Filter Set

    Using ISMN data:

  1. A A Nadeem and Y Y Zha and L S Shi and G L Ran and S Ali and Z Jahangir and M M Afzal and M Awais (2022). Multi-Scale Assessment of SMAP Level 3 and Level 4 Soil Moisture Products over the Soil Moisture Network within the ShanDian River (SMN-SDR) Basin, China. Remote Sensing, 14, 982. ARTN 982 10.3390/rs14040982
  2. A Karami and H R Moradi and A Mousivand and A I J M van Dijk and L Renzullo (2022). Using ensemble learning to take advantage of high-resolution radar backscatter in conjunction with surface features to disaggregate SMAP soil moisture product. International Journal of Remote Sensing, 43, 894-914. 10.1080/01431161.2021.2022239
  3. A. Rahman, V. Maggioni, X. Zhang, P. Houser, T. Sauer and D. M. Mocko (2022). The Joint Assimilation of Remotely Sensed Leaf Area Index and Surface Soil Moisture into a Land Surface Model. Remote Sensing,, 14. 10.3390/rs14030437
  4. B. Fang, V. Lakshmi, M. Cosh, P.-W. Liu, R. Bindlish and T. J. Jackson (2022). A global 1-km downscaled SMAP soil moisture product based on thermal inertia theory. Vadose Zone Journal,, 21. https://doi.org/10.1002/vzj2.20182
  5. Claudia Gessner and Erich M Fischer and Urs Beyerle and Reto Knutti (2022). Multi-year drought storylines for Europe and North America from an iteratively perturbed global climate model. Weather and Climate Extremes, 38, 100512. 10.1016/j.wace.2022.100512
  6. D Kumawat and M Olyaei and L Gao and A Ebtehaj (2022). Passive Microwave Retrieval of Soil Moisture Below Snowpack at L-Band Using SMAP Observations. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-16. Artn 4415216 10.1109/Tgrs.2022.3216324
  7. E S Krueger and M R Levi and K O Achieng and J D Bolten and J D Carlson and N C Coops and Z A Holden and B I Magi and A J Rigden and T E Ochsner (2022). Using soil moisture information to better understand and predict wildfire danger: a review of recent developments and outstanding questions. International Journal of Wildland Fire. 10.1071/Wf22056
  8. F. Lei, V. Senyurek, M. Kurum, A. C. Gurbuz, D. Boyd, R. Moorhead, W. T. Crow and O. Eroglu (2022). Quasi-global machine learning-based soil moisture estimates at high spatio-temporal scales using CYGNSS and SMAP observations. Remote Sensing of Environment,, 276. 10.1016/j.rse.2022.113041
  9. F. Meng, M. Luo, C. Sa, M. Wang and Y. Bao (2022). Quantitative assessment of the effects of climate, vegetation, soil and groundwater on soil moisture spatiotemporal variability in the Mongolian Plateau. Sci Total Environ,, 809, 152198. 10.1016/j.scitotenv.2021.152198
  10. F Mohseni and S M Mirmazloumi and M Mokhtarzade and S Jamali and S Homayouni (2022). Global Evaluation of SMAP/Sentinel-1 Soil Moisture Products. Remote Sensing, 14, 4624. ARTN 4624 10.3390/rs14184624
  11. G Kaplan and T Rashid and M Gasparovic and A Pietrelli and V Ferrara (2022). Monitoring war-generated environmental security using remote sensing: A review. Land Degradation & Development, 33, 1513-1526. 10.1002/ldr.4249
  12. G Portal and M Vall-llossera and M Piles and T Jagdhuber and A Camps and M Pablos and C Lopez-Martinez and N N Das and D Entekhabi (2022). Impact of Incidence Angle Diversity on SMOS and Sentinel-1 Soil Moisture Retrievals at Coarse and Fine Scales. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-18. Artn 4412218 10.1109/Tgrs.2022.3187467
  13. H F Zhao and J Li and Q Q Yuan and L P Lin and L W Yue and H Z Xu (2022). Downscaling of soil moisture products using deep learning: Comparison and analysis on Tibetan Plateau. Journal of Hydrology, 607, 127570. ARTN 127570 10.1016/j.jhydrol.2022.127570
  14. H. H. Nguyen, S. Cho and M. Choi (2022). Spatial soil moisture estimation in agro-pastoral transitional zone based on synergistic use of SAR and optical-thermal satellite images. Agricultural and Forest Meteorology,, 312. 10.1016/j.agrformet.2021.108719
  15. Hoang Hai Nguyen and Seongkeun Cho and Minha Choi (2022). Spatial soil moisture estimation in agro-pastoral transitional zone based on synergistic use of SAR and optical-thermal satellite images. Agricultural and Forest Meteorology, 312, 108719. https://doi.org/10.1016/j.agrformet.2021.108719
  16. H T Jiang and S X Chen and X H Li and J A Wu and J Zhang and L F Wu (2022). A Novel Method for Long Time Series Passive Microwave Soil Moisture Downscaling over Central Tibet Plateau. Remote Sensing, 14, 2902. ARTN 2902 10.3390/rs14122902
  17. H T Wang and Q Q Yuan and H F Zhao and H Z Xu (2022). In-situ and triple-collocation based assessments of CYGNSS-R soil moisture compared with satellite and merged estimates quasi-globally. Journal of Hydrology, 615, 128716. ARTN 128716 10.1016/j.jhydrol.2022.128716
  18. I Hajdu and I Yule and M White (2022). The Patitapu Soil Moisture Network (PTSMN) dataset and its deployment in New Zealand's hill country. Agricultural Water Management, 274, 107915. ARTN 107915 10.1016/j.agwat.2022.107915
  19. I P Senanayake and I Y Yeo and G R Hancock and G R Willgoose (2022). A decadal record of soil moisture space-time variability over a south-east Australian catchment. Hydrological Processes, 36, e14770. ARTN e14770 10.1002/hyp.14770
  20. J A Ahmad and B A Forman and S V Kumar (2022). Soil moisture estimation in South Asia via assimilation of SMAP retrievals. Hydrology and Earth System Sciences, 26, 2221-2243. 10.5194/hess-26-2221-2022
  21. J Chung and Y Lee and J Kim and C Jung and S Kim (2022). Soil Moisture Content Estimation Based on Sentinel-1 SAR Imagery Using an Artificial Neural Network and Hydrological Components. Remote Sensing, 14, 465. ARTN 465 10.3390/rs14030465
  22. J. Dong, F. Lei and W. T. Crow (2022). Land transpiration-evaporation partitioning errors responsible for modeled summertime warm bias in the central United States. Nat Commun,, 13, 336. 10.1038/s41467-021-27938-6
  23. J. Lee, S. Park, J. Im, C. Yoo and E. Seo (2022). Improved soil moisture estimation: Synergistic use of satellite observations and land surface models over CONUS based on machine learning. Journal of Hydrology,, 609. 10.1016/j.jhydrol.2022.127749
  24. J T Liu and F Rahmani and K Lawson and C P Shen (2022). A Multiscale Deep Learning Model for Soil Moisture Integrating Satellite and In Situ Data. Geophysical Research Letters, 49, e2021GL096847. ARTN e2021GL096847 10.1029/2021GL096847
  25. K Morrison and W Wagner (2022). Soil Moisture and Soil Depth Retrieval Using the Coupled Phase-Amplitude Behavior of C-Band Radar Backscatter in the Presence of Sub-Surface Scattering. Canadian Journal of Remote Sensing, 48, 779-792. 10.1080/07038992.2022.2120858
  26. L. Gao, A. Ebtehaj, J. Cohen and J.-P. Wigneron (2022). Variability and Changes of Unfrozen Soils Below Snowpack. Geophysical Research Letters,, 49. https://doi.org/10.1029/2021GL095354
  27. L. Gao, Q. Gao, H. Zhang, X. Li, M. J. Chaubell, A. Ebtehaj, L. Shen and J.-P. Wigneron (2022). A deep neural network based SMAP soil moisture product. Remote Sensing of Environment,, 277. 10.1016/j.rse.2022.113059
  28. L. Zhu, R. Si, X. Shen and J. P. Walker (2022). An advanced change detection method for time-series soil moisture retrieval from Sentinel-1. Remote Sensing of Environment,, 279. 10.1016/j.rse.2022.113137
  29. M F Celik and M S Isik and O Yuzugullu and N Fajraoui and E Erten (2022). Soil Moisture Prediction from Remote Sensing Images Coupled with Climate, Soil Texture and Topography via Deep Learning. Remote Sensing, 14, 5584. ARTN 5584 10.3390/rs14215584
  30. M Karamouz and R S Alipour and M Roohinia and M Fereshtehpour (2022). A Remote Sensing Driven Soil Moisture Estimator: Uncertain Downscaling With Geostatistically Based Use of Ancillary Data. Water Resources Research, 58, e2022WR031946. ARTN e2022WR031946 10.1029/2022WR031946
  31. M Y Xu and N Yao and H X Yang and J Xu and A N Hu and L G G de Goncalves and G Liu (2022). Downscaling SMAP soil moisture using a wide & deep learning method over the Continental United States. Journal of Hydrology, 609, 127784. ARTN 127784 10.1016/j.jhydrol.2022.127784
  32. N MacBean and C Bacour and N Raoult and V Bastrikov and E N Koffi and S Kuppel and F Maignan and C Ottle and M Peaucelle and D Santaren and P Peylin (2022). Quantifying and Reducing Uncertainty in Global Carbon Cycle Predictions: Lessons and Perspectives From 15 Years of Data Assimilation Studies With the ORCHIDEE Terrestrial Biosphere Model. Global Biogeochemical Cycles, 36, e2021GB007177. ARTN e2021GB007177 10.1029/2021GB007177
  33. O D Kozhushko and M V Boiko and M Yu Kovbasa and P M Martyniuk and O M Stepanchenko and N V Uvarov (2022). Field scale computer modeling of soil moisture with dynamic nudging assimilation algorithm. Mathematical Modeling and Computing, 9, 203-216. 10.23939/mmc2022.02.203
  34. P. Konkathi and L. Karthikeyan (2022). Error and uncertainty characterization of soil moisture and VOD retrievals obtained from L-band SMAP radiometer. Remote Sensing of Environment,, 280. 10.1016/j.rse.2022.113146
  35. P Leng and Z L Li and Q Y Liao and Y J Geng and Q Y Yan and X Zhang and G F Shang (2022). Enhanced Surface Soil Moisture Retrieval at High Spatial Resolution From the Integration of Satellite Observations and Soil Pedotransfer Functions. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-11. Artn 4513711 10.1109/Tgrs.2022.3222493
  36. P. Luo, Y. Song, X. Huang, H. Ma, J. Liu, Y. Yao and L. Meng (2022). Identifying determinants of spatio-temporal disparities in soil moisture of the Northern Hemisphere using a geographically optimal zones-based heterogeneity model. ISPRS Journal of Photogrammetry and Remote Sensing,, 185, 111-128. 10.1016/j.isprsjprs.2022.01.009
  37. P. Song, Y. Zhang, J. Guo, J. Shi, T. Zhao and B. Tong (2022). A 1?km daily surface soil moisture dataset of enhanced coverage under all-weather conditions over China in 2003�2019. Earth System Science Data,, 14, 2613-2637. 10.5194/essd-14-2613-2022
  38. Q Xie and L Jia and M Menenti and G Hu (2022). Global soil moisture data fusion by Triple Collocation Analysis from 2011 to 2018. Sci Data, 9, 687. 10.1038/s41597-022-01772-x
  39. Q Zhang and Q Q Yuan and T Y Jin and M P Song and F J Sun (2022). SGD-SM 2.0: an improved seamless global daily soil moisture long-term dataset from 2002 to 2022. Earth System Science Data, 14, 4473-4488. 10.5194/essd-14-4473-2022
  40. R Araki and F Branger and I Wiekenkamp and H McMillan (2022). A signature-based approach to quantify soil moisture dynamics under contrasting land-uses. Hydrological Processes, 36, e14553. ARTN e14553 10.1002/hyp.14553
  41. R Khandan and J P Wigneron and S Bonafoni and A P Biazar and M Gholamnia (2022). Assimilation of Satellite-Derived Soil Moisture and Brightness Temperature in Land Surface Models: A Review. Remote Sensing, 14, 770. ARTN 770 10.3390/rs14030770
  42. R Meyer and W M Zhang and S J Kragh and M Andreasen and K H Jensen and R Fensholt and S Stisen and M C Looms (2022). Exploring the combined use of SMAP and Sentinel-1 data for downscaling soil moisture beyond the 1 km scale. Hydrology and Earth System Sciences, 26, 3337-3357. 10.5194/hess-26-3337-2022
  43. R. Souissi, M. Zribi, C. Corbari, M. Mancini, S. Muddu, S. K. Tomer, D. B. Upadhyaya and A. Al Bitar (2022). Integrating process-related information into an artificial neural network for root-zone soil moisture prediction. Hydrology and Earth System Sciences,, 26, 3263-3297. 10.5194/hess-26-3263-2022
  44. S. A. Wakigari and R. Leconte (2022). 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. Remote Sensing,, 14. 10.3390/rs14030776
  45. S Fathololoumi and M K Firozjaei and A Biswas (2022). Improving spatial resolution of satellite soil water index (SWI) maps under clear-sky conditions using a machine learning approach. Journal of Hydrology, 615, 128709. ARTN 128709 10.1016/j.jhydrol.2022.128709
  46. S Feng and X Huang and S Zhao and Z Qin and J Fan and S Zhao (2022). Evaluation of Several Satellite-Based Soil Moisture Products in the Continental US. Sensors (Basel), 22, 9977. 10.3390/s22249977
  47. S. Huang, X. Zhang, N. Chen, H. Ma, J. Zeng, P. Fu, W.-H. Nam and D. Niyogi (2022). Generating high-accuracy and cloud-free surface soil moisture at 1 km resolution by point-surface data fusion over the Southwestern U.S. Agricultural and Forest Meteorology,, 321. 10.1016/j.agrformet.2022.108985
  48. S. Nativel, E. Ayari, N. Rodriguez-Fernandez, N. Baghdadi, R. Madelon, C. Albergel and M. Zribi (2022). Hybrid Methodology Using Sentinel-1/Sentinel-2 for Soil Moisture Estimation. Remote Sensing,, 14. 10.3390/rs14102434
  49. S O and R Orth and U Weber and S K Park (2022). High-resolution European daily soil moisture derived with machine learning (2003-2020). Sci Data, 9, 701. 10.1038/s41597-022-01785-6
  50. S P Li and Y Sawada (2022). Soil moisture-vegetation interaction from near-global in-situ soil moisture measurements. Environmental Research Letters, 17, 114028. ARTN 114028 10.1088/1748-9326/ac9c1f
  51. S V Travova and V M Stepanenko and A I Medvedev and M A Tolstykh and V Y Bogomolov (2022). Quality of Soil Simulation by the INM RAS-MSU Soil Scheme as a Part of the SL-AV Weather Prediction Model. Russian Meteorology and Hydrology, 47, 159-173. 10.3103/S1068373922030013
  52. S Y Bunting and M J Ascott and D C Gooddy and J P Bloomfield (2022). Towards improved global estimates and model representations of water storage in the unsaturated zone. Hydrogeology Journal, 30, 1933-1936. 10.1007/s10040-022-02520-6
  53. S Z Huang and X Zhang and N C Chen and H L Ma and P Fu and J Z Dong and X H Gu and W H Nam and L Xu and G Rab and D Niyogi (2022). A Novel Fusion Method for Generating Surface Soil Moisture Data With High Accuracy, High Spatial Resolution, and High Spatio-Temporal Continuity. Water Resources Research, 58, e2021WR030827. ARTN e2021WR030827 10.1029/2021WR030827
  54. T Yang and W Wan and J D Wang and B J Liu and Z G Sun (2022). A Physics-Based Algorithm to Couple CYGNSS Surface Reflectivity and SMAP Brightness Temperature Estimates for Accurate Soil Moisture Retrieval. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-15. Artn 4409715 10.1109/Tgrs.2022.3156959
  55. V A Walker and A Colliander and J S Kimball (2022). Satellite Retrievals of Probabilistic Freeze-Thaw Conditions From SMAP and AMSR Brightness Temperatures. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-11. Artn 4411211 10.1109/Tgrs.2022.3174807
  56. Wan, W. and Liu, B. and Guo, Z. and Lu, F. and Niu, X. and Li, H. and Ji, R. and Cheng, J. and Li, W. and Chen, X. and Yang, J. and Bai, Z. (2022). Initial Evaluation of the First Chinese GNSS-R Mission BuFeng-1 A/B for Soil Moisture Estimation. IEEE Geoscience and Remote Sensing Letters, 19, 1-5. 10.1109/LGRS.2021.3097003
  57. X D Zheng and S Z Huang and J Peng and G Y Leng and Q Huang and W Fang and Y Guo (2022). Flash Droughts Identification Based on an Improved Framework and Their Contrasting Impacts on Vegetation Over the Loess Plateau, China. Water Resources Research, 58, e2021WR031464. ARTN e2021WR031464 10.1029/2021WR031464
  58. X J Meng and Y B Yang and J Y Zeng and J Peng and J Hu (2022). Improvement of AMSR2 Soil Moisture Retrieval Using a Soil-Vegetation Temperature Decomposition Algorithm. IEEE Geoscience and Remote Sensing Letters, 19, 1-5. Artn 2507805 10.1109/Lgrs.2022.3218518
  59. X J Wu (2022). Assessment of Effective Roughness Parameters for Simulating Sentinel-1A Observation and Retrieving Soil Moisture over Sparsely Vegetated Field. Remote Sensing, 14, 6020. ARTN 6020 10.3390/rs14236020
  60. X W Fan and X S Zhao and X Pan and Y W Liu and Y B Liu (2022). Investigating multiple causes of time-varying SMAP soil moisture biases based on core validation sites data. Journal of Hydrology, 612, 128151. ARTN 128151 10.1016/j.jhydrol.2022.128151
  61. X. Xi, P. Gentine, Q. Zhuang and S. Kim (2022). Evaluating the Variability of Surface Soil Moisture Simulated Within CMIP5 Using SMAP Data. Journal of Geophysical Research: Atmospheres,, 127. https://doi.org/10.1029/2021JD035363
  62. X X Min and Y L Shangguan and D L Li and Z Shi (2022). Improving the fusion of global soil moisture datasets from SMAP, SMOS, ASCAT, and MERRA2 by considering the non-zero error covariance. International Journal of Applied Earth Observation and Geoinformation, 113, 103016. ARTN 103016 10.1016/j.jag.2022.103016
  63. Y Bai and T J Zhao and L Jia and M H Cosh and J C Shi and Z Q Peng and X J Li and J P Wigneron (2022). A multi-temporal and multi-angular approach for systematically retrieving soil moisture and vegetation optical depth from SMOS data. Remote Sensing of Environment, 280, 113190. ARTN 113190 10.1016/j.rse.2022.113190
  64. Y D Ji and Y Li and N Yao and A Biswas and X G Chen and L C Li and A Pulatov and F G Liu (2022). Multivariate global agricultural drought frequency analysis using kernel density estimation. Ecological Engineering, 177, 106550. ARTN 106550 10.1016/j.ecoleng.2022.106550
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  66. Y H Deng and X Y Li and F Z Shi and L N Chai and S J Zhao and M K Ding and Q W Liao (2022). Nonlinear effects of thermokarst lakes on peripheral vegetation greenness across the Qinghai-Tibet Plateau using stable isotopes and satellite detection. Remote Sensing of Environment, 280, 113215. ARTN 113215 10.1016/j.rse.2022.113215
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  69. Y L Cai and P R Fan and S Lang and M Y Li and Y Muhammad and A X Liu (2022). Downscaling of SMAP Soil Moisture Data by Using a Deep Belief Network. Remote Sensing, 14, 5681. ARTN 5681 10.3390/rs14225681
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