99 citations as recorded by crossref.

1. The SMOS-Derived Soil Water EXtent and equivalent layer thickness facilitate determination of soil water resources B. Usowicz et al. https://doi.org/10.1038/s41598-020-75475-x
2. Multi-Scale Assessment of SMAP Level 3 and Level 4 Soil Moisture Products over the Soil Moisture Network within the ShanDian River (SMN-SDR) Basin, China A. Nadeem et al. https://doi.org/10.3390/rs14040982
3. Optimizing a backscatter forward operator using Sentinel-1 data over irrigated land S. Modanesi et al. https://doi.org/10.5194/hess-25-6283-2021
4. 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
5. Improved Hydrological Simulation Using SMAP Data: Relative Impacts of Model Calibration and Data Assimilation R. Koster et al. https://doi.org/10.1175/JHM-D-17-0228.1
6. Parameterization of Vegetation Scattering Albedo in the Tau-Omega Model for Soil Moisture Retrieval on Croplands C. Park et al. https://doi.org/10.3390/rs12182939
7. Assimilation of Satellite-Derived Soil Moisture and Brightness Temperature in Land Surface Models: A Review R. Khandan et al. https://doi.org/10.3390/rs14030770
8. Impact of Soil Moisture Data Resolution on Soil Moisture and Surface Heat Flux Estimates through Data Assimilation: A Case Study in the Southern Great Plains Y. Lu et al. https://doi.org/10.1175/JHM-D-18-0234.1
9. Regional data sets of high-resolution (1 and 6 km) irrigation estimates from space J. Dari et al. https://doi.org/10.5194/essd-15-1555-2023
10. Comparison of different assimilation methodologies of groundwater levels to improve predictions of root zone soil moisture with an integrated terrestrial system model H. Zhang et al. https://doi.org/10.1016/j.advwatres.2017.11.003
11. Retrieving Soil Physical Properties by Assimilating SMAP Brightness Temperature Observations into the Community Land Model H. Zhao et al. https://doi.org/10.3390/s23052620
12. A seamless global daily soil moisture dataset (2010–2015) harmonized from SMOS observations and SMAP-era assimilation modeling X. Wang et al. https://doi.org/10.1080/17538947.2025.2609469
13. Comparing Seven Variants of the Ensemble Kalman Filter: How Many Synthetic Experiments Are Needed? J. Keller et al. https://doi.org/10.1029/2018WR023374
14. Net irrigation requirement under different climate scenarios using AquaCrop over Europe L. Busschaert et al. https://doi.org/10.5194/hess-26-3731-2022
15. Challenges and benefits of quantifying irrigation through the assimilation of Sentinel-1 backscatter observations into Noah-MP S. Modanesi et al. https://doi.org/10.5194/hess-26-4685-2022
16. Performance analysis of regional AquaCrop (v6.1) biomass and surface soil moisture simulations using satellite and in situ observations S. de Roos et al. https://doi.org/10.5194/gmd-14-7309-2021
17. Joint Sentinel‐1 and SMAP data assimilation to improve soil moisture estimates H. Lievens et al. https://doi.org/10.1002/2017GL073904
18. 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
19. More severe drought detected by the assimilation of brightness temperature and terrestrial water storage anomalies in Texas during 2010–2013 W. Chen et al. https://doi.org/10.1016/j.jhydrol.2021.126802
20. Assimilation of Cosmogenic Neutron Counts for Improved Soil Moisture Prediction in a Distributed Land Surface Model A. Patil et al. https://doi.org/10.3389/frwa.2021.729592
21. Modelling the passive microwave signature from land surfaces: A review of recent results and application to the L-band SMOS & SMAP soil moisture retrieval algorithms J. Wigneron et al. https://doi.org/10.1016/j.rse.2017.01.024
22. Assimilation of SMAP and ASCAT soil moisture retrievals into the JULES land surface model using the Local Ensemble Transform Kalman Filter E. Seo et al. https://doi.org/10.1016/j.rse.2020.112222
23. Assimilation of SMAP Brightness Temperature Observations in the GEOS Land–Atmosphere Data Assimilation System R. Reichle et al. https://doi.org/10.1109/JSTARS.2021.3118595
24. Merging active and passive microwave observations in soil moisture data assimilation J. Kolassa et al. https://doi.org/10.1016/j.rse.2017.01.015
25. SMOS-IC data record of soil moisture and L-VOD: Historical development, applications and perspectives J. Wigneron et al. https://doi.org/10.1016/j.rse.2020.112238
26. Assimilation of Passive L-band Microwave Brightness Temperatures in the Canadian Land Data Assimilation System: Impacts on Short-Range Warm Season Numerical Weather Prediction M. Carrera et al. https://doi.org/10.1175/JHM-D-18-0133.1
27. Using data assimilation to optimize pedotransfer functions using field-scale in situ soil moisture observations E. Cooper et al. https://doi.org/10.5194/hess-25-2445-2021
28. Assessing the Feasibility of Satellite‐Based Thresholds for Hydrologically Driven Landsliding M. Thomas et al. https://doi.org/10.1029/2019WR025577
29. Mapping Surface Heat Fluxes by Assimilating SMAP Soil Moisture and GOES Land Surface Temperature Data Y. Lu et al. https://doi.org/10.1002/2017WR021415
30. The Land Variational Ensemble Data Assimilation Framework: LAVENDAR v1.0.0 E. Pinnington et al. https://doi.org/10.5194/gmd-13-55-2020
31. Improving Robustness of Hydrologic Ensemble Predictions Through Probabilistic Pre‐ and Post‐Processing in Sequential Data Assimilation S. Wang et al. https://doi.org/10.1002/2018WR022546
32. Evaluation of State and Bias Estimates for Assimilation of SMOS Retrievals Into Conceptual Rainfall-Runoff Models V. Pauwels et al. https://doi.org/10.3389/frwa.2020.00004
33. Improved groundwater table and L-band brightness temperature estimates for Northern Hemisphere peatlands using new model physics and SMOS observations in a global data assimilation framework M. Bechtold et al. https://doi.org/10.1016/j.rse.2020.111805
34. SMOS brightness temperature assimilation into the Community Land Model D. Rains et al. https://doi.org/10.5194/hess-21-5929-2017
35. Impacts of Spatiotemporal Gaps in Satellite Soil Moisture Data on Hydrological Data Assimilation K. Mohammed et al. https://doi.org/10.3390/w15020321
36. Monitoring Soil Moisture Drought over Northern High Latitudes from Space J. Blyverket et al. https://doi.org/10.3390/rs11101200
37. The Value of SMAP for Long-Term Soil Moisture Estimation With the Help of Deep Learning K. Fang et al. https://doi.org/10.1109/TGRS.2018.2872131
38. Using the Angular Dependence of the Polarization Ratio to Analyze Aircraft Passive Microwave Measurements Over Land R. de Jeu et al. https://doi.org/10.1109/TGRS.2026.3657427
39. Four decades of microwave satellite soil moisture observations: Part 2. Product validation and inter-satellite comparisons L. Karthikeyan et al. https://doi.org/10.1016/j.advwatres.2017.09.010
40. A Novel Fusion Method for Generating Surface Soil Moisture Data With High Accuracy, High Spatial Resolution, and High Spatio‐Temporal Continuity S. Huang et al. https://doi.org/10.1029/2021WR030827
41. Prediction of Active Microwave Backscatter Over Snow-Covered Terrain Across Western Colorado Using a Land Surface Model and Support Vector Machine Regression J. Park et al. https://doi.org/10.1109/JSTARS.2021.3053945
42. Preliminary Assimilation of Satellite Derived Land Surface Temperature from SEVIRI in the Surface Scheme of the AROME-France Model M. Sassi et al. https://doi.org/10.16993/tellusa.48
43. Reappraisal of the roughness effect parameterization schemes for L-band radiometry over bare soil B. Peng et al. https://doi.org/10.1016/j.rse.2017.07.006
44. Retrieving accurate soil moisture over the Tibetan Plateau using multi-source remote sensing data assimilation with simultaneous state and parameter estimations W. Chen et al. https://doi.org/10.1175/JHM-D-20-0298.1
45. Improvement of the soil-atmosphere interactions and subsequent heavy precipitation modelling by enhanced initialization using remotely sensed 1 km soil moisture information S. Helgert & S. Khodayar https://doi.org/10.1016/j.rse.2020.111812
46. Perspective on satellite-based land data assimilation to estimate water cycle components in an era of advanced data availability and model sophistication G. De Lannoy et al. https://doi.org/10.3389/frwa.2022.981745
47. Estimation of Snow Mass Information via Assimilation of C-Band Synthetic Aperture Radar Backscatter Observations Into an Advanced Land Surface Model J. Park et al. https://doi.org/10.1109/JSTARS.2021.3133513
48. Field scale computer modeling of soil moisture with dynamic nudging assimilation algorithm O. Kozhushko et al. https://doi.org/10.23939/mmc2022.02.203
49. The impact of multi-sensor land data assimilation on river discharge estimation W. Wu et al. https://doi.org/10.1016/j.rse.2022.113138
50. Benefits and pitfalls of irrigation timing and water amounts derived from satellite soil moisture L. Zappa et al. https://doi.org/10.1016/j.agwat.2024.108773
51. Assimilation of Soil Moisture and Ocean Salinity (SMOS) brightness temperature into a large-scale distributed conceptual hydrological model to improve soil moisture predictions: the Murray–Darling basin in Australia as a test case R. Hostache et al. https://doi.org/10.5194/hess-24-4793-2020
52. Leveraging Soil Moisture Assimilation in Permafrost Affected Regions A. Pradhan et al. https://doi.org/10.3390/rs15061532
53. Evaluation of GEOS-Simulated L-Band Microwave Brightness Temperature Using Aquarius Observations over Non-Frozen Land across North America J. Park et al. https://doi.org/10.3390/rs12183098
54. Fusion of In-Situ Soil Moisture and Land Surface Model Estimates Using Localized Ensemble Optimum Interpolation over China L. Jiang et al. https://doi.org/10.1007/s13351-020-0033-7
55. Validation practices for satellite soil moisture retrievals: What are (the) errors? A. Gruber et al. https://doi.org/10.1016/j.rse.2020.111806
56. Improving Soil Moisture Estimation via Assimilation of Remote Sensing Product into the DSSAT Crop Model and Its Effect on Agricultural Drought Monitoring H. Zhou et al. https://doi.org/10.3390/rs14133187
57. A Dielectric Mixing Model Accounting for Soil Organic Matter C. Park et al. https://doi.org/10.2136/vzj2019.04.0036
58. Effect of Assimilating SMAP Soil Moisture on CO2 and CH4 Fluxes through Direct Insertion in a Land Surface Model Z. Zhang et al. https://doi.org/10.3390/rs14102405
59. Improving Soil Moisture and Surface Turbulent Heat Flux Estimates by Assimilation of SMAP Brightness Temperatures or Soil Moisture Retrievals and GOES Land Surface Temperature Retrievals Y. Lu et al. https://doi.org/10.1175/JHM-D-19-0130.1
60. SHui, an EU-Chinese cooperative project to optimize soil and water management in agricultural areas in the XXI century J. Gómez et al. https://doi.org/10.1016/j.iswcr.2020.01.001
61. A Comparison of Passive Microwave Emission Models for Estimating Brightness Temperature at L- and P-Bands Under Bare and Vegetated Soil Conditions F. Brakhasi et al. https://doi.org/10.1109/JSTARS.2023.3344764
62. Evaluation of a cosmic-ray neutron sensor network for improved land surface model prediction R. Baatz et al. https://doi.org/10.5194/hess-21-2509-2017
63. Impact of remotely sensed soil moisture and precipitation on soil moisture prediction in a data assimilation system with the JULES land surface model E. Pinnington et al. https://doi.org/10.5194/hess-22-2575-2018
64. Spatially enhanced passive microwave derived soil moisture: Capabilities and opportunities S. Sabaghy et al. https://doi.org/10.1016/j.rse.2018.02.065
65. Assimilation of SMOS brightness temperatures in the ECMWF Integrated Forecasting System J. Muñoz‐Sabater et al. https://doi.org/10.1002/qj.3577
66. Assimilating ASCAT normalized backscatter and slope into the land surface model ISBA-A-gs using a Deep Neural Network as the observation operator: Case studies at ISMN stations in western Europe X. Shan et al. https://doi.org/10.1016/j.rse.2024.114167
67. Assimilating SMOS Brightness Temperature for Hydrologic Model Parameters and Soil Moisture Estimation with an Immune Evolutionary Strategy F. Ju et al. https://doi.org/10.3390/rs12101556
68. 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
69. Improving soil moisture prediction of a high-resolution land surface model by parameterising pedotransfer functions through assimilation of SMAP satellite data E. Pinnington et al. https://doi.org/10.5194/hess-25-1617-2021
70. Multi-sensor land data assimilation: Toward a robust global soil moisture and snow estimation L. Zhao & Z. Yang https://doi.org/10.1016/j.rse.2018.06.033
71. On the impact of C-band in place of L-band radar for SMAP downscaling E. Ghafari et al. https://doi.org/10.1016/j.rse.2020.112111
72. A Monte Carlo based adaptive Kalman filtering framework for soil moisture data assimilation A. Gruber et al. https://doi.org/10.1016/j.rse.2019.04.003
73. Inhomogeneous Anisotropic Analysis of the Available Water Content of the Upper Soil Layer According to Ground-Based and Remote Sensing on the Territory of Russia P. Bykov et al. https://doi.org/10.1109/TGRS.2022.3202609
74. Triple collocation-based multi-source evaporation and transpiration merging J. Park et al. https://doi.org/10.1016/j.agrformet.2023.109353
75. Assessment of the SMAP Level-4 Surface and Root-Zone Soil Moisture Product Using In Situ Measurements R. Reichle et al. https://doi.org/10.1175/JHM-D-17-0063.1
76. Global Assessment of the SMAP Level-4 Surface and Root-Zone Soil Moisture Product Using Assimilation Diagnostics R. Reichle et al. https://doi.org/10.1175/JHM-D-17-0130.1
77. New Approach for Calculating the Effective Dielectric Constant of the Moist Soil for Microwaves C. Park et al. https://doi.org/10.3390/rs9070732
78. Toward a Surface Soil Moisture Product at High Spatiotemporal Resolution: Temporally Interpolated, Spatially Disaggregated SMOS Data Y. Malbéteau et al. https://doi.org/10.1175/JHM-D-16-0280.1
79. Satellite Microwave Radiometry for the Observation of Land Surfaces: A General Review C. Vittucci & M. Picchiani https://doi.org/10.3390/s26051638
80. Data Assimilation to Extract Soil Moisture Information from SMAP Observations J. Kolassa et al. https://doi.org/10.3390/rs9111179
81. Confronting Soil Moisture Dynamics from the ORCHIDEE Land Surface Model With the ESA-CCI Product: Perspectives for Data Assimilation N. Raoult et al. https://doi.org/10.3390/rs10111786
82. Assimilation of Satellite Soil Moisture for Improved Atmospheric Reanalyses C. Draper & R. Reichle https://doi.org/10.1175/MWR-D-18-0393.1
83. A novel approach for soil moisture retrieval from Sentinel-1 SAR via temporal stability-based backscatter analysis S. Lee et al. https://doi.org/10.1016/j.rse.2026.115319
84. Joint assimilation of satellite-based surface soil moisture and vegetation conditions into the Noah-MP land surface model Z. Heyvaert et al. https://doi.org/10.1016/j.srs.2024.100129
85. SMOS brightness temperature forward modelling and long term monitoring at ECMWF P. de Rosnay et al. https://doi.org/10.1016/j.rse.2019.111424
86. Towards effective drought monitoring in the Middle East and North Africa (MENA) region: implications from assimilating leaf area index and soil moisture into the Noah-MP land surface model for Morocco W. Nie et al. https://doi.org/10.5194/hess-26-2365-2022
87. Synergistic Calibration of a Hydrological Model Using Discharge and Remotely Sensed Soil Moisture in the Paraná River Basin A. Fleischmann et al. https://doi.org/10.3390/rs13163256
88. Uncertainty in soil moisture retrievals: An ensemble approach using SMOS L-band microwave data J. Quets et al. https://doi.org/10.1016/j.rse.2019.05.008
89. An Evaluation of the EnKF vs. EnOI and the Assimilation of SMAP, SMOS and ESA CCI Soil Moisture Data over the Contiguous US J. Blyverket et al. https://doi.org/10.3390/rs11050478
90. Assimilation of synthetic observations of radar backscatters at Ku-band improves SWE estimates N. Leroux et al. https://doi.org/10.5194/tc-20-2773-2026
91. Validation of Remotely Sensed and Modeled Soil Moisture at Forested and Unforested NEON Sites E. Ayres et al. https://doi.org/10.1109/JSTARS.2024.3430928
92. Comparing the Assimilation of SMOS Brightness Temperatures and Soil Moisture Products on Hydrological Simulation in the Canadian Land Surface Scheme M. Nambiar et al. https://doi.org/10.3390/rs12203405
93. L-Band Microwave Satellite Data and Model Simulations Over the Dry Chaco to Estimate Soil Moisture, Soil Temperature, Vegetation, and Soil Salinity F. Vincent et al. https://doi.org/10.1109/JSTARS.2022.3193636
94. The value of soil temperature data versus soil moisture data for state, parameter, and flux estimation in unsaturated flow model R. Kandala et al. https://doi.org/10.1002/vzj2.20298
95. Land data assimilation of satellite‐based surface soil moisture: Impact on atmospheric simulations over the contiguous United States Z. Heyvaert et al. https://doi.org/10.1002/qj.70052
96. Optical and Thermal Remote Sensing for Monitoring Agricultural Drought Q. Qin et al. https://doi.org/10.3390/rs13245092
97. Assessment of MERRA-2 Land Surface Energy Flux Estimates C. Draper et al. https://doi.org/10.1175/JCLI-D-17-0121.1
98. Quality Control and Evaluation of the Observed Daily Data in the North American Soil Moisture Database W. Liao et al. https://doi.org/10.1007/s13351-019-8121-2
99. Multivariate data assimilation of GRACE, SMOS, SMAP measurements for improved regional soil moisture and groundwater storage estimates N. Tangdamrongsub et al. https://doi.org/10.1016/j.advwatres.2019.103477