392 citations as recorded by crossref.

1. Water Body Mapping Using Long Time Series Sentinel-1 SAR Data in Poyang Lake G. Shen et al. https://doi.org/10.3390/w14121902
2. NASA’s MODIS/VIIRS Global Water Reservoir Product Suite from Moderate Resolution Remote Sensing Data Y. Li et al. https://doi.org/10.3390/rs13040565
3. Identifying Flood Events over the Poyang Lake Basin Using Multiple Satellite Remote Sensing Observations, Hydrological Models and In Situ Data H. Zhou et al. https://doi.org/10.3390/rs10050713
4. Possible role of anthropogenic climate change in the record-breaking 2020 Lake Victoria levels and floods R. Pietroiusti et al. https://doi.org/10.5194/esd-15-225-2024
5. Tracking lake surface elevations with proportional hypsometric relationships, Landsat imagery, and multiple DEMs D. Weekley & X. Li https://doi.org/10.1029/2020WR027666
6. Streamflow prediction in “geopolitically ungauged” basins using satellite observations and regionalization at subcontinental scale T. Du et al. https://doi.org/10.1016/j.jhydrol.2020.125016
7. A Large-Scale Evaluation of SWOT-Derived Water Surface Elevations: Precision Drivers and Strategies to Enhance Data Availability T. Lappicy et al. https://doi.org/10.3390/rs18101609
8. Long-Term Change of Lake Water Storage and Its Response to Climate Change for Typical Lakes in Arid Xinjiang, China Z. Huang et al. https://doi.org/10.3390/w15081444
9. Evaporation from the hypersaline Aral Sea in Central Asia X. Ma et al. https://doi.org/10.1016/j.scitotenv.2023.168412
10. Remote Sensing-Based Long-Term Assessment of Water Dynamics and Influencing Factors in Abaya and Chamo Lakes, East African Rift Valley, Ethiopia B. Mulu et al. https://doi.org/10.1177/11786221241299932
11. Hydro-meteorological drivers of spatiotemporal variability in lake water storage across China: Evidence from ICESat-2 H. Xu et al. https://doi.org/10.1016/j.jhydrol.2026.135449
12. Recent global decline in endorheic basin water storages J. Wang et al. https://doi.org/10.1038/s41561-018-0265-7
13. Monitoring drought in ungauged areas using satellite altimetry: The Standardized River Stage Index R. Zhong et al. https://doi.org/10.1016/j.jhydrol.2022.128308
14. Improving mean water lake surface elevation estimates using dense lidar measurements from the GEDI satellite mission F. Frappart et al. https://doi.org/10.1016/j.rsase.2024.101213
15. Overcoming hydrological forecasting challenges through augmented adaptive deep algorithms: a case study of the great lakes across Canada and the U.S M. Zeynoddin et al. https://doi.org/10.1007/s00704-025-05819-y
16. Multi-Objective Calibration of a Pre-Modern Nile Hydrologic Model Using Recovered Records I. Munyejuru & J. Stagge https://doi.org/10.3390/hydrology13040114
17. Virtual water gauge from the Synthetic Aperture Radar (SAR) altimeters for small reservoirs in tropical regions M. Abdul Majid et al. https://doi.org/10.1016/j.asr.2025.11.060
18. From abundance to aridity: The institutional drivers behind Lake Urmia's decline M. Taraghi et al. https://doi.org/10.1016/j.envdev.2025.101205
19. Reconstructing reservoir water levels using basin-scale GRACE TWS and machine learning: A case study in the Upper Parana River Basin J. Besnier et al. https://doi.org/10.1016/j.ejrh.2026.103503
20. An era of Sentinels in flood management: Potential of Sentinel-1, -2, and -3 satellites for effective flood management S. Kuntla https://doi.org/10.1515/geo-2020-0325
21. Fusing SAR image and CYGNSS data for monitoring river water level changes by machine learning Y. Jia et al. https://doi.org/10.1016/j.rse.2025.114927
22. The accuracy of the Sentinel-3A altimetry over Polish rivers M. Halicki & T. Niedzielski https://doi.org/10.1016/j.jhydrol.2021.127355
23. Deriving daily water levels from satellite altimetry and land surface temperature for sparsely gauged catchments: A case study for the Mekong River H. Pham et al. https://doi.org/10.1016/j.rse.2018.04.034
24. Observation-based analysis of groundwater storage in Vietnam’s Central Highlands: influence of hydro-climatic variability S. Sayyadi et al. https://doi.org/10.1080/02626667.2025.2568558
25. Monitoring water depth, surface area and volume changes in Lake Victoria: integrating the bathymetry map and remote sensing data during 1993–2016 A. Sichangi & G. Makokha https://doi.org/10.1007/s40808-017-0311-2
26. The impact of the satellite ground track shift on the accuracy of altimetric measurements on rivers: A case study of the Sentinel-3 altimetry on the Odra/Oder River M. Halicki et al. https://doi.org/10.1016/j.jhydrol.2022.128761
27. Limits to GRACE-based groundwater storage monitoring in a 23 000 km2 coastal basin: evidence from the Lower Kutai Basin, Indonesia R. Taylor et al. https://doi.org/10.5194/hess-30-4771-2026
28. Transitioning from MODIS to VIIRS Global Water Reservoir Product D. Shah et al. https://doi.org/10.1038/s41597-024-03028-2
29. Satellite Laser Altimetry Reveals a Net Water Mass Gain in Global Lakes With Spatial Heterogeneity in the Early 21st Century S. Luo et al. https://doi.org/10.1029/2021GL096676
30. Satellite-based water surface slope over a small mountain river in northern China H. Lyu & F. Tian https://doi.org/10.1016/j.jhydrol.2024.131576
31. Polygon-Informed Cross-Track Altimetry (PICTA): Estimating river water level profiles with the Sentinel-6 altimeter F. Ehlers et al. https://doi.org/10.1016/j.rse.2024.114479
32. Synergy between Satellite Altimetry and Optical Water Quality Data towards Improved Estimation of Lakes Ecological Status A. Ansper-Toomsalu et al. https://doi.org/10.3390/rs13040770
33. Comparison of the Hydrological Dynamics of Poyang Lake in the Wet and Dry Seasons F. Sun et al. https://doi.org/10.3390/rs13050985
34. The Potential Applications of Satellite Altimetry with SARAL/AltiKa for Indian Inland Waters S. Ghosh et al. https://doi.org/10.1007/s40010-017-0463-5
35. Estimating and Assessing Monthly Water Level Changes of Reservoirs and Lakes in Jiangsu Province Using Sentinel-3 Radar Altimetry Data J. Xu et al. https://doi.org/10.3390/rs16050808
36. Investigating Hydroclimatic Variables Trends on the Natural Lakes of Western Greece Using Earth Observation Data N. Gourgouletis & E. Baltas https://doi.org/10.3390/s23042056
37. Potential of ENVISAT Radar Altimetry for Water Level Monitoring in the Pantanal Wetland D. Dettmering et al. https://doi.org/10.3390/rs8070596
38. Monitoring and Analysis of Spatiotemporal Variations in Poyang Lake (2016–2024) Using Sentinel-1/2 Imagery Y. Sun & H. Jin https://doi.org/10.62051/ijnres.v7n3.03
39. Shrinking Tonlé Sap and the recent intensification of sand mining in the Cambodian Mekong River W. NG & E. Park https://doi.org/10.1016/j.scitotenv.2021.146180
40. Unveiling cascading lag effects of wetland methane emissions: Evidence from Lake Chad in Africa R. Liu et al. https://doi.org/10.1126/sciadv.adx9866
41. Long-term lake water levels in central Chile using satellite altimetry and conceptual hydrological modelling approaches M. Pedreros-Guarda et al. https://doi.org/10.1016/j.asr.2025.05.091
42. Improvement of Deep Learning Models for River Water Level Prediction Using Complex Network Method D. Kim et al. https://doi.org/10.3390/w14030466
43. Changing Pattern of Water Level Trends in Eurasian Endorheic Lakes as a Response to the Recent Climate Variability X. Zhang et al. https://doi.org/10.3390/rs13183705
44. Airborne Laser Scanning for calibration and validation of inshore satellite altimetry: A proof of concept A. Zlinszky et al. https://doi.org/10.1016/j.rse.2017.04.027
45. A multi-sensor satellite imagery approach to monitor on-farm reservoirs V. Perin et al. https://doi.org/10.1016/j.rse.2021.112796
46. Mapping near-daily large lake water surface topography by integrating SWOT swath observations with multi-mission satellite radar altimetry S. Shu et al. https://doi.org/10.1016/j.jhydrol.2026.135724
47. A novel approach for river discharge prediction in Lancang Mekong River Basin: Incorporation of multisource remote sensing and LSTM model T. Aryal et al. https://doi.org/10.1016/j.ejrh.2026.103326
48. Reconstruction of Effective Cross-Sections from DEMs and Water Surface Elevation I. Rezende et al. https://doi.org/10.3390/rs17061020
49. Monitoring Large-Scale Inland Water Dynamics by Fusing Sentinel-1 SAR and Sentinel-3 Altimetry Data and by Analyzing Causal Effects of Snowmelt Y. Tsai et al. https://doi.org/10.3390/rs12233896
50. Generation and analysis of stage-fall-discharge laws from coupled hydrological-hydraulic river network model integrating sparse multi-satellite data T. Malou et al. https://doi.org/10.1016/j.jhydrol.2021.126993
51. Improving Sentinel-3 SAR Mode Processing Over Lake Using Numerical Simulations F. Boy et al. https://doi.org/10.1109/TGRS.2021.3137034
52. Characterization of the hydro-geological regime of fractured aquifers in Benin (West-Africa) using multi-satellites and models F. Oussou et al. https://doi.org/10.1016/j.ejrh.2021.100987
53. A Scalable Framework for Reservoir Water-Level Monitoring Using Optical–SAR Integration in Data-Sparse Regions K. Han et al. https://doi.org/10.1109/TGRS.2025.3624378
54. What Is Going on with Intrasecular Water Level Fluctuations in Great Lakes of Eurasia? N. Filatov & V. Malinin https://doi.org/10.1134/S1028334X25608430
55. Monitoring of Hydrological Resources in Surface Water Change by Satellite Altimetry W. Li et al. https://doi.org/10.3390/rs14194904
56. Brazil’s Amazon Oxygen Crisis: How Lives and Health Were Sacrificed During the Peak of COVID-19 to Promote an Agenda with Long-Term Consequences for the Environment, Indigenous Peoples, and Health L. Ferrante & P. Fearnside https://doi.org/10.1007/s40615-023-01626-1
57. Exploring the Performance of SWOT Satellite to Monitor Lake Volumes: A Case Study of Chinese Lakes R. Jing et al. https://doi.org/10.34133/remotesensing.1026
58. Mapping bathymetry on Tibetan Plateau lakes using ICESat-2 laser altimetry H. He et al. https://doi.org/10.1080/01431161.2025.2496531
59. Effects of upstream and downstream boundary conditions on lacustrine shallow-water delta morphologies: A numerical modeling approach P. Nota et al. https://doi.org/10.1016/j.marpetgeo.2024.106966
60. Potential and Limitations of Satellite Altimetry Constellations for Monitoring Surface Water Storage Changes—A Case Study in the Mississippi Basin D. Dettmering et al. https://doi.org/10.3390/rs12203320
61. Surface water dynamics of Africa: Analysing continental trends and identifying drivers for major lakes and reservoirs P. Sogno et al. https://doi.org/10.1080/01431161.2024.2412802
62. Statistical and Machine Learning Methods for River Water Level Time Series Construction Using Satellite Altimetry N. Semenova et al. https://doi.org/10.1134/S001095252460121X
63. On the capacity of ICESat-2 laser altimetry for river level retrieval: An investigation in the Ohio River basin H. Li et al. https://doi.org/10.1016/j.jhydrol.2023.130277
64. Enhanced Satellite Monitoring of Reservoir Water Storage Variations in the Yangtze River Basin from 1990 to 2023 Y. Wang et al. https://doi.org/10.34133/remotesensing.0384
65. SWOT-derived high-resolution lake surface topography for enhanced lake water level retrieval X. Luo et al. https://doi.org/10.1080/15481603.2026.2707798
66. Satellite altimetry for Indian reservoirs K. Verma et al. https://doi.org/10.1016/j.wse.2021.09.001
67. The Potential of EO Data for Enhanced Flood Monitoring and Forecasting: A Consortium Assessment A. Tarpanelli et al. https://doi.org/10.1007/s10712-026-09935-w
68. Hydrological model calibration in data-deficient basins using satellite altimetry and a hydrodynamic model H. Lyu et al. https://doi.org/10.1016/j.jhydrol.2024.132022
69. Stage‐Slope‐Discharge Relationships Upstream of River Confluences Revealed by Satellite Altimetry J. Liu et al. https://doi.org/10.1029/2023GL106394
70. Divergent hydrologic regimes of mega-rivers originated from High Mountain Asia uncovered by satellite virtual station-densified water levels F. Zeng et al. https://doi.org/10.1016/j.jhydrol.2025.133214
71. Influence of local geoid variation on water surface elevation estimates derived from multi-mission altimetry for Lake Namco L. Jiang et al. https://doi.org/10.1016/j.rse.2018.11.004
72. Satellite-based drought analysis in the Zambezi River Basin: Was the 2019 drought the most extreme in several decades as locally perceived? P. Hulsman et al. https://doi.org/10.1016/j.ejrh.2021.100789
73. Global intercomparison of satellite-derived variability in reservoir storage S. Cooley et al. https://doi.org/10.1088/1748-9326/ade903
74. Reservoir-Triggered Earthquakes Around the Atatürk Dam (Southeastern Turkey) P. Büyükakpınar et al. https://doi.org/10.3389/feart.2021.663385
75. High Cadence Monitoring of Reservoir Volume Fluctuations Using PlanetScope Imagery M. Karaman https://doi.org/10.1016/j.jhydrol.2022.127456
76. A hydraulic model of the Amur River informed by ICESat-2 elevation P. Bauer-Gottwein et al. https://doi.org/10.1080/02626667.2023.2245811
77. Using Sentinel-1A DInSAR interferometry and Landsat 8 data for monitoring water level changes in two lakes in Crete, Greece D. Alexakis et al. https://doi.org/10.1080/10106049.2018.1434685
78. Flood Modeling and Prediction Using Earth Observation Data G. Schumann et al. https://doi.org/10.1007/s10712-022-09751-y
79. GeoFormer: Geography-Aware Adaptive Transformer with Multi-Scale Temporal Fusion for Global Reservoir Water Level Forecasting X. Wu et al. https://doi.org/10.3390/math14040676
80. Combining multisource satellite data to estimate storage variation of a lake in the Rift Valley Basin, Ethiopia W. Asfaw et al. https://doi.org/10.1016/j.jag.2020.102095
81. High-resolution surface water dynamics in Earth’s small and medium-sized reservoirs G. Donchyts et al. https://doi.org/10.1038/s41598-022-17074-6
82. Validation of Sentinel-6MF based lake levels – An assessment with in situ data and other satellite altimetry data B. Nilsson & K. Nielsen https://doi.org/10.1016/j.asr.2024.04.006
83. An Innovative Coastal Altimetry Waveform Processing Approach Based on Wave-Transformer Classifier M. Li et al. https://doi.org/10.3390/rs18121950
84. ENSO-based outlook of droughts and agricultural outcomes in Afghanistan S. Shukla et al. https://doi.org/10.1016/j.wace.2024.100697
85. Exploitation of satellite earth observation datasets for hydrologic analysis of large reservoirs in Thailand L. Zeng et al. https://doi.org/10.1016/j.jhydrol.2025.133085
86. Estimating groundwater recharge across Africa during 2003–2023 using GRACE-derived groundwater storage changes V. Ferreira et al. https://doi.org/10.1016/j.ejrh.2024.102046
87. A review of globally available data sources for modelling the Water-Energy-Food Nexus J. Lodge et al. https://doi.org/10.1016/j.earscirev.2023.104485
88. Inland Surface Waters Quantity Monitored from Remote Sensing J. Cretaux et al. https://doi.org/10.1007/s10712-023-09803-x
89. Global sea-level budget 1993–present https://doi.org/10.5194/essd-10-1551-2018
90. High accuracy water level estimation using super-resolution Sentinel-1 data Y. Jia et al. https://doi.org/10.1016/j.jag.2026.105156
91. Riparian Zone DEM Generation From Time-Series Sentinel-1 and Corresponding Water Level: A Novel Waterline Method J. Tan et al. https://doi.org/10.1109/TGRS.2022.3170342
92. Long-Term Discharge Estimation for the Lower Mississippi River Using Satellite Altimetry and Remote Sensing Images D. Scherer et al. https://doi.org/10.3390/rs12172693
93. A review of the evolution of satellite radar altimetry for hydrological Monitoring: Water level and discharge estimation B. kumar et al. https://doi.org/10.1016/j.pce.2025.104039
94. Niger discharge from radar altimetry: bridging gaps between gauge and altimetry time series S. Schröder et al. https://doi.org/10.5194/hess-23-4113-2019
95. Significant changes and driving mechanism of Bosten Lake in Xinjiang, China Q. Li et al. https://doi.org/10.2166/nh.2025.101
96. Accuracy assessment of remotely sensed data to analyze lake water balance in semi-arid region J. Bhattacharjee et al. https://doi.org/10.1016/j.scitotenv.2021.149034
97. Surface freshwater storage and recent trends in the Amazon Basin from spaceborne GNSS-R and multi-satellite observations (2019–2024) Z. Ma et al. https://doi.org/10.1016/j.srs.2026.100445
98. Automatic Detection of Inland Water Bodies along Altimetry Tracks for Estimating Surface Water Storage Variations in the Congo Basin F. Frappart et al. https://doi.org/10.3390/rs13193804
99. Estimating discharge of the Ganga River from satellite altimeter data A. Rai et al. https://doi.org/10.1016/j.jhydrol.2021.126860
100. Variations in water storage of Bosten Lake, China, over the last two decades based on multi-source satellite data C. Cheng et al. https://doi.org/10.1016/j.ejrh.2023.101496
101. Impacts of water level changes in the fauna, flora and physical properties over the Balkhash Lake watershed K. Isbekov et al. https://doi.org/10.1111/lre.12263
102. Satellite observed recent rising water levels of global lakes and reservoirs N. Xu et al. https://doi.org/10.1088/1748-9326/ac78f8
103. Impact of land use on water quality in buffer zones at different scales in the Poyang Lake, middle reaches of the Yangtze River basin W. Wang et al. https://doi.org/10.1016/j.scitotenv.2023.165161
104. Integrating satellite altimetry and SAR technology for Manchar Lake water monitoring S. Jamali et al. https://doi.org/10.1007/s43832-025-00292-0
105. Self-Supervised Reservoir Water Area Detection Across Multi-Source Optical Imagery G. Mo et al. https://doi.org/10.3390/rs18060918
106. Investigating the changes in agricultural land use and actual evapotranspiration of the Urmia Lake basin based on FAO’s WaPOR database R. Barideh & F. Nasimi https://doi.org/10.1016/j.agwat.2022.107509
107. Geomorphic control on stage-area hysteresis in three of the largest floodplain lakes W. Ang et al. https://doi.org/10.1016/j.jhydrol.2022.128574
108. Lake Urmia restoration success story: A natural trend or a planned remedy? Z. Nikraftar et al. https://doi.org/10.1016/j.jglr.2021.03.012
109. Hybrid Neural Network - Variational Data Assimilation algorithm to infer river discharges from SWOT-like data K. LARNIER & J. MONNIER https://doi.org/10.1007/s10596-023-10225-2
110. A global dataset of reservoir in-situ water levels for hydrological and remote sensing applications M. Zhang et al. https://doi.org/10.1038/s41597-026-07091-9
111. An uncertainty-aware multi-source remote sensing framework for integrated inland water monitoring: linking water level, surface extent, storage, and water quality F. Li et al. https://doi.org/10.1016/j.srs.2026.100470
112. Water level status of Indian reservoirs: A synoptic view from altimeter observations P. Thakur et al. https://doi.org/10.1016/j.asr.2020.06.015
113. A Comparison of Multiple DEMs and Satellite Altimetric Data in Lake Volume Monitoring C. Yuan et al. https://doi.org/10.3390/rs16060974
114. Satellite-based estimates of declining groundwater storage in the transboundary Cambodia-Mekong River Delta Aquifer of the Lower Mekong region, Southeast Asia S. Upadhyay et al. https://doi.org/10.1007/s10040-023-02746-y
115. Spatiotemporal characteristics of lake water level, surface area, and storage changes in Central East Africa from 2018 to 2024 M. Liao et al. https://doi.org/10.1016/j.ejrh.2025.102842
116. Volumetric Analysis of Reservoirs in Drought-Prone Areas Using Remote Sensing Products T. Bhagwat et al. https://doi.org/10.3390/rs11171974
117. Multivariate extremes in lakes R. Woolway et al. https://doi.org/10.1038/s41467-024-49012-7
118. Environmental determinants of E. coli, link with the diarrheal diseases, and indication of vulnerability criteria in tropical West Africa (Kapore, Burkina Faso) E. Robert et al. https://doi.org/10.1371/journal.pntd.0009634
119. Treating the Hooking Effect in Satellite Altimetry Data: A Case Study along the Mekong River and Its Tributaries E. Boergens et al. https://doi.org/10.3390/rs8020091
120. ICESat‐2 Based River Surface Slope and Its Impact on Water Level Time Series From Satellite Altimetry D. Scherer et al. https://doi.org/10.1029/2022WR032842
121. Combination of multi-mission altimetry data along the Mekong River with spatio-temporal kriging E. Boergens et al. https://doi.org/10.1007/s00190-016-0980-z
122. High-temporal-resolution monitoring of reservoir water storage of the Lancang-Mekong River Y. Wang et al. https://doi.org/10.1016/j.rse.2023.113575
123. Hydrologic changes of Aral Sea: A reveal by the combination of radar altimeter data and optical images F. Sun & R. Ma https://doi.org/10.1080/19475683.2019.1626909
124. Global River Radar Altimetry Time Series (GRRATS): new river elevation earth science data records for the hydrologic community S. Coss et al. https://doi.org/10.5194/essd-12-137-2020
125. Thirty Years of Satellite Altimetry Technology: A Retrospective, Current Status, and Trend Analysis of Inland Water Body Monitoring Research H. Li et al. https://doi.org/10.3390/w18151793
126. Extreme water level changes in global lakes revealed by altimetry satellites since the 2000s B. Bai et al. https://doi.org/10.1016/j.jag.2024.103694
127. Reconstructing Lake Storage for the Major Water Bodies in the Aral Sea Basin Using Multi-DEM Hypsometry S. Huang et al. https://doi.org/10.3390/rs18050763
128. Characterization of the hydro-geological regime of Yangtze River basin using remotely-sensed and modeled products V. Ferreira et al. https://doi.org/10.1016/j.scitotenv.2020.137354
129. High Precision Altimetry Using Spaceborne Grazing Angle GNSS-Reflectometry Over Tonlé Sap Lake M. Scott et al. https://doi.org/10.1109/JSTARS.2026.3662584
130. Monitoring Recent Fluctuations of the Southern Pool of Lake Chad Using Multiple Remote Sensing Data: Implications for Water Balance Analysis W. Zhu et al. https://doi.org/10.3390/rs9101032
131. Monitoring reservoir water elevation changes using Jason-2/3 altimetry satellite missions: exploring the capabilities of JASTER (Jason-2/3 Altimetry Stand-Alone Tool for Enhanced Research) N. Ross et al. https://doi.org/10.1080/17538947.2024.2406387
132. The Total Drainable Water Storage of the Amazon River Basin: A First Estimate Using GRACE M. Tourian et al. https://doi.org/10.1029/2017WR021674
133. Total Hg and methylmercury dynamics in a river-floodplain system in the Western Amazon: Influence of seasonality, organic matter and physical and chemical parameters I. Pestana et al. https://doi.org/10.1016/j.scitotenv.2018.11.388
134. An approach for global monitoring of surface water extent variations in reservoirs using MODIS data A. Khandelwal et al. https://doi.org/10.1016/j.rse.2017.05.039
135. AltEx: An open source web application and toolkit for accessing and exploring altimetry datasets K. Markert et al. https://doi.org/10.1016/j.envsoft.2019.03.021
136. Caspian Sea level changes during instrumental period, its impact and forecast: A review H. Lahijani et al. https://doi.org/10.1016/j.earscirev.2023.104428
137. A global lake and reservoir volume analysis using a surface water dataset and satellite altimetry T. Busker et al. https://doi.org/10.5194/hess-23-669-2019
138. Enhancing reservoir water level time series in the Mekong river basin by improving area-elevation curves and integrating multi-source satellite data Y. Chen et al. https://doi.org/10.1016/j.ejrh.2026.103593
139. Remote sensing-based high-resolution reservoir drought index for identifying the occurrence and propagation of hydrological droughts in a large river basin L. Chang et al. https://doi.org/10.1016/j.rse.2025.114859
140. Evaluation of historic and operational satellite radar altimetry missions for constructing consistent long-term lake water level records S. Shu et al. https://doi.org/10.5194/hess-25-1643-2021
141. Arctic Sea Ice Lead Detection from Chinese HY-2B Radar Altimeter Data W. Zhong et al. https://doi.org/10.3390/rs15020516
142. Validation and application of water levels derived from Sentinel-3A for the Brahmaputra River Q. Huang et al. https://doi.org/10.1007/s11431-019-9535-3
143. Daily river water levels from multi-mission altimetry: A reach-based regression method using the unique SWOT data geometry M. Halicki et al. https://doi.org/10.1016/j.jhydrol.2026.135367
144. Evaluation of a new 18-year MODIS-derived surface water fraction dataset for constructing Mediterranean wetland open surface water dynamics L. Li et al. https://doi.org/10.1016/j.jhydrol.2020.124956
145. Inferring elevation variation of lakes and reservoirs from areal extents: Calibrating with altimeter and in situ data A. Nguy-Robertson et al. https://doi.org/10.1016/j.rsase.2018.01.001
146. Remote Sensing of Storage Fluctuations of Poorly Gauged Reservoirs and State Space Model (SSM)-Based Estimation A. Singh et al. https://doi.org/10.3390/rs71215872
147. Towards the use of satellite remote sensing to validate reservoir storage in global hydrological models: methodology and pilot study in the CONUS K. Otta et al. https://doi.org/10.1088/3033-4942/adc47b
148. Characterizing channel-floodplain connectivity using satellite altimetry: Mechanism, hydrogeomorphic control, and sediment budget E. Park https://doi.org/10.1016/j.rse.2020.111783
149. Monitoring Reservoir Drought Dynamics with Landsat and Radar/Lidar Altimetry Time Series in Persistently Cloudy Eastern Brazil J. Van Den Hoek et al. https://doi.org/10.3390/rs11070827
150. Characterizing water body changes in Poyang lake using multi-source remote sensing data W. Wang et al. https://doi.org/10.1016/j.envdev.2023.100909
151. Revealing upstream dam operations under information asymmetry for integrated water resources management K. Han et al. https://doi.org/10.1088/1748-9326/ae9967
152. Mapping 10 m monthly surface water dynamics in the Yangtze River basin from 2017 to 2020 using a robust ATMC algorithm Y. Liu et al. https://doi.org/10.1016/j.jhydrol.2023.130327
153. SDUST2023VGGA: a global ocean vertical gradient of gravity anomaly model determined from multidirectional data from mean sea surface R. Zhou et al. https://doi.org/10.5194/essd-17-817-2025
154. An improved approach to monitoring Brahmaputra River water levels using retracked altimetry data Q. Huang et al. https://doi.org/10.1016/j.rse.2018.04.018
155. Changing Patterns of Lakes on The Southern Tibetan Plateau Based on Multi-Source Satellite Data F. Sun et al. https://doi.org/10.3390/rs12203450
156. Bridging the divide between inland water quantity and quality with satellite remote sensing: An interdisciplinary review E. Ellis et al. https://doi.org/10.1002/wat2.1725
157. Retracking multiple off-nadir river elevations with Sentinel-3 and Sentinel-6A altimetry missions G. Calassou et al. https://doi.org/10.1016/j.asr.2026.06.078
158. An Effective Low-Cost Remote Sensing Approach to Reconstruct the Long-Term and Dense Time Series of Area and Storage Variations for Large Lakes S. Luo et al. https://doi.org/10.3390/s19194247
159. Monitoring the 2024 Abrupt Flood Event in East Dongting Lake via Deep Learning and Multisource Remote Sensing Data Y. Xiao et al. https://doi.org/10.1109/JSTARS.2026.3653452
160. GeoDAR: georeferenced global dams and reservoirs dataset for bridging attributes and geolocations J. Wang et al. https://doi.org/10.5194/essd-14-1869-2022
161. Monitoring Surface Water Inundation of Poyang Lake and Dongting Lake in China Using Sentinel-1 SAR Images Z. Wang et al. https://doi.org/10.3390/rs14143473
162. Hydraulic Model Calibration Using CryoSat‐2 Observations in the Zambezi Catchment C. Kittel et al. https://doi.org/10.1029/2020WR029261
163. Rainfall-runoff modelling using river-stage time series in the absence of reliable discharge information: a case study in the semi-arid Mara River basin P. Hulsman et al. https://doi.org/10.5194/hess-22-5081-2018
164. Remotely sensed reservoir water storage dynamics (1984–2015) and the influence of climate variability and management at a global scale J. Hou et al. https://doi.org/10.5194/hess-26-3785-2022
165. Estimating Reservoir Storage Variations by Combining Sentinel-2 and 3 Measurements in the Yliki Reservoir, Greece N. Gourgouletis et al. https://doi.org/10.3390/rs14081860
166. Contribution of Sentinel-3A Radar Altimetry Data to the Study of the Water Level Variations in Lake Buyo (West of Côte d’Ivoire) S. Oularé et al. https://doi.org/10.3390/rs14215602
167. Estimating continental river basin discharges using multiple remote sensing data sets A. Sichangi et al. https://doi.org/10.1016/j.rse.2016.03.019
168. Increasing Cropland Area and Its Associated Human-Induced Water Consumption Put Ebinur Lake at Risk of Drying Up H. Zeng et al. https://doi.org/10.34133/remotesensing.0496
169. Comparison of Typical Alpine Lake Surface Elevation Variations and Different Driving Forces by Remote Sensing Altimetry Method Y. Pan et al. https://doi.org/10.3390/ijerph192417090
170. Gap filling of water level time series with water area using remote sensing data: a comparative performance analysis of polynomial functions, XGBoost, Random Forest and Support Vector Machine O. Narin https://doi.org/10.1080/02626667.2025.2458555
171. Reservoir Storage Retrieval From Multi-Source Satellite Remote Sensing and Artificial Intelligence: A Comprehensive Review G. Mo et al. https://doi.org/10.1109/ACCESS.2026.3683976
172. Detecting Lake Level Change From 1992 to 2019 of Zhari Namco in Tibet Using Altimetry Data of TOPEX/Poseidon and Jason-1/2/3 Missions M. Sun et al. https://doi.org/10.3389/feart.2021.640553
173. High resolution river and estuarine water level topography from nadir altimetry L. Fenoglio et al. https://doi.org/10.1016/j.jhydrol.2026.135681
174. Dynamics of human adaptation to flood risk in the Sudd wetlands, South Sudan D. Chol et al. https://doi.org/10.1007/s10113-026-02664-1
175. Tracking mass‐wasting sediments in the large Brahmaputra River system A. Dixit et al. https://doi.org/10.1002/esp.70297
176. Imbalance in lake variability but not embodying driving factors on the Qinghai-Tibetan Plateau calls on heterogeneous lake management X. Leng et al. https://doi.org/10.1016/j.jenvman.2023.119887
177. Maximizing Multi‐Decadal Water Surface Elevation Estimates With Landsat Imagery and Elevation/Bathymetry Datasets D. Weekley & X. Li https://doi.org/10.1029/2021WR029680
178. Evaluating long-term water storage trends in small catchments and aquifers from a joint inversion of 20 years of GRACE/GRACE-FO mission data A. Kvas et al. https://doi.org/10.1093/gji/ggad468
179. Self‐Validating Deep Learning for Recovering Terrestrial Water Storage From Gravity and Altimetry Measurements C. Irrgang et al. https://doi.org/10.1029/2020GL089258
180. Monitoring Spatial-Temporal Variations of Lake Level in Western China Using ICESat-1 and CryoSat-2 Satellite Altimetry J. Chen & Z. Duan https://doi.org/10.3390/rs14225709
181. Watching the Grand Ethiopian Renaissance Dam from a distance: Implications for sustainable water management of the Nile water K. Abdelmohsen et al. https://doi.org/10.1093/pnasnexus/pgae219
182. Multi-Source Remote Sensing Reveals Multi-Timescale Variations of Lakes on the Tibetan Plateau: A Case Study of Three Representative Lakes J. Wu et al. https://doi.org/10.3390/w18151899
183. Advancing Scalable Methods for Surface Water Monitoring: A Novel Integration of Satellite Observations and Machine Learning Techniques M. Renshaw & L. Magruder https://doi.org/10.3390/geosciences15070255
184. A 1985–2023 time series dataset of absolute reservoir storage in Mainland Southeast Asia (MSEA-Res) S. Mahto et al. https://doi.org/10.5194/essd-17-2693-2025
185. Automated Generation of Lakes and Reservoirs Water Elevation Changes From Satellite Radar Altimetry M. Okeowo et al. https://doi.org/10.1109/JSTARS.2017.2684081
186. Indus river water level monitoring using satellite radar altimetry A. Zaidi et al. https://doi.org/10.1016/j.asr.2020.03.044
187. Recent Abnormal Hydrologic Behavior of Tibetan Lakes Observed by Multi-Mission Altimeters P. Zhan et al. https://doi.org/10.3390/rs12182986
188. Volume Variations of Small Inland Water Bodies from a Combination of Satellite Altimetry and Optical Imagery C. Schwatke et al. https://doi.org/10.3390/rs12101606
189. Enhanced water level monitoring for small and complex inland water bodies using multi-satellite remote sensing K. Han et al. https://doi.org/10.1016/j.envsoft.2024.106169
190. The performance of CryoSat-2 fully-focussed SAR for inland water-level estimation M. Kleinherenbrink et al. https://doi.org/10.1016/j.rse.2019.111589
191. Trend shifts and hydroclimatic drivers of Caspian Sea Level (2003–2024) revealed by GRACE and multi-source observations M. Wang et al. https://doi.org/10.1016/j.ejrh.2026.103335
192. Feasibility of monitoring floodplain on-farm water storages by integrating airborne and satellite LiDAR altimetry with optical remote sensing J. Peña-Arancibia et al. https://doi.org/10.1016/j.rse.2024.113992
193. Measuring Off-nadir river water levels and slopes from altimeter Fully-Focused SAR mode J. Chen et al. https://doi.org/10.1016/j.jhydrol.2024.132553
194. SWOT versus legacy satellite missions: assessing water surface elevation and surface water extent in complex river settings T. Bhattarai et al. https://doi.org/10.1080/10106049.2026.2679903
195. Benchmarking reservoir operation schemes for large-scale hydrological models J. Casado-Rodríguez et al. https://doi.org/10.5194/hess-30-4629-2026
196. Evaluation and integration of spaceborne altimetry missions for inland water level monitoring: a multi-country study O. Narin https://doi.org/10.1016/j.asr.2025.09.078
197. Naive Bayes classification-based surface water gap-filling from partially contaminated optical remote sensing image B. Bai et al. https://doi.org/10.1016/j.jhydrol.2022.128791
198. CryoSat-2 Altimetry Applications over Rivers and Lakes L. Jiang et al. https://doi.org/10.3390/w9030211
199. Mapping seamless surface water dynamics over East Africa semimonthly at a 10-meter resolution in 2017–2023 by integrating Sentinel-1/2 data Z. Wang et al. https://doi.org/10.1016/j.isprsjprs.2025.04.032
200. Time-series surface water gap filling based on spatiotemporal neighbourhood similarity B. Bai et al. https://doi.org/10.1016/j.jag.2022.102882
201. Surface water monitoring in small water bodies: potential and limits of multi-sensor Landsat time series A. Ogilvie et al. https://doi.org/10.5194/hess-22-4349-2018
202. Validation of Sentinel-3A Based Lake Level over US and Canada K. Nielsen et al. https://doi.org/10.3390/rs12172835
203. A Study on Crustal Deformation Patterns induced by Large-Scale River Storage Variation using a Line-Load Model J. Eom & H. Rim https://doi.org/10.5467/JKESS.2025.46.1.64
204. Impacts of agricultural expansion on floodplain water and sediment budgets in the Mekong River E. Park et al. https://doi.org/10.1016/j.jhydrol.2021.127296
205. Challenges and Evolution of Water Level Monitoring towards a Comprehensive, World-Scale Coverage with Remote Sensing M. Machefer et al. https://doi.org/10.3390/rs14153513
206. A novel Multiple Persistent Peaks (MPP) retracker to improve global inland water level monitoring from satellite radar altimetry L. Ke et al. https://doi.org/10.1016/j.rse.2025.114744
207. Analysis of Retrackers’ Performances and Water Level Retrieval over the Ebro River Basin Using Sentinel-3 Q. Gao et al. https://doi.org/10.3390/rs11060718
208. Monitoring spatial and temporal dynamics of wetland vegetation and their response to hydrological conditions in a large seasonal lake with time series Landsat data H. Peng et al. https://doi.org/10.1016/j.ecolind.2022.109283
209. Monitoring Multi-Temporal Changes of Lakes on the Tibetan Plateau Using Multi-Source Remote Sensing Data from 1992 to 2019: A Case Study of Lake Zhari Namco J. Wu et al. https://doi.org/10.1007/s12583-022-1639-8
210. Global Characterization of Inland Water Reservoirs Using ICESat‐2 Altimetry and Climate Reanalysis J. Ryan et al. https://doi.org/10.1029/2020GL088543
211. Data-model comparison of temporal variability in long-term time series of large-scale soil moisture L. Verrot & G. Destouni https://doi.org/10.1002/2016JD025209
212. Using RGISTools to Estimate Water Levels in Reservoirs and Lakes A. Militino et al. https://doi.org/10.3390/rs12121934
213. Investigating different timescales of terrestrial water storage changes in the northeastern Tibetan Plateau P. Zhan et al. https://doi.org/10.1016/j.jhydrol.2022.127608
214. Evaluation of multi-mode CryoSat-2 altimetry data over the Po River against in situ data and a hydrodynamic model R. Schneider et al. https://doi.org/10.1016/j.advwatres.2017.11.027
215. Use of non-polar orbiting satellite radar altimeters of the Jason series for estimation of river input to the Arctic Ocean E. Zakharova et al. https://doi.org/10.1016/j.jhydrol.2018.10.068
216. Global Estimation and Assessment of Monthly Lake/Reservoir Water Level Changes Using ICESat-2 ATL13 Products N. Xu et al. https://doi.org/10.3390/rs13142744
217. Altimetry river water level retrieval over complex environments: assessment and diagnosis of different strategies T. Xia et al. https://doi.org/10.1016/j.srs.2026.100363
218. Estimating seasonal water budgets in global lakes by using multi-source remote sensing measurements T. Chen et al. https://doi.org/10.1016/j.jhydrol.2020.125781
219. Mapping Wetlands in Zambia Using Seasonal Backscatter Signatures Derived from ENVISAT ASAR Time Series S. Schlaffer et al. https://doi.org/10.3390/rs8050402
220. How Many Pan-Arctic Lakes Are Observed by ICESat-2 in Space and Time? T. Chen et al. https://doi.org/10.3390/rs14235971
221. Construction of High Spatial-Temporal Water Body Dataset in China Based on Sentinel-1 Archives and GEE Y. Li et al. https://doi.org/10.3390/rs12152413
222. SWOT mission enables high-precision and wide-coverage lake water levels monitoring on the Tibetan Plateau S. Wu et al. https://doi.org/10.1016/j.ejrh.2025.102357
223. Daily time series of river water levels derived from a seasonal linear model using multisource satellite products under uncertainty H. Pham et al. https://doi.org/10.1016/j.jhydrol.2021.126783
224. Modeling of water level trends and characterizing potential influencing factors in Lake Baringo in Kenya D. Kimtai et al. https://doi.org/10.1007/s43832-024-00105-w
225. Brine shrimp cysts in the West Aral Sea from satellite observations K. Mao et al. https://doi.org/10.1080/01431161.2025.2535745
226. Application of CryoSat-2 altimetry data for river analysis and modelling R. Schneider et al. https://doi.org/10.5194/hess-21-751-2017
227. Interrelationships Between NDVI, Surface Water, and Regional Hydro-Climatic Variables In the Sudd Wetland G. Persico et al. https://doi.org/10.1007/s13157-024-01851-2
228. Characterizing the Water Storage Variation of Kusai Lake by Constructing Time Series from Multisource Remote Sensing Data Z. Huang et al. https://doi.org/10.3390/rs16010128
229. Trends in the Ionic Composition of Lake Ladoga M. Guseva & V. Shmakova https://doi.org/10.3103/S1068373924030087
230. Evaluation and Validation of CryoSat-2-Derived Water Levels Using In Situ Lake Data from China Z. Liu et al. https://doi.org/10.3390/rs11080899
231. Evaluation using in-situ observations from national governments and Citizen Scientists suggests nadir altimeters can accurately measure water level changes regardless of lake area A. Gómez et al. https://doi.org/10.1080/15481603.2025.2543521
232. Hydrological variability and loading deformation in the Yangtze river basin based on modern geodetic means D. Ren et al. https://doi.org/10.1080/27669645.2022.2098895
233. Water level estimation from radar altimetry data in lowland temperate climate rivers R. Tamašauskas et al. https://doi.org/10.1080/01431161.2026.2660252
234. Monitoring river discharge from space: An optimization approach with uncertainty quantification for small ungauged rivers D. Scherer et al. https://doi.org/10.1016/j.rse.2024.114434
235. Constructing long-term high-frequency time series of global lake and reservoir areas using Landsat imagery F. Yao et al. https://doi.org/10.1016/j.rse.2019.111210
236. Uncertainties on the combined use of ICESat and ICESat-2 observations to monitor lake levels S. Luo & C. Song https://doi.org/10.3389/frwa.2023.1279444
237. Accurate estimation of lake levels by the spatio-temporal modeling of satellite altimetry data Y. Hu et al. https://doi.org/10.1016/j.rse.2023.113681
238. An XGBoost Error Correction Model for Improving Monthly Lake Water Level Estimation on Qiangtang Plateau Y. Li et al. https://doi.org/10.1109/TGRS.2025.3599941
239. KF-MFWL: A High-Resolution Time Series Construction Algorithm for Lake Water Levels Based on Multisource Altimeter Satellites and Meteorological Data Fusion W. Jiang et al. https://doi.org/10.1109/TGRS.2024.3491859
240. Evaluation of GRACE mascon solutions for small spatial scales and localized mass sources L. Zhang et al. https://doi.org/10.1093/gji/ggz198
241. Anthropogenic and climatic drivers of the 2022 mega-flood in Pakistan A. Arshad et al. https://doi.org/10.1038/s44304-025-00109-z
242. Comparison and correction of satellite measurements using in-situ observations of lake surface heights: A case study in lake Baikal V. Vuglinsky et al. https://doi.org/10.1016/j.asr.2022.12.046
243. Climate-driven 21st century Caspian Sea level decline estimated from CMIP6 projections R. Samant & M. Prange https://doi.org/10.1038/s43247-023-01017-8
244. HydroSat: geometric quantities of the global water cycle from geodetic satellites M. Tourian et al. https://doi.org/10.5194/essd-14-2463-2022
245. GRDL: A New Global Reservoir Area‐Storage‐Depth Data Set Derived Through Deep Learning‐Based Bathymetry Reconstruction Z. Hao et al. https://doi.org/10.1029/2023WR035781
246. Decoding the complex fluvial-lacustrine sedimentary interactions: Insights from the Neogene Guantao Formation in the Bohai Bay Basin and Modern Poyang Lake, China Z. Sun et al. https://doi.org/10.1016/j.marpetgeo.2024.107007
247. Mapping drawdown-zone bathymetry using SWOT observations: implications for global monitoring of lake inundation and storage changes L. Liu et al. https://doi.org/10.1016/j.jhydrol.2026.135130
248. A data assimilation system combining CryoSat-2 data and hydrodynamic river models R. Schneider et al. https://doi.org/10.1016/j.jhydrol.2017.11.052
249. A dataset of lake level changes in China between 2002 and 2023 using multi-altimeter data S. Ma et al. https://doi.org/10.1080/20964471.2023.2295632
250. A review of freely accessible global datasets for the study of floods, droughts and their interactions with human societies S. Lindersson et al. https://doi.org/10.1002/wat2.1424
251. A dataset for lake level changes in the Tibetan Plateau from 2002 and 2010 to 2021 using multi-altimeter data J. Chen et al. https://doi.org/10.5194/essd-17-4235-2025
252. Machine learning based classification of lake ice and open water from Sentinel-3 SAR altimetry waveforms J. Mugunthan et al. https://doi.org/10.1016/j.rse.2023.113891
253. Changes in the water environment and its major driving factors in Poyang Lake from 2016 to 2019, China W. Wang et al. https://doi.org/10.1007/s11356-022-22136-3
254. Water Level Change Monitoring Based on a New Denoising Algorithm Using Data from Landsat and ICESat-2: A Case Study of Miyun Reservoir in Beijing J. Xie et al. https://doi.org/10.3390/rs14174344
255. Forecasting River Water Levels at Virtual Stations of Altimetry Satellites M. Halicki et al. https://doi.org/10.1109/JSTARS.2026.3681863
256. Integrating spatiotemporal similarity for robust gap-filling in continuous surface water mapping with uncertainty quantification Z. Xiao et al. https://doi.org/10.1016/j.isprsjprs.2026.02.021
257. Hydrological regime of Sahelian small waterbodies from combined Sentinel-2 MSI and Sentinel-3 Synthetic Aperture Radar Altimeter data M. de Fleury et al. https://doi.org/10.5194/hess-27-2189-2023
258. Monitoring global reservoirs using ICESat-2: Assessment on spatial coverage and application potential T. Chen et al. https://doi.org/10.1016/j.jhydrol.2021.127257
259. GLOBAL COMPARISON OF WATER SURFACE AREA VARIABILITY BETWEEN NATURAL LAKES AND RESERVOIRS D. TOKUDA et al. https://doi.org/10.2208/journalofjsce.24-00212
260. GEDI: A New LiDAR Altimetry to Obtain the Water Levels of More Lakes on the Tibetan Plateau J. Wu et al. https://doi.org/10.1109/JSTARS.2023.3268558
261. Sentinel-2 high-resolution data for river discharge monitoring P. Filippucci et al. https://doi.org/10.1016/j.rse.2022.113255
262. Rating Curve Modeling Using Machine Learning: A Case Study in the Largest Gauging Stations in the Amazon River V. Paca et al. https://doi.org/10.3390/rs18091337
263. Rising Lake Levels as a Distinct Flood Hazard: An Integrated Risk Assessment Framework for Semi-Arid Inland Lake Basins N. Kiplangat et al. https://doi.org/10.3390/w18151887
264. Evaluating the Performance of Seven Ongoing Satellite Altimetry Missions for Measuring Inland Water Levels of the Great Lakes Z. An et al. https://doi.org/10.3390/s22249718
265. A review of altimetry waveform retracking for inland water levels X. Deng et al. https://doi.org/10.1016/j.geog.2025.02.001
266. Analysis of Surface Water Areal changes using Remote Sensing Data H. Murray & M. Khaki https://doi.org/10.21926/aeer.2103019
267. Observing the tidal pulse of rivers from wide-swath satellite altimetry M. Hart-Davis et al. https://doi.org/10.1038/s41586-026-10287-z
268. A Combination Model Based on Sequential General Variational Mode Decomposition Method for Improving Lake Water Level Prediction O. Ibrahim et al. https://doi.org/10.1007/s40710-026-00846-x
269. Understanding and conceptualization of the hydrogeology and groundwater flow dynamics of the Nyando River Basin in Western Kenya S. Mutua et al. https://doi.org/10.1016/j.ejrh.2020.100766
270. An Automatic Google Earth Engine Tool for Generating Lake Water Area Time Series From Satellite Imagery O. Elmi & A. Ahrari https://doi.org/10.1109/LGRS.2025.3528323
271. High-resolution water level and storage variation datasets for 338 reservoirs in China during 2010–2021 Y. Shen et al. https://doi.org/10.5194/essd-14-5671-2022
272. GloLakes: water storage dynamics for 27 000 lakes globally from 1984 to present derived from satellite altimetry and optical imaging J. Hou et al. https://doi.org/10.5194/essd-16-201-2024
273. Modelling the water balance of Lake Victoria (East Africa) – Part 1: Observational analysis I. Vanderkelen et al. https://doi.org/10.5194/hess-22-5509-2018
274. Observing water level extremes in the Mekong River Basin: The benefit of long-repeat orbit missions in a multi-mission satellite altimetry approach E. Boergens et al. https://doi.org/10.1016/j.jhydrol.2018.12.041
275. Machine learning revealed monthly change characteristics of lakes on the Tibet Plateau over the past two decades J. Wu et al. https://doi.org/10.1016/j.ejrh.2025.102839
276. Remote Sensing-Based Extension of GRDC Discharge Time Series - A Monthly Product with Uncertainty Estimates O. Elmi et al. https://doi.org/10.1038/s41597-024-03078-6
277. Modeling Spatio-Temporal Surface Elevation Changes in Argentino and Viedma Lakes, Patagonia, Employing ICESat-2 F. Suad Corbetta et al. https://doi.org/10.3390/rs18070993
278. ALES+: Adapting a homogenous ocean retracker for satellite altimetry to sea ice leads, coastal and inland waters M. Passaro et al. https://doi.org/10.1016/j.rse.2018.02.074
279. Estimating river water level using satellite altimetry and water surface temperature data in a regulated subtropical river Q. Wang & H. Wang https://doi.org/10.1016/j.ejrh.2025.102376
280. River discharge estimation from radar altimetry: Assessment of satellite performance, river scales and methods E. Zakharova et al. https://doi.org/10.1016/j.jhydrol.2020.124561
281. Daily Continuous River Discharge Estimation for Ungauged Basins Using a Hydrologic Model Calibrated by Satellite Altimetry: Implications for the SWOT Mission Q. Huang et al. https://doi.org/10.1029/2020WR027309
282. Lower Crust Viscosity in Central Tibet Inferred From InSAR Derived Deformation Around Siling Co Lake After Its Rapid Expansion in the 2000s W. Fan et al. https://doi.org/10.1029/2023GL104863
283. AltiMaP: altimetry mapping procedure for hydrography data M. Revel et al. https://doi.org/10.5194/essd-16-75-2024
284. Monitoring inland water via Sentinel satellite constellation: A review and perspective F. Zeng et al. https://doi.org/10.1016/j.isprsjprs.2023.09.011
285. Topographically informed satellite monitoring of reservoir storage: A multi-regional evaluation across contrasting hydroclimatic settings I. Fuentes et al. https://doi.org/10.1016/j.ejrh.2026.103603
286. Potential of SWOT for Monitoring Water Volumes in Sahelian Ponds and Lakes M. Grippa et al. https://doi.org/10.1109/JSTARS.2019.2901434
287. Buffering the impacts of extreme climate variability in the highly engineered Tigris Euphrates river system K. Abdelmohsen et al. https://doi.org/10.1038/s41598-022-07891-0
288. Satellite-derived multivariate world-wide lake physical variable timeseries for climate studies L. Carrea et al. https://doi.org/10.1038/s41597-022-01889-z
289. Anzali Wetland Crisis: Unraveling the Decline of Iran's Ecological Gem M. Mahdian et al. https://doi.org/10.1029/2023JD039538
290. Exploring the potential of Sentinel-3 and Sentinel-6 SAR altimetry measurements for discharge estimation: Case studies from the Rhine and Po rivers E. Zakharova et al. https://doi.org/10.1016/j.jhydrol.2026.135211
291. Trends and Innovations in Surface Water Monitoring via Satellite Altimetry: A 34-Year Bibliometric Review Z. Huang et al. https://doi.org/10.3390/rs16162886
292. Dramatic decrease of flood frequency in the Mekong Delta due to river-bed mining and dyke construction E. Park et al. https://doi.org/10.1016/j.scitotenv.2020.138066
293. Accuracy of Copernicus Altimeter Water Level Data in Italian Rivers Accounting for Narrow River Sections C. Deidda et al. https://doi.org/10.3390/rs13214456
294. How boundary conditions govern lacustrine delta growth and slope curvature: Insights from numerical modelling P. Nota et al. https://doi.org/10.1111/sed.70124
295. On the Desiccation of the South Aral Sea Observed from Spaceborne Missions A. Singh et al. https://doi.org/10.3390/rs10050793
296. Synthesis of the ICESat/ICESat-2 and CryoSat-2 observations to reconstruct time series of lake level Y. Feng et al. https://doi.org/10.1080/17538947.2023.2166134
297. Study on the Variations in Water Storage in Lake Qinghai Based on Multi-Source Satellite Data J. Wang et al. https://doi.org/10.3390/rs15071746
298. Monitoring of Inland Water Levels by Satellite Altimetry and Deep Learning F. Nino et al. https://doi.org/10.1109/TGRS.2021.3138329
299. Using altimetry observations combined with GRACE to select parameter sets of a hydrological model in a data-scarce region P. Hulsman et al. https://doi.org/10.5194/hess-24-3331-2020
300. Water and hydropower reservoirs: High temporal resolution time series derived from MODIS data to characterize seasonality and variability I. Klein et al. https://doi.org/10.1016/j.rse.2020.112207
301. Seasonal Amplitude of Water Storage Variations of the Yangtze–Huai Plain Lake Group: Implicaion for Floodwater Storage Capacity L. Song et al. https://doi.org/10.3389/fenvs.2022.838883
302. RECOG RL01: correcting GRACE total water storage estimates for global lakes/reservoirs and earthquakes S. Deggim et al. https://doi.org/10.5194/essd-13-2227-2021
303. Assessing the impact of climate change on flood patterns in downstream Nigeria using machine learning and geospatial techniques (2018–2024) D. Eteh et al. https://doi.org/10.1007/s44288-025-00178-7
304. ICESat-2 river surface slope (IRIS): A global reach-scale water surface slope dataset D. Scherer et al. https://doi.org/10.1038/s41597-023-02215-x
305. Reconciling the water balance of large lake systems A. Gronewold et al. https://doi.org/10.1016/j.advwatres.2020.103505
306. On the role of the troposphere in satellite altimetry M. Fernandes et al. https://doi.org/10.1016/j.rse.2020.112149
307. Satellite Altimetry-based Extension of global-scale in situ river discharge Measurements (SAEM) P. Saemian et al. https://doi.org/10.5194/essd-17-2063-2025
308. Hydroclimatic analysis of rising water levels in the Great rift Valley Lakes of Kenya M. Herrnegger et al. https://doi.org/10.1016/j.ejrh.2021.100857
309. Integrating ICESat-2 altimetry and machine learning to estimate the seasonal water level and storage variations of national-scale lakes in China L. Song et al. https://doi.org/10.1016/j.rse.2023.113657
310. Amazon Hydrology From Space: Scientific Advances and Future Challenges A. Fassoni‐Andrade et al. https://doi.org/10.1029/2020RG000728
311. Assessing the accuracy of satellite radar altimetry for inland water level monitoring S. Camici et al. https://doi.org/10.1016/j.jhydrol.2025.133556
312. Robust, Long-term Lake Level Change from Multiple Satellite Altimeters in Tibet: Observing the Rapid Rise of Ngangzi Co over a New Wetland H. Wang et al. https://doi.org/10.3390/rs11050558
313. A Review of Earth Observation-Based Analyses for Major River Basins S. Uereyen & C. Kuenzer https://doi.org/10.3390/rs11242951
314. Rising lake levels in central East Africa are driven by increasing rainfall and land-use intensification A. Byrne et al. https://doi.org/10.1016/j.ejrh.2024.101999
315. GLWaterlevel-analyst: An open-source remote sensing toolbox in QGIS for global lake water level analysis and prediction S. Shu et al. https://doi.org/10.1016/j.rsase.2026.102144
316. Understanding the association between climate variability and the Nile's water level fluctuations and water storage changes during 1992–2016 M. Khaki et al. https://doi.org/10.1016/j.scitotenv.2018.07.212
317. Densifying and Optimizing the Water Level Series for Large Lakes from Multi-Orbit ICESat-2 Observations T. Chen et al. https://doi.org/10.3390/rs15030780
318. River discharge estimation at daily resolution from satellite altimetry over an entire river basin M. Tourian et al. https://doi.org/10.1016/j.jhydrol.2017.01.009
319. Satellite observations reveal 13 years of reservoir filling strategies, operating rules, and hydrological alterations in the Upper Mekong River basin D. Vu et al. https://doi.org/10.5194/hess-26-2345-2022
320. An altimeter height extraction technique for dynamically changing rivers of South and South-East Asia N. Biswas et al. https://doi.org/10.1016/j.rse.2018.10.033
321. Monitoring surface water area variations of reservoirs using daily MODIS images by exploring sub-pixel information F. Ling et al. https://doi.org/10.1016/j.isprsjprs.2020.08.008
322. When timing matters—misdesigned dam filling impacts hydropower sustainability M. Zaniolo et al. https://doi.org/10.1038/s41467-021-23323-5
323. Automated Extraction of Consistent Time-Variable Water Surfaces of Lakes and Reservoirs Based on Landsat and Sentinel-2 C. Schwatke et al. https://doi.org/10.3390/rs11091010
324. Lake Water Footprint Determination Using Linear Clustering-based Algorithm and Lake Water Changes in the Tibetan Plateau from 2002 to 2020 G. Qiao & H. Li https://doi.org/10.14358/PERS.21-00047R2
325. Incertitudes des niveaux d’eau dérivés de l’altimétrie satellitaire pour des étendues d’eau soumises à l’action de la glace J. Ziyad et al. https://doi.org/10.1080/07038992.2020.1780906
326. River levels from multi mission altimetry, a statistical approach K. Nielsen et al. https://doi.org/10.1016/j.rse.2021.112876
327. Improving SAR Altimeter in Complex Terrain Using Slope-Based Height Correction W. Qin et al. https://doi.org/10.1109/TGRS.2025.3546579
328. Monitoring monthly dynamics of Nile River Basin surface water by combining Sentinel-1 SAR and Sentinel-2 multispectral imagery J. Xu et al. https://doi.org/10.1016/j.ejrh.2025.102860
329. Global instantaneous monitoring of reservoir water levels using ICESat-2 photon-counting LiDAR X. Jiang et al. https://doi.org/10.1080/15481603.2026.2642616
330. Modeling groundwater levels using satellite images and machine learning N. Nagkoulis et al. https://doi.org/10.1007/s10040-025-02960-w
331. Widespread decline in terrestrial water storage and its link to teleconnections across Asia and eastern Europe X. Liu et al. https://doi.org/10.5194/hess-24-3663-2020
332. Was Drought Really the Trigger Behind the Syrian Civil War in 2011? A. Karnieli et al. https://doi.org/10.3390/w11081564
333. Enhanced Monitoring of Lake Balkhash Water Level Dynamics in Central Asia Using Integrated GEDI and ICESat‐2 Altimetry Y. Zhang et al. https://doi.org/10.1002/hyp.70331
334. Interannual variations of terrestrial water storage in the East African Rift region E. Boergens et al. https://doi.org/10.5194/hess-28-4733-2024
335. Recent Changes in Land Water Storage and its Contribution to Sea Level Variations Y. Wada et al. https://doi.org/10.1007/s10712-016-9399-6
336. Satellites reveal widespread decline in global lake water storage F. Yao et al. https://doi.org/10.1126/science.abo2812
337. Tracking the Mekong river’s water levels with satellite radar altimetry T. Ali et al. https://doi.org/10.1007/s43832-025-00249-3
338. Dynamic water level changes in Qinghai Lake from integrating refined ICESat-2 and GEDI altimetry data (2018–2021) Z. Zhang et al. https://doi.org/10.1016/j.jhydrol.2022.129007
339. A GRACE/GFO Empirical Low-Pass Filter to Extract the Mass Changes in Nicaragua G. Jian et al. https://doi.org/10.3390/rs15112805
340. Hydrological and ecological consequences of the Kakhovka dam collapse S. Mhanna et al. https://doi.org/10.1088/1748-9326/ae4d5f
341. Long term displacement observation of the Atatürk Dam, Turkey by multi-temporal InSAR analysis C. Bayik et al. https://doi.org/10.1016/j.actaastro.2021.09.022
342. Satellite-based remote sensing data set of global surface water storage change from 1992 to 2018 R. Tortini et al. https://doi.org/10.5194/essd-12-1141-2020
343. Water Resources in Africa under Global Change: Monitoring Surface Waters from Space F. Papa et al. https://doi.org/10.1007/s10712-022-09700-9
344. CryoSat-2 radar altimetry for monitoring freshwater resources of China L. Jiang et al. https://doi.org/10.1016/j.rse.2017.08.015
345. CryoSat-2 Full Bit Rate Level 1A processing and validation for inland water applications P. Moore et al. https://doi.org/10.1016/j.asr.2017.12.015
346. Lake Topography and Active Storage From Satellite Observations of Flood Frequency A. Fassoni‐Andrade et al. https://doi.org/10.1029/2019WR026362
347. GLOBAL COMPARISON OF WATER SURFACE AREA VARIABILITY BETWEEN NATURAL LAKES AND RESERVOIRS D. TOKUDA et al. https://doi.org/10.2208/jscejhe.78.2_I_505
348. A multi-parameter optimized sub-waveform retracker for monitoring river water levels using SAR altimetry X. Gao et al. https://doi.org/10.1016/j.rse.2025.114838
349. Characterizing the 2022 Extreme Drought Event over the Poyang Lake Basin Using Multiple Satellite Remote Sensing Observations and In Situ Data S. Liu et al. https://doi.org/10.3390/rs15215125
350. Paleohydrology repeating? Regional hydrological change may lead to an overflow and cross-mixing of an alkaline and a freshwater lake in East Africa M. Herrnegger et al. https://doi.org/10.1016/j.ejrh.2024.101951
351. Retrieving water surface elevation over arctic thermokarst lakes using ICESat-2 and Sentinel-3 alti metry data S. Yekeen et al. https://doi.org/10.1016/j.rsase.2025.101751
352. Vertical bedrock shifts reveal summer water storage in Greenland ice sheet J. Ran et al. https://doi.org/10.1038/s41586-024-08096-3
353. Synergistic use of multi-sensor altimetry and multispectral data for bathymetric mapping of Bhadra Reservoir A. Nalluri & H. Ramesh https://doi.org/10.1007/s11600-026-01850-3
354. Water Storage Monitoring in the Aral Sea and its Endorheic Basin from Multisatellite Data and a Hydrological Model D. Tao et al. https://doi.org/10.3390/rs12152408
355. How can SWOT derived water surface elevations help calibrating a distributed hydrological model? G. Patidar et al. https://doi.org/10.1016/j.jhydrol.2025.132968
356. Res-CN (Reservoir dataset in China): hydrometeorological time series and landscape attributes across 3254 Chinese reservoirs Y. Shen et al. https://doi.org/10.5194/essd-15-2781-2023
357. River Levels Derived with CryoSat-2 SAR Data Classification—A Case Study in the Mekong River Basin E. Boergens et al. https://doi.org/10.3390/rs9121238
358. The use of B-splines to represent the topography of river networks E. Boergens et al. https://doi.org/10.1007/s13137-021-00188-w
359. Water Resources in Africa: The Role of Earth Observation Data and Hydrodynamic Modeling to Derive River Discharge A. Tarpanelli et al. https://doi.org/10.1007/s10712-022-09744-x
360. A fast and efficient method to estimate inland water levels using CYGNSS L1 data and DTMs: Application to Floods, lakes and reservoirs monitoring Z. Ma et al. https://doi.org/10.1016/j.jhydrol.2024.132258
361. SWOT Level 2 Lake Single-Pass Product: The L2_HR_LakeSP Data Preliminary Analysis for Water Level Monitoring A. Hamoudzadeh et al. https://doi.org/10.3390/rs16071244
362. Ten years of Lake Taupō surface height estimates using the GNSS interferometric reflectometry L. Holden & K. Larson https://doi.org/10.1007/s00190-021-01523-7
363. Comparative insights into independent and hybrid modeling strategies for effective river water level prediction and management O. Ibrahim et al. https://doi.org/10.1007/s40808-025-02611-9
364. Terrestrial Water Storage Component Changes Derived from Multisource Data and Their Responses to ENSO in Nicaragua G. Jian et al. https://doi.org/10.3390/rs14236012
365. Integration of interferometry and altimetry techniques for comping and reducing the desertification phenomena in Iraq M. Aryan & A. Bin Rasib https://doi.org/10.1088/1755-1315/1274/1/012011
366. Interannual Variability of Water Level in Two Largest Lakes of Europe A. Kostianoy et al. https://doi.org/10.3390/rs14030659
367. Altimetry for the future: Building on 25 years of progress S. Abdalla et al. https://doi.org/10.1016/j.asr.2021.01.022
368. Lake and reservoir volume variations in South America from radar altimetry, ICESat laser altimetry, and GRACE time-variable gravity C. Carabajal & J. Boy https://doi.org/10.1016/j.asr.2020.04.022
369. Towards the continuous monitoring of the extreme events through satellite radar altimetry observations R. Belloni et al. https://doi.org/10.1016/j.jhydrol.2021.126870
370. Analysis of Salt Lake Volume Dynamics Using Sentinel-1 Based SBAS Measurements: A Case Study of Lake Tuz, Turkey B. Bilgilioğlu et al. https://doi.org/10.3390/rs13142701
371. A New Framework of 17 Hydrological Ecosystem Services (HESS17) for Supporting River Basin Planning and Environmental Monitoring L. Ha et al. https://doi.org/10.3390/su15076182
372. Statistical evaluation of Jason-2 satellite altimetry products in a trans-boundary river basin; the case study of the White Volta River basin in Ghana S. Darko et al. https://doi.org/10.1007/s40808-022-01655-5
373. Performance Assessment of ICESat-2 Laser Altimeter Data for Water-Level Measurement over Lakes and Reservoirs in China C. Yuan et al. https://doi.org/10.3390/rs12050770
374. Production, Validation and Morphometric Analysis of a Digital Terrain Model for Lake Trichonis Using Geospatial Technologies and Hydroacoustics T. Perivolioti et al. https://doi.org/10.3390/ijgi10020091
375. Lake Level Estimation Based on CryoSat-2 SAR Altimetry and Multi-Looked Waveform Classification F. Göttl et al. https://doi.org/10.3390/rs8110885
376. Daily River Discharge Estimation Using Multi-Mission Radar Altimetry Data and Ensemble Learning Regression in the Lower Mekong River Basin D. Kim et al. https://doi.org/10.3390/rs11222684
377. A comparative study of satellite altimetry-based and DEM-based methods for estimating lake water volume changes H. Li et al. https://doi.org/10.2166/wst.2024.086
378. Research progress on aerospace electromagnetic information technology: acquisition, transmission and intelligent processing Y. JIN et al. https://doi.org/10.1360/SSI-2024-0393
379. Longterm multisource satellite data fusion reveals dynamic expansion of lake water area and storage in a hyperarid basin of China C. Zhang et al. https://doi.org/10.1016/j.jhydrol.2022.127888
380. Inland Water Body Surface Height Retrievals Using CYGNSS Delay Doppler Maps J. Zhang & Y. Morton https://doi.org/10.1109/TGRS.2023.3330410
381. Machine learning based predictive model for forecasting and managing the economic impact of cross border dam water release in Nigeria E. Woherem et al. https://doi.org/10.1007/s44163-026-01003-x
382. Improving the Representation of Long‐Term Storage Variations With Conceptual Hydrological Models in Data‐Scarce Regions P. Hulsman et al. https://doi.org/10.1029/2020WR028837
383. Temporal Analysis of Reservoirs, Lakes, and Rivers in the Euphrates–Tigris Basin from Multi-Sensor Data Between 2018 and 2022 O. Narin et al. https://doi.org/10.3390/rs17162913
384. A spatiotemporal deep learning framework integrating CNN-BiLSTM and attention mechanisms for GRACE data downscaling in Yunnan Province Y. He et al. https://doi.org/10.1016/j.cageo.2026.106117
385. Retrieving time series of river water extent from global inland water data sets O. Elmi & M. Tourian https://doi.org/10.1016/j.jhydrol.2022.128880
386. Satellite altimetry reveals intensifying global river water level variability C. Fang et al. https://doi.org/10.1038/s41467-025-67682-9
387. Drowned Landscapes: The Rediscovered Archaeological Heritage of the Mosul Dam Reservoir P. Sconzo et al. https://doi.org/10.1086/724419
388. Crossing the Threshold: Land Cover Change Triggers Hydrological Regime Shift in Brazil’s Itaipu Hydropower Region J. Besnier et al. https://doi.org/10.3390/rs18060848
389. Sensitivity to Mass Changes of Lakes, Subsurface Hydrology and Glaciers of the Quantum Technology Gravity Gradients and Time Observations of Satellite MOCAST+ T. Pivetta et al. https://doi.org/10.3390/rs14174278
390. The MOCAST+ Study on a Quantum Gradiometry Satellite Mission with Atomic Clocks F. Migliaccio et al. https://doi.org/10.1007/s10712-022-09760-x
391. GRACE Detected Rise of Groundwater in the Sahelian Niger River Basin S. Werth et al. https://doi.org/10.1002/2017JB014845
392. Using Multisource Satellite Data to Investigate Lake Area, Water Level, and Water Storage Changes of Terminal Lakes in Ungauged Regions C. Zhang et al. https://doi.org/10.3390/rs13163221