366 citations as recorded by crossref.

1. Optimized deep learning architecture for predicting maximum temperatures in key Egyptian regions using hybrid genetic algorithm and mountain Gazelle optimizer E. Houssein et al. https://doi.org/10.1007/s10462-025-11247-1
2. A global hydrology research agenda fit for the 2030s R. Wilby https://doi.org/10.2166/nh.2019.100
3. Denitrification in wetlands: A review towards a quantification at global scale C. Martínez-Espinosa et al. https://doi.org/10.1016/j.scitotenv.2020.142398
4. 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
5. Personal weather stations in hydrology and meteorology: a review of applications, challenges and future directions P. Mazzoglio et al. https://doi.org/10.1080/02626667.2026.2665735
6. Reconstruction of Rainfall Field Using Earth–Space Links Network: A Compressed Sensing Approach Y. Zhao et al. https://doi.org/10.3390/rs14194966
7. Integrating remote sensing and GIS-based AHP for flood hazard assessment in the Damodar River Basin, India M. Khadav et al. https://doi.org/10.1016/j.eve.2026.100123
8. Space-based observations of crustal deflections for drought characterization in Brazil V. Ferreira et al. https://doi.org/10.1016/j.scitotenv.2018.06.277
9. Multi-sensor assimilation of SMOS brightness temperature and GRACE terrestrial water storage observations for soil moisture and shallow groundwater estimation M. Girotto et al. https://doi.org/10.1016/j.rse.2019.04.001
10. A New Soil Moisture Downscaling Approach for SMAP, SMOS, and ASCAT by Predicting Sub-Grid Variability C. Montzka et al. https://doi.org/10.3390/rs10030427
11. Recent advance in earth observation big data for hydrology L. Chen & L. Wang https://doi.org/10.1080/20964471.2018.1435072
12. The Global Water Cycle Budget: A Chronological Review M. Vargas Godoy et al. https://doi.org/10.1007/s10712-021-09652-6
13. The water resources of tropical West Africa: problems, progress, and prospects C. Ndehedehe https://doi.org/10.1007/s11600-019-00260-y
14. On the Interest of Optical Remote Sensing for Seasonal Snowmelt Parameterization, Applied to the Everest Region (Nepal) B. Bouchard et al. https://doi.org/10.3390/rs11222598
15. The role of topography, soil, and remotely sensed vegetation condition towards predicting crop yield T. Franz et al. https://doi.org/10.1016/j.fcr.2020.107788
16. Rainfall estimates from opportunistic sensors in Germany across spatio-temporal scales M. Graf et al. https://doi.org/10.1016/j.ejrh.2021.100883
17. Water body classification from high-resolution optical remote sensing imagery: Achievements and perspectives Y. Li et al. https://doi.org/10.1016/j.isprsjprs.2022.03.013
18. Towards analysis ready data of optical CubeSat images: Demonstrating a hierarchical normalization framework at a wetland site Z. Li et al. https://doi.org/10.1016/j.jag.2021.102502
19. Estimating Fine‐Scale Transpiration From UAV‐Derived Thermal Imagery and Atmospheric Profiles B. Morgan & K. Caylor https://doi.org/10.1029/2023WR035251
20. On the Use of Unmanned Aerial Systems for Environmental Monitoring S. Manfreda et al. https://doi.org/10.3390/rs10040641
21. An Integrative Information Aqueduct to Close the Gaps between Satellite Observation of Water Cycle and Local Sustainable Management of Water Resources Z. Su et al. https://doi.org/10.3390/w12051495
22. Deep Learning for Satellite Image Time-Series Analysis: A review L. Miller et al. https://doi.org/10.1109/MGRS.2024.3393010
23. Coupling between evapotranspiration, water use efficiency, and evaporative stress index strengthens after wildfires in New Mexico, USA R. Joshi et al. https://doi.org/10.1016/j.jag.2024.104238
24. Hydrological mass balance of boreal watersheds in the Canadian Shield S. Nadeau et al. https://doi.org/10.1016/j.jhydrol.2022.128236
25. Achieving Breakthroughs in Global Hydrologic Science by Unlocking the Power of Multisensor, Multidisciplinary Earth Observations M. Durand et al. https://doi.org/10.1029/2021AV000455
26. Tipping Bucket Rain Gauges in Hydrological Research: Summary on Measurement Uncertainties, Calibration, and Error Reduction Strategies D. Segovia-Cardozo et al. https://doi.org/10.3390/s23125385
27. Downscaling Multispectral Satellite Images Without Colocated High-Resolution Data: A Stochastic Approach Based on Training Images F. Oriani et al. https://doi.org/10.1109/TGRS.2020.3008015
28. Do satellite-based products suffice for rainfall observations over data-sparse complex terrains? Evidence from the North-Western Himalayas A. Dogra et al. https://doi.org/10.1016/j.rse.2023.113855
29. Uncovering Flooding Mechanisms Across the Contiguous United States Through Interpretive Deep Learning on Representative Catchments S. Jiang et al. https://doi.org/10.1029/2021WR030185
30. Partitioning evapotranspiration and its long-term evolution in a dry pine forest using measurement-based estimates of soil evaporation R. Qubaja et al. https://doi.org/10.1016/j.agrformet.2019.107831
31. Unmanned Aerial System multispectral mapping for low and variable solar irradiance conditions: Potential of tensor decomposition S. Wang et al. https://doi.org/10.1016/j.isprsjprs.2019.06.017
32. Application of machine learning and emerging remote sensing techniques in hydrology: A state-of-the-art review and current research trends A. Saha & S. Chandra Pal https://doi.org/10.1016/j.jhydrol.2024.130907
33. Advances in uncertainty quantification for water resources applications V. Ciriello et al. https://doi.org/10.1007/s00477-021-01998-y
34. Disruptive Technologies for Improving Water Security in Large River Basins N. Harshadeep & W. Young https://doi.org/10.3390/w12102783
35. Event-based hydrology and sedimentation in paired watersheds under commercial eucalyptus and grasslands in the Brazilian Pampa biome É. Ebling et al. https://doi.org/10.1016/j.iswcr.2020.10.008
36. Toward catchment hydro‐biogeochemical theories L. Li et al. https://doi.org/10.1002/wat2.1495
37. Use of unmanned aerial vehicles for efficient beach litter monitoring C. Martin et al. https://doi.org/10.1016/j.marpolbul.2018.04.045
38. Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions F. Zheng et al. https://doi.org/10.1029/2018RG000616
39. Deep learning fusion modeling of reference evapotranspiration with multi-source remote sensing data through addressing noise impacts M. Sadeghzadeh et al. https://doi.org/10.1016/j.atech.2026.101891
40. The Value of Distributed High-Resolution UAV-Borne Observations of Water Surface Elevation for River Management and Hydrodynamic Modeling L. Jiang et al. https://doi.org/10.3390/rs12071171
41. An Assessment of the Impacts of Climate Variability and Change in KwaZulu-Natal Province, South Africa M. Ndlovu et al. https://doi.org/10.3390/atmos12040427
42. A comprehensive review of various environmental factors' roles in remote sensing techniques for assessing surface water quality M. Diganta et al. https://doi.org/10.1016/j.scitotenv.2024.177180
43. A novel quality control model of rainfall estimation with videos – A survey based on multi-surveillance cameras X. Wang et al. https://doi.org/10.1016/j.jhydrol.2021.127312
44. Improving the Predictive Skill of a Distributed Hydrological Model by Calibration on Spatial Patterns With Multiple Satellite Data Sets M. Dembélé et al. https://doi.org/10.1029/2019WR026085
45. Benchmark Datasets for Satellite Image Time Series Classification: A Review A. Zhang et al. https://doi.org/10.3390/rs18101581
46. Satellite Remote Sensing for Water Resources Management: Potential for Supporting Sustainable Development in Data‐Poor Regions J. Sheffield et al. https://doi.org/10.1029/2017WR022437
47. Global Sensitivity of Simulated Water Balance Indicators Under Future Climate Change in the Colorado Basin K. Bennett et al. https://doi.org/10.1002/2017WR020471
48. Hydro-meteorological factors and human activities contribute comparably to weekly water quality dynamics in the Yangtze River Basin L. Li et al. https://doi.org/10.1016/j.jhydrol.2026.135025
49. Multisource remote sensing data facilitate ecohydrological simulations without runoff calibration D. Yang et al. https://doi.org/10.1002/hyp.14773
50. Using Multi-Spectral UAV Imagery to Extract Tree Crop Structural Properties and Assess Pruning Effects K. Johansen et al. https://doi.org/10.3390/rs10060854
51. A Satellite–UAV–USV Collaborative Monitoring Framework for Cross-Scale Assessment of River Restoration Effectiveness: A Case Study of the Nihe River Basin, China G. Chen et al. https://doi.org/10.3390/rs18121934
52. Behind the scenes of streamflow model performance L. Bouaziz et al. https://doi.org/10.5194/hess-25-1069-2021
53. Towards SDG 15: Using Remote Sensing to Restore Our Lands, From the Coastal Fringe to the Deep Desert J. Blanco-Sacristán et al. https://doi.org/10.3389/frym.2024.1393515
54. Paying more attention on backgrounds: Background-centric attention for UAV detection X. Lin et al. https://doi.org/10.1016/j.neunet.2025.107182
55. Drones for Flood Monitoring, Mapping and Detection: A Bibliometric Review U. Iqbal et al. https://doi.org/10.3390/drones7010032
56. Toward a definition of Essential Mountain Climate Variables J. Thornton et al. https://doi.org/10.1016/j.oneear.2021.05.005
57. Underlying Fundamentals of Kalman Filtering for River Network Modeling C. Emery et al. https://doi.org/10.1175/JHM-D-19-0084.1
58. The application of satellite sensors, current state of utilization, and sources of remote sensing dataset in hydrology for water resource management D. Abegeja https://doi.org/10.2166/wh.2024.102
59. From Earth Observation to Water Intelligence: Remote Sensing for Integrated and Sustainable Water Resource Management A. Tiwari https://doi.org/10.3390/w18131631
60. Assimilation of Satellite Soil Moisture Products for River Flow Prediction: An Extensive Experiment in Over 700 Catchments Throughout Europe D. De Santis et al. https://doi.org/10.1029/2021WR029643
61. Cloud computing and global satellite products for potential groundwater recharge estimation: application to the Basin of Mexico E. Guzmán-Hidalgo et al. https://doi.org/10.1007/s40808-026-02930-5
62. Continuous Plant-Based and Remote Sensing for Determination of Fruit Tree Water Status A. Carella et al. https://doi.org/10.3390/horticulturae10050516
63. Utilization of Remote Sensing Data in the Simulation of the Water and Heat Regime of Land Areas: A Review of Publications E. Muzylev https://doi.org/10.31857/S0321059623700025
64. Evaluating the Global State of Ecosystems and Natural Resources: Within and Beyond the SDGs C. Dickens et al. https://doi.org/10.3390/su12187381
65. A Random Forest Machine Learning Approach for the Retrieval of Leaf Chlorophyll Content in Wheat S. Shah et al. https://doi.org/10.3390/rs11080920
66. A validation study of citizen science-based rainfall observation X. Wang et al. https://doi.org/10.1088/1361-6501/ad3be2
67. Small Arctic rivers mapped from Sentinel-2 satellite imagery and ArcticDEM X. Lu et al. https://doi.org/10.1016/j.jhydrol.2020.124689
68. Optimal Temporal Filtering of the Cosmic-Ray Neutron Signal to Reduce Soil Moisture Uncertainty P. Davies et al. https://doi.org/10.3390/s22239143
69. Continuous data assimilation for the Richards equation of unsaturated flow: A computational study A. Rowley et al. https://doi.org/10.1002/vzj2.70094
70. Large-scale sensitivities of groundwater and surface water to groundwater withdrawal M. Bierkens et al. https://doi.org/10.5194/hess-25-5859-2021
71. Tracking Dynamic Northern Surface Water Changes with High-Frequency Planet CubeSat Imagery S. Cooley et al. https://doi.org/10.3390/rs9121306
72. Improving satellite remote sensing estimates of the global terrestrial water cycle via neural network modeling M. Heberger et al. https://doi.org/10.1016/j.jhydrol.2025.133825
73. Why is arid zone hydrology a scientific desert - and how does the hydrological community move forward? H. Wheater https://doi.org/10.1016/j.envsoft.2025.106561
74. Wireless Telecommunication Links for Rainfall Monitoring: Deep Learning Approach and Experimental Results F. Diba et al. https://doi.org/10.1109/ACCESS.2021.3076781
75. On the Impacts of Observation Location, Timing, and Frequency on Flood Extent Assimilation Performance A. Dasgupta et al. https://doi.org/10.1029/2020WR028238
76. Synergistic Effects of High‐Resolution Factors for Improving Soil Moisture Simulations Over China P. Ji et al. https://doi.org/10.1029/2023WR035513
77. Quality analysis of water level series obtained by altimetric radar satellite along the São Francisco River L. Martins et al. https://doi.org/10.1590/2318-0331.262120210069
78. Similarities and Improvements of GPM Dual-Frequency Precipitation Radar (DPR) upon TRMM Precipitation Radar (PR) in Global Precipitation Rate Estimation, Type Classification and Vertical Profiling J. Gao et al. https://doi.org/10.3390/rs9111142
79. A situated proposal for a grounded approach to socio-hydrology M. Zwarteveen et al. https://doi.org/10.1016/j.jhydrol.2025.134828
80. An Effective Method for Generating Spatiotemporally Continuous 30 m Vegetation Products X. Li et al. https://doi.org/10.3390/rs13040719
81. 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
82. Divergent vegetation greening's direct impacts on land-atmosphere water and carbon exchanges in the northeastern Tibetan Plateau Y. Luo et al. https://doi.org/10.1016/j.gloplacha.2025.104825
83. Ground-Based Soil Moisture Retrieval Using the Correlation Between Dual-Polarization GNSS-R Interference Patterns M. El Hajj et al. https://doi.org/10.1109/TGRS.2023.3337841
84. Using Unmanned Aerial Vehicles to assess the rehabilitation performance of open cut coal mines K. Johansen et al. https://doi.org/10.1016/j.jclepro.2018.10.287
85. 100 Years of Progress in Hydrology C. Peters-Lidard et al. https://doi.org/10.1175/AMSMONOGRAPHS-D-18-0019.1
86. Advances in the Remote Sensing of Terrestrial Evaporation M. McCabe et al. https://doi.org/10.3390/rs11091138
87. State-of-the-art hydrological datasets exhibit low water balance consistency globally H. Huang et al. https://doi.org/10.5194/essd-18-3109-2026
88. Sustainable development of the drone industry: Toward an integrated support system J. Chen et al. https://doi.org/10.1016/j.rtbm.2026.101799
89. Monitoring Total Suspended Solids and Chlorophyll-a Concentrations in Turbid Waters: A Case Study of the Pearl River Estuary and Coast Using Machine Learning J. Liu et al. https://doi.org/10.3390/rs15235559
90. Dye tracing and concentration mapping in coastal waters using unmanned aerial vehicles K. Johansen et al. https://doi.org/10.1038/s41598-022-05189-9
91. Reconstructing and Nowcasting the Rainfall Field by a CML Network P. Zhang et al. https://doi.org/10.1029/2023EA002909
92. An integrated framework of deep learning and entropy theory for enhanced high-dimensional permeability field identification in heterogeneous aquifers M. Cao et al. https://doi.org/10.1016/j.watres.2024.122706
93. Numerical Insights Into the Effects of Model Geometric Distortion in Laboratory Experiments of Urban Flooding X. Li et al. https://doi.org/10.1029/2019WR026774
94. VideoARD: An Analysis-Ready Multi-Level Data Model for Remote Sensing Video Y. Wu et al. https://doi.org/10.3390/rs17223746
95. Advances and challenges in the water sciences in Brazil: a community synthesis of the XXIII Brazilian Water Resources Symposium R. Paiva et al. https://doi.org/10.1590/2318-0331.252020200136
96. Rain Discrimination with Machine Learning Classifiers for Opportunistic Rain Detection System Using Satellite Micro-Wave Links C. Gianoglio et al. https://doi.org/10.3390/s23031202
97. BESS-STAIR: a framework to estimate daily, 30 m, and all-weather crop evapotranspiration using multi-source satellite data for the US Corn Belt C. Jiang et al. https://doi.org/10.5194/hess-24-1251-2020
98. 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
99. Reliability of satellite-derived precipitation data in driving hydrological simulations: A case study of the upper Huaihe River basin, China F. Chen et al. https://doi.org/10.1016/j.jhydrol.2022.128076
100. Early season prediction of within-field crop yield variability by assimilating CubeSat data into a crop model M. Ziliani et al. https://doi.org/10.1016/j.agrformet.2021.108736
101. Data-driven attribution of evapotranspiration dynamics in the Heihe River Basin: Controlling factors from site measurements to regional satellite observations Z. Lin et al. https://doi.org/10.1016/j.agwat.2026.110324
102. An ensemble of 48 physically perturbed model estimates of the 1∕8° terrestrial water budget over the conterminous United States, 1980–2015 H. Zheng et al. https://doi.org/10.5194/essd-15-2755-2023
103. HESS Opinions: Towards a common vision for the future of hydrological observatories P. Nasta et al. https://doi.org/10.5194/hess-29-465-2025
104. Land–atmospheric feedbacks during droughts and heatwaves: state of the science and current challenges D. Miralles et al. https://doi.org/10.1111/nyas.13912
105. The Temporal Improvement of Earth's Mass Transport Estimated by Coupling GRACE‐FO With a Chinese Polar Gravity Satellite Mission Z. Yan et al. https://doi.org/10.1029/2023JB027157
106. Unmanned Aerial Vehicles in Hydrology and Water Management: Applications, Challenges, and Perspectives B. Acharya et al. https://doi.org/10.1029/2021WR029925
107. Predicting Channel Conveyance and Characterizing Planform Using River Bathymetry via Satellite Image Compilation (RiBaSIC) Algorithm for DEM-Based Hydrodynamic Modeling M. Bhuyian & A. Kalyanapu https://doi.org/10.3390/rs12172799
108. Statistical Study of Rainfall Inversion Using the Earth-Space Link at the Ku Band: Optimization and Validation for 1 Year of Data Y. Zhao et al. https://doi.org/10.1109/JSTARS.2021.3111336
109. Winter storm Uri and temporary drought relief in the western climate divisions of Texas A. Veettil et al. https://doi.org/10.1016/j.scitotenv.2022.155336
110. Potential of satellite and reanalysis evaporation datasets for hydrological modelling under various model calibration strategies M. Dembélé et al. https://doi.org/10.1016/j.advwatres.2020.103667
111. Hydraulic Simulation of Step–Pool Channels in Mountain Streams Using High-Density Topographic Observation S. WAKAMATSU et al. https://doi.org/10.3178/jjshwr.39.1927
112. Drought monitoring based on a new combined remote sensing index across the transitional area between humid and arid regions in China Y. Zhang et al. https://doi.org/10.1016/j.atmosres.2021.105850
113. Constraining Mountain Streamflow Constituents by Integrating Citizen Scientist Acquired Geochemical Samples and Sentinel‐1 SAR Wet Snow Time‐Series for the Shimshal Catchment in the Karakoram Mountains of Pakistan J. Lund et al. https://doi.org/10.1029/2022WR032171
114. Assimilation of NDVI data in a land surface – Vegetation model for leaf area index predictions in a tree-grass ecosystem N. Montaldo et al. https://doi.org/10.1080/17538947.2023.2259226
115. A multi-scale intensive monitoring network for soil hydrological processes in semi-arid northwest China B. Zhang et al. https://doi.org/10.1016/j.scib.2026.02.010
116. Threshold trigger embedded gas sensor for low-power and real-time atmospheric NO2 monitoring using drones M. Chae et al. https://doi.org/10.1016/j.cej.2025.160605
117. Unpiloted Aerial Vehicle (UAV) image velocimetry for validation of two-dimensional hydraulic model simulations C. Masafu et al. https://doi.org/10.1016/j.jhydrol.2022.128217
118. Spatial assessment of the performance of multiple high‐resolution satellite‐based precipitation data sets over the Middle East A. El Kenawy et al. https://doi.org/10.1002/joc.5968
119. Emerging trends in global freshwater availability M. Rodell et al. https://doi.org/10.1038/s41586-018-0123-1
120. Monitoring coastal water flow dynamics using sub-daily high-resolution SkySat satellite and UAV-based imagery K. Johansen et al. https://doi.org/10.1016/j.watres.2022.118531
121. Progress in Developing Scale-Able Approaches to Field-Scale Water Accounting Based on Remote Sensing R. Vervoort et al. https://doi.org/10.3390/su14052732
122. Evaluating satellite-derived evapotranspiration estimates against ground-based measurements in arid Upper Orange catchment, South Africa T. Dube et al. https://doi.org/10.1080/10106049.2026.2630432
123. Assessment of Precipitation and Soil Water Content and Their Effects on Evaporation in Haihe River Basin Based on Remote Sensing Technology Z. Shaoyi & S. Jichao https://doi.org/10.1134/S0097807825601323
124. Multiple Spatial and Temporal Scales Evaluation of Eight Satellite Precipitation Products in a Mountainous Catchment of South China B. Guo et al. https://doi.org/10.3390/rs15051373
125. HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community C. Shen et al. https://doi.org/10.5194/hess-22-5639-2018
126. Evaluating the Potential of Different Evapotranspiration Datasets for Distributed Hydrological Model Calibration X. Guo et al. https://doi.org/10.3390/rs14030629
127. Nordic hydrological frontier in the 21st century H. Marttila et al. https://doi.org/10.2166/nh.2022.120
128. Event‐Based Evaluation of the GPM Multisatellite Merged Precipitation Product From 2014 to 2018 Over China: Methods and Results R. Li et al. https://doi.org/10.1029/2020JD033692
129. Drought: A Common Environmental Disaster I. Orimoloye et al. https://doi.org/10.3390/atmos13010111
130. Assessing and optimizing high-resolution global river streamflow estimates with triple collocation analysis M. Sun et al. https://doi.org/10.1016/j.jhydrol.2026.135122
131. Amazon Hydrology From Space: Scientific Advances and Future Challenges A. Fassoni‐Andrade et al. https://doi.org/10.1029/2020RG000728
132. Remote sensing of river corridors: A review of current trends and future directions C. Tomsett & J. Leyland https://doi.org/10.1002/rra.3479
133. Improved Understanding of the Link Between Catchment‐Scale Vegetation Accessible Storage and Satellite‐Derived Soil Water Index L. Bouaziz et al. https://doi.org/10.1029/2019WR026365
134. CubeSats in Hydrology: Ultrahigh‐Resolution Insights Into Vegetation Dynamics and Terrestrial Evaporation M. McCabe et al. https://doi.org/10.1002/2017WR022240
135. Improving parameter regionalization learning for spatialized differentiable hydrological models by assimilation of satellite-based soil moisture data M. Ettalbi et al. https://doi.org/10.1016/j.jhydrol.2025.133300
136. Integrated approaches to understanding and reducing drought impact on food security across scales X. He et al. https://doi.org/10.1016/j.cosust.2019.09.006
137. Low-cost electronic sensors for environmental research: Pitfalls and opportunities K. Chan et al. https://doi.org/10.1177/0309133320956567
138. A new regionalization of rainfall patterns based on wavelet transform information and hierarchical cluster analysis in northeastern Algeria B. Zerouali et al. https://doi.org/10.1007/s00704-021-03883-8
139. Value of sun-induced chlorophyll fluorescence for quantifying hydrological states and fluxes: Current status and challenges F. Jonard et al. https://doi.org/10.1016/j.agrformet.2020.108088
140. Mediterranean intermittent rivers and ephemeral streams: Challenges in monitoring complexity A. Borg Galea et al. https://doi.org/10.1002/eco.2149
141. Implications of model selection: a comparison of publicly available, conterminous US-extent hydrologic component estimates S. Saxe et al. https://doi.org/10.5194/hess-25-1529-2021
142. A new pixel-to-object method for evaluating the capability of the GPM IMERG product to quantify precipitation systems R. Li et al. https://doi.org/10.1016/j.jhydrol.2022.128476
143. Surveillance audio-based rainfall observation: An enhanced strategy for extreme rainfall observation X. Wang et al. https://doi.org/10.1016/j.apacoust.2023.109581
144. 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
145. Streamflow Generation From Catchments of Contrasting Lithologies: The Role of Soil Properties, Topography, and Catchment Size D. Xiao et al. https://doi.org/10.1029/2018WR023736
146. Large-scale flash flood warning in China using deep learning G. Zhao et al. https://doi.org/10.1016/j.jhydrol.2021.127222
147. Emerging technologies and radical collaboration to advance predictive understanding of watershed hydrobiogeochemistry S. Hubbard et al. https://doi.org/10.1002/hyp.13807
148. A dual-channel framework for video-based rainfall intensity estimation using weakly supervised rain streak extraction J. Xu et al. https://doi.org/10.1016/j.jhydrol.2026.136439
149. Suitability of 17 gridded rainfall and temperature datasets for large-scale hydrological modelling in West Africa M. Dembélé et al. https://doi.org/10.5194/hess-24-5379-2020
150. A Novel Approach to Measuring Urban Waterlogging Depth from Images Based on Mask Region-Based Convolutional Neural Network J. Huang et al. https://doi.org/10.3390/su12052149
151. Microwave Resolution Enhancement Using Distributed Small Spacecraft Architectures T. Himani & A. Jones https://doi.org/10.1109/TGRS.2020.3034541
152. Evaluating reanalysis datasets as meteorological input for estimating reference evapotranspiration in Africa and Southwest Asia B. Tran et al. https://doi.org/10.1080/02626667.2025.2600682
153. Potential Added Value of Incorporating Human Water Use on the Simulation of Evapotranspiration and Precipitation in a Continental‐Scale Bedrock‐to‐Atmosphere Modeling System: A Validation Study Considering Observational Uncertainty J. Keune et al. https://doi.org/10.1029/2019MS001657
154. Revealing potential areas of water resources using integrated remote-sensing data and GIS-based analytical hierarchy process M. Abdelkareem & F. Abdalla https://doi.org/10.1080/10106049.2021.2005155
155. A New Orbiting Deployable System for Small Satellite Observations for Ecology and Earth Observation E. Martellato et al. https://doi.org/10.3390/rs14092066
156. Estimation of Small-Stream Water Surface Elevation Using UAV Photogrammetry and Deep Learning R. Szostak et al. https://doi.org/10.3390/rs16081458
157. Advances in the application of smartphones in hydrology L. Li et al. https://doi.org/10.1080/02508060.2023.2204636
158. Hydrometeorological Monitoring Using Opportunistic Sensing Networks in the Amsterdam Metropolitan Area L. de Vos et al. https://doi.org/10.1175/BAMS-D-19-0091.1
159. Combining multi-indicators with machine-learning algorithms for maize yield early prediction at the county-level in China M. Cheng et al. https://doi.org/10.1016/j.agrformet.2022.109057
160. Revisiting parameter sensitivities in the variable infiltration capacity model across a hydroclimatic gradient U. Sepúlveda et al. https://doi.org/10.5194/hess-26-3419-2022
161. Deep shared proxy construction hashing for cross-modal remote sensing image fast target retrieval L. Han et al. https://doi.org/10.1016/j.isprsjprs.2024.10.004
162. Information Communication and Computation Technologies (ICCT) for Agricultural and Environmental Information Systems for Society 5.0 S. Aithal & P. Aithal https://doi.org/10.47992/IJAEML.2581.7000.0213
163. An improved representative of stomatal models for predicting diurnal stomatal conductance at low irradiance and vapor pressure deficit in tropical rainforest trees W. Xue et al. https://doi.org/10.1016/j.agrformet.2024.110098
164. A remote sensing-based method for river discharge estimation in data-scarce regions Y. Guo et al. https://doi.org/10.1080/17538947.2026.2730681
165. Exploring the use of underground gravity monitoring to evaluate radar estimates of heavy rainfall L. Delobbe et al. https://doi.org/10.5194/hess-23-93-2019
166. Research on Real-Time Rainfall Intensity Monitoring Methods Based on Deep Learning and Audio Signals in the Semi-Arid Region of Northwest China Y. Wang et al. https://doi.org/10.3390/atmos17020131
167. Structure from Motion of Multi-Angle RPAS Imagery Complements Larger-Scale Airborne Lidar Data for Cost-Effective Snow Monitoring in Mountain Forests P. Broxton & W. van Leeuwen https://doi.org/10.3390/rs12142311
168. Research on Multi-Source Precipitation Fusion Based on Classification and Regression Machine Learning Methods—A Case Study of the Min River Basin in the Eastern Source of the Qinghai–Tibet Plateau S. Liu et al. https://doi.org/10.3390/rs17243982
169. Capturing the Diurnal Cycle of Land Surface Temperature Using an Unmanned Aerial Vehicle Y. Malbéteau et al. https://doi.org/10.3390/rs10091407
170. Sensitivity of Evapotranspiration Components in Remote Sensing-Based Models C. Talsma et al. https://doi.org/10.3390/rs10101601
171. Earth Observation-Based Operational Estimation of Soil Moisture and Evapotranspiration for Agricultural Crops in Support of Sustainable Water Management G. Petropoulos et al. https://doi.org/10.3390/su10010181
172. Deep learning for water quality W. Zhi et al. https://doi.org/10.1038/s44221-024-00202-z
173. Spatial heterogeneity of agricultural evapotranspiration as quantified by satellite and flux tower sources A. Goodwell et al. https://doi.org/10.1016/j.agrformet.2025.110608
174. Causes and implications of groundwater depletion in India: A review S. Dangar et al. https://doi.org/10.1016/j.jhydrol.2021.126103
175. Mapping groundwater abstractions from irrigated agriculture: big data, inverse modeling, and a satellite–model fusion approach O. López Valencia et al. https://doi.org/10.5194/hess-24-5251-2020
176. Mapping the condition of macadamia tree crops using multi-spectral UAV and WorldView-3 imagery K. Johansen et al. https://doi.org/10.1016/j.isprsjprs.2020.04.017
177. Mapping the Natural Distribution of Bamboo and Related Carbon Stocks in the Tropics Using Google Earth Engine, Phenological Behavior, Landsat 8, and Sentinel-2 M. Venkatappa et al. https://doi.org/10.3390/rs12183109
178. CubeSats deliver new insights into agricultural water use at daily and 3 m resolutions B. Aragon et al. https://doi.org/10.1038/s41598-021-91646-w
179. Digital insights: bridging the phenotype-to-genotype divide M. McCabe & M. Tester https://doi.org/10.1093/jxb/erab108
180. Remote Sensing Satellites for Land Applications: A Review S. Kumar & V. Bhagat https://doi.org/10.21523/gcj1.18020203
181. Climate data empathy S. Brönnimann & J. Wintzer https://doi.org/10.1002/wcc.559
182. A Digital Twin of the terrestrial water cycle: a glimpse into the future through high-resolution Earth observations L. Brocca et al. https://doi.org/10.3389/fsci.2023.1190191
183. Center pivot field delineation and mapping: A satellite-driven object-based image analysis approach for national scale accounting K. Johansen et al. https://doi.org/10.1016/j.isprsjprs.2021.02.019
184. Seasonal variations of vegetative indices and their correlation with evapotranspiration and soil water storage in a small agricultural catchment T. Li et al. https://doi.org/10.17221/60/2023-SWR
185. The SPAtial EFficiency metric (SPAEF): multiple-component evaluation of spatial patterns for optimization of hydrological models J. Koch et al. https://doi.org/10.5194/gmd-11-1873-2018
186. Multivariate temperature prediction model based on CNN-BiLSTM and RandomForest X. Bai et al. https://doi.org/10.1007/s11227-024-06689-3
187. Towards Estimating Land Evaporation at Field Scales Using GLEAM B. Martens et al. https://doi.org/10.3390/rs10111720
188. Recent Advances in Land–Atmosphere Interactions and Atmospheric Water Cycle Feedbacks Under Climate Change N. Li et al. https://doi.org/10.3390/atmos17070644
189. Urban Heat Island Effect: Remote Sensing Monitoring and Assessment—Methods, Applications, and Future Directions L. Zhao et al. https://doi.org/10.3390/atmos16070791
190. Validation of the EUMETSAT CMSAF global interpolated gridded rainfall estimation (GIRAFE v1) dataset for Türkiye M. Pekin et al. https://doi.org/10.1007/s41324-025-00625-0
191. From Slide Rule to Big Data: How Data Science is Changing Water Science and Engineering J. Hering https://doi.org/10.1061/(ASCE)EE.1943-7870.0001578
192. From Hydrometeorology to River Water Quality: Can a Deep Learning Model Predict Dissolved Oxygen at the Continental Scale? W. Zhi et al. https://doi.org/10.1021/acs.est.0c06783
193. Analysing River Systems with Time Series Data Using Path Queries in Graph Databases E. Bollen et al. https://doi.org/10.3390/ijgi12030094
194. Plot-level field monitoring with Sentinel-2 and PlanetScope data for examination of sewage sludge disposal impact F. Kovács & Z. Ladányi https://doi.org/10.5937/gp26-37964
195. Developing Data‐Rich Video of Surface Water–Groundwater Interactions for Public Engagement C. Christenson et al. https://doi.org/10.1111/gwat.13165
196. Evaluations of MODIS and microwave based satellite evapotranspiration products under varied cloud conditions over East Asia forests Y. Wang et al. https://doi.org/10.1016/j.rse.2021.112606
197. Demystifying Earth Observation Through Co-Creation Pathways for Flood Resilience in Some African Informal Cities S. Yunus et al. https://doi.org/10.3390/su18073266
198. An improved Terra–Aqua MODIS snow cover and Randolph Glacier Inventory 6.0 combined product (MOYDGL06*) for high-mountain Asia between 2002 and 2018 S. Muhammad & A. Thapa https://doi.org/10.5194/essd-12-345-2020
199. Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows H. Aasen et al. https://doi.org/10.3390/rs10071091
200. Role of Unmanned Aerial Systems for Natural Resource Management P. Mishra & A. Rai https://doi.org/10.1007/s12524-020-01230-4
201. Comparing and combining data-driven and model-driven approaches to monitor wheat green area index with high spatio-temporal resolution satellites M. Dong et al. https://doi.org/10.1016/j.rse.2024.114118
202. Fetching Ecosystem Monitoring Data in Extreme Areas via a Drone-Enabled Internet of Remote Things M. Zhang et al. https://doi.org/10.1109/JIOT.2022.3195302
203. Sliding Time Master Digital Image Correlation Analyses of CubeSat Images for landslide Monitoring: The Rattlesnake Hills Landslide (USA) P. Mazzanti et al. https://doi.org/10.3390/rs12040592
204. Time Series Analysis for the Adaptive Prediction of Total Phosphorus in the Yangtze River: A Machine Learning Approach T. Ma & X. Chen https://doi.org/10.3390/w17040603
205. Estimating rainfall intensity from surveillance audio: A hybrid model-data-driven framework X. Wang et al. https://doi.org/10.1016/j.jhydrol.2025.133295
206. Weakly Supervised Phenology-Guided Refinement of WorldCover Cropland Maps in Hyperarid Regions L. Lin et al. https://doi.org/10.1109/TGRS.2026.3697170
207. Estimation of water storage changes in small endorheic lakes in Northern Kazakhstan V. Yapiyev et al. https://doi.org/10.1016/j.jaridenv.2018.09.008
208. HESS Opinions: Participatory Digital eARth Twin Hydrology systems (DARTHs) for everyone – a blueprint for hydrologists R. Rigon et al. https://doi.org/10.5194/hess-26-4773-2022
209. Estimating Stream Bank and Bed Erosion and Deposition with Innovative and Traditional Methods P. Koutalakis et al. https://doi.org/10.3390/land13020232
210. Using rapid repeat SAR interferometry to improve hydrodynamic models of flood propagation in coastal wetlands X. Zhang et al. https://doi.org/10.1016/j.advwatres.2021.104088
211. Leveraging the Google Earth Engine for Drought Assessment Using Global Soil Moisture Data N. Sazib et al. https://doi.org/10.3390/rs10081265
212. Assessing the impact of computing infrastructure on transboundary water pollution control: Evidence from a quasi-natural experiment in China X. Liu et al. https://doi.org/10.1007/s10668-026-07598-6
213. Seeing the Disturbed Forest for the Trees: Remote Sensing Is Underutilized to Quantify Critical Zone Response to Unprecedented Disturbance K. Hwang et al. https://doi.org/10.1029/2022EF003314
214. Estimation of surface runoff using satellite data in arid regions: case study of Jalajil Dam R. Alharbi https://doi.org/10.1007/s12517-023-11726-1
215. Multi-sensor remote sensing for drought characterization: current status, opportunities and a roadmap for the future W. Jiao et al. https://doi.org/10.1016/j.rse.2021.112313
216. Understanding the response of fruit crops to drought stress and irrigation needs under climate change conditi N. Čereković et al. https://doi.org/10.63356/agrores.2025.007
217. Spatial analysis approaches for the evaluation and protection of groundwater resources in large watersheds of the Canadian Shield S. Nadeau et al. https://doi.org/10.1007/s10040-021-02367-3
218. Flood Mapping Proposal in Small Watersheds: A Case Study of the Rebollos and Miranda Ephemeral Streams (Cartagena, Spain) V. Betancourt-Suárez et al. https://doi.org/10.3390/w13010102
219. Comparison of remote sensing feature tracking techniques using high-resolution UAV images for small mountain glacier surface velocity measurement S. Sheshangosht et al. https://doi.org/10.1016/j.rsase.2025.101781
220. Modeling the behavior of water reservoir operators via eigenbehavior analysis M. Giuliani & J. Herman https://doi.org/10.1016/j.advwatres.2018.10.021
221. Prospects of Improving Agricultural and Water Productivity through Unmanned Aerial Vehicles L. Nhamo et al. https://doi.org/10.3390/agriculture10070256
222. Water Balance in Alpine Catchments by Sentinel Data R. Perico et al. https://doi.org/10.1029/2021WR031355
223. 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
224. Satellite and In Situ Observations for Advancing Global Earth Surface Modelling: A Review G. Balsamo et al. https://doi.org/10.3390/rs10122038
225. Global Assessment of the Standardized Evapotranspiration Deficit Index (SEDI) for Drought Analysis and Monitoring S. Vicente-Serrano et al. https://doi.org/10.1175/JCLI-D-17-0775.1
226. Assessing seasonal river-wetland connectivity using remote sensing-based monitoring in tropical environments D. Irvine et al. https://doi.org/10.1016/j.ecolind.2026.114624
227. High-resolution satellite products improve hydrological modeling in northern Italy L. Alfieri et al. https://doi.org/10.5194/hess-26-3921-2022
228. Accuracy Assessment of Remote Sensing-Derived Evapotranspiration Products Against Eddy Covariance Measurements in Tensift Al-Haouz Semi-Arid Region, Morocco Y. Manyari et al. https://doi.org/10.3390/atmos16121407
229. Harnessing big data to rethink land heterogeneity in Earth system models N. Chaney et al. https://doi.org/10.5194/hess-22-3311-2018
230. Research on the Method of Rainfall Field Retrieval Based on the Combination of Earth–Space Links and Horizontal Microwave Links Y. Zhao et al. https://doi.org/10.3390/rs14092220
231. High spatial resolution monitoring land surface energy, water and CO2 fluxes from an Unmanned Aerial System S. Wang et al. https://doi.org/10.1016/j.rse.2019.03.040
232. Soil moisture dynamics and enhancement of satellite-derived soil moisture responses to rainfall events across land use and land cover types Q. Wang et al. https://doi.org/10.1016/j.catena.2026.110394
233. Improving hydrological simulations by incorporating GRACE data for model calibration P. Bai et al. https://doi.org/10.1016/j.jhydrol.2017.12.025
234. Citizen science for hydrological risk reduction and resilience building J. Paul et al. https://doi.org/10.1002/wat2.1262
235. Value of microwave soil moisture and thermal-infrared evapotranspiration retrievals for the mapping of irrigation coverage W. Crow et al. https://doi.org/10.1016/j.jag.2025.104773
236. A Review of Current and Potential Applications of Remote Sensing to Study the Water Status of Horticultural Crops D. Gautam & V. Pagay https://doi.org/10.3390/agronomy10010140
237. Satellite Video Remote Sensing for Flood Model Validation C. Masafu & R. Williams https://doi.org/10.1029/2023WR034545
238. Evapotranspiration Variations of the Minjiang River Basin in Southeastern China from 2000 to 2019 Y. Lu et al. https://doi.org/10.3390/atmos13040562
239. Co-regulation of water use and canopy temperature in desert trees B. Morgan et al. https://doi.org/10.1016/j.agrformet.2025.110929
240. HAQCCN: A Hybrid Quantum–Classical Convolutional Network with asymmetric kernels for remote sensing image classification L. Chen et al. https://doi.org/10.2298/CSIS251029015C
241. 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
242. Hydrologic model parameter estimation in snow-dominated headwater catchments using multiple observation datasets L. North et al. https://doi.org/10.5194/hess-30-5195-2026
243. A Physically Informed Transformation of Sentinel-1 SAR Observations for Streamflow Inference in Small and Complex Rivers K. Han et al. https://doi.org/10.1109/JSTARS.2026.3735355
244. An Agenda for Land Data Assimilation Priorities: Realizing the Promise of Terrestrial Water, Energy, and Vegetation Observations From Space S. Kumar et al. https://doi.org/10.1029/2022MS003259
245. Drone-Enabled Internet-of-Things Relay for Environmental Monitoring in Remote Areas Without Public Networks M. Zhang & X. Li https://doi.org/10.1109/JIOT.2020.2988249
246. A new robust discharge estimation method applied in the context of SWOT satellite data processing I. Gejadze et al. https://doi.org/10.1016/j.jhydrol.2022.127909
247. Evapotranspiration, gross primary productivity and water use efficiency over a high-density olive orchard using ground and satellite based data J. Elfarkh et al. https://doi.org/10.1016/j.agwat.2023.108423
248. Crop coefficient and water requirement for oil palm (Elaeis guineensis Jacq.) on the nursery based on radiation evaporation method N. Ichwan et al. https://doi.org/10.1088/1755-1315/260/1/012041
249. Ensemble flood forecasting: Current status and future opportunities W. Wu et al. https://doi.org/10.1002/wat2.1432
250. Quantifying mangrove carbon assimilation rates using UAV imagery J. Blanco-Sacristán et al. https://doi.org/10.1038/s41598-024-55090-w
251. Research Trends in the Use of Remote Sensing for Inland Water Quality Science: Moving Towards Multidisciplinary Applications S. Topp et al. https://doi.org/10.3390/w12010169
252. A database system for querying of river networks: facilitating monitoring and prediction applications E. Bollen et al. https://doi.org/10.2166/ws.2021.433
253. Estimating rainfall intensity based on surveillance audio and deep-learning M. Wang et al. https://doi.org/10.1016/j.ese.2024.100450
254. Reconstruction and Nowcasting of Rainfall Field by Oblique Earth-Space Links Network: Preliminary Results from Numerical Simulation M. Xian et al. https://doi.org/10.3390/rs12213598
255. Detecting Streamflow in Dryland Rivers Using CubeSats Z. Wang & E. Vivoni https://doi.org/10.1029/2022GL098729
256. High Resolution, Annual Maps of Field Boundaries for Smallholder-Dominated Croplands at National Scales L. Estes et al. https://doi.org/10.3389/frai.2021.744863
257. Winter Wheat Yield Estimation Based on Sparrow Search Algorithm Combined with Random Forest: A Case Study in Henan Province, China X. Shi et al. https://doi.org/10.1007/s11769-024-1421-1
258. Practical Data Products From Cosmic-Ray Neutron Sensing for Hydrological Applications T. Franz et al. https://doi.org/10.3389/frwa.2020.00009
259. A Basin Approach to a Hydrological Service Delivery System in the Amur River Basin S. Borsch et al. https://doi.org/10.3390/geosciences8030093
260. Large‐Domain Multisite Precipitation Generation: Operational Blueprint and Demonstration for 1,000 Sites S. Papalexiou et al. https://doi.org/10.1029/2022WR034094
261. Comment on “Can assimilation of crowdsourced data in hydrological modelling improve flood prediction?” by Mazzoleni et al. (2017) D. Viero https://doi.org/10.5194/hess-22-171-2018
262. Improving Backscatter-Based Surface Water Classification in Arid Environments Through Interferometric Coherence D. Festa et al. https://doi.org/10.3390/rs18172966
263. Investigation on the Informatization of Archives Management of Hydraulic Project Based on Internet of Things 康. 王 https://doi.org/10.12677/JWRR.2022.115060
264. Rainfall Monitoring Based on Next-Generation Millimeter-Wave Backhaul Technologies in a Dense Urban Environment C. Han et al. https://doi.org/10.3390/rs12061045
265. Editorial – Towards FAIR and SQUARE hydrological data C. Cudennec et al. https://doi.org/10.1080/02626667.2020.1739397
266. Multi-Scenario Integration Comparison of CMADS and TMPA Datasets for Hydro-Climatic Simulation over Ganjiang River Basin, China Q. Wang et al. https://doi.org/10.3390/w12113243
267. Rainfall observation using surveillance audio X. Wang et al. https://doi.org/10.1016/j.apacoust.2021.108478
268. Rainfall intensity estimation at night using deep learning and urban surveillance cameras in Jiangsu Province, China X. Wang et al. https://doi.org/10.1016/j.ejrh.2026.103112
269. Remote Sensing of Water: The Observation-to-Inference Arc Across Six Decades and Toward an AI-Native Future D. Ames https://doi.org/10.3390/rs18081127
270. The activities of the international precipitation working group V. Levizzani et al. https://doi.org/10.1002/qj.3214
271. Calibration and Validation of the Cosmic Ray Neutron Rover for Soil Water Mapping within Two South African Land Classes T. Vather et al. https://doi.org/10.3390/hydrology6030065
272. Synergistic Use of Ground-Based GNSS-R and Sentinel-2 Imagery for Soil Moisture Estimation Across an Irrigated Grassland M. El Hajj et al. https://doi.org/10.1109/TGRS.2025.3555000
273. Overcoming the Challenges of Thermal Infrared Orthomosaics Using a Swath-Based Approach to Correct for Dynamic Temperature and Wind Effects Y. Malbéteau et al. https://doi.org/10.3390/rs13163255
274. Embracing large language model (LLM) technologies in hydrology research Z. Ma et al. https://doi.org/10.1088/3033-4942/addd43
275. Water accounting for water management at the river basin scale in India: approaches and gaps N. Bassi et al. https://doi.org/10.2166/wp.2020.080
276. Multiyear Mapping of Water Demand at Crop Level: An End-to-End Workflow Based on High-Resolution Crop Type Maps and Meteorological Data G. Weikmann et al. https://doi.org/10.1109/JSTARS.2023.3294107
277. K-sharp: A segmented regression approach for image sharpening and normalization B. Aragon et al. https://doi.org/10.1016/j.srs.2023.100095
278. Near-infrared surveillance video-based rain gauge X. Wang et al. https://doi.org/10.1016/j.jhydrol.2023.129173
279. Performance Superiority of Dual-Pair Cartwheel/Pendulum Over Bender-Type in Four-Satellite Gravity Constellations: An Equitable Evaluation Z. Zeng et al. https://doi.org/10.1109/JSTARS.2026.3663481
280. A new cost-performance grid to compare different flood modelling approaches R. Hdeib et al. https://doi.org/10.1080/02626667.2021.1873346
281. Satellite-Enabled Internet of Remote Things Network Transmits Field Data from the Most Remote Areas of the Tibetan Plateau Y. Chen et al. https://doi.org/10.3390/s22103713
282. Citizens AND HYdrology (CANDHY): conceptualizing a transdisciplinary framework for citizen science addressing hydrological challenges F. Nardi et al. https://doi.org/10.1080/02626667.2020.1849707
283. Monitoring spatially heterogeneous riparian vegetation around permanent waterholes: A method to integrate LiDAR and Landsat data for enhanced ecological interpretation of Landsat fPAR time-series M. Henriques et al. https://doi.org/10.1016/j.rse.2024.114382
284. Automated Georectification and Mosaicking of UAV-Based Hyperspectral Imagery from Push-Broom Sensors Y. Angel et al. https://doi.org/10.3390/rs12010034
285. Daily Retrieval of NDVI and LAI at 3 m Resolution via the Fusion of CubeSat, Landsat, and MODIS Data R. Houborg & M. McCabe https://doi.org/10.3390/rs10060890
286. Hydrological process knowledge in catchment modelling – Lessons and perspectives from 60 years development J. Refsgaard et al. https://doi.org/10.1002/hyp.14463
287. A multidimensional vine copula hydrological analysis framework for assessing compound risks of blue and green water: Implications for integrated water resources management Y. Qiu et al. https://doi.org/10.1016/j.eiar.2026.108454
288. Water-energy-food nexus: Concepts, questions and methodologies C. Zhang et al. https://doi.org/10.1016/j.jclepro.2018.05.194
289. Hybrid process-based and deep learning for river nutrient prediction under limited monitoring data J. Tang et al. https://doi.org/10.1016/j.jhydrol.2026.135098
290. Estimating daily evapotranspiration based on a model of evaporative fraction (EF) for mixed pixels F. Li et al. https://doi.org/10.5194/hess-23-949-2019
291. Supervised multi-regional segmentation machine learning architecture for digital twin applications in coastal regions M. Ahmadi et al. https://doi.org/10.1007/s11852-024-01038-1
292. Comparison of Simple Temporal and Climatological Baselines, Deterministic Spatial Interpolation, and Hybrid Machine-Learning Methods for Imputing Precipitation Data Using ERA5-Land Climate Data Y. Tektaş & N. Polat https://doi.org/10.3390/atmos17080727
293. Automated Extraction of Surface Water Extent from Sentinel-1 Data W. Huang et al. https://doi.org/10.3390/rs10050797
294. Improving total phosphorus prediction and monitoring in data scarce streams using a hybrid machine learning framework A. Kumar & K. Zhang https://doi.org/10.1016/j.jhydrol.2025.134636
295. Multi-sensor and multi-platform consistency and interoperability between UAV, Planet CubeSat, Sentinel-2, and Landsat reflectance data J. Jiang et al. https://doi.org/10.1080/15481603.2022.2083791
296. Exploring the Potential of Sub-daily Microwave Remote Sensing Observations for Estimating Evaporation in Forests E. Tronquo et al. https://doi.org/10.1109/JSTARS.2026.3679245
297. Constraining the Assimilation of SWOT Observations With Hydraulic Geometry Relations K. Andreadis et al. https://doi.org/10.1029/2019WR026611
298. Rational gaze: Presenting the open-source HYPEtools R package for analysis, visualization, and interpretation of hydrological models and datasets C. Brendel et al. https://doi.org/10.1016/j.envsoft.2024.106094
299. Design flood estimation for global river networks based on machine learning models G. Zhao et al. https://doi.org/10.5194/hess-25-5981-2021
300. A hybrid training approach for leaf area index estimation via Cubist and random forests machine-learning R. Houborg & M. McCabe https://doi.org/10.1016/j.isprsjprs.2017.10.004
301. Rule-based safety prediction models for rural two-lane run-off-road crashes S. Das et al. https://doi.org/10.1016/j.ijtst.2020.08.001
302. Remote sensing and modeling fusion for investigating the ecosystem water-carbon coupling processes P. Sun et al. https://doi.org/10.1016/j.scitotenv.2019.134064
303. Mapping Apennines river paths along different hydrological conditions from satellite images: A description of the method and potential applications S. Giacomelli et al. https://doi.org/10.1002/rra.4182
304. Managing data of sensor-equipped transportation networks using graph databases E. Bollen et al. https://doi.org/10.5194/gi-13-353-2024
305. Fusion of Multi-Satellite Data and Artificial Neural Network for Predicting Total Discharge J. Seo & S. Lee https://doi.org/10.3390/rs12142248
306. Reconstructing monthly 0.25° terrestrial evapotranspiration data in a remote arid region using Bayesian-driven ensemble learning method F. Ochege et al. https://doi.org/10.1016/j.jhydrol.2024.131115
307. БПЛА в геоэкологических и гидрологических исследованиях . Федоровский https://doi.org/10.35735/26870509_2026_25_5
308. Utilization of Remote Sensing Data in the Simulation of the Water and Heat Regime of Land Areas: A Review of Publications E. Muzylev https://doi.org/10.1134/S0097807823700021
309. Next-Generation Digital Ecosystem for Climate Data Mining and Knowledge Discovery: A Review of Digital Data Collection Technologies A. Hsu et al. https://doi.org/10.3389/fdata.2020.00029
310. High-Resolution Stress Detection in Crops: Integrating Satellite and Drone Remote Sensing for Resilient Agriculture C. Ojeilua et al. https://doi.org/10.62154/ajasfr.2025.019.01037
311. Mobile phone technologies for disaster risk reduction J. Paul et al. https://doi.org/10.1016/j.crm.2021.100296
312. Areal extent of vegetative cover: A challenge to regional upscaling of methane emissions J. Melack & L. Hess https://doi.org/10.1016/j.aquabot.2022.103592
313. Towards a coherent flood forecasting framework for Canada: Local to global implications L. Arnal et al. https://doi.org/10.1111/jfr3.12895
314. Assessing hydrological drought propagation through assimilation of GRACE for groundwater storage anomalies modelling in northeastern Mexico S. Arciniega-Esparza et al. https://doi.org/10.1016/j.jhydrol.2025.133826
315. Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate V. Levizzani & E. Cattani https://doi.org/10.3390/rs11192301
316. Actual Evapotranspiration from UAV Images: A Multi-Sensor Data Fusion Approach A. Mokhtari et al. https://doi.org/10.3390/rs13122315
317. Monitoring water crisis from space across a Mediterranean region W. Baba et al. https://doi.org/10.1038/s41598-025-06240-1
318. Assimilation of soil moisture and canopy cover data improves maize simulation using an under-calibrated crop model Y. Lu et al. https://doi.org/10.1016/j.agwat.2021.106884
319. Assessing remote sensing as a tool to monitor hydrological stress in Irish catchments with Freshwater Pearl Mussel populations M. Kuemmerlen et al. https://doi.org/10.1016/j.scitotenv.2021.150807
320. Rainfall Monitoring Based on Machine Learning by Earth-Space Link in the Ku Band M. Xian et al. https://doi.org/10.1109/JSTARS.2020.3004375
321. Predicting algal blooms: Are we overlooking groundwater? A. Brookfield et al. https://doi.org/10.1016/j.scitotenv.2020.144442
322. Comparison of methods for green cover assessment: Options of aerial photography and satellite imagery in Japanese municipalities Y. Sofue et al. https://doi.org/10.1016/j.indic.2025.100982
323. Significant stream chemistry response to temperature variations in a high-elevation mountain watershed W. Zhi et al. https://doi.org/10.1038/s43247-020-00039-w
324. Spatial and Temporal Evaluation of Gridded Precipitation Products over the Mountainous Lake Tana Basin, Ethiopia S. Ewnetu et al. https://doi.org/10.3390/w17243536
325. The radiometric accuracy of the 8-band multi-spectral surface reflectance from the planet SuperDove constellation Y. Tu et al. https://doi.org/10.1016/j.jag.2022.103035
326. Experimental and Numerical Study of the Effect of Model Geometric Distortion on Laboratory Modeling of Urban Flooding X. Li et al. https://doi.org/10.1029/2021WR029666
327. Capitalizing on Cellular Technology—Opportunities and Challenges for Near Ground Weather Monitoring H. Messer https://doi.org/10.3390/environments5070073
328. Temporal interpolation of land surface fluxes derived from remote sensing – results with an unmanned aerial system S. Wang et al. https://doi.org/10.5194/hess-24-3643-2020
329. Evaluation of the ERA5 reanalysis as a potential reference dataset for hydrological modelling over North America M. Tarek et al. https://doi.org/10.5194/hess-24-2527-2020
330. Soil hydrology in the Earth system H. Vereecken et al. https://doi.org/10.1038/s43017-022-00324-6
331. Monitoring Irrigation in Small Orchards with Cosmic-Ray Neutron Sensors C. Brogi et al. https://doi.org/10.3390/s23052378
332. Estimation of raindrop size distribution and rain rate with infrared surveillance camera in dark conditions J. Lee et al. https://doi.org/10.5194/amt-16-707-2023
333. Mapping data poverty in the water environment: national-scale assessment of gaps and biases in the Philippine hydrometric network R. Cornelio & C. Ringor https://doi.org/10.1080/07900627.2026.2721419
334. Response time of global deltas to changes in fluvial sediment supply J. Wang et al. https://doi.org/10.1038/s41467-025-60531-9
335. Remote Sensing Big Data for Water Environment Monitoring: Current Status, Challenges, and Future Prospects J. Chen et al. https://doi.org/10.1029/2021EF002289
336. Assessing Hydrological Modelling Driven by Different Precipitation Datasets via the SMAP Soil Moisture Product and Gauged Streamflow Data L. Yi et al. https://doi.org/10.3390/rs10121872
337. Spatiotemporal Evaluation and Uncertainty Assessment of Multi-Source Evapotranspiration Products Across Hong Kong: Implications for Mai Po Wetland Water-Balance Monitoring F. Quayson et al. https://doi.org/10.3390/w18151897
338. Integrating LiDAR data and multi-temporal aerial imagery to map wetland inundation dynamics using Google Earth Engine Q. Wu et al. https://doi.org/10.1016/j.rse.2019.04.015
339. The pantropical response of soil moisture to El Niño K. Solander et al. https://doi.org/10.5194/hess-24-2303-2020
340. A blended census and multiscale remote sensing approach to probabilistic cropland mapping in complex landscapes I. Mohammed et al. https://doi.org/10.1016/j.isprsjprs.2020.01.024
341. Reviews and syntheses: Turning the challenges of partitioning ecosystem evaporation and transpiration into opportunities P. Stoy et al. https://doi.org/10.5194/bg-16-3747-2019
342. Predicting land susceptibility to atmospheric dust emissions in central Iran by combining integrated data mining and a regional climate model H. Gholami et al. https://doi.org/10.1016/j.apr.2021.03.005
343. Climatological and Hydrological Observations for the South American Andes: In situ Stations, Satellite, and Reanalysis Data Sets T. Condom et al. https://doi.org/10.3389/feart.2020.00092
344. Sensing human health from Space: An assessment of applications and big data platforms D. Sengupta et al. https://doi.org/10.1016/j.rsase.2025.101701
345. Watershed Reactive Transport L. Li https://doi.org/10.2138/rmg.2018.85.13
346. Evaluation of Heavy Precipitation Simulated by the WRF Model Using 4D-Var Data Assimilation with TRMM 3B42 and GPM IMERG over the Huaihe River Basin, China L. Yi et al. https://doi.org/10.3390/rs10040646
347. Soil Temperature, Environment, and Moisture Monitoring Network—a low-cost sensing network for Alabama N. Perlaky et al. https://doi.org/10.1007/s10661-025-14166-4
348. A Cubesat enabled Spatio-Temporal Enhancement Method (CESTEM) utilizing Planet, Landsat and MODIS data R. Houborg & M. McCabe https://doi.org/10.1016/j.rse.2018.02.067
349. Decision-support systems for water management C. Wardropper & A. Brookfield https://doi.org/10.1016/j.jhydrol.2022.127928
350. SMAP-HydroBlocks, a 30-m satellite-based soil moisture dataset for the conterminous US N. Vergopolan et al. https://doi.org/10.1038/s41597-021-01050-2
351. Introducing of modeling techniques in the research of POPs in breast milk – A pilot study G. Jovanović et al. https://doi.org/10.1016/j.ecoenv.2019.01.087
352. How well does the IMERG satellite precipitation product capture the timing of precipitation events? R. Li et al. https://doi.org/10.1016/j.jhydrol.2023.129563
353. A novel deep learning approach for high-precision rainfall intensity inversion using urban surveillance audio J. Feng et al. https://doi.org/10.1016/j.asr.2025.10.070
354. An Efficient Method for Generating UAV-Based Hyperspectral Mosaics Using Push-Broom Sensors J. JuanManuel et al. https://doi.org/10.1109/JSTARS.2021.3088945
355. Intra-Season Crop Height Variability at Commercial Farm Scales Using a Fixed-Wing UAV M. Ziliani et al. https://doi.org/10.3390/rs10122007
356. Planning remote sensing emergency services: Bridging the gap between remote sensing science and emergency practice in China X. Zheng et al. https://doi.org/10.1016/j.ssci.2021.105346
357. Identifying ENSO-related interannual and decadal variability on terrestrial water storage S. Cheon et al. https://doi.org/10.1038/s41598-021-92729-4
358. Advancing river monitoring using image-based techniques: challenges and opportunities S. Manfreda et al. https://doi.org/10.1080/02626667.2024.2333846
359. Knowledge formalization for Earth Science informed decision-making: The GEOEssential Knowledge Base P. Mazzetti et al. https://doi.org/10.1016/j.envsci.2021.12.023
360. Shyft v4.8: a framework for uncertainty assessment and distributed hydrologic modeling for operational hydrology J. Burkhart et al. https://doi.org/10.5194/gmd-14-821-2021
361. High-Frequency Multi-Satellite Observations of Brahmaputra River Hydrology and Floodplain Dynamics F. Abdullah et al. https://doi.org/10.3390/hydrology13020060
362. Forecasting drought using machine learning: a systematic literature review R. Oyarzabal et al. https://doi.org/10.1007/s11069-025-07195-2
363. A Spatial-Enhanced LSE-SFIM Algorithm for Hyperspectral and Multispectral Images Fusion Y. Wang et al. https://doi.org/10.3390/rs13244967
364. A review of open data for studying global groundwater in social–ecological systems X. Huggins et al. https://doi.org/10.1088/1748-9326/adf127
365. Machine Learning‐Based Modeling of Vegetation Leaf Area Index and Gross Primary Productivity Across North America and Comparison With a Process‐Based Model Z. Zhang et al. https://doi.org/10.1029/2021MS002802
366. Stratified and adaptive regression for NDVI-based land surface temperature downscaling over agricultural fields J. Elfarkh et al. https://doi.org/10.1016/j.jag.2026.105456