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. De Roos, Shannon and De Lannoy, Gabrielle and Raes, Dirk (2021). A Regional Version of the Aquacrop Model Evaluated with Satellite Retrievals and Backscatter Data. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 6572-6574. 10.1109/IGARSS47720.2021.9554287
  5. 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
  6. 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
  7. Fernandez-Moran, Roberto and Piles, María and Camps-Valls, Gustau and Wigneron, Jean-Pierre and Li, Xiaojun and Wang, Mengjia and Fan, Lei and Al-Yaari, Amen and Gómez-Chova, Luis (2021). Towards a Better Understanding of Effective Temperature Modelling in the SMOS-IC Retrieval Algorithm. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 6221-6224. 10.1109/IGARSS47720.2021.9553065
  8. 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
  9. 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
  10. 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
  11. Guglielmo, Magda and Tang, Fiona and Pasut, Chiara and Maggi, Federico (2021). SOIL-WATERGRIDS, mapping dynamic changes in soil moisture and depth of water table from 1970 to 2014. Scientific Data, 8. 10.1038/s41597-021-01032-4
  12. 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
  13. Hegazi, Ehab H. and Yang, Lingbo and Huang, Jingfeng (2021). A Convolutional Neural Network Algorithm for Soil Moisture Prediction from Sentinel-1 SAR Images. Remote Sensing, 13, 24. 10.3390/rs13244964
  14. 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
  15. Hongliang Ma and Jiangyuan Zeng and Xiang Zhang and Peng Fu and Donghai Zheng and Jean-Pierre Wigneron and Nengcheng Chen and Dev Niyogi (2021). Evaluation of six satellite- and model-based surface soil temperature datasets using global ground-based observations. Remote Sensing of Environment, 264, 112605.
  16. 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
  17. 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
  18. 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
  19. 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
  20. Lei, Fangni and Senyurek, Volkan and Kurum, Mehmet and Gurbuz, Ali and Boyd, Dylan and Moorhead, Robert (2021). Quasi-Global GNSS-R Soil Moisture Retrievals at High Spatio-Temporal Resolution from Cygnss and Smap Data. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 6303-6306. 10.1109/IGARSS47720.2021.9554005
  21. 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
  22. Ling, X. and Huang, Y. and Guo, W. and Wang, Y. and Chen, C. and Qiu, B. and Ge, J. and Qin, K. and Xue, Y. and Peng, J. (2021). Comprehensive evaluation of satellite-based and reanalysis soil moisture products using in situ observations over China. Hydrology and Earth System Sciences, 25, 7, 4209--4229. 10.5194/hess-25-4209-2021
  23. L. Karthikeyan and Ashok K. Mishra (2021). Multi-layer high-resolution soil moisture estimation using machine learning over the United States. Remote Sensing of Environment, 266, 112706.
  24. Mahmoodi, Alireza and Rodríguez-Fernández, Nemesio J. and Richaume, Philippe and Kerr, Yann H. (2021). Global Estimation of Surface Soil Moisture Using Neural Networks Trained by In-Situ Measurements and Passive L-Band Telemetry. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 6996-6999. 10.1109/IGARSS47720.2021.9555011
  25. Manali Pal and Rajib Maity (2021). Assimilation of remote sensing based surface soil moisture to develop a spatially varying vertical soil moisture profile database for entire Indian mainland. Journal of Hydrology, 601, 126807.
  26. 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
  27. Min Luo and Chula Sa and Fanhao Meng and Yongchao Duan and Tie Liu and Yuhai Bao (2021). Assessing remotely sensed and reanalysis products in characterizing surface soil moisture in the Mongolian Plateau. International Journal of Digital Earth, 14, 10, 1255-1272. 10.1080/17538947.2020.1820590
  28. Mira, Nuno Cirne and Catalão, João and Nico, Giovanni (2021). Observing Soil Moisture Change Using C-Band Interferometry using Machine Learning Regression. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 6343-6346. 10.1109/IGARSS47720.2021.9554692
  29. Mounir Abassi,El M’kaddem Kheddioui (2021). Estimation of soil moisture using SAR and Optical imagery in Area with Semi-arid and rainy seasons. European Journal of Molecular & Clinical Medicine, 8, 3, 2708-2716
  30. 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
  31. Ontel, Irina and Irimescu, Anisoara and Boldeanu, George and Mihailescu, Denis and Angearu, Claudiu-Valeriu and Nertan, Argentina and Craciunescu, Vasile and Negreanu, Stefan (2021). Assessment of Soil Moisture Anomaly Sensitivity to Detect Drought Spatio-Temporal Variability in Romania. Sensors, 21, 24. 10.3390/s21248371
  32. 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
  33. 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
  34. 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.
  35. Shi, Yajie and Liang, Yueji and Ren, Chao and Lai, Jianmin and Ding, Qin and Hu, Xinmiao (2021). Investigating the Effects of Meteorological Data Rainfall and Temperature on GNSS-R Soil Moisture Inversion. 2021 IEEE Specialist Meeting on Reflectometry using GNSS and other Signals of Opportunity (GNSS+R), 97-100. 10.1109/GNSSR53802.2021.9617574
  36. Shi, Yajie and Ren, Chao and Yan, Zhiheng and Lai, Jianmin (2021). High Spatial-Temporal Resolution Estimation of Ground-Based Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) Soil Moisture Using the Genetic Algorithm Back Propagation (GA-BP) Neural Network. ISPRS International Journal of Geo-Information, 10, 9. 10.3390/ijgi10090623
  37. 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
  38. 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
  39. 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
  40. Tsagkatakis, Grigorios and Moghaddam, Mahta and Tsakalides, Panagiotis (2021). Deep multi-modal satellite and in-situ observation fusion for Soil Moisture retrieval. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 6339-6342. 10.1109/IGARSS47720.2021.9553848
  41. 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
  42. W C Adinugroho and R Imanuddin and H Krisnawati and A Syaugi and P B Santosa and M A Qirom and L B Prasetyo (2021). Exploring the potential of soil moisture maps using Sentinel Imagery as a Proxy for groundwater levels in peat. {IOP} Conference Series: Earth and Environmental Science, 874, 1, 012011. 10.1088/1755-1315/874/1/012011
  43. Yajie Shi and Chao Ren and Zhiheng Yan and Jianmin Lai (2021). Improving soil moisture retrieval from GNSS-interferometric reflectometry: parameters optimization and data fusion via neural network. International Journal of Remote Sensing, 42, 23, 9085-9108. 10.1080/01431161.2021.1988186
  44. 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
  45. Yang, Zhihui and Zhao, Jun and Liu, Jialiang and Wen, Yuanyuan and Wang, Yanqiang (2021). Soil Moisture Retrieval Using Microwave Remote Sensing Data and a Deep Belief Network in the Naqu Region of the Tibetan Plateau. Sustainability, 13, 22. 10.3390/su132212635
  46. 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
  47. 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.
  48. Zhang, Lijie and Zeng, Yijian and Zhuang, Ruodan and Szabó, Brigitta and Manfreda, Salvatore and Han, Qianqian and Su, Zhongbo (2021). In Situ Observation-Constrained Global Surface Soil Moisture Using Random Forest Model. Remote Sensing, 13, 23. 10.3390/rs13234893
  49. Zhang, Ling and Zhang, Zixuan and Xue, Zhaohui and Li, Hao (2021). 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. Water, 13, 15. 10.3390/w13152003
  50. 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