144 citations as recorded by crossref.

1. Satellite soil moisture for agricultural drought monitoring: Assessment of the SMOS derived Soil Water Deficit Index J. Martínez-Fernández et al. https://doi.org/10.1016/j.rse.2016.02.064
2. Spatial Downscaling of Soil Moisture Product to Generate High-Resolution Data: A Multi-Source Approach over Heterogeneous Landscapes in Kenya A. Abebe et al. https://doi.org/10.3390/rs17101763
3. Multi-layer high-resolution soil moisture estimation using machine learning over the United States L. Karthikeyan & A. Mishra https://doi.org/10.1016/j.rse.2021.112706
4. Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors H. Beck et al. https://doi.org/10.5194/hess-25-17-2021
5. A Convolutional Neural Network Algorithm for Soil Moisture Prediction from Sentinel-1 SAR Images E. Hegazi et al. https://doi.org/10.3390/rs13244964
6. Large-scale monitoring of soil moisture using Temperature Vegetation Quantitative Index (TVQI) and exponential filtering: A case study in Beijing Z. Zhao et al. https://doi.org/10.1016/j.agwat.2021.106896
7. Automated Quality Control of In Situ Soil Moisture from the North American Soil Moisture Database Using NLDAS-2 Products Y. Xia et al. https://doi.org/10.1175/JAMC-D-14-0275.1
8. A geospatial method for estimating soil moisture variability in prehistoric agricultural landscapes A. Gillreath-Brown et al. https://doi.org/10.1371/journal.pone.0220457
9. Evaluation of microwave remote sensing for monitoring live fuel moisture content in the Mediterranean region L. Fan et al. https://doi.org/10.1016/j.rse.2017.11.020
10. Evaluation of soil moisture data products over Indian region and analysis of spatio-temporal characteristics with respect to monsoon rainfall A. Sathyanadh et al. https://doi.org/10.1016/j.jhydrol.2016.08.040
11. Assessment of Root Zone Soil Moisture Estimations from SMAP, SMOS and MODIS Observations M. Pablos et al. https://doi.org/10.3390/rs10070981
12. Assessment of long-term multisource surface and subsurface soil moisture products and estimate methods on the Tibetan Plateau P. Zhang et al. https://doi.org/10.1016/j.jhydrol.2024.131713
13. A data-driven approach using the remotely sensed soil moisture product to identify water-demand in agricultural regions G. Singh & N. Das https://doi.org/10.1016/j.scitotenv.2022.155893
14. AI-Driven Integration of Sentinel-1 SAR for High-Resolution Soil Water Content Estimation to Enhance Precision Irrigation in Smallholder Maize Systems, Vhembe District G. Nxumalo et al. https://doi.org/10.3390/w18040499
15. Testing of observation operators designed to estimate profile soil moisture from surface measurements X. Gao et al. https://doi.org/10.1002/hyp.13344
16. Evaporative demand delays soil moisture accumulation timing during the dry-to-wet season transition in the Sahel C. Chalmers et al. https://doi.org/10.1088/1748-9326/aea2ae
17. A global framework for subsurface soil moisture estimation: Coupling fractal Richards equation with Bayesian optimization Z. Zhu et al. https://doi.org/10.1016/j.rse.2026.115318
18. A comparison of machine learning algorithms for predicting gross primary productivity of the Western Ghats region in India using reanalysis and satellite data G. Agarwal et al. https://doi.org/10.1007/s12040-025-02525-1
19. Assessment of remotely sensed soil moisture products and their quality improvement: A case study in South Korea M. Kwon & D. Han https://doi.org/10.1016/j.jher.2019.04.002
20. A Fusion Strategy for High-Accuracy Multilayer Soil Moisture Downscaling and Mapping X. Zhang et al. https://doi.org/10.1109/JSTARS.2025.3576126
21. Improving operational flood ensemble prediction by the assimilation of satellite soil moisture: comparison between lumped and semi-distributed schemes C. Alvarez-Garreton et al. https://doi.org/10.5194/hess-19-1659-2015
22. Soil moisture information can improve shallow landslide forecasting using the hydrometeorological threshold approach P. Marino et al. https://doi.org/10.1007/s10346-020-01420-8
23. Dynamics of Soil Moisture and Its Response to Meteorological Factors at Different Depths in an Arid Land, Northwest China W. Li et al. https://doi.org/10.3390/atmos17030232
24. 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
25. Global scale error assessments of soil moisture estimates from microwave-based active and passive satellites and land surface models over forest and mixed irrigated/dryland agriculture regions H. Kim et al. https://doi.org/10.1016/j.rse.2020.112052
26. Integrating machine learning models with ground sensors to enhance soil moisture prediction in agroecosystems of Texas G. Tefera et al. https://doi.org/10.1016/j.compag.2025.111358
27. Evaluating controls of soil properties and climatic conditions on the use of an exponential filter for converting near surface to root zone soil moisture contents T. Wang et al. https://doi.org/10.1016/j.jhydrol.2017.03.055
28. Comprehensive quality assessment of satellite- and model-based soil moisture products against the COSMOS network in Germany T. Schmidt et al. https://doi.org/10.1016/j.rse.2023.113930
29. Estimation of root zone soil moisture using passive microwave remote sensing: A case study for rice and wheat crops for three states in the Indo-Gangetic basin A. Sure & O. Dikshit https://doi.org/10.1016/j.jenvman.2018.12.109
30. Uncertainty estimation for a new exponential-filter-based long-term root-zone soil moisture dataset from Copernicus Climate Change Service (C3S) surface observations A. Pasik et al. https://doi.org/10.5194/gmd-16-4957-2023
31. Using remotely sensed soil moisture for land–atmosphere coupling diagnostics: The role of surface vs. root-zone soil moisture variability M. Hirschi et al. https://doi.org/10.1016/j.rse.2014.08.030
32. Comparative study of soil moisture estimations from SMOS satellite mission, GLDAS database, and cosmic-ray neutrons measurements at COSMOS station in Eastern Poland M. Kędzior & J. Zawadzki https://doi.org/10.1016/j.geoderma.2016.07.023
33. An Evaluation of the North American Regional Reanalysis Simulated Soil Moisture Conditions during the 2011–13 Drought Period R. Leeper et al. https://doi.org/10.1175/JHM-D-16-0132.1
34. An Exponential Filter Model-Based Root-Zone Soil Moisture Estimation Methodology from Multiple Datasets Y. Yang et al. https://doi.org/10.3390/rs14081785
35. Estimation of 100 m root zone soil moisture by downscaling 1 km soil water index with machine learning and multiple geodata T. Mahmood et al. https://doi.org/10.1007/s10661-024-12969-5
36. Towards soil moisture profile estimation in the root zone using L- and P-band radiometer observations: A coherent modelling approach F. Brakhasi et al. https://doi.org/10.1016/j.srs.2023.100079
37. Comparison of NLDAS-2 Simulated and NASMD Observed Daily Soil Moisture. Part I: Comparison and Analysis Y. Xia et al. https://doi.org/10.1175/JHM-D-14-0096.1
38. Soil Moisture Prediction in Pavement Layers Using LSTM Neural Networks A. Tophel et al. https://doi.org/10.1007/s10706-025-03293-x
39. Evaluation of the SMOS-Derived Soil Water Deficit Index as Agricultural Drought Index in Northeast of Brazil F. Paredes-Trejo & H. Barbosa https://doi.org/10.3390/w9060377
40. Multi-decadal analysis of root-zone soil moisture applying the exponential filter across CONUS K. Tobin et al. https://doi.org/10.5194/hess-21-4403-2017
41. C-band radar data and in situ measurements for the monitoring of wheat crops in a semi-arid area (center of Morocco) N. Ouaadi et al. https://doi.org/10.5194/essd-13-3707-2021
42. High Spatial and Temporal Soil Moisture Retrieval in Agricultural Areas Using Multi-Orbit and Vegetation Adapted Sentinel-1 SAR Time Series D. Mengen et al. https://doi.org/10.3390/rs15092282
43. Using lagged dependence to identify (de)coupled surface and subsurface soil moisture values C. Carranza et al. https://doi.org/10.5194/hess-22-2255-2018
44. Impacts of hydrometeorological controls on vegetation productivity: Evidence from satellite observations and reanalysis Y. Liu et al. https://doi.org/10.1016/j.ecolind.2024.111976
45. Hydrological Storage Length Scales Represented by Remote Sensing Estimates of Soil Moisture and Precipitation R. Akbar et al. https://doi.org/10.1002/2017WR021508
46. The diagnosis about spatio-temporal characteristics and driving factors of flash drought and its prediction over typical humid and semi-arid basins in China Q. Zhu & Y. Wang https://doi.org/10.1175/JHM-D-21-0062.1
47. Root-zone soil moisture estimation based on remote sensing data and deep learning Y. A et al. https://doi.org/10.1016/j.envres.2022.113278
48. Retrieving long-term topsoil moisture in Qingtongxia irrigation district using a modified OPTRAM model S. Du et al. https://doi.org/10.1016/j.ejrh.2025.103074
49. Comparison of SMOS, modelled andin situlong-term soil moisture series in the northwest of Spain A. Gumuzzio et al. https://doi.org/10.1080/02626667.2016.1151981
50. Physics‐Constrained Machine Learning of Evapotranspiration W. Zhao et al. https://doi.org/10.1029/2019GL085291
51. A quality assessment of the soil water index by the propagation of ASCAT soil moisture error estimates through an exponential filter D. De Santis & D. Biondi https://doi.org/10.1080/01431161.2017.1382745
52. Soil Moisture Monitoring in Iran by Implementing Satellite Data into the Root-Zone SMAR Model F. Gheybi et al. https://doi.org/10.3390/hydrology6020044
53. Comparing the Normalized Difference Infrared Index (NDII) with root zone storage in a lumped conceptual model N. Sriwongsitanon et al. https://doi.org/10.5194/hess-20-3361-2016
54. Root-zone plant available water estimation using the SMOS-derived soil water index Á. González-Zamora et al. https://doi.org/10.1016/j.advwatres.2016.08.001
55. Soil moisture profile estimation by combining P-band SAR polarimetry with hydrological and multi-layer scattering models A. Fluhrer et al. https://doi.org/10.1016/j.rse.2024.114067
56. Using InSAR for Surface Deformation Monitoring and Active Layer Thickness Retrieval in the Heihe River Basin on the Northeast Qinghai‐Tibet Plateau S. Peng et al. https://doi.org/10.1029/2022JF006782
57. Enhanced 1-km Soil Moisture Mapping over Nebraska Using Segmented Quantile Mapping of SMAP 9-km Data J. Kodikara et al. https://doi.org/10.1080/02626667.2026.2747793
58. Estimating root zone soil moisture using the SMAR model and regression method at a headwater catchment with complex terrain Y. Qi et al. https://doi.org/10.1016/j.geoderma.2024.117144
59. 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
60. Retrieving Surface and Rootzone Soil Moisture Using Microwave Remote Sensing S. Thaggahalli Nagaraju & A. Pathak https://doi.org/10.1007/s12524-024-01881-7
61. Microwave Retrievals of Soil Moisture Improve Grassland Wildfire Predictions A. Rigden et al. https://doi.org/10.1029/2020GL091410
62. Combined influence of soil moisture and atmospheric evaporative demand is important for accurately predicting US maize yields A. Rigden et al. https://doi.org/10.1038/s43016-020-0028-7
63. 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
64. Evaluation of Soil Water Availability (SWA) Based on Hydrological Modelling in Arid and Semi-Arid Areas: A Case Study in Handan City, China X. Gao et al. https://doi.org/10.3390/w8080360
65. On the use of the critical event concept for quantifying soil moisture dynamics T. Wang et al. https://doi.org/10.1016/j.geoderma.2018.08.013
66. Deep learning approach for vertical soil moisture profile estimation using hydrometeorological data M. Khan & R. Maity https://doi.org/10.1080/02626667.2025.2584637
67. An advanced error correction methodology for merging in-situ observed and model-based soil moisture Z. Wu et al. https://doi.org/10.1016/j.jhydrol.2018.09.018
68. SMOS data as a source of the agricultural drought information: Case study of the Vistula catchment, Poland M. Kędzior & J. Zawadzki https://doi.org/10.1016/j.geoderma.2017.07.018
69. Global-scale assessment and combination of SMAP with ASCAT (active) and AMSR2 (passive) soil moisture products H. Kim et al. https://doi.org/10.1016/j.rse.2017.10.026
70. Evaluating the drought response of CMIP5 models using global gross primary productivity, leaf area, precipitation, and soil moisture data Y. Huang et al. https://doi.org/10.1002/2016GB005480
71. Detecting groundwater dependence and woody vegetation restoration with NDVI and moisture trend analyses in an Indonesian karst savanna P. Godwin et al. https://doi.org/10.3389/frsen.2024.1280712
72. Evaluation of nine sub-daily soil moisture model products over China using high-resolution in situ observations Y. Chen & H. Yuan https://doi.org/10.1016/j.jhydrol.2020.125054
73. Climate change increases the interannual variance of summer crop yields globally through changes in temperature and water supply J. Proctor et al. https://doi.org/10.1126/sciadv.ady3575
74. More accurate specification of water supply shows its importance for global crop production J. Proctor et al. https://doi.org/10.1038/s43016-022-00592-x
75. From near surface to root zone soil water losses: a new model validated with field TDR and remotely sensed data T. Martini et al. https://doi.org/10.1016/j.jhydrol.2026.135355
76. Multilayer Soil Moisture Mapping at a Regional Scale from Multisource Data via a Machine Learning Method L. Zeng et al. https://doi.org/10.3390/rs11030284
77. Kenyan tea is made with heat and water: how will climate change influence its yield? A. Rigden et al. https://doi.org/10.1088/1748-9326/ab70be
78. The Feasibility of Remotely Sensed Near-Infrared Reflectance for Soil Moisture Estimation for Agricultural Water Management E. Babaeian & M. Tuller https://doi.org/10.3390/rs15112736
79. Predicting rootzone soil moisture from surface observations in cropland using an exponential filter P. Rossini & A. Patrignani https://doi.org/10.1002/saj2.20319
80. Retrieval of Subsurface Soil Moisture and Vegetation Water Content From Multifrequency SoOp Reflectometry: Sensitivity Analysis S. Kim et al. https://doi.org/10.1109/TGRS.2023.3284800
81. Estimation of Landscape Soil Water Losses from Satellite Observations of Soil Moisture R. Akbar et al. https://doi.org/10.1175/JHM-D-17-0200.1
82. Remote Sensing, Geophysics, and Modeling to Support Precision Agriculture—Part 2: Irrigation Management A. Pradipta et al. https://doi.org/10.3390/w14071157
83. Predicting root zone soil moisture using observations at 2121 sites across China J. Tian et al. https://doi.org/10.1016/j.scitotenv.2022.157425
84. Estimation of subsurface soil moisture from surface soil moisture in cold mountainous areas J. Tian et al. https://doi.org/10.5194/hess-24-4659-2020
85. An improved process-oriented hydro-biogeochemical model for simulating dynamic fluxes of methane and nitrous oxide in alpine ecosystems with seasonally frozen soils W. Zhang et al. https://doi.org/10.5194/bg-18-4211-2021
86. A novel framework for pixel-wise estimation of irrigation water use by integrating remote sensing and reanalysis data L. Zhang et al. https://doi.org/10.1016/j.agwat.2025.110077
87. Estimation of root zone soil moisture from ground and remotely sensed soil information with multisensor data fusion and automated machine learning E. Babaeian et al. https://doi.org/10.1016/j.rse.2021.112434
88. Landscape Water Storage and Subsurface Correlation From Satellite Surface Soil Moisture and Precipitation Observations D. Short Gianotti et al. https://doi.org/10.1029/2019WR025332
89. Assessment of SMOS Root Zone Soil Moisture: A Comparative Study Using SMAP, ERA5, and GLDAS N. Ojha et al. https://doi.org/10.1109/ACCESS.2024.3404123
90. Enhanced sequential soil moisture estimation using an ensemble deep learning filter without linear-Gaussian constraints T. Zhang et al. https://doi.org/10.1016/j.jhydrol.2025.134810
91. Evaluation of Soil Moisture in CMIP6 Multimodel Simulations Over Conterminous China A. Wang et al. https://doi.org/10.1029/2022JD037072
92. Spatio-temporal changes in global root zone soil moisture from 1981 to 2017 X. Luo et al. https://doi.org/10.1016/j.jhydrol.2023.130297
93. Long-Term Characteristics of Surface Soil Moisture over the Tibetan Plateau and Its Response to Climate Change C. Zhu et al. https://doi.org/10.3390/rs15184414
94. The Raam regional soil moisture monitoring network in the Netherlands H. Benninga et al. https://doi.org/10.5194/essd-10-61-2018
95. Passive Microwave Remote Sensing Soil Moisture Data in Agricultural Drought Monitoring: Application in Northeastern China T. Cheng et al. https://doi.org/10.3390/w13192777
96. CYGNSS Soil Moisture Performance in Guinea Savanna Region: Extended and Quadruple Collocation Evidence from Benue State, Nigeria S. Ajoniloju et al. https://doi.org/10.3390/rs18193267
97. Comparison of Three Methods for Vertical Extrapolation of Soil Moisture in Oklahoma N. Zhang et al. https://doi.org/10.2136/vzj2017.04.0085
98. Spatial representativeness of soil moisture using in situ, remote sensing, and land reanalysis data N. Nicolai‐Shaw et al. https://doi.org/10.1002/2015JD023305
99. Extension of cosmic-ray neutron probe measurement depth for improving field scale root-zone soil moisture estimation by coupling with representative in-situ sensors H. Nguyen et al. https://doi.org/10.1016/j.jhydrol.2019.02.018
100. Analyzing Spatio-Temporal Factors to Estimate the Response Time between SMOS and In-Situ Soil Moisture at Different Depths C. Herbert et al. https://doi.org/10.3390/rs12162614
101. Remotely Sensed Soil Moisture Can Capture Dynamics Relevant to Plant Water Uptake A. Feldman et al. https://doi.org/10.1029/2022WR033814
102. High-Resolution SMAP-Derived Root-Zone Soil Moisture Using an Exponential Filter Model Calibrated per Land Cover Type V. Stefan et al. https://doi.org/10.3390/rs13061112
103. Assessing and comparing the subseasonal variations of summer soil moisture of satellite products over eastern China Y. Zhou et al. https://doi.org/10.1002/joc.8073
104. Does Afternoon Precipitation Occur Preferentially over Dry or Wet Soils in Oklahoma? T. Ford et al. https://doi.org/10.1175/JHM-D-14-0005.1
105. Estimating field-scale root zone soil moisture using the cosmic-ray neutron probe A. Peterson et al. https://doi.org/10.5194/hess-20-1373-2016
106. The Impact of Vertical Measurement Depth on the Information Content of Soil Moisture for Latent Heat Flux Estimation J. Qiu et al. https://doi.org/10.1175/JHM-D-16-0044.1
107. Improved subsurface soil moisture prediction from surface soil moisture through the integration of the (de)coupling effect Z. Xu et al. https://doi.org/10.1016/j.jhydrol.2022.127634
108. Enhancing root-zone soil moisture estimation using Richards' equation and dynamic surface soil moisture data X. Zha et al. https://doi.org/10.1016/j.agwat.2025.109460
109. Short-Term and Long-Term Surface Soil Moisture Memory Time Scales Are Spatially Anticorrelated at Global Scales K. McColl et al. https://doi.org/10.1175/JHM-D-18-0141.1
110. Soil Moisture and Sea Surface Salinity Derived from Satellite-Borne Sensors J. Boutin et al. https://doi.org/10.1007/s10712-023-09798-5
111. Parametric and non-parametric indices for agricultural drought assessment using ESACCI soil moisture data over the Southern Plateau and Hills, India H. Palagiri & M. Pal https://doi.org/10.1016/j.jag.2024.104175
112. Assimilation of SMOS brightness temperatures or soil moisture retrievals into a land surface model G. De Lannoy & R. Reichle https://doi.org/10.5194/hess-20-4895-2016
113. Comparison of Long-Term Changes in Non-Linear Aggregated Drought Index Calibrated by MERRA–2 and NDII Soil Moisture Proxies F. Mathivha & N. Mbatha https://doi.org/10.3390/w14010026
114. Improvement of rootzone soil moisture estimation over the Tibetan Plateau based on the exponential filter model and Copernicus Climate Change Service (C3S) surface observations Y. Song et al. https://doi.org/10.1016/j.jhydrol.2025.133882
115. The Coupling of Soil Water and Leaf Phenology Observed by Satellite During Australian Bushfires in 2019–2020 Y. Liu et al. https://doi.org/10.1029/2021JD036279
116. Comprehensive Error Analysis of CLDAS Soil Moisture Over Arid and Semiarid Regions R. Guluzade et al. https://doi.org/10.1109/JSTARS.2023.3271512
117. Evaluation and analysis of root zone soil moisture products in the frozen soil of the western Daxinganling Mountains based on In-Situ data W. Shan et al. https://doi.org/10.1016/j.coldregions.2025.104694
118. Assessment of SMADI and SWDI agricultural drought indices using remotely sensed root zone soil moisture M. Pablos et al. https://doi.org/10.5194/piahs-380-55-2018
119. A Review of Root Zone Soil Moisture Estimation Methods Based on Remote Sensing M. Li et al. https://doi.org/10.3390/rs15225361
120. Parameter optimisation for a better representation of drought by LSMs: inverse modelling vs. sequential data assimilation H. Dewaele et al. https://doi.org/10.5194/hess-21-4861-2017
121. Spatial-temporal simulation and prediction of root zone soil moisture based on Hydrus-1D and CNN-LSTM-attention models in Yutian Oasis, southern Xinjiang, China X. LÜ et al. https://doi.org/10.1016/j.pedsph.2024.10.008
122. Evaluation of root zone soil moisture products over the Huai River basin E. Liu et al. https://doi.org/10.5194/hess-28-2375-2024
123. Satellite Chlorophyll Fluorescence and Soil Moisture Observations Lead to Advances in the Predictive Understanding of Global Terrestrial Coupled Carbon‐Water Cycles B. Qiu et al. https://doi.org/10.1002/2017GB005744
124. Noninvasive 2D and 3D Mapping of Root Zone Soil Moisture Through the Detection of Coarse Roots With Ground‐Penetrating Radar X. Liu et al. https://doi.org/10.1029/2019WR026930
125. Spatial and Temporal Distribution of Soil Moisture at the Catchment Scale Using Remotely-Sensed Energy Fluxes T. Alexandridis et al. https://doi.org/10.3390/w8010032
126. Root zone soil moisture mapping at very high spatial resolution using radar-derived surface soil moisture product N. Ouaadi et al. https://doi.org/10.1016/j.agwat.2025.109507
127. Late-fall satellite-based soil moisture observations show clear connections to subsequent spring streamflow R. Koster et al. https://doi.org/10.1038/s41467-023-39318-3
128. Relationships among soil moisture at various depths under diverse climate, land cover and soil texture N. Li et al. https://doi.org/10.1016/j.scitotenv.2024.174583
129. Two-step fusion method for generating 1 km seamless multi-layer soil moisture with high accuracy in the Qinghai-Tibet plateau S. Huang et al. https://doi.org/10.1016/j.isprsjprs.2023.02.009
130. Robustness of critical soil moisture to curve-fitting methods and its variability with soil depth, soil texture, and climatic conditions: insights from lysimeter data in Germany X. Lu et al. https://doi.org/10.1016/j.jhydrol.2026.134959
131. A New Optical Remote Sensing Technique for High-Resolution Mapping of Soil Moisture E. Babaeian et al. https://doi.org/10.3389/fdata.2019.00037
132. Performance evaluation of soil moisture profile estimation through entropy-based and exponential filter models V. Mishra et al. https://doi.org/10.1080/02626667.2020.1730846
133. 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
134. Satellite-Based Assessment of Meteorological and Agricultural Drought in Mainland Southeast Asia Y. Li et al. https://doi.org/10.1109/JSTARS.2022.3190438
135. Improvement of multi‐layer soil moisture prediction using support vector machines and ensemble Kalman filter coupled with remote sensing soil moisture datasets over an agriculture dominant basin in China Q. Zhu et al. https://doi.org/10.1002/hyp.14154
136. Springtime soil moisture variability and its changing environmental drivers: a CMIP6 multi-model ensemble analysis for the subtropical East Asian region S. Koralegedara et al. https://doi.org/10.1186/s40562-025-00436-z
137. Quantifying the potential of using Soil Moisture Active Passive (SMAP) soil moisture variability to predict subsurface water dynamics A. Nayak et al. https://doi.org/10.5194/hess-29-215-2025
138. Remote sensing of root zone soil moisture: A review of methods and products A. Kasim et al. https://doi.org/10.1016/j.jhydrol.2025.133002
139. 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
140. Evaluation of subsurface soil water content estimate methods: Maximum entropy vs. exponential filter H. Luo et al. https://doi.org/10.1016/j.jhydrol.2024.132007
141. Regional assimilation of in situ observed soil moisture into the VIC model considering spatial variability J. Zhou et al. https://doi.org/10.1080/02626667.2019.1662024
142. Estimation and Assessment of the Root Zone Soil Moisture from Near-Surface Measurements over Huai River Basin E. Liu et al. https://doi.org/10.3390/atmos14010124
143. A regional model-based algorithm to simulate root zone soil moisture with multi-source datasets Y. Liu et al. https://doi.org/10.1016/j.agwat.2025.109803
144. Root-zone soil moisture estimation from assimilation of downscaled Soil Moisture and Ocean Salinity data G. Dumedah et al. https://doi.org/10.1016/j.advwatres.2015.07.021