387 citations as recorded by crossref.

1. Toward Global Soil Moisture Monitoring With Sentinel-1: Harnessing Assets and Overcoming Obstacles B. Bauer-Marschallinger et al. https://doi.org/10.1109/TGRS.2018.2858004
2. Spatial composition of AMSR2 soil moisture products by conditional merging technique with ground soil moisture data D. Kim et al. https://doi.org/10.1007/s00477-016-1300-0
3. A review of spatial downscaling of satellite remotely sensed soil moisture J. Peng et al. https://doi.org/10.1002/2016RG000543
4. Feasibility of irrigation monitoring with cosmic-ray neutron sensors C. Brogi et al. https://doi.org/10.5194/gi-11-451-2022
5. Assimilation of Cosmic‐Ray Neutron Counts for the Estimation of Soil Ice Content on the Eastern Tibetan Plateau S. Mwangi et al. https://doi.org/10.1029/2019JD031529
6. The Potsdam Soil Moisture Observatory: high-coverage reference observations at kilometer scale P. Grosse et al. https://doi.org/10.5194/essd-18-1703-2026
7. Estimating the uncertainties of satellite derived soil moisture at global scale F. Gibon et al. https://doi.org/10.1016/j.srs.2024.100147
8. 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
9. 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
10. SOIL-WATERGRIDS, mapping dynamic changes in soil moisture and depth of water table from 1970 to 2014 M. Guglielmo et al. https://doi.org/10.1038/s41597-021-01032-4
11. The Applicability of the Cosmic Ray Neutron Sensor to Simultaneously Monitor Soil Water Content and Biomass in an Acacia mearnsii Forest T. Vather et al. https://doi.org/10.3390/hydrology7030048
12. Technical Note: Revisiting the general calibration of cosmic-ray neutron sensors to estimate soil water content M. Heistermann et al. https://doi.org/10.5194/hess-28-989-2024
13. First implementation of a new cross-disciplinary observation strategy for heavy precipitation events from formation to flooding A. Wieser et al. https://doi.org/10.1007/s12665-023-11050-7
14. Vom Punkt zur Fläche in der Messung des Wasseräquivalents der Schneedecke – Mehrwert von Cosmic-Ray Neutron Sensoren in der regionalen Schneemodellierung P. Schattan et al. https://doi.org/10.1007/s00506-018-0500-x
15. Measurement of secondary cosmic-ray neutrons near the geomagnetic North Pole R. Woolf et al. https://doi.org/10.1016/j.jenvrad.2019.01.001
16. BLOSM: Boron-based large-scale observation of soil moisture: First laboratory results of a cost-efficient neutron detector E. van Amelrooij et al. https://doi.org/10.1016/j.ohx.2022.e00342
17. Towards disentangling heterogeneous soil moisture patterns in cosmic-ray neutron sensor footprints D. Rasche et al. https://doi.org/10.5194/hess-25-6547-2021
18. The role of sampling strategy on apparent temporal stability of soil moisture under subtropical hydroclimatic conditions L. Gao et al. https://doi.org/10.2478/johh-2019-0006
19. Very High Spatial Resolution Downscaled SMAP Radiometer Soil Moisture in the CONUS Using VIIRS/MODIS Data B. Fang et al. https://doi.org/10.1109/JSTARS.2021.3076026
20. Continuous monitoring of snowpack dynamics in alpine terrain by aboveground neutron sensing P. Schattan et al. https://doi.org/10.1002/2016WR020234
21. Large-Scale Boron-Lined Neutron Detection Systems as a 3He Alternative for Cosmic Ray Neutron Sensing J. Weimar et al. https://doi.org/10.3389/frwa.2020.00016
22. L-Band Soil Moisture Retrievals Using Microwave Based Temperature and Filtering. Towards Model-Independent Climate Data Records R. van der Schalie et al. https://doi.org/10.3390/rs13132480
23. Evaluation of reanalysis soil moisture products using cosmic ray neutron sensor observations across the globe Y. Zheng et al. https://doi.org/10.5194/hess-28-1999-2024
24. Smart networks of autonomous in-situ soil sensors X. Chavanne & J. Frangi https://doi.org/10.1080/19648189.2022.2135026
25. Assessment of Root Zone Soil Moisture Estimations from SMAP, SMOS and MODIS Observations M. Pablos et al. https://doi.org/10.3390/rs10070981
26. SMAP soil moisture improves global evapotranspiration A. Purdy et al. https://doi.org/10.1016/j.rse.2018.09.023
27. The complementary value of cosmic-ray neutron sensing and snow covered area products for snow hydrological modelling P. Schattan et al. https://doi.org/10.1016/j.rse.2019.111603
28. Downscaling of seasonal soil moisture forecasts using satellite data S. Schneider et al. https://doi.org/10.5194/hess-18-2899-2014
29. RETRACTED ARTICLE: Examining the variation of soil moisture from cosmic-ray neutron probes footprint: experimental results from a COSMOS-UK site O. Howells et al. https://doi.org/10.1007/s12665-022-10721-1
30. Dynamic groundwater recharge simulations based on cosmic‐ray neutron sensing in a tropical wet experimental basin L. Barbosa et al. https://doi.org/10.1002/vzj2.20145
31. Estimates of Global Surface Hydrology and Heat Fluxes from the Community Land Model (CLM4.5) with Four Atmospheric Forcing Datasets A. Wang et al. https://doi.org/10.1175/JHM-D-16-0041.1
32. Practical Data Products From Cosmic-Ray Neutron Sensing for Hydrological Applications T. Franz et al. https://doi.org/10.3389/frwa.2020.00009
33. Spatial soil moisture estimation in agro-pastoral transitional zone based on synergistic use of SAR and optical-thermal satellite images H. Nguyen et al. https://doi.org/10.1016/j.agrformet.2021.108719
34. Novel Cosmic Ray Neutron Sensor Accurately Captures Field-Scale Soil Moisture Trends under Heterogeneous Soil Textures K. Flynn et al. https://doi.org/10.3390/w13213038
35. On the Information Content of Cosmic‐Ray Neutron Data in the Inverse Estimation of Soil Hydraulic Properties G. Brunetti et al. https://doi.org/10.2136/vzj2018.06.0123
36. Using Cosmic-Ray Neutron Probes in Validating Satellite Soil Moisture Products and Land Surface Models M. Duygu & Z. Akyürek https://doi.org/10.3390/w11071362
37. Evaluation of the RF-Based Downscaled SMAP and SMOS Products Using Multi-Source Data over an Alpine Mountains Basin, Northwest China Y. Wen et al. https://doi.org/10.3390/w13202875
38. A Study on the Establishment of a Korean Soil Moisture Network (2): Measurement of Intermediate-Scale Soil Moisture Using a Cosmic-Ray Sensor J. Jeong et al. https://doi.org/10.9798/KOSHAM.2018.18.7.83
39. Error Estimation for Soil Moisture Measurements With Cosmic Ray Neutron Sensing and Implications for Rover Surveys J. Jakobi et al. https://doi.org/10.3389/frwa.2020.00010
40. A linked geomorphological and geophysical modelling methodology applied to an active landslide J. Boyd et al. https://doi.org/10.1007/s10346-021-01666-w
41. Effects of rain and soil moisture on background neutron measurements with the SuperMISTI neutron array A. Hutcheson et al. https://doi.org/10.1016/j.radmeas.2017.03.029
42. Estimation of root zone soil moisture at point scale based on soil water measurements from cosmic-ray neutron sensing in a karst catchment X. Li et al. https://doi.org/10.1016/j.agwat.2023.108511
43. High-resolution drought simulations and comparison to soil moisture observations in Germany F. Boeing et al. https://doi.org/10.5194/hess-26-5137-2022
44. Examining the relationship between intermediate-scale soil moisture and terrestrial evaporation within a semi-arid grassland R. Jana et al. https://doi.org/10.5194/hess-20-3987-2016
45. Applicability of cosmic-ray neutron sensing for measuring soil water content to heterogeneous landscapes under subtropical hydroclimatic conditions Z. Pang et al. https://doi.org/10.1016/j.jhydrol.2021.126068
46. Improving calibration and validation of cosmic-ray neutron sensors in the light of spatial sensitivity M. Schrön et al. https://doi.org/10.5194/hess-21-5009-2017
47. Monitoring soil water content and its salinity with high-precision and low-cost in-situ sensors X. Chavanne & J. Frangi https://doi.org/10.1080/19648189.2022.2051076
48. Evaluation of the ESA CCI soil moisture product using ground-based observations W. Dorigo et al. https://doi.org/10.1016/j.rse.2014.07.023
49. Multi-scale assessment of a cosmic-ray neutron probe observation of soil moisture for surface layer applications in a mountainous forest environment J. Jeong et al. https://doi.org/10.1016/j.agrformet.2024.110155
50. Applicability of cosmic-ray neutron sensor for measuring soil moisture at the agricultural-pastoral ecotone in northwest China X. Tan et al. https://doi.org/10.1007/s11430-020-9650-2
51. Intensity of the neutron component of cosmic rays and air humidity V. Yanchukovsky et al. https://doi.org/10.12737/szf-101202405
52. Integration of hydrogeophysical datasets and empirical orthogonal functions for improved irrigation water management C. Finkenbiner et al. https://doi.org/10.1007/s11119-018-9582-5
53. Three years of soil moisture observations by a dense cosmic-ray neutron sensing cluster at an agricultural research site in north-east Germany M. Heistermann et al. https://doi.org/10.5194/essd-15-3243-2023
54. Kozmik Işın Nötron Sayacı ile Ölçülen Toprak Neminin Hidrolojik Modellerde Kullanımı M. DUYGU & Z. AKYÜREK https://doi.org/10.18400/tekderg.986949
55. Mesoscale spatial variability of soil‐water content in an alpine meadow on the northern Tibetan Plateau X. Zhu et al. https://doi.org/10.1002/hyp.13479
56. How to make advances in hydrological modelling K. Beven https://doi.org/10.2166/nh.2019.134
57. Employing NDVI as vegetation correction variable to improve soil moisture measurements of mobile cosmic-ray neutron sensor near the Qilian Mountains S. Wu et al. https://doi.org/10.1016/j.geoderma.2023.116764
58. Towards a methodology for testing models as hypotheses in the inexact sciences K. Beven https://doi.org/10.1098/rspa.2018.0862
59. Calibration and Validation of the COSMOS Rover for Surface Soil Moisture Measurement J. Dong et al. https://doi.org/10.2136/vzj2013.08.0148
60. Global-scale evaluation of SMAP, SMOS and ASCAT soil moisture products using triple collocation F. Chen et al. https://doi.org/10.1016/j.rse.2018.05.008
61. Lunar analog study using portable gamma-ray neutron detector: Radiochemical mapping of silicic-to-basaltic volcanic terrains in the San Francisco Volcanic Field, Arizona D. Ölçek et al. https://doi.org/10.1016/j.icarus.2026.117131
62. Correction of systematic model forcing bias of CLM using assimilation of cosmic-ray Neutrons and land surface temperature: a study in the Heihe Catchment, China X. Han et al. https://doi.org/10.5194/hess-19-615-2015
63. Impacts of model initialization on an integrated surface water–groundwater model H. Ajami et al. https://doi.org/10.1002/hyp.10478
64. Continuous and autonomous snow water equivalent measurements by a cosmic ray sensor on an alpine glacier R. Gugerli et al. https://doi.org/10.5194/tc-13-3413-2019
65. Improving the ESA CCI daily soil moisture time series with physically-based land surface model datasets using a Fourier time-filtering method E. Seo & P. Dirmeyer https://doi.org/10.1175/JHM-D-21-0120.1
66. Soil water content determination with cosmic-ray neutron sensor: Correcting aboveground hydrogen effects with thermal/fast neutron ratio Z. Tian et al. https://doi.org/10.1016/j.jhydrol.2016.07.004
67. Study of Cosmic Ray Intensity (CRI) along with Solar Wind Parameters and Geomagnetic Indices from Different Stations . Chali Idosa Uga & . Binod Adhikari https://doi.org/10.1134/S0010952523600026
68. Comparison of Soil Water Estimates From Cosmic-Ray Neutron and Capacity Sensors in a Semi-arid Pine Forest: Which Is Able to Better Assess the Role of Environmental Conditions and Thinning? M. González-Sanchis et al. https://doi.org/10.3389/frwa.2020.552508
69. Spatiotemporal Change Analysis of Soil Moisture Based on Downscaling Technology in Africa Z. Yuan et al. https://doi.org/10.3390/w14010074
70. Microwave retrievals of soil moisture and vegetation optical depth with improved resolution using a combined constrained inversion algorithm: Application for SMAP satellite L. Gao et al. https://doi.org/10.1016/j.rse.2020.111662
71. Calibration of the W-PIE neutron spectrometer at CERF reference facility A. Cirillo & M. Caresana https://doi.org/10.1093/rpd/ncac203
72. Sentinel-1 SAR and Temporal Lag Soil Moisture Estimation at Instrumented Field Sites: A Stacked Ensemble Approach P. Wang & Q. Jiang https://doi.org/10.3390/rs18152483
73. Snow water equivalent measurement in the Arctic based on cosmic ray neutron attenuation A. Jitnikovitch et al. https://doi.org/10.5194/tc-15-5227-2021
74. Linking Remote Sensing and Geodiversity and Their Traits Relevant to Biodiversity—Part I: Soil Characteristics A. Lausch et al. https://doi.org/10.3390/rs11202356
75. Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals A. Gruber et al. https://doi.org/10.1109/TGRS.2017.2734070
76. Evidence of a union between organic carbon and water content in soil H. Manns et al. https://doi.org/10.1139/cjss-2015-0084
77. Sensitive Feature Evaluation for Soil Moisture Retrieval Based on Multi-Source Remote Sensing Data with Few In-Situ Measurements: A Case Study of the Continental U.S. L. Zhang et al. https://doi.org/10.3390/w13152003
78. A study of field-scale soil moisture variability using the COsmic-ray Soil Moisture Observing System (COSMOS) at IITM Pune site M. Mujumdar et al. https://doi.org/10.1016/j.jhydrol.2021.126102
79. Improved representation of soil moisture processes through incorporation of cosmic-ray neutron count measurements in a large-scale hydrologic model E. Fatima et al. https://doi.org/10.5194/hess-28-5419-2024
80. SMCR: A first satellite-derived all-weather daily/1-km Soil Moisture Climatological Record (1980–2023) S. Zhai et al. https://doi.org/10.1038/s41597-025-06432-4
81. Improving the theoretical underpinnings of process‐based hydrologic models M. Clark et al. https://doi.org/10.1002/2015WR017910
82. Local- and Plot-Scale Measurements of Soil Moisture: Time and Spatially Resolved Field Techniques in Plain, Hill and Mountain Sites G. Raffelli et al. https://doi.org/10.3390/w9090706
83. Cropland observatory nodes (CRONOS): Proximal, integrated soil-plant-atmosphere monitoring systems D. Diggins et al. https://doi.org/10.1016/j.atech.2024.100737
84. A Multilayer Soil Moisture Dataset Based on the Gravimetric Method in China and Its Characteristics A. Wang & X. Shi https://doi.org/10.1175/JHM-D-19-0035.1
85. DRYP 1.0: a parsimonious hydrological model of DRYland Partitioning of the water balance E. Quichimbo et al. https://doi.org/10.5194/gmd-14-6893-2021
86. 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
87. Emerging methods for noninvasive sensing of soil moisture dynamics from field to catchment scale: a review H. Bogena et al. https://doi.org/10.1002/wat2.1097
88. Towards Precision Agriculture: IoT-Enabled Intelligent Irrigation Systems Using Deep Learning Neural Network P. Kashyap et al. https://doi.org/10.1109/JSEN.2021.3069266
89. Influence of snow spatial variability on cosmic ray neutron snow water equivalent (SWE): case study in a northern prairie H. Kim et al. https://doi.org/10.5194/tc-19-3177-2025
90. Cosmic-Ray Neutron Sensor Backpack for Assessing Spatial and Temporal Variations in Soil Water Content in an Agroforestry System in Northern Spain L. Gaspar et al. https://doi.org/10.3390/land14040744
91. ESA CCI Soil Moisture GAPFILLED: an independent global gap-free satellite climate data record with uncertainty estimates W. Preimesberger et al. https://doi.org/10.5194/essd-17-4305-2025
92. Toward Large‐Scale Soil Moisture Monitoring Using Rail‐Based Cosmic Ray Neutron Sensing D. Altdorff et al. https://doi.org/10.1029/2022WR033514
93. Improving the realism of hydrologic model functioning through multivariate parameter estimation O. Rakovec et al. https://doi.org/10.1002/2016WR019430
94. Capturing Field‐Scale Soil Moisture Dynamics in Ireland Using Cosmic‐Ray Neutron Sensing K. Shishkin et al. https://doi.org/10.1111/ejss.70367
95. Assessment and intercomparison of 23 global satellite and model-based soil moisture products using cosmic ray neutron sensing observations over Europe P. Helili et al. https://doi.org/10.1016/j.rse.2025.115207
96. Assimilation of Satellite Soil Moisture for Improved Atmospheric Reanalyses C. Draper & R. Reichle https://doi.org/10.1175/MWR-D-18-0393.1
97. Pore Ice Content and Unfrozen Water Content Coexistence in Partially Frozen Soils: A State-of-the-Art Review of Mechanisms, Measurement Technology and Modeling Methods M. Waseem et al. https://doi.org/10.3390/geotechnics5040080
98. Multiscale soil moisture estimates using static and roving cosmic-ray soil moisture sensors D. McJannet et al. https://doi.org/10.5194/hess-21-6049-2017
99. Sampling frequency significantly influenced surface soil moisture dynamics but not its prediction accuracy in an arid mountain forest X. Zhu et al. https://doi.org/10.1016/j.ejrh.2024.102065
100. Data-driven scaling methods for soil moisture cosmic ray neutron sensors R. Baatz et al. https://doi.org/10.5194/hess-29-2583-2025
101. A profile shape correction to reduce the vertical sensitivity of cosmic‐ray neutron sensing of soil moisture L. Scheiffele et al. https://doi.org/10.1002/vzj2.20083
102. Enhancing soil moisture retrievals from cosmic ray neutron sensor in a peri-urban agricultural landscape M. Noč et al. https://doi.org/10.1016/j.agwat.2026.110564
103. Present status of soil moisture estimation over the African continent L. Myeni et al. https://doi.org/10.1016/j.ejrh.2018.11.004
104. Does a tradeoff between temporal stability and sampling frequency contribute to the prediction accuracy of soil moisture in alternative stable states? X. Zhu et al. https://doi.org/10.1016/j.geoderma.2026.117751
105. Translating aboveground cosmic-ray neutron intensity to high-frequency soil moisture profiles at sub-kilometer scale R. Rosolem et al. https://doi.org/10.5194/hess-18-4363-2014
106. Intercomparison of cosmic-ray neutron sensors and water balance monitoring in an urban environment M. Schrön et al. https://doi.org/10.5194/gi-7-83-2018
107. Validation of Soil Moisture Data Products From the NASA SMAP Mission A. Colliander et al. https://doi.org/10.1109/JSTARS.2021.3124743
108. A D-vine copula quantile regression approach for soil moisture retrieval from dual polarimetric SAR Sentinel-1 over vegetated terrains H. Nguyen et al. https://doi.org/10.1016/j.rse.2021.112283
109. A Machine Learning-Based Geostatistical Downscaling Method for Coarse-Resolution Soil Moisture Products Y. Jin et al. https://doi.org/10.1109/JSTARS.2020.3035386
110. Improving measurement technology for the design of sustainable cities E. Pardyjak & R. Stoll https://doi.org/10.1088/1361-6501/aa7c77
111. Optimal Temporal Filtering of the Cosmic-Ray Neutron Signal to Reduce Soil Moisture Uncertainty P. Davies et al. https://doi.org/10.3390/s22239143
112. Event-Based Decentralized Approach of Root Zone Soil Moisture Increase Dynamics C. Zhang et al. https://doi.org/10.1177/11786221261429782
113. Response functions for detectors in cosmic ray neutron sensing M. Köhli et al. https://doi.org/10.1016/j.nima.2018.06.052
114. Application of Cosmic-Ray Neutron Sensor Method to Calculate Field Water Use Efficiency X. Chen et al. https://doi.org/10.3390/w14091518
115. Neutrons on Rails: Transregional Monitoring of Soil Moisture and Snow Water Equivalent M. Schrön et al. https://doi.org/10.1029/2021GL093924
116. On the observed time evolution of cosmic rays in a new time domain C. Varotsos et al. https://doi.org/10.1016/j.actaastro.2024.09.034
117. Practical considerations for using petrophysics and geoelectrical methods on clay rich landslides J. Boyd et al. https://doi.org/10.1016/j.enggeo.2024.107506
118. Summer Soil Moisture Spatiotemporal Variability in Southeastern Arizona S. Stillman et al. https://doi.org/10.1175/JHM-D-13-0173.1
119. Hybrid Methodology Using Sentinel-1/Sentinel-2 for Soil Moisture Estimation S. Nativel et al. https://doi.org/10.3390/rs14102434
120. Unlocking the synergistic potential of sensor technologies in grassland research K. Jindo et al. https://doi.org/10.1007/s44397-025-00020-2
121. Quasi-SMAP: A SMAP-Referenced Long-Term Daily Surface Soil Moisture Record From 1981 to 2022 Y. Wang et al. https://doi.org/10.1109/JSTARS.2026.3701537
122. Advances in soil moisture retrieval from multispectral remote sensing using unoccupied aircraft systems and machine learning techniques S. Araya et al. https://doi.org/10.5194/hess-25-2739-2021
123. Quantifying the effect of salinity on dielectric-based soil moisture measurements using COSMOS records J. Wang et al. https://doi.org/10.1016/j.jhydrol.2024.131925
124. Evaluation of Model-Based Soil Moisture Drought Monitoring over Three Key Regions in China Y. Li et al. https://doi.org/10.1175/JAMC-D-17-0118.1
125. Ensemble deep learning towards high-resolution soil-moisture mapping for enhanced water management in California's Central Valley A. Azedou et al. https://doi.org/10.1016/j.envsoft.2025.106824
126. Monitoring Irrigation in Small Orchards with Cosmic-Ray Neutron Sensors C. Brogi et al. https://doi.org/10.3390/s23052378
127. Feasibility analysis of using inverse modeling for estimating field-scale evapotranspiration in maize and soybean fields from soil water content monitoring networks F. Foolad et al. https://doi.org/10.5194/hess-21-1263-2017
128. Soil moisture retrieval from Sentinel-1: Lessons learned after more than a decade in orbit M. Rahmati et al. https://doi.org/10.1016/j.rse.2025.115146
129. Spatial prediction of near surface soil water retention functions using hydrogeophysics and empirical orthogonal functions J. Gibson & T. Franz https://doi.org/10.1016/j.jhydrol.2018.03.046
130. Downscaling SMAP soil moisture using a wide & deep learning method over the Continental United States M. Xu et al. https://doi.org/10.1016/j.jhydrol.2022.127784
131. Use of cosmic-ray neutron sensors for soil moisture monitoring in forests I. Heidbüchel et al. https://doi.org/10.5194/hess-20-1269-2016
132. Cosmic Ray Neutron Soil Moisture Estimation Using Physically Based Site‐Specific Conversion Functions M. Andreasen et al. https://doi.org/10.1029/2019WR026588
133. Sensing Methodologies in Agriculture for Soil Moisture and Nutrient Monitoring B. Kashyap & R. Kumar https://doi.org/10.1109/ACCESS.2021.3052478
134. The Bode hydrological observatory: a platform for integrated, interdisciplinary hydro-ecological research within the TERENO Harz/Central German Lowland Observatory U. Wollschläger et al. https://doi.org/10.1007/s12665-016-6327-5
135. Effect of Biomass Water Dynamics in Cosmic-Ray Neutron Sensor Observations: A Long-Term Analysis of Maize–Soybean Rotation in Nebraska T. Morris et al. https://doi.org/10.3390/s24134094
136. Combining static and portable Cosmic ray neutron sensor data to assess catchment scale heterogeneity in soil water storage and their integrated role in catchment runoff response K. Dimitrova-Petrova et al. https://doi.org/10.1016/j.jhydrol.2021.126659
137. Estimating snow water equivalent using cosmic‐ray neutron sensors from the COSMOS‐UK network J. Wallbank et al. https://doi.org/10.1002/hyp.14048
138. Combining cosmic-ray neutron sensor and fallout 137Cs to explore the connection of soil water content with soil redistribution in an agroforestry hillslope L. Gaspar et al. https://doi.org/10.1016/j.envres.2023.116451
139. Estimation and evaluation of high-resolution soil moisture from merged model and Earth observation data in the Great Britain J. Peng et al. https://doi.org/10.1016/j.rse.2021.112610
140. Autonomous ice sheet surface mass balance measurements from cosmic rays I. Howat et al. https://doi.org/10.5194/tc-12-2099-2018
141. Advancements in dielectric soil moisture sensor Calibration: A comprehensive review of methods and techniques S. Mane et al. https://doi.org/10.1016/j.compag.2024.108686
142. 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
143. AMSR2 Soil Moisture Downscaling Using Temperature and Vegetation Data B. Fang et al. https://doi.org/10.3390/rs10101575
144. Sensing Area‐Average Snow Water Equivalent with Cosmic‐Ray Neutrons: The Influence of Fractional Snow Cover P. Schattan et al. https://doi.org/10.1029/2019WR025647
145. Cosmic-Ray neutron Sensor PYthon tool (crspy 1.2.1): an open-source tool for the processing of cosmic-ray neutron and soil moisture data D. Power et al. https://doi.org/10.5194/gmd-14-7287-2021
146. Ecohydrology with unmanned aerial vehicles E. Vivoni et al. https://doi.org/10.1890/ES14-00217.1
147. Measuring and mapping moisture content in agricultural fields by neutron-gamma analysis G. Yakubova et al. https://doi.org/10.1016/j.still.2024.106444
148. A dense network of cosmic-ray neutron sensors for soil moisture observation in a highly instrumented pre-Alpine headwater catchment in Germany B. Fersch et al. https://doi.org/10.5194/essd-12-2289-2020
149. Evaluation of the accuracy and response to recorded irrigation events of remote sensing and reanalysis soil moisture products in a typical irrigation district R. Zhu et al. https://doi.org/10.1016/j.agwat.2026.110822
150. Application of DSSAT model to evaluate the effects of tillage methods on soil water balance during drought period A. Irkiso et al. https://doi.org/10.1007/s40808-025-02334-x
151. Potential of snow data to improve the consistency and robustness of a semi-distributed hydrological model using the SAFRAN input dataset D. Ruelland https://doi.org/10.1016/j.jhydrol.2024.130820
152. Monitoring soil water content at a heterogeneous oil sand reclamation site using a cosmic-ray soil moisture probe M. Sigouin et al. https://doi.org/10.1016/j.jhydrol.2016.10.026
153. A fused soil moisture product integrating linear fusion method and error characteristics approach A. Singh et al. https://doi.org/10.1080/01431161.2026.2653239
154. The future of Earth observation in hydrology M. McCabe et al. https://doi.org/10.5194/hess-21-3879-2017
155. Snow shielding factors for cosmogenic nuclide dating inferred from Monte Carlo neutron transport simulations C. Zweck et al. https://doi.org/10.1016/j.epsl.2013.07.023
156. Dynamic Neural Network Modelling of Soil Moisture Content for Predictive Irrigation Scheduling O. Adeyemi et al. https://doi.org/10.3390/s18103408
157. Intensity of the neutron component of cosmic rays and air humidity V. Yanchukovsky et al. https://doi.org/10.12737/stp-101202405
158. A terrestrial observatory approach to the integrated investigation of the effects of deforestation on water, energy, and matter fluxes H. Bogena et al. https://doi.org/10.1007/s11430-014-4911-7
159. 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
160. Evaluation of the soil water content of two managed ecosystems using cosmic‐ray neutron sensing on China's Loess Plateau C. Liu et al. https://doi.org/10.1111/ejss.13339
161. High latitude observation of the Forbush decrease during the May 2024 solar storms with muon and neutron detectors on Svalbard F. Riggi et al. https://doi.org/10.1016/j.asr.2025.05.023
162. Intercomparison of Earth Observation products for hyper-resolution hydrological modelling over Europe A. García-García et al. https://doi.org/10.1016/j.rse.2025.115131
163. An empirical vegetation correction for soil water content quantification using cosmic ray probes R. Baatz et al. https://doi.org/10.1002/2014WR016443
164. Exploring the potential of SCAT-SAR SWI for soil moisture retrievals at selected COSMOS-UK sites O. Howells et al. https://doi.org/10.1080/01431161.2021.1988185
165. Construction and estimation of soil moisture site with FDR and COSMIC-ray (SM-FC) sensors for calibration/validation of satellite-based and COSMIC-ray soil moisture products in Sungkyunkwan university, South Korea H. Kim et al. https://doi.org/10.3741/JKWRA.2016.49.2.133
166. URANOS v1.0 – the Ultra Rapid Adaptable Neutron-Only Simulation for Environmental Research M. Köhli et al. https://doi.org/10.5194/gmd-16-449-2023
167. Mesoscale soil moisture measurements along the rover route using the mobile cosmic-ray neutron sensing in the eastern Tibetan Plateau Y. Zhang et al. https://doi.org/10.1016/j.geoderma.2024.117046
168. Two Datasets over South Tyrol and Tyrol Areas to Understand and Characterize Water Resource Dynamics in Mountain Regions L. De Gregorio et al. https://doi.org/10.3390/data9110136
169. Evaluating Sentinel-1/-2 and MODIS fractional snow cover products for applications in alpine cosmic ray neutron snow monitoring N. Krebs et al. https://doi.org/10.1016/j.rsase.2025.101812
170. Can soil water measurements at a certain depth be used to estimate mean soil water content of a soil profile at a point or at a hillslope scale? W. Hu & B. Si https://doi.org/10.1016/j.jhydrol.2014.01.053
171. A seamless global daily 5 km soil moisture product from 1982 to 2021 using AVHRR satellite data and an attention-based deep learning model Y. Zhang et al. https://doi.org/10.5194/essd-17-5181-2025
172. On the spatio-temporal dynamics of soil moisture at the field scale H. Vereecken et al. https://doi.org/10.1016/j.jhydrol.2013.11.061
173. The LAB-Net Soil Moisture Network: Application to Thermal Remote Sensing and Surface Energy Balance C. Mattar et al. https://doi.org/10.3390/data1010006
174. Footprint characteristics revised for field‐scale soil moisture monitoring with cosmic‐ray neutrons M. Köhli et al. https://doi.org/10.1002/2015WR017169
175. Multi-Layer Soil Moisture Profiling Based on BKA-CNN by Integrating Sentinel-1/2 SAR and Multispectral Data M. Jiao et al. https://doi.org/10.3390/agronomy15112542
176. Experimental Evidence of Ground Albedo Neutron Impact on Soft Error Rate for Nanoscale Devices G. Hubert & L. Artola https://doi.org/10.1109/TNS.2018.2877263
177. Assessment of neutrons from secondary cosmic rays at mountain altitudes – Geant4 simulations of environmental parameters including soil moisture and snow cover T. Brall et al. https://doi.org/10.5194/tc-15-4769-2021
178. Refractive index-based soil moisture sensor E. Sim et al. https://doi.org/10.46670/JSST.2021.30.6.415
179. Evaluation of an operational real-time irrigation scheduling scheme for drip irrigated citrus fields in Picassent, Spain D. Li et al. https://doi.org/10.1016/j.agwat.2018.06.022
180. A Two-Source Normalized Soil Thermal Inertia Model for Estimating Field-Scale Soil Moisture from MODIS and ASTER Data G. Hao et al. https://doi.org/10.3390/rs14051215
181. Estimation of Soil Moisture Content Based on Fractional Differential and Optimal Spectral Index W. Li et al. https://doi.org/10.3390/agronomy14010184
182. Managing spatial irrigation using remote-sensing-based evapotranspiration and soil water adaptive control model M. Maguire et al. https://doi.org/10.1016/j.agwat.2022.107838
183. Soil moisture sensor network design for hydrological applications L. Zhuo et al. https://doi.org/10.5194/hess-24-2577-2020
184. Calibration of a distributed SWE model using MODIS snow cover maps andin situmeasurements N. Besic et al. https://doi.org/10.1080/2150704X.2014.897399
185. Measurements and Observations in the XXI century (MOXXI): innovation and multi-disciplinarity to sense the hydrological cycle F. Tauro et al. https://doi.org/10.1080/02626667.2017.1420191
186. Relationship between cosmic ray intensity and Earth's magnetic field: Cross-wavelet analysis during intense and moderate geomagnetic storms C. Uga et al. https://doi.org/10.1063/5.0201135
187. Heat as a tracer to quantify processes and properties in the vadose zone: A review L. Halloran et al. https://doi.org/10.1016/j.earscirev.2016.06.009
188. Cyber-Innovated Watershed Research at the Shale Hills Critical Zone Observatory X. Yu et al. https://doi.org/10.1109/JSYST.2015.2484219
189. A dataset of soil moisture observations using the cosmic-ray neutron method in a Quercus variabilis plantation at Xiaolangdi Station (2016–2021) J. DU et al. https://doi.org/10.11922/11-6035.nesdc.2025.0220.zh
190. The COsmic-ray Soil Moisture Interaction Code (COSMIC) for use in data assimilation J. Shuttleworth et al. https://doi.org/10.5194/hess-17-3205-2013
191. An innovative neutron spectrometer for soil moisture measurements A. Cirillo et al. https://doi.org/10.1140/epjp/s13360-021-01976-x
192. High‐Resolution Shortwave Infrared Imaging of Water Infiltration into Dry Soil M. Sadeghi et al. https://doi.org/10.2136/vzj2017.09.0167
193. Virtual baseline to improve anomaly detection of SHM systems with non-stationary data S. Kamali et al. https://doi.org/10.1016/j.ymssp.2024.111968
194. Soil hydrology: Recent methodological advances, challenges, and perspectives H. Vereecken et al. https://doi.org/10.1002/2014WR016852
195. Precocious 19th century soil carbon science B. Minasny et al. https://doi.org/10.1016/j.geodrs.2020.e00306
196. Hydrometeorology of the catastrophic Blanco river flood in South Texas, May 2015 C. Furl et al. https://doi.org/10.1016/j.ejrh.2017.12.001
197. Inverse modelling of cosmic‐ray soil moisture for field‐scale soil hydraulic parameters C. Rivera Villarreyes et al. https://doi.org/10.1111/ejss.12162
198. Soil moisture at local scale: Measurements and simulations N. Romano https://doi.org/10.1016/j.jhydrol.2014.01.026
199. Observation on Soil Moisture of Irrigation Cropland by Cosmic-Ray Probe . Zhongli Zhu et al. https://doi.org/10.1109/LGRS.2014.2346784
200. Drought dynamics across the hydrological cycle – an extensive validation of the National Hydrological Model of Denmark R. Schneider et al. https://doi.org/10.5194/hess-30-4019-2026
201. Calibration of a non-invasive cosmic-ray probe for wide area snow water equivalent measurement M. Sigouin & B. Si https://doi.org/10.5194/tc-10-1181-2016
202. Global soil moisture data derived through machine learning trained with in-situ measurements S. O. & R. Orth https://doi.org/10.1038/s41597-021-00964-1
203. Spatio-temporal soil moisture retrieval at the catchment scale using a dense network of cosmic-ray neutron sensors M. Heistermann et al. https://doi.org/10.5194/hess-25-4807-2021
204. Accuracy and precision of the cosmic‐ray neutron sensor for soil moisture estimation at humid environments J. Iwema et al. https://doi.org/10.1002/hyp.14419
205. Machine learning based estimation of field-scale daily, high resolution, multi-depth soil moisture for the Western and Midwestern United States Y. Xia et al. https://doi.org/10.7717/peerj.14275
206. COSMIC-SWAMP: IoT Processing of Cosmic Ray Soil Moisture Sensors P. Stowell et al. https://doi.org/10.1109/ACCESS.2026.3682125
207. Data assimilation of soil water flow via ensemble Kalman filter: Infusing soil moisture data at different scales P. Zhu et al. https://doi.org/10.1016/j.jhydrol.2017.10.078
208. Mesoscale soil moisture survey by mobile cosmic-ray neutron sensor across various landscapes in the Heihe River Basin S. Wu et al. https://doi.org/10.1016/j.rcar.2024.01.001
209. The AmeriFlux network: A coalition of the willing K. Novick et al. https://doi.org/10.1016/j.agrformet.2017.10.009
210. Spatiotemporal Analysis of Soil Moisture Variation in the Jiangsu Water Supply Area of the South-to-North Water Diversion Using ESA CCI Data Y. Wang et al. https://doi.org/10.3390/rs14020256
211. Land Use Impacts on Coral Reef Health: A Ridge-to-Reef Perspective R. Carlson et al. https://doi.org/10.3389/fmars.2019.00562
212. Potential of Thermal Neutrons to Correct Cosmic‐Ray Neutron Soil Moisture Content Measurements for Dynamic Biomass Effects J. Jakobi et al. https://doi.org/10.1029/2022WR031972
213. An Alternative Incoming Correction for Cosmic‐Ray Neutron Sensing Observations Using Local Muon Measurement L. Stevanato et al. https://doi.org/10.1029/2021GL095383
214. Conceptual modelling to assess how the interplay of hydrological connectivity, catchment storage and tracer dynamics controls nonstationary water age estimates C. Birkel et al. https://doi.org/10.1002/hyp.10414
215. Continuous data assimilation for the Richards equation of unsaturated flow: A computational study A. Rowley et al. https://doi.org/10.1002/vzj2.70094
216. Investigating the possibility of leakage detection in water distribution networks using cosmic ray neutrons in the thermal region L. Sostero et al. https://doi.org/10.1016/j.nima.2023.168951
217. 高灵敏度无线传感对煤炭含水率的精细探测研究 陈. Chen Lina et al. https://doi.org/10.3788/LOP242386
218. Evaluation of variable rate irrigation using a remote-sensing-based model J. Barker et al. https://doi.org/10.1016/j.agwat.2018.02.022
219. Monitoring of Snowpack Dynamics With Cosmic-Ray Neutron Probes: A Comparison of Four Conversion Methods H. Bogena et al. https://doi.org/10.3389/frwa.2020.00019
220. A soil moisture experiment for validating high-resolution satellite products and monitoring irrigation at agricultural field scale W. Wang et al. https://doi.org/10.1016/j.agwat.2024.109071
221. Comparing AMSR-E soil moisture estimates to the extended record of the U.S. Climate Reference Network (USCRN) E. Coopersmith et al. https://doi.org/10.1016/j.advwatres.2015.09.003
222. Accounting for biomass water equivalent variations in soil moisture retrievals from cosmic ray neutron sensor S. Al-Mashharawi et al. https://doi.org/10.1016/j.agwat.2025.109493
223. Evaluation of the soil water content using cosmic-ray neutron probe in a heterogeneous monsoon climate-dominated region H. Nguyen et al. https://doi.org/10.1016/j.advwatres.2017.07.020
224. Surface soil water content and its controlling factors in a small karst catchment T. Fu et al. https://doi.org/10.1007/s12665-016-6222-0
225. Synergies for Soil Moisture Retrieval Across Scales From Airborne Polarimetric SAR, Cosmic Ray Neutron Roving, and an In Situ Sensor Network B. Fersch et al. https://doi.org/10.1029/2018WR023337
226. Depth extrapolation of field-scale soil moisture time series derived with cosmic-ray neutron sensing (CRNS) using the soil moisture analytical relationship (SMAR) model D. Rasche et al. https://doi.org/10.5194/soil-10-655-2024
227. 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
228. Time-variant error characterization of SMAP and ASCAT soil moisture using Triple Collocation Analysis K. Wu et al. https://doi.org/10.1016/j.rse.2021.112324
229. Status and Perspectives on the Cosmic‐Ray Neutron Method for Soil Moisture Estimation and Other Environmental Science Applications M. Andreasen et al. https://doi.org/10.2136/vzj2017.04.0086
230. Soil moisture research in Kenya based on a systematic review of monitoring modelling and hydrological applications I. Kipkemoi et al. https://doi.org/10.1007/s44378-026-00304-y
231. Evolution of forest precipitation water storage measurement methods J. Friesen et al. https://doi.org/10.1002/hyp.10376
232. Combining Cosmic-Ray Neutron and Capacitance Sensors and Fuzzy Inference to Spatially Quantify Soil Moisture Distribution A. Almeida et al. https://doi.org/10.1109/JSEN.2014.2345376
233. Preface: Illuminating soil's hidden dimensions, a decade of progress and future directions in agrogeophysics research A. Romero-Ruiz et al. https://doi.org/10.5194/soil-11-1041-2025
234. Soil Moisture Measuring Techniques and Factors Affecting the Moisture Dynamics: A Comprehensive Review M. Rasheed et al. https://doi.org/10.3390/su141811538
235. A Review of Advanced Soil Moisture Monitoring Techniques for Slope Stability Assessment Y. Yao et al. https://doi.org/10.3390/w17030390
236. Continental satellite soil moisture data assimilation improves root-zone moisture analysis for water resources assessment L. Renzullo et al. https://doi.org/10.1016/j.jhydrol.2014.08.008
237. Soil Moisture and Air Humidity Dependence of the Above-Ground Cosmic-Ray Neutron Intensity M. Köhli et al. https://doi.org/10.3389/frwa.2020.544847
238. Evaluating satellite-derived soil moisture data for improving the internal consistency of process-based ecohydrological modelling D. Duethmann et al. https://doi.org/10.1016/j.jhydrol.2022.128462
239. Making sense of cosmic-ray soil moisture measurements and eddy covariance data with regard to crop water use and field water balance E. Wang et al. https://doi.org/10.1016/j.agwat.2018.04.017
240. Satellite Soil Moisture: Review of Theory and Applications in Water Resources P. Srivastava https://doi.org/10.1007/s11269-017-1722-6
241. Validation of Spaceborne and Modelled Surface Soil Moisture Products with Cosmic-Ray Neutron Probes C. Montzka et al. https://doi.org/10.3390/rs9020103
242. Prediction of profile soil moisture for one land use using measurements at a soil depth of other land uses in a karst depression X. Li et al. https://doi.org/10.1007/s11368-018-2138-5
243. Improving the Seasonal Representation of ASCAT Soil Moisture and Vegetation Dynamics in a Temperate Climate I. Pfeil et al. https://doi.org/10.3390/rs10111788
244. Analysing Seasonal Differences between a Soil Water Balance Model and in Situ Soil Moisture Measurementsat Nine Locations Across Europe B. Kurnik et al. https://doi.org/10.1007/s10666-013-9377-z
245. Diverging sensitivity of soil water stress to changing snowmelt timing in the Western U.S. A. Harpold https://doi.org/10.1016/j.advwatres.2016.03.017
246. The merging of radiative transfer based surface soil moisture data from SMOS and AMSR-E R. van der Schalie et al. https://doi.org/10.1016/j.rse.2016.11.026
247. Field scale soil water prediction based on areal soil moisture measurements using cosmic-ray neutron sensing in a karst landscape X. Li et al. https://doi.org/10.1016/j.jhydrol.2021.127395
248. Multiscale and Multivariate Evaluation of Water Fluxes and States over European River Basins O. Rakovec et al. https://doi.org/10.1175/JHM-D-15-0054.1
249. Quantifying the Representativeness Errors Caused by Scale Transformation of Remote Sensing Data in Stochastic Ensemble Data Assimilation F. Liu et al. https://doi.org/10.1109/JSTARS.2022.3149957
250. Mapping soil moisture with the OPtical TRApezoid Model (OPTRAM) based on long-term MODIS observations E. Babaeian et al. https://doi.org/10.1016/j.rse.2018.04.029
251. A global comparison of alternate AMSR2 soil moisture products: Why do they differ? S. Kim et al. https://doi.org/10.1016/j.rse.2015.02.002
252. Accounting for seasonal retrieval errors in the merging of multi-sensor satellite soil moisture products P. Stradiotti et al. https://doi.org/10.1016/j.srs.2025.100242
253. Rainfall estimation from in situ soil moisture observations at several sites in Europe: an evaluation of the SM2RAIN algorithm L. Brocca et al. https://doi.org/10.1515/johh-2015-0016
254. 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
255. Mapping mesoscale soil moisture using a model-data fusion approach J. Peraza et al. https://doi.org/10.1016/j.jhydrol.2025.132768
256. Exploring the use of thermal neutron counts to track orchard phenological development S. Al-Mashharawi et al. https://doi.org/10.3389/frwa.2026.1749654
257. Soil moisture retrieval over agricultural fields from L-band multi-incidence and multitemporal PolSAR observations using polarimetric decomposition techniques H. Shi et al. https://doi.org/10.1016/j.rse.2021.112485
258. Remote Sensing of Complex Permittivity and Penetration Depth of Soils Using P-Band SAR Polarimetry A. Fluhrer et al. https://doi.org/10.3390/rs14122755
259. Effects of Biomass and Soil Water Content Distribution on Cosmic Ray Neutron Probe Measurement Q. Wang et al. https://doi.org/10.3390/w15152766
260. Hyperresolution information and hyperresolution ignorance in modelling the hydrology of the land surface K. Beven et al. https://doi.org/10.1007/s11430-014-5003-4
261. Global Evaluation of SMAP/Sentinel-1 Soil Moisture Products F. Mohseni et al. https://doi.org/10.3390/rs14184624
262. Trends and variability of water balance components over a tropical savanna and Eucalyptus forest in Australia Y. Kang et al. https://doi.org/10.2166/wcc.2021.374
263. Analysis of Cosmic Ray Fluxes at Different Stations during Geomagnetic Storms using Wavelet Based Approaches: Continuous Wavelet Transform and Multi-Resolution Analysis . Chali Idosa Uga et al. https://doi.org/10.1134/S0016793223600418
264. A Merged CYGNSS Soil Moisture Product Using a Minimum Variance Estimator E. Hodges et al. https://doi.org/10.1109/TGRS.2025.3597532
265. REMOTE SENSING TECHNOLOGIES IN ASSESSING SOIL MOISTURE IN FLOOD-PRONE AREAS: REVIEW A. Bondarovich et al. https://doi.org/10.53360/2788-7995-2025-3(19)-12
266. Evaluation and validation of a high spatial resolution satellite soil moisture product over the Continental United States B. Fang et al. https://doi.org/10.1016/j.jhydrol.2020.125043
267. CRONUS-Earth cosmogenic 36Cl calibration S. Marrero et al. https://doi.org/10.1016/j.quageo.2015.10.002
268. Soil water content in southern England derived from a cosmic‐ray soil moisture observing system – COSMOS‐UK J. Evans et al. https://doi.org/10.1002/hyp.10929
269. A 21-year dataset (2000–2020) of gap-free global daily surface soil moisture at 1-km grid resolution C. Zheng et al. https://doi.org/10.1038/s41597-023-01991-w
270. Detection of soil moisture anomalies based on Sentinel-1 F. Greifeneder et al. https://doi.org/10.1016/j.pce.2018.11.009
271. Miscellaneous methods for determination of unfrozen water content in frozen soils S. Feng et al. https://doi.org/10.1016/j.jhydrol.2024.130802
272. Leveraging Limited ISMN Soil Moisture Measurements to Develop the HYDRUS-1D Model and Explore the Potential of Remotely Sensed Precipitation for Soil Moisture Estimates in the Northern Territory, Australia M. Usman & C. Ndehedehe https://doi.org/10.3390/rs17223723
273. A change in perspective: downhole cosmic-ray neutron sensing for the estimation of soil moisture D. Rasche et al. https://doi.org/10.5194/hess-27-3059-2023
274. Investigating temporal field sampling strategies for site-specific calibration of three soil moisture–neutron intensity parameterisation methods J. Iwema et al. https://doi.org/10.5194/hess-19-3203-2015
275. Cosmic-ray neutron sensors provide scale-appropriate soil water content and vegetation observations for eddy covariance stations in agricultural ecosystems C. Brogi et al. https://doi.org/10.1016/j.agrformet.2025.110731
276. A Review of Root Zone Soil Moisture Estimation Methods Based on Remote Sensing M. Li et al. https://doi.org/10.3390/rs15225361
277. Integrated modelling of soil moisture by evaluating backscattering models Dubois, Oh and IoT sensor development for field moisture estimation A. Shakya et al. https://doi.org/10.1007/s40808-023-01693-7
278. Correction efficiency and error characteristics for cosmic-ray soil moisture on mountainous terrain J. Jeong et al. https://doi.org/10.1016/j.jhydrol.2021.126657
279. A Novel Cosmic-Ray Neutron Sensor for Soil Moisture Estimation over Large Areas L. Stevanato et al. https://doi.org/10.3390/agriculture9090202
280. Comparison of Met Office regional model soil moisture with COSMOS‐UK field‐scale in situ observations E. Cooper et al. https://doi.org/10.1002/asl.1236
281. 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
282. First results from the ENTOTO neutron monitor: Quantifying the waiting time distribution R. Strauss et al. https://doi.org/10.1016/j.asr.2022.05.016
283. Multi-method soil moisture monitoring at two temperate forest stands in Germany J. Fäth & C. Kneisel https://doi.org/10.1007/s42452-024-06262-w
284. Development and validation of an operational multi-layered model for estimation of soil moisture at point-scale in South Africa L. Myeni et al. https://doi.org/10.1080/02571862.2021.1970832
285. Improving subseasonal forecast skill in the Norwegian Climate Prediction Model using soil moisture data assimilation A. Nair et al. https://doi.org/10.1007/s00382-024-07444-3
286. Detecting Ground Level Enhancements Using Soil Moisture Sensor Networks A. Hands et al. https://doi.org/10.1029/2021SW002800
287. Using Cosmic-Ray Neutron Probes to Monitor Landscape Scale Soil Water Content in Mixed Land Use Agricultural Systems T. Franz et al. https://doi.org/10.1155/2016/4323742
288. A Comparative Analysis of Remote Sensing Soil Moisture Datasets Fusion Methods: Novel LSTM Approach Versus Widely Used Triple Collocation Technique H. Zhao et al. https://doi.org/10.1109/JSTARS.2024.3455549
289. Soil Texture, Soil Moisture, and Sentinel-1 Backscattering: Towards the Retrieval of Field-Scale Soil Hydrological Properties C. Stanyer et al. https://doi.org/10.3390/rs17030542
290. Soil Texture Often Exerts a Stronger Influence Than Precipitation on Mesoscale Soil Moisture Patterns J. Dong & T. Ochsner https://doi.org/10.1002/2017WR021692
291. Theory and application of measuring mesoscale soil moisture by cosmic-ray fast neutron probe Q. Jiao et al. https://doi.org/10.1088/1755-1315/17/1/012147
292. Sensor Networks, Data Processing, and Inference: The Hydrology Challenge A. Zanella et al. https://doi.org/10.1109/ACCESS.2023.3318739
293. Testing a novel sensor design to jointly measure cosmic-ray neutrons, muons and gamma rays for non-invasive soil moisture estimation S. Gianessi et al. https://doi.org/10.5194/gi-13-9-2024
294. Field-scale moisture estimates using COSMOS sensors: A validation study with temporary networks and Leaf-Area-Indices E. Coopersmith et al. https://doi.org/10.1016/j.jhydrol.2014.07.060
295. Improving Land Surface Hydrological Simulations in China Using CLDAS Meteorological Forcing Data J. Liu et al. https://doi.org/10.1007/s13351-019-9067-0
296. A Novel Lithium Foil Cosmic-Ray Neutron Detector for Measuring Field-Scale Soil Moisture A. Patrignani et al. https://doi.org/10.3389/frwa.2021.673185
297. 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
298. Application of cosmic-ray neutron sensing to monitor soil water content in an alpine meadow ecosystem on the northern Tibetan Plateau X. Zhu et al. https://doi.org/10.1016/j.jhydrol.2016.02.038
299. Uncertainty, sensitivity and improvements in soil moisture estimation with cosmic-ray neutron sensing G. Baroni et al. https://doi.org/10.1016/j.jhydrol.2018.07.053
300. Estimating surface soil moisture from satellite observations using a generalized regression neural network trained on sparse ground-based measurements in the continental U.S Q. Yuan et al. https://doi.org/10.1016/j.jhydrol.2019.124351
301. Variability of hydrogen sources within nominally anhydrous soil across a desert-oasis ecoregion, Northwest China S. Wang et al. https://doi.org/10.1016/j.catena.2024.108187
302. From initiation of convective storms to their impact — the Swabian MOSES 2023 campaign in southwestern Germany J. Handwerker et al. https://doi.org/10.3389/feart.2025.1555755
303. Comment on ‘Examining the variation of soil moisture from cosmic-ray neutron probes footprint: experimental results from a COSMOS-UK site’ by Howells, O.D., Petropoulos, G.P., et al., Environ Earth Sci 82, 41 (2023) L. Scheiffele et al. https://doi.org/10.1007/s12665-023-11186-6
304. Detecting intercepted snow on mountain needleleaf forest canopies using satellite remote sensing Z. Lv & J. Pomeroy https://doi.org/10.1016/j.rse.2019.111222
305. Retrieval of High-Resolution Soil Moisture through Combination of Sentinel-1 and Sentinel-2 Data C. Ma et al. https://doi.org/10.3390/rs12142303
306. A global-scale intercomparison of Triple Collocation Analysis- and ground-based soil moisture time-variant errors derived from different rescaling techniques K. Wu et al. https://doi.org/10.1016/j.rse.2022.113387
307. Review of numerical solution of Richardson–Richards equation for variably saturated flow in soils Y. Zha et al. https://doi.org/10.1002/wat2.1364
308. Mesoscale unfrozen water content measurements in frozen oasis soils using combined cosmic-ray neutron sensing and time domain reflectometry networks Y. Zhang et al. https://doi.org/10.1016/j.agwat.2025.109600
309. Signal contribution of distant areas to cosmic-ray neutron sensors – implications for footprint and sensitivity M. Schrön et al. https://doi.org/10.5194/hess-27-723-2023
310. Virtual Joint Field Campaign: a framework of synthetic landscapes to assess multiscale measurement methods of water storage T. Francke et al. https://doi.org/10.5194/gmd-18-819-2025
311. Bias-blind and bias-aware assimilation of leaf area index into the Noah-MP land surface model over Europe S. Scherrer et al. https://doi.org/10.5194/hess-27-4087-2023
312. Cosmic‐ray Neutron Rover Surveys of Field Soil Moisture and the Influence of Roads M. Schrön et al. https://doi.org/10.1029/2017WR021719
313. Scintillating thermal neutron detectors for cosmic ray soil moisture monitoring P. Stowell et al. https://doi.org/10.1088/1748-0221/16/11/P11039
314. Analysis of cosmic ray-induced neutron spectra recorded during PIRATA oceanographic cruises G. Hubert et al. https://doi.org/10.1016/j.jastp.2025.106690
315. Incoming Neutron Flux Corrections for Cosmic‐Ray Soil and Snow Sensors Using the Global Neutron Monitor Network D. McJannet & D. Desilets https://doi.org/10.1029/2022WR033889
316. 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
317. Future increases in soil moisture drought frequency at UK monitoring sites: merging the JULES land model with observations and convection-permitting UK climate projections M. Szczykulska et al. https://doi.org/10.1088/1748-9326/ad7045
318. A new approach for joint assimilation of cosmic-ray neutron soil moisture and groundwater level data into an integrated terrestrial model F. Li et al. https://doi.org/10.5194/hess-29-6419-2025
319. Land surface model performance using cosmic-ray and point-scale soil moisture measurements for calibration J. Iwema et al. https://doi.org/10.5194/hess-21-2843-2017
320. Global SMOS Soil Moisture Retrievals from The Land Parameter Retrieval Model R. Schalie et al. https://doi.org/10.1016/j.jag.2015.08.005
321. Application and accuracy of cosmic-ray neutron probes in three soil textures on the Loess Plateau, China Q. Wang et al. https://doi.org/10.1016/j.jhydrol.2018.11.073
322. Can Drip Irrigation be Scheduled with Cosmic‐Ray Neutron Sensing? D. Li et al. https://doi.org/10.2136/vzj2019.05.0053
323. A review of globally available data sources for modelling the Water-Energy-Food Nexus J. Lodge et al. https://doi.org/10.1016/j.earscirev.2023.104485
324. Opportunities and challenges in using catchment-scale storage estimates from cosmic ray neutron sensors for rainfall-runoff modelling K. Dimitrova-Petrova et al. https://doi.org/10.1016/j.jhydrol.2020.124878
325. Sensitivity of soil water availability to changing snowmelt timing in the western U.S. A. Harpold & N. Molotch https://doi.org/10.1002/2015GL065855
326. 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
327. 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
328. High-resolution European daily soil moisture derived with machine learning (2003–2020) S. O et al. https://doi.org/10.1038/s41597-022-01785-6
329. Probabilistic prediction by means of the propagation of response variable uncertainty through a Monte Carlo approach in regression random forest: Application to soil moisture regionalization S. Dega et al. https://doi.org/10.3389/fenvs.2023.1009191
330. The Indian COSMOS Network (ICON): Validating L-Band Remote Sensing and Modelled Soil Moisture Data Products D. Upadhyaya et al. https://doi.org/10.3390/rs13030537
331. Ground and Satellite-Based Methods of Measuring Deformation at a UK Landslide Observatory: Comparison and Integration K. Kelevitz et al. https://doi.org/10.3390/rs14122836
332. The International Soil Moisture Network: serving Earth system science for over a decade W. Dorigo et al. https://doi.org/10.5194/hess-25-5749-2021
333. Evaluation of 22 Precipitation and 23 Soil Moisture Products over a Semiarid Area in Southeastern Arizona* S. Stillman et al. https://doi.org/10.1175/JHM-D-15-0007.1
334. Field Testing of Gamma-Spectroscopy Method for Soil Water Content Estimation in an Agricultural Field S. Becker et al. https://doi.org/10.3390/s24072223
335. Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology A. Gruber et al. https://doi.org/10.5194/essd-11-717-2019
336. A Soil Moisture Data Assimilation System for Pakistan Using PODEn4DVar and CLM4.5 T. Mahmood et al. https://doi.org/10.1007/s13351-019-9020-2
337. Techniques for ground-based soil moisture measurement: a detailed overview M. Mukhlisin et al. https://doi.org/10.1007/s12517-021-08263-0
338. Evaluation of Backscattering Models and Support Vector Machine for the Retrieval of Bare Soil Moisture from Sentinel-1 Data J. Ezzahar et al. https://doi.org/10.3390/rs12010072
339. Closing the water balance with cosmic-ray soil moisture measurements and assessing their relation to evapotranspiration in two semiarid watersheds A. Schreiner-McGraw et al. https://doi.org/10.5194/hess-20-329-2016
340. Deriving temporally continuous soil moisture estimations at fine resolution by downscaling remotely sensed product Y. Jin et al. https://doi.org/10.1016/j.jag.2018.01.010
341. A universal calibration function for determination of soil moisture with cosmic-ray neutrons T. Franz et al. https://doi.org/10.5194/hess-17-453-2013
342. Simultaneous soil moisture and properties estimation for a drip irrigated field by assimilating cosmic-ray neutron intensity X. Han et al. https://doi.org/10.1016/j.jhydrol.2016.05.050
343. An assessment of L-band surface soil moisture products from SMOS and SMAP in the tropical areas H. Ma et al. https://doi.org/10.1016/j.rse.2022.113344
344. Combined analysis of satellite and ground data for winter wheat yield forecasting C. Broms et al. https://doi.org/10.1016/j.atech.2022.100107
345. A scaling approach for the assessment of biomass changes and rainfall interception using cosmic-ray neutron sensing G. Baroni & S. Oswald https://doi.org/10.1016/j.jhydrol.2015.03.053
346. The synergies of SMAP enhanced and MODIS products in a random forest regression for estimating 1 km soil moisture over Africa using Google Earth Engine F. Mohseni et al. https://doi.org/10.1080/20964471.2023.2257905
347. Soil moisture measurements by Cosmic-Ray neutron sensing: A critical review M. Köhli https://doi.org/10.1016/j.geoderma.2025.117626
348. Dynamic coupling between soil properties and water content in shrink-swell soils: Effects on surface hydrologic partitioning R. Souza et al. https://doi.org/10.1016/j.advwatres.2024.104630
349. A deep learning approach for SMAP soil moisture downscaling informed by thermal inertia theory M. Xu et al. https://doi.org/10.1016/j.jag.2025.104370
350. Modeling cosmic ray neutron field measurements M. Andreasen et al. https://doi.org/10.1002/2015WR018236
351. Soil water content monitoring for irrigation management: A geostatistical analysis J. Barker et al. https://doi.org/10.1016/j.agwat.2017.03.024
352. A Sensor to Monitor Soil Moisture, Salinity, and Temperature Profiles for Wireless Networks X. Chavanne & J. Frangi https://doi.org/10.3390/jsan13030032
353. Soil moisture monitoring with cosmogenic neutrons: an asset for the development and assessment of soil moisture products in the state of Brandenburg (Germany) M. Heistermann et al. https://doi.org/10.5194/nhess-26-465-2026
354. Application of temporal stability analysis in depth-scaling estimated soil water content by cosmic-ray neutron probe on the northern Tibetan Plateau X. Zhu et al. https://doi.org/10.1016/j.jhydrol.2017.01.019
355. Soil moisture observation in a forested headwater catchment: combining a dense cosmic-ray neutron sensor network with roving and hydrogravimetry at the TERENO site Wüstebach M. Heistermann et al. https://doi.org/10.5194/essd-14-2501-2022
356. Calibration of a large-size wide-range neutron spectrometer A. Cirillo & M. Caresana https://doi.org/10.1140/epjp/s13360-022-02945-8
357. 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
358. COSMOS-UK: national soil moisture and hydrometeorology data for environmental science research H. Cooper et al. https://doi.org/10.5194/essd-13-1737-2021
359. Interaction of convective organization with monsoon precipitation, atmosphere, surface and sea: The 2016 INCOMPASS field campaign in India A. Turner et al. https://doi.org/10.1002/qj.3633
360. On the Variability of the Barometric Effect and Its Relation to Cosmic-Ray Neutron Sensing P. Davies et al. https://doi.org/10.3390/s26030925
361. Assessment of irrigation physics in a land surface modeling framework using non-traditional and human-practice datasets P. Lawston et al. https://doi.org/10.5194/hess-21-2953-2017
362. SMRFR: A global multilayer soil moisture dataset generated using Random Forest from multi-source data Y. Liu et al. https://doi.org/10.1038/s41597-025-05511-w
363. Quantifying mesoscale soil moisture with the cosmic-ray rover B. Chrisman & M. Zreda https://doi.org/10.5194/hess-17-5097-2013
364. Incorporation of globally available datasets into the roving cosmic-ray neutron probe method for estimating field-scale soil water content W. Avery et al. https://doi.org/10.5194/hess-20-3859-2016
365. A time series deep learning algorithm for soil moisture retrieval from Sentinel-1 data using Transformer L. Ndekelu et al. https://doi.org/10.1016/j.srs.2026.100414
366. Study of Secondary Scattering/Albedo Neutron Fields and Their Impacts on SER as Function of Scene Topologies G. Hubert & L. Artola https://doi.org/10.1109/TNS.2019.2943213
367. Background issues encountered by cold-neutron chopper spectrometer AMATERAS T. Kikuchi et al. https://doi.org/10.1016/j.physb.2019.02.015
368. Soil Moisture Estimation by Assimilating L-Band Microwave Brightness Temperature with Geostatistics and Observation Localization X. Han et al. https://doi.org/10.1371/journal.pone.0116435
369. Data Assimilation and Modeling Frontiers in Soil–Water Systems Y. Zhao https://doi.org/10.3390/w18040440
370. Comparing the Accuracy of Soil Moisture Estimates Derived from Bulk and Energy-Resolved Gamma Radiation Measurements S. Akter et al. https://doi.org/10.3390/s25144453
371. Field‐scale soil moisture estimation in complex alpine terrain using cosmic‐ray neutron sensors: Biomass correction, vertical extension, and multi‐scale heterogeneity W. Kang et al. https://doi.org/10.1002/vzj2.70101
372. Collection: Datasets From Real-Time In-Situ Soil Monitoring for Agriculture 2025 K. Moore et al. https://doi.org/10.1109/IEEEDATA.2025.3612373
373. Footprint radius of a cosmic-ray neutron probe for measuring soil-water content and its spatiotemporal variability in an alpine meadow ecosystem X. Zhu et al. https://doi.org/10.1016/j.jhydrol.2018.01.022
374. The Impact of SMOS Soil Moisture Data Assimilation within the Operational Global Flood Awareness System (GloFAS) C. Baugh et al. https://doi.org/10.3390/rs12091490
375. Beyond Helium-3: Instruments for Cosmic-Ray Neutron Sensing Based on Boron-10 Neutron Detectors M. Köhli & J. Weimar https://doi.org/10.3390/instruments10020031
376. State of the Art in Large-Scale Soil Moisture Monitoring T. Ochsner et al. https://doi.org/10.2136/sssaj2013.03.0093
377. Investigating the Potential of Cosmic-Ray Neutron Sensing for Estimating Soil Water Content in Farmland and Mountainous Areas Y. Jiang et al. https://doi.org/10.3390/w15081500
378. Enhanced Estimation of Root Zone Soil Moisture at 1 km Resolution Using SMAR Model and MODIS-Based Downscaled AMSR2 Soil Moisture Data M. Farokhi et al. https://doi.org/10.3390/s21155211
379. Adjusting diurnal error in dielectric-based in situ soil moisture measurements via Fourier time-filtering using land surface model datasets J. Han et al. https://doi.org/10.5194/hess-30-2207-2026
380. Cosmic-ray neutron transport at a forest field site: the sensitivity to various environmental conditions with focus on biomass and canopy interception M. Andreasen et al. https://doi.org/10.5194/hess-21-1875-2017
381. 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
382. Comparison of soil strength measurements of agricultural soils in Nebraska W. Wieder et al. https://doi.org/10.1016/j.jterra.2018.02.003
383. CRNPy: An Open-Source Python Library for Cosmic-Ray Neutron Probe Data Processing J. Peraza Rud et al. https://doi.org/10.21105/joss.06025
384. Impact of Rescaling Approaches in Simple Fusion of Soil Moisture Products M. Afshar et al. https://doi.org/10.1029/2019WR025111
385. Soil Moisture from Fusion of Scatterometer and SAR: Closing the Scale Gap with Temporal Filtering B. Bauer-Marschallinger et al. https://doi.org/10.3390/rs10071030
386. A Decade of Data‐Driven Water Budgets: Synthesis and Bibliometric Review K. Moyers et al. https://doi.org/10.1029/2022WR034310
387. Evapotranspiration prediction for European forest sites does not improve with assimilation of in situ soil water content data L. Strebel et al. https://doi.org/10.5194/hess-28-1001-2024