Filter Set

    Using ISMN data:

  1. Anna Balenzano and Francesco Mattia and Giuseppe Satalino and Francesco P. Lovergine and Davide Palmisano and Jian Peng and Philip Marzahn and Urs Wegmüller and Oliver Cartus and Katarzyna Dabrowska-Zielinska and Jan P. Musial and Malcolm W.J. Davidson and Valentijn R.N. Pauwels and Michael H. Cosh and Heather McNairn and Joel T. Johnson and Jeffrey P. Walker and Simon H. Yueh and Dara Entekhabi and Yann H. Kerr and Thomas J. Jackson (2021). Sentinel-1 soil moisture at 1 km resolution: a validation study. Remote Sensing of Environment, 263, 112554. 10.1016/j.rse.2021.112554
  2. Bin Fang and Prakrut Kansara and Chelsea Dandridge and Venkat Lakshmi (2021). Drought monitoring using high spatial resolution soil moisture data over Australia in 2015–2019. Journal of Hydrology, 594, 125960. 10.1016/j.jhydrol.2021.125960
  3. Chen, Y. and Feng, X. and Fu, B. (2021). An improved global remote-sensing-based surface soil moisture (RSSSM) dataset covering 2003--2018. Earth System Science Data, 13, 1, 1--31. 10.5194/essd-13-1-2021
  4. Erlingis, Jessica M. and Rodell, Matthew and Peters-Lidard, Christa D. and Li, Bailing and Kumar, Sujay V. and Famiglietti, James S. and Granger, Stephanie L. and Hurley, John V. and Liu, Pang-Wei and Mocko, David M. (2021). A High-Resolution Land Data Assimilation System Optimized for the Western United States. JAWRA Journal of the American Water Resources Association. 10.1111/1752-1688.12910
  5. Fang, Bin and Lakshmi, Venkat and Cosh, Michael H. and Hain, Christopher (2021). Very High Spatial Resolution Downscaled SMAP Radiometer Soil Moisture in the CONUS Using VIIRS/MODIS Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 4946-4965. 10.1109/JSTARS.2021.3076026
  6. Greifeneder, Felix and Notarnicola, Claudia and Wagner, Wolfgang (2021). A Machine Learning-Based Approach for Surface Soil Moisture Estimations with Google Earth Engine. Remote Sensing, 13, 11. 10.3390/rs13112099
  7. Grillakis, Manolis G. and Koutroulis, Aristeidis G. and Alexakis, Dimitrios D. and Polykretis, Christos and Daliakopoulos, Ioannis N. (2021). Regionalizing Root-Zone Soil Moisture Estimates From ESA CCI Soil Water Index Using Machine Learning and Information on Soil, Vegetation, and Climate. Water Resources Research, 57, 5, e2020WR029249. 10.1029/2020WR029249
  8. Guevara, M. and Taufer, M. and Vargas, R. (2021). Gap-free global annual soil moisture: 15\,km grids for 1991--2018. Earth System Science Data, 13, 4, 1711--1735. 10.5194/essd-13-1711-2021
  9. Gupta, Dileep Kumar and Srivastava, Prashant K. and Singh, Ankita and Petropoulos, George P. and Stathopoulos, Nikolaos and Prasad, Rajendra (2021). SMAP Soil Moisture Product Assessment over Wales, U.K., Using Observations from the WSMN Ground Monitoring Network. Sustainability, 13, 11. 10.3390/su13116019
  10. He, Liming and Chen, Jing M. and Mostovoy, Georgy and Gonsamo, Alemu (2021). Soil Moisture Active Passive Improves Global Soil Moisture Simulation in a Land Surface Scheme and Reveals Strong Irrigation Signals Over Farmlands. Geophysical Research Letters, 48, 8, e2021GL092658. 10.1029/2021GL092658
  11. J. Martínez-Fernández and A. González-Zamora and L. Almendra-Martín (2021). Soil moisture memory and soil properties: An analysis with the stored precipitation fraction. Journal of Hydrology, 593, 125622. 10.1016/j.jhydrol.2020.125622
  12. Kai Wu and Dongryeol Ryu and Lei Nie and Hong Shu (2021). Time-variant error characterization of SMAP and ASCAT soil moisture using Triple Collocation Analysis. Remote Sensing of Environment, 256, 112324. 10.1016/j.rse.2021.112324
  13. Kim, Seokhyeon and Sharma, Ashish and Liu, Yi and Young, Sean (2021). Rethinking Satellite Data Merging: From Averaging to SNR Optimization. IEEE Transactions on Geoscience and Remote Sensing. 10.36227/techrxiv.14214035
  14. Laura Almendra-Martín and José Martínez-Fernández and María Piles and Ángel González-Zamora (2021). Comparison of gap-filling techniques applied to the CCI soil moisture database in Southern Europe. Remote Sensing of Environment, 258, 112377. 10.1016/j.rse.2021.112377
  15. Li, Mingxing and Wu, Peili and Sexton, David MH and Ma, Zhuguo (2021). Potential shifts in climate zones under a future global warming scenario using soil moisture classification. Climate Dynamics, 56, 7, 2071--2092. 10.1007/s00382-020-05576-w
  16. Mina Moradizadeh and Prashant K. Srivastava (2021). A new model for an improved AMSR2 satellite soil moisture retrieval over agricultural areas. Computers and Electronics in Agriculture, 186, 106205. 10.1016/j.compag.2021.106205
  17. Ojha, Nitu and Merlin, Olivier and Suere, Christophe and Escorihuela, Maria José (2021). Extending the Spatio-Temporal Applicability of DISPATCH Soil Moisture Downscaling Algorithm: A Study Case Using SMAP, MODIS and Sentinel-3 Data. Frontiers in Environmental Science, 9, 40. 10.3389/fenvs.2021.555216
  18. Ramsauer, Thomas and Weiß, Thomas and Löw, Alexander and Marzahn, Philip (2021). RADOLAN_API: An Hourly Soil Moisture Data Set Based on Weather Radar, Soil Properties and Reanalysis Temperature Data. Remote Sensing, 13, 9. 10.3390/rs13091712
  19. Raoult, Nina and Ottl{\'e}, Catherine and Peylin, Philippe and Bastrikov, Vladislav and Maugis, Pascal (2021). Evaluating and Optimizing Surface Soil Moisture Drydowns in the ORCHIDEE Land Surface Model at In Situ Locations. Journal of Hydrometeorology, 22, 4, 1025--1043. 10.1175/JHM-D-20-0115.1
  20. Runze Zhang and Seokhyeon Kim and Ashish Sharma and Venkat Lakshmi (2021). Identifying relative strengths of SMAP, SMOS-IC, and ASCAT to capture temporal variability. Remote Sensing of Environment, 252, 112126. https://doi.org/10.1016/j.rse.2020.112126
  21. Steele-Dunne, Susan C. and Hahn, Sebastian and Wagner, Wolfgang and Vreugdenhil, Mariette (2021). Towards Including Dynamic Vegetation Parameters in the EUMETSAT H SAF ASCAT Soil Moisture Products. Remote Sensing, 13, 8. 10.3390/rs13081463
  22. Sungmin, O and Orth, Rene (2021). Global soil moisture data derived through machine learning trained with in-situ measurements. Scientific Data, 8, 1, 1--14. 10.1038/s41597-021-00964-1
  23. Sun, Hao and Cui, Yajing (2021). Evaluating Downscaling Factors of Microwave Satellite Soil Moisture Based on Machine Learning Method. Remote Sensing, 13, 1. 10.3390/rs13010133
  24. van der Schalie, Robin and van der Vliet, Mendy and Rodríguez-Fernández, Nemesio and Dorigo, Wouter A. and Scanlon, Tracy and Preimesberger, Wolfgang and Madelon, Rémi and de Jeu, Richard A. M. (2021). L-Band Soil Moisture Retrievals Using Microwave Based Temperature and Filtering. Towards Model-Independent Climate Data Records. Remote Sensing, 13, 13. 10.3390/rs13132480
  25. Yangxiaoyue Liu and Yuke Zhou and Ning Lu and Ronglin Tang and Naijing Liu and Yong Li and Ji Yang and Wenlong Jing and Chenghu Zhou (2021). Comprehensive assessment of Fengyun-3 satellites derived soil moisture with in-situ measurements across the globe. Journal of Hydrology, 594, 125949. 10.1016/j.jhydrol.2020.125949
  26. Yao, Panpan and Lu, Hui and Shi, Jiancheng and Zhao, Tianjie and Yang, Kun and Cosh, Michael H and Gianotti, Daniel J Short and Entekhabi, Dara (2021). A long term global daily soil moisture dataset derived from AMSR-E and AMSR2 (2002--2019). Scientific data, 8, 1, 1--16. 10.1038/s41597-021-00925-8
  27. Yawei Wang and Pei Leng and Jian Peng and Philip Marzahn and Ralf Ludwig (2021). Global assessments of two blended microwave soil moisture products CCI and SMOPS with in-situ measurements and reanalysis data. International Journal of Applied Earth Observation and Geoinformation, 94, 102234. https://doi.org/10.1016/j.jag.2020.102234
  28. Zhang, Q. and Yuan, Q. and Li, J. and Wang, Y. and Sun, F. and Zhang, L. (2021). Generating seamless global daily AMSR2 soil moisture (SGD-SM) long-term products for the years 2013--2019. Earth System Science Data, 13, 3, 1385--1401. 10.5194/essd-13-1385-2021