61 citations as recorded by crossref.

1. Low‐Rank Gap Filling and Downscaling for SMAP Soil Moisture Datasets K. Beale et al. https://doi.org/10.1002/eco.70024
2. Multivariate remotely sensed and in-situ data assimilation for enhancing community WRF-Hydro model forecasting P. Abbaszadeh et al. https://doi.org/10.1016/j.advwatres.2020.103721
3. Mechanism-learning coupling paradigms for parameter inversion and simulation in earth surface systems H. Shen & L. Zhang https://doi.org/10.1007/s11430-022-9999-9
4. Spatial Downscaling and Gap-Filling of SMAP Soil Moisture to High Resolution Using MODIS Surface Variables and Machine Learning Approaches over ShanDian River Basin, China A. Nadeem et al. https://doi.org/10.3390/rs15030812
5. Deep learning in environmental remote sensing: Achievements and challenges Q. Yuan et al. https://doi.org/10.1016/j.rse.2020.111716
6. Surface soil moisture retrieval based on transfer learning using SAR data on a local scale E. Hemmati & M. Sahebi https://doi.org/10.1080/01431161.2024.2329529
7. Spatial Downscaling of Satellite-Based Soil Moisture Products Using Machine Learning Techniques: A Review I. Senanayake et al. https://doi.org/10.3390/rs16122067
8. A review of forward modelling and retrieval approaches for forest soil moisture and vegetation optical depth using L-band radiometry A. Colliander et al. https://doi.org/10.1016/j.rse.2025.115158
9. Improving SMAP freeze-thaw retrievals for pavements using effective soil temperature from GEOS-5: Evaluation against in situ road temperature data over the U.S S. Kraatz et al. https://doi.org/10.1016/j.rse.2019.111545
10. An Integrated Learning Framework for Seamless High-Resolution Soil Moisture Estimation Y. Jing et al. https://doi.org/10.1109/TGRS.2024.3461717
11. 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
12. A Multiscale Deep Learning Model for Soil Moisture Integrating Satellite and In Situ Data J. Liu et al. https://doi.org/10.1029/2021GL096847
13. Long-Term and High-Resolution Global Time Series of Brightness Temperature from Copula-Based Fusion of SMAP Enhanced and SMOS Data C. Lorenz et al. https://doi.org/10.3390/rs10111842
14. Soil-Permittivity Estimation Under Grassland Using Machine-Learning and Polarimetric Decomposition Techniques R. Hansch et al. https://doi.org/10.1109/TGRS.2020.3010104
15. Estimating and Downscaling ESA-CCI Soil Moisture Using Multi-Source Remote Sensing Images and Stacking-Based Ensemble Learning Algorithms in the Shandian River Basin, China L. Wang & Y. Gao https://doi.org/10.3390/rs17040716
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17. How accurately can we retrieve irrigation timing and water amounts from (satellite) soil moisture? L. Zappa et al. https://doi.org/10.1016/j.jag.2022.102979
18. Soil moisture downscaling in the state of Oklahoma: Employing advanced machine learning S. Koujani et al. https://doi.org/10.1016/j.jastp.2025.106454
19. A Bayesian Deep Image Prior Downscaling Approach for High-Resolution Soil Moisture Estimation Y. Fang et al. https://doi.org/10.1109/JSTARS.2022.3177081
20. Spatiotemporal Downscaling of SMAP Soil Moisture With an Integrated Machine Learning and Land Data Assimilation Framework J. Tian et al. https://doi.org/10.1109/JSTARS.2026.3707028
21. Improved downscaling of microwave-based surface soil moisture over a typical subtropical monsoon region L. Li et al. https://doi.org/10.1016/j.jhydrol.2023.130431
22. Estimating catchment scale soil moisture at a high spatial resolution: Integrating remote sensing and machine learning I. Senanayake et al. https://doi.org/10.1016/j.scitotenv.2021.145924
23. 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
24. An Intercomparison Study of Algorithms for Downscaling SMAP Radiometer Soil Moisture Retrievals L. Fang et al. https://doi.org/10.1175/JHM-D-19-0034.1
25. 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
26. An Approach for Downscaling SMAP Soil Moisture by Combining Sentinel-1 SAR and MODIS Data J. Bai et al. https://doi.org/10.3390/rs11232736
27. Gap-free global annual soil moisture: 15 km grids for 1991–2018 M. Guevara et al. https://doi.org/10.5194/essd-13-1711-2021
28. Deriving Field Scale Soil Moisture from Satellite Observations and Ground Measurements in a Hilly Agricultural Region L. Zappa et al. https://doi.org/10.3390/rs11222596
29. Near‐real‐time one‐kilometre Soil Moisture Active Passive soil moisture data product J. Yin et al. https://doi.org/10.1002/hyp.13857
30. Mapping near-real-time soil moisture dynamics over Tasmania with transfer learning M. Widyastuti et al. https://doi.org/10.5194/soil-11-287-2025
31. Using of Remote Sensing-Based Auxiliary Variables for Soil Moisture Scaling and Mapping Z. Zhao et al. https://doi.org/10.3390/rs14143373
32. Global land surface evapotranspiration monitoring by ETMonitor model driven by multi-source satellite earth observations C. Zheng et al. https://doi.org/10.1016/j.jhydrol.2022.128444
33. An Operational Downscaling Method of Solar-Induced Chlorophyll Fluorescence (SIF) for Regional Drought Monitoring Z. Hong et al. https://doi.org/10.3390/agriculture12040547
34. 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
35. A Semi-Physical Approach for Downscaling Satellite Soil Moisture Data in a Typical Cold Alpine Area, Northwest China Z. Cao et al. https://doi.org/10.3390/rs13030509
36. 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
37. Downscaling CLDAS Soil Moisture Product by Integrating Sentinel-1 and Sentinel-2 Data over Agricultural Area H. Ma et al. https://doi.org/10.1080/07038992.2022.2114891
38. A Remote Sensing Driven Soil Moisture Estimator: Uncertain Downscaling With Geostatistically Based Use of Ancillary Data M. Karamouz et al. https://doi.org/10.1029/2022WR031946
39. A New Approach for Soil Moisture Downscaling in the Presence of Seasonal Difference R. Yan & J. Bai https://doi.org/10.3390/rs12172818
40. An Overview of Machine-Learning Methods for Soil Moisture Estimation M. Taheri et al. https://doi.org/10.3390/w17111638
41. 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
42. Estimating soil moisture at farm scale with high spatial resolution: integrating remote sensing data, and machine learning D. Wang et al. https://doi.org/10.1016/j.jhydrol.2025.134707
43. Spatial Downscaling Methods of Soil Moisture Based on Multisource Remote Sensing Data and Its Application S. Chen et al. https://doi.org/10.3390/w11071401
44. Disaggregating satellite soil moisture products based on soil thermal inertia: A comparison of a downscaling model built at two spatial scales I. Senanayake et al. https://doi.org/10.1016/j.jhydrol.2020.125894
45. Assessing the Potential of Combined SMAP and In-Situ Soil Moisture for Improving Streamflow Forecast S. Wakigari & R. Leconte https://doi.org/10.3390/hydrology10020031
46. Mapping Heavy Metals in Agricultural Soils Using a Hybrid HASM–ANN Model: A Case Study of the Eastern Longquan Mountain Region, China K. Wang et al. https://doi.org/10.3390/app16115402
47. Assessing the influence of model inputs on performance of the EMT + VS soil moisture downscaling model for a large foothills region in Northern Colorado S. Fischer et al. https://doi.org/10.1016/j.jhydrol.2024.132397
48. Space–time variability in soil moisture droughts in the Himalayan region S. Nepal et al. https://doi.org/10.5194/hess-25-1761-2021
49. A machine learning-based approach for generating high-resolution soil moisture from SMAP products Y. Zhang et al. https://doi.org/10.1080/10106049.2022.2105406
50. Development of the global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M) Z. Zhang et al. https://doi.org/10.5194/essd-13-2001-2021
51. Improving SMAP soil moisture spatial resolution in different climatic conditions using remote sensing data F. Imanpour et al. https://doi.org/10.1007/s10661-023-12107-7
52. 地球表层特征参量反演与模拟的机理<bold>-</bold>学习耦合范式 焕. 沈 & 良. 张 https://doi.org/10.1360/SSTe-2022-0089
53. A Spatial Downscaling Method for Remote Sensing Soil Moisture Using Adaptive Weighted Stacking Strategy M. Xing et al. https://doi.org/10.1109/TGRS.2025.3626415
54. A Value-Consistent Method for Downscaling SMAP Passive Soil Moisture With MODIS Products Using Self-Adaptive Window F. Wen et al. https://doi.org/10.1109/TGRS.2019.2941696
55. A Microwave–Optical Multi-Stage Synergistic Daily 30 m Soil Moisture Downscaling Framework H. Xie et al. https://doi.org/10.3390/rs17223677
56. Improving high-resolution soil moisture mapping in China via dense ground-based observations and multi-source data fusion Z. Yao et al. https://doi.org/10.1016/j.agwat.2026.110570
57. Daily High-Resolution Land Surface Freeze/Thaw Detection Using Sentinel-1 and AMSR2 Data J. Wang et al. https://doi.org/10.3390/rs14122854
58. Enhancing Spatial Resolution of SMAP Soil Moisture Products through Spatial Downscaling over a Large Watershed: A Case Study for the Susquehanna River Basin in the Northeastern United States S. Wakigari & R. Leconte https://doi.org/10.3390/rs14030776
59. Enhanced Surface Soil Moisture Retrieval at High Spatial Resolution From the Integration of Satellite Observations and Soil Pedotransfer Functions P. Leng et al. https://doi.org/10.1109/TGRS.2022.3222493
60. Potential of Remote Sensing Images for Soil Moisture Retrieving Using Ensemble Learning Methods in Vegetation-Covered Area Y. Gao et al. https://doi.org/10.1109/JSTARS.2023.3311096
61. A Multi-Layer Perceptron Approach to Downscaling Geostationary Land Surface Temperature in Urban Areas A. Hurduc et al. https://doi.org/10.3390/rs17010045