Filter Set

    Using ISMN data:

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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
  18. 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
  19. 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
  20. 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
  21. Y. Kwon, S. V. Kumar, M. Navari, D. M. Mocko, E. M. Kemp, J. W. Wegiel, J. V. Geiger and R. Bindlish (2022). Irrigation characterization improved by the direct use of SMAP soil moisture anomalies within a data assimilation system. Environmental Research Letters,, 17. 10.1088/1748-9326/ac7f49
  22. Y. Zhang, S. Liang, Z. Zhu, H. Ma and T. He (2022). Soil moisture content retrieval from Landsat 8 data using ensemble learning. ISPRS Journal of Photogrammetry and Remote Sensing,, 185, 32-47. 10.1016/j.isprsjprs.2022.01.005