2020.026","article-title":"Using long short-term memory networks for river flow prediction","volume":"51","author":"Xu","year":"2020","journal-title":"Hydrol. Res."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"90069","DOI":"10.1109\/ACCESS.2020.2993874","article-title":"Streamflow Prediction Using Deep Learning Neural Network: Case Study of Yangtze River","volume":"8","author":"Liu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.rse.2014.08.006","article-title":"Drought and flood monitoring for a large karst plateau in Southwest China using extended GRACE data","volume":"155","author":"Long","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1038\/s41558-019-0456-2","article-title":"Contributions of GRACE to understanding climate change","volume":"9","author":"Tapley","year":"2019","journal-title":"Nat. Clim. Chang."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"105331","DOI":"10.1016\/j.atmosres.2020.105331","article-title":"Quantifying uncertainty sources in extreme flow projections for three watersheds with different climate features in China","volume":"249","author":"Zhang","year":"2021","journal-title":"Atmos. Res."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Fok, H.S., and He, Q. (2018). Water Level Reconstruction Based on Satellite Gravimetry in the Yangtze River Basin. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7070286"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/12\/2272\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:12:53Z","timestamp":1760163173000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/12\/2272"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,10]]},"references-count":94,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["rs13122272"],"URL":"https:\/\/doi.org\/10.3390\/rs13122272","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,10]]}}}