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  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
  29. A. Gruber and G. De Lannoy and C. Albergel and A. Al-Yaari and L. Brocca and J.-C. Calvet and A. Colliander and M. Cosh and W. Crow and W. Dorigo and C. Draper and M. Hirschi and Y. Kerr and A. Konings and W. Lahoz and K. McColl and C. Montzka and J. Muñoz-Sabater and J. Peng and R. Reichle and P. Richaume and C. Rüdiger and T. Scanlon and R. {van der Schalie} and J.-P. Wigneron and W. Wagner (2020). Validation practices for satellite soil moisture retrievals: What are (the) errors?. Remote Sensing of Environment, 244, 111806. https://doi.org/10.1016/j.rse.2020.111806
  30. Akhilesh S. Nair and Rohit Mangla and Thiruvengadam P and J. Indu (2020). Remote sensing data assimilation. Hydrological Sciences Journal, 1--33. 10.1080/02626667.2020.1761021
  31. Albergel, C. and Zheng, Y. and Bonan, B. and Dutra, E. and Rodriguez-Fernandez, N. and Munier, S. and Draper, C. and de Rosnay, P. and Munoz-Sabater, J. and Balsamo, G. and Fairbairn, D. and Meurey, C. and Calvet, J.-C. (2020). Data assimilation for continuous global assessment of severe conditions over terrestrial surfaces. Hydrology and Earth System Sciences, 24, 9, 4291--4316. 10.5194/hess-24-4291-2020
  32. Beck, H. E. and Pan, M. and Miralles, D. G. and Reichle, R. H. and Dorigo, W. A. and Hahn, S. and Sheffield, J. and Karthikeyan, L. and Balsamo, G. and Parinussa, R. M. and van Dijk, A. I. J. M. and Du, J. and Kimball, J. S. and Vergopolan, N. and Wood, E. F. (2020). Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors. Hydrology and Earth System Sciences Discussions, 2020, 1--35. 10.5194/hess-2020-184
  33. Bin Fang and Venkataraman Lakshmi and Rajat Bindlish and Thomas J. Jackson and Pang-Wei Liu (2020). Evaluation and validation of a high spatial resolution satellite soil moisture product over the Continental United States. Journal of Hydrology, 588, 125043. https://doi.org/10.1016/j.jhydrol.2020.125043
  34. Chen, Y. and Feng, X. and Fu, B. (2020). A new dataset of satellite observation-based global surface soil moisture covering 2003--2018. Earth System Science Data Discussions, 2020, 1--46. 10.5194/essd-2020-59
  35. Clara Chew and Eric Small (2020). Estimating inundation extent using CYGNSS data: A conceptual modeling study. Remote Sensing of Environment, 246, 111869. https://doi.org/10.1016/j.rse.2020.111869
  36. Deng, Yuanhong and Wang, Shijie and Bai, Xiaoyong and Luo, Guangjie and Wu, Luhua and Chen, Fei and Wang, Jinfeng and Li, Chaojun and Yang, Yujie and Hu, Zeyin and others (2020). Vegetation greening intensified soil drying in some semi-arid and arid areas of the world. Agricultural and Forest Meteorology, 292, 108103. https://doi.org/10.1016/j.agrformet.2020.108103
  37. Foucras, Myriam and Zribi, Mehrez and Albergel, Clement and Baghdadi, Nicolas and Calvet, Jean-Christophe and Pellarin, Thierry (2020). Estimating 500-m Resolution Soil Moisture Using Sentinel-1 and Optical Data Synergy. Water, 12, 3, 866. 10.3390/w12030866
  38. Hagan, Daniel Fiifi Tawia and Wang, Guojie and Kim, Seokhyeon and Parinussa, Robert M. and Liu, Yi and Ullah, Waheed and Bhatti, Asher Samuel and Ma, Xiaowen and Jiang, Tong and Su, Buda (2020). Maximizing Temporal Correlations in Long-Term Global Satellite Soil Moisture Data-Merging. Remote Sensing, 12, 13, 2164. 10.3390/rs12132164
  39. Han, Yizhi and Bai, Xiaojing and Shao, Wei and Wang, Jie (2020). Retrieval of Soil Moisture by Integrating Sentinel-1A and MODIS Data over Agricultural Fields. Water, 12, 6. 10.3390/w12061726
  40. Herbert, Christoph and Pablos, Miriam and Vall-llossera, Merce and Camps, Adriano and Martinez-Fernandez, Jose (2020). Analyzing Spatio-Temporal Factors to Estimate the Response Time between SMOS and In-Situ Soil Moisture at Different Depths. Remote Sensing, 12, 16, 2614. 10.3390/rs12162614
  41. Jianzhi Dong and Wade T. Crow and Kenneth J. Tobin and Michael H. Cosh and David D. Bosch and Patrick J. Starks and Mark Seyfried and Chandra Holifield Collins (2020). Comparison of microwave remote sensing and land surface modeling for surface soil moisture climatology estimation. Remote Sensing of Environment, 242, 111756. https://doi.org/10.1016/j.rse.2020.111756
  42. Kovacevic, Jovan and Cvijetinovic, Zeljko and Stancic, Nikola and Brodic, Nenad and Mihajlovic, Dragan (2020). New Downscaling Approach Using ESA CCI SM Products for Obtaining High Resolution Surface Soil Moisture. Remote Sensing, 12, 7, 1119. 10.3390/rs12071119
  43. Lei Xu and Nengcheng Chen and Xiang Zhang and Hamid Moradkhani and Chong Zhang and Chuli Hu (2020). In-situ and triple-collocation based evaluations of eight global root zone soil moisture products. Remote Sensing of Environment, 254, 112248. https://doi.org/10.1016/j.rse.2020.112248
  44. L. Gao and M. Sadeghi and A. F. Feldman and A. Ebtehaj (2020). A Spatially Constrained Multichannel Algorithm for Inversion of a First-Order Microwave Emission Model at L-Band. IEEE Transactions on Geoscience and Remote Sensing, 1--13. 10.1109/TGRS.2020.2987490
  45. Li, Mingxing and Wu, Peili and Ma, Zhuguo (2020). A comprehensive evaluation of soil moisture and soil temperature from third-generation atmospheric and land reanalysis data sets. International Journal of Climatology. 10.1002/joc.6549
  46. Lin, Liao-Fan and Pu, Zhaoxia (2020). Improving Near-Surface Short-Range Weather Forecasts Using Strongly Coupled Land--Atmosphere Data Assimilation with GSI-EnKF. Monthly Weather Review, 148, 7, 2863--2888. 10.1175/MWR-D-19-0370.1
  47. Lun Gao and Morteza Sadeghi and Ardeshir Ebtehaj (2020). Microwave retrievals of soil moisture and vegetation optical depth with improved resolution using a combined constrained inversion algorithm: Application for SMAP satellite. Remote Sensing of Environment, 239, 111662. https://doi.org/10.1016/j.rse.2020.111662
  48. Ma, Chunfeng and Li, Xin and McCabe, Matthew F. (2020). Retrieval of High-Resolution Soil Moisture through Combination of Sentinel-1 and Sentinel-2 Data. Remote Sensing, 12, 14, 2303. 10.3390/rs12142303
  49. Mimeau, L. and Tramblay, Y. and Brocca, L. and Massari, C. and Camici, S. and Finaud-Guyot, P. (2020). Modeling the response of soil moisture to climate variability in the Mediterranean region. Hydrology and Earth System Sciences Discussions, 2020, 1--29. 10.5194/hess-2020-302
  50. M. Link and M. Drusch and K. Scipal (2020). Soil Moisture Information Content in SMOS, SMAP, AMSR2, and ASCAT Level-1 Data Over Selected In Situ Sites. IEEE Geoscience and Remote Sensing Letters, 17, 7, 1213--1217. 10.1109/LGRS.2019.2940633
  51. Moreno-Martinez, Alvaro and Piles, Maria and Munoz-Mari, Jordi and Campos-Taberner, Manuel and Adsuara, Jose E. and Mateo, Anna and Perez-Suay, Adrian and Javier Garcia-Haro, Francisco and Camps-Valls, Gustau (2020). Machine Learning Methods for Spatial and Temporal Parameter Estimation. Hyperspectral Image Analysis: Advances in Machine Learning and Signal Processing, 5--35, Cham.. 10.1007/978-3-030-38617-7_2
  52. Morteza Sadeghi and Lun Gao and Ardeshir Ebtehaj and Jean-Pierre Wigneron and Wade T. Crow and John T. Reager and Arthur W. Warrick (2020). Retrieving global surface soil moisture from GRACE satellite gravity data. Journal of Hydrology, 584, 124717. https://doi.org/10.1016/j.jhydrol.2020.124717
  53. Naz, Bibi S. and Kollet, Stefan and Franssen, Harrie-Jan Hendricks and Montzka, Carsten and Kurtz, Wolfgang (2020). A 3km spatially and temporally consistent European daily soil moisture reanalysis from 2000 to 2015. Scientific Data, 7, 1, 111. 10.1038/s41597-020-0450-6
  54. Peijun Li and Yuanyuan Zha and Chak-Hau Michael Tso and Liangsheng Shi and Danyang Yu and Yonggen Zhang and Wenzhi Zeng (2020). Data assimilation of uncalibrated soil moisture measurements from frequency-domain reflectometry. Geoderma, 374, 114432. https://doi.org/10.1016/j.geoderma.2020.114432
  55. Portal, Gerard and Jagdhuber, Thomas and Vall-llossera, Merce and Camps, Adriano and Pablos, Miriam and Entekhabi, Dara and Piles, Maria (2020). Assessment of Multi-Scale SMOS and SMAP Soil Moisture Products across the Iberian Peninsula. Remote Sensing, 12, 3, 570. 10.3390/rs12030570
  56. Sara Sadri and Ming Pan and Yoshihide Wada and Noemi Vergopolan and Justin Sheffield and James S. Famiglietti and Yann Kerr and Eric Wood (2020). A global near-real-time soil moisture index monitor for food security using integrated SMOS and SMAP. Remote Sensing of Environment, 246, 111864. https://doi.org/10.1016/j.rse.2020.111864
  57. Sebastian Helgert and Samiro Khodayar (2020). Improvement of the soil-atmosphere interactions and subsequent heavy precipitation modelling by enhanced initialization using remotely sensed 1 km soil moisture information. Remote Sensing of Environment, 246, 111812. https://doi.org/10.1016/j.rse.2020.111812
  58. Sebastien Verrier (2020). Multifractal and multiscale entropy scaling of in-situ soil moisture time series: Study of SMOSMANIA network data, southwestern France. Journal of Hydrology, 585, 124821. https://doi.org/10.1016/j.jhydrol.2020.124821
  59. Senyurek, Volkan and Lei, Fangni and Boyd, Dylan and Kurum, Mehmet and Gurbuz, Ali Cafer and Moorhead, Robert (2020). Machine Learning-Based CYGNSS Soil Moisture Estimates over ISMN sites in CONUS. Remote Sensing, 12, 7, 1168. 10.3390/rs12071168
  60. Solander, K. C. and Newman, B. D. and Carioca de Araujo, A. and Barnard, H. R. and Berry, Z. C. and Bonal, D. and Bretfeld, M. and Burban, B. and Antonio Candido, L. and Celleri, R. and Chambers, J. Q. and Christoffersen, B. O. and Detto, M. and Dorigo, W. A. and Ewers, B. E. and Jose Filgueiras Ferreira, S. and Knohl, A. and Leung, L. R. and McDowell, N. G. and Miller, G. R. and Terezinha Ferreira Monteiro, M. and Moore, G. W. and Negron-Juarez, R. and Saleska, S. R. and Stiegler, C. and Tomasella, J. and Xu, C. (2020). The pantropical response of soil moisture to El Nino. Hydrology and Earth System Sciences, 24, 5, 2303--2322. 10.5194/hess-24-2303-2020
  61. Souissi, Roïya and Al Bitar, Ahmad and Zribi, Mehrez (2020). Accuracy and Transferability of Artificial Neural Networks in Predicting in Situ Root-Zone Soil Moisture for Various Regions across the Globe. Water, 12, 11. 10.3390/w12113109
  62. Suman, Swati and Srivastava, Prashant K. and Petropoulos, George P. and Pandey, Dharmendra K. and O{\textquoteright}Neill, Peggy E. (2020). Appraisal of SMAP Operational Soil Moisture Product from a Global Perspective. Remote Sensing, 12, 12, 1977. 10.3390/rs12121977
  63. Sun, Hao and Zhou, Baichi and Zhang, Chuanjun and Liu, Hongxing and Yang, Bo (2020). DSCALE\_mod16: A Model for Disaggregating Microwave Satellite Soil Moisture with Land Surface Evapotranspiration Products and Gridded Meteorological Data. Remote Sensing, 12, 6, 980. 10.3390/rs12060980
  64. Wang, Lei and Fang, Shibo and Pei, Zhifang and Zhu, Yongchao and Khoi, Dao Nguyen and Han, Wei (2020). Using FengYun-3C VSM Data and Multivariate Models to Estimate Land Surface Soil Moisture. Remote Sensing, 12, 6, 1038. 10.3390/rs12061038
  65. Wang, Yakun and Shi, Liangsheng and Lin, Lin and Holzman, Mauro and Carmona, Facundo and Zhang, Qiuru (2020). A robust data-worth analysis framework for soil moisture flow by hybridizing sequential data assimilation and machine learning. Vadose Zone Journal, 19, 1, e20026. 10.1002/vzj2.20026
  66. Xaver, Angelika and Zappa, Luca and Rab, Gerhard and Pfeil, Isabella and Vreugdenhil, Mariette and Hemment, Drew and Dorigo, Wouter Arnoud (2020). Evaluating the suitability of the consumer low-cost Parrot Flower Power soil moisture sensor for scientific environmental applications. Geoscientific Instrumentation, Methods and Data Systems, 9, 1, 117--139. 10.5194/gi-9-117-2020
  67. Yangxiaoyue Liu and Wenlong Jing and Qi Wang and Xiaolin Xia (2020). Generating high-resolution daily soil moisture by using spatial downscaling techniques: a comparison of six machine learning algorithms. Advances in Water Resources, 141, 103601. https://doi.org/10.1016/j.advwatres.2020.103601
  68. Yuanhong Deng and Shijie Wang and Xiaoyong Bai and Guangjie Luo and Luhua Wu and Fei Chen and Jinfeng Wang and Qin Li and Chaojun Li and Yujie Yang and Zeyin Hu and Shiqi Tian (2020). Spatiotemporal dynamics of soil moisture in the karst areas of China based on reanalysis and observations data. Journal of Hydrology, 585, 124744. https://doi.org/10.1016/j.jhydrol.2020.124744
  69. Zappa, Luca and Woods, Mel and Hemment, Drew and Xaver, Angelika and Dorigo, Wouter (2020). Evaluation of Remotely Sensed Soil Moisture Products using Crowdsourced Measurements. SPIE, 660 -- 672. 10.1117/12.2571913
  70. Abbaszadeh, P., Moradkhani, H., & Zhan, X. (2019). Downscaling SMAP radiometer soil moisture over the CONUS using an ensemble learning method. Water Resources Research, 55, 324-344. https://doi.org/10.1029/2018WR023354
  71. Adamolekun, O. (2019). Field validation of proximal sensors on a typical Prairie field. https://hdl.handle.net/1993/33950
  72. Afshar, M., Yilmaz, M., & Crow, W. (2019). Impact of Rescaling Approaches in Simple Fusion of Soil Moisture Products. Water Resources Research, 55, 7804-7825. https://doi.org/10.1029/2019WR025111
  73. Albergel, C., Zheng, Y., Bonan, B., Dutra, E., Rodríguez-Fernández, N., Munier, S., Draper, C., de Rosnay, P., Muñoz-Sabater, J., Balsamo, G., Fairbairn, D., Meurey, C., & Calvet, J.-C. (2019). Data assimilation for continuous global assessment of severe conditions over terrestrial surfaces. Hydrol. Earth Syst. Sci. Discuss. In review. https://doi.org/10.5194/hess-2019-534
  74. Almagbile, A., Zeitoun, M., Hazaymeh, K., Sammour, H. A., & Sababha, N. (2019). Statistical analysis of estimated and observed soil moisture in sub-humid climate in north-western Jordan. Environmental monitoring and assessment, 191, 96. https://doi.org/10.1007/s10661-019-7230-9
  75. Al-Yaari, A., Ducharne, A., Cheruy, F., Crow, W.T., & Wigneron, J.P. (2019). Satellite-based soil moisture provides missing link between summertime precipitation and surface temperature biases in CMIP5 simulations over conterminous United States. Sci Rep, 9, 1657. https://doi.org/10.1038/s41598-018-38309-5
  76. Al-Yaari, A., Wigneron, J.P., Dorigo, W., Colliander, A., Pellarin, T., Hahn, S., Mialon, A., Richaume, P., Fernandez-Moran, R., Fan, L., Kerr, Y.H., & De Lannoy, G. (2019). Assessment and inter-comparison of recently developed/reprocessed microwave satellite soil moisture products using ISMN ground-based measurements. Remote Sensing of Environment, 224, 289-303. https://doi.org/10.1016/j.rse.2019.02.008
  77. Araghi, A., Adamowski, J., Martinez, C.J., & Olesen, J.E. (2019). Projections of future soil temperature in northeast Iran. Geoderma, 349, 11-24. https://doi.org/10.1016/j.geoderma.2019.04.034
  78. Ardeshir Ebtehaj and Rafael L. Bras (2019). A physically constrained inversion for high-resolution passive microwave retrieval of soil moisture and vegetation water content in L-band. Remote Sensing of Environment, 233, 111346. https://doi.org/10.1016/j.rse.2019.111346
  79. Arora, B., Dwivedi, D., Faybishenko, B., Jana, R. B., & Wainwright, H. M. (2019). Understanding and predicting vadose zone processes. Reviews in Mineralogy and Geochemistry, 85, 303-328. https://doi.org/10.2138/rmg.2019.85.10
  80. Asmuß, T., Bechtold, M., & Tiemeyer, B. (2019). On the Potential of Sentinel-1 for High Resolution Monitoring of Water Table Dynamics in Grasslands on Organic Soils. Remote Sensing 2019, 11, 1659. https://doi.org/10.3390/rs11141659
  81. Babaeian, E., Sadeghi, M., Jones, S.B., Montzka, C., Vereecken, H., & Tuller, M. (2019). Ground. Proximal and Satellite Remote Sensing of Soil Moisture. Reviews of Geophysics. https://doi.org/10.1029/2018rg000618
  82. Baczyk, M. K., Gromek, A., Kulpa, K., Gurdak, R., & Grzybowski, P. (2019). Neural Network-Based Soil Moisture Estimation Using Satellite SAR Data. 2019 Signal Processing Symposium (SPSympo). https://doi.org/10.1109/SPS.2019.8881987
  83. Baik, J., Zohaib, M., Kim, U., Aadil, M., & Choi, M. (2019). Agricultural drought assessment based on multiple soil moisture products. Journal of arid environments, 167, 43-55. https://doi.org/10.1016/j.jaridenv.2019.04.007
  84. Bai, L., Long, D., & Yan, L. (2019). Estimation of surface soil moisture with downscaled land surface temperatures using a data fusion approach for heterogeneous agricultural land. Water Resources Research, 55, 1105-1128. https://doi.org/10.1029/2018WR024162
  85. Bai, L., Lv, X., & Li, X. (2019). Evaluation of Two SMAP Soil Moisture Retrievals Using Modeled-and Ground-Based Measurements. Remote Sensing 2019, 11, 2891. https://doi.org/10.3390/rs11242891
  86. Baldwin, D., Manfreda, S., Lin, H., & Smithwick, E. A. (2019). Estimating Root Zone Soil Moisture Across the Eastern United States with Passive Microwave Satellite Data and a Simple Hydrologic Model. Remote Sensing 2019, 11, 2013. https://doi.org/10.3390/rs11172013
  87. Barbosa, L. R., Lira, N. B. d., Coelho, V. H. R., Silans, A. M. B. P. d., Gadêlha, A. N., & Almeida, C. d. N. (2019). Stability of Soil Moisture Patterns Retrieved at Different Temporal Resolutions in a Tropical Watershed. Revista Brasileira de Ciência do Solo, 43. https://doi.org/10.1590/18069657rbcs20180236
  88. Berthelin, R., Rinderer, M., Andreo, B., Baker, A., Kilian, D., Leonhardt, G., Lotz, A., Lichtenwoehrer, K., Mudarra, M., Padilla, I. Y., Pantoja Agreda, F., Rosolem, R., Vale, A., & Hartmann, A. (2019). A soil moisture monitoring network to characterize karstic recharge and evapotranspiration at five representative sites across the globe. Geosci. Instrum. Method. Data Syst., 9, 11–23. https://doi.org/10.5194/gi-9-11-2020
  89. Blyverket, J. (2019). Land Surface Data Assimilation of Satellite Derived Surface Soil Moisture: Towards an Integrated Representation of the Arctic Hydrological Cycle. https://bora.uib.no/handle/1956/20940
  90. Blyverket, J., Hamer, P., Bertino, L., Albergel, C., Fairbairn, D., & Lahoz, W. (2019). An Evaluation of the EnKF vs. EnOI and the Assimilation of SMAP, SMOS and ESA CCI Soil Moisture Data over the Contiguous US. Remote Sensing, 11. https://doi.org/10.3390/rs11050478
  91. Blyverket, J., Hamer, P. D., Bertino, L., Albergel, C., Fairbairn, D., & Lahoz, W. A. (2019). Improving soil moisture estimates over the contiguous US using satellite retrievals and ensemble based data assimilation techniques. Preprints. https://doi.org/10.20944/preprints201901.0224.v1
  92. Caldwell, T. G., Bongiovanni, T., Cosh, M. H., Jackson, T. J., Colliander, A., Abolt, C. J., et al. (2019). The Texas Soil Observation Network: A Comprehensive Soil Moisture Dataset for Remote Sensing and Land Surface Model Validation. Vadose Zone Journal, 18. https://doi.org/10.2136/vzj2019.04.0034
  93. Carrera, M. L., Bilodeau, B., Bélair, S., Abrahamowicz, M., Russell, A., & Wang, X. (2019). Assimilation of passive L-band microwave brightness temperatures in the Canadian Land Data Assimilation System: Impacts on short-range warm season Numerical Weather Prediction. Journal of Hydrometeorology, 20, 1053-1079. https://doi.org/10.1175/JHM-D-18-0133.1
  94. Chen, Y., Sun, L., Wang, W., & Pei, Z. (2019). Application of Sentinel 2 data for drought monitoring in Texas, America. 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics). https://doi.org/10.1109/Agro-Geoinformatics.2019.8820491
  95. Chipade, R. A. (2019). Soil moisture retrieval using indigenously developed NavIC-GPS-SBAS receiver. Coordinates. https://www.researchgate.net/publication/333641239
  96. Chuchón Prado, R. (2019). Láminas de riego en el cultivo de papa (Solanum tuberosum L.) variedad “unica” mediante riego por goteo en La Molina. Universidad Nacional Agraria La Molina. http://repositorio.lamolina.edu.pe/handle/UNALM/4245
  97. Crow, W. T. (2019). Utility of soil moisture data products for natural disaster applications. Elsevier Extreme Hydroclimatic Events and Multivariate Hazards in a Changing Environment. https://doi.org/10.1016/B978-0-12-814899-0.00003-1
  98. Dasgupta, K., Das, K., & Padmanaban, M. (2019). Soil Moisture Evaluation Using Machine Learning Techniques on Synthetic Aperture Radar (SAR) And Land Surface Model. IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. https://doi.org/10.1109/IGARSS.2019.8900220
  99. Das, K., Singh, J., & Hazra, J. (2019). Comparison of Smap, Gldas and Simulated Soil Moisture Datasets Over A Malaysian Region. IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. https://doi.org/10.1109/IGARSS.2019.8900589
  100. Das, N. N., Entekhabi, D., Dunbar, R. S., Chaubell, M. J., Colliander, A., Yueh, S., et al. (2019). The SMAP and Copernicus Sentinel 1A/B microwave active-passive high resolution surface soil moisture product. Remote Sensing of Environment, 233, 111380. https://doi.org/10.1016/j.rse.2019.111380
  101. Deng, K.A.K., Lamine, S., Pavlides, A., Petropoulos, G.P., Bao, Y., Srivastava, P.K., & Guan, Y. (2019). Large scale operational soil moisture mapping from passive MW radiometry: SMOS product evaluation in Europe & USA. International Journal of Applied Earth Observation and Geoinformation, 80, 206-217. https://doi.org/10.1016/j.jag.2019.04.015
  102. Deng, K., Lamine, S., Pavlides, A., Petropoulos, G., Srivastava, P., Bao, Y., Hristopulos, D., & Anagnostopoulos, V. (2019). Operational Soil Moisture from ASCAT in Support of Water Resources Management. Remote Sensing, 11. https://doi.org/10.3390/rs11050579
  103. Deng, Y., Wang, S., Bai, X., Wu, L., Cao, Y., Li, H., (2019). Comparison of soil moisture products from microwave remote sensing, land model, and reanalysis using global ground observations. Hydrological Processes, 34, 836– 851. https://doi.org/10.1002/hyp.13636
  104. Di, Chongli and Wang, Tiejun and Istanbulluoglu, Erkan and Jayawardena, A. and Li, Si-Liang and Chen, Xi (2019). Deterministic chaotic dynamics in soil moisture across Nebraska. Journal of Hydrology, 578. 10.1016/j.jhydrol.2019.124048
  105. Dorigo, W. A., Himmelbauer, I., Xaver, A., Zappa, L., Aberer, D., Schremmer, L., Preimesberger, W., Scanlon, T. (2019). The International Soil Moisture Network (ISMN): Status and Update. IDEAS+ CAL/VAL Workshop #7, Wageningen, Netherlands.
  106. Draper, Clara and Reichle, Rolf H. (2019). Assimilation of Satellite Soil Moisture for Improved Atmospheric Reanalyses. Monthly Weather Review, 147, 6, 2163-2188. 10.1175/MWR-D-18-0393.1
  107. Eroglu, Orhan and Kurum, Mehmet and Boyd, Dylan and Gurbuz, Ali Cafer (2019). High Spatio-Temporal Resolution CYGNSS Soil Moisture Estimates Using Artificial Neural Networks. Remote Sensing, 11, 19, 2272. 10.3390/rs11192272
  108. Fairbairn, David and de Rosnay, Patricia and Browne, Philip A. (2019). The New Stand-Alone Surface Analysis at ECMWF: Implications for Land–Atmosphere DA Coupling. Journal of Hydrometeorology, 20, 10, 2023-2042. 10.1175/JHM-D-19-0074.1
  109. Fairbairn, David and de Rosnay, Patricia and Browne, Philip and Albergel, Clement and Isaksen, Lars (2019). H SAF root-zone soil moisture products from ASCAT assimilation.
  110. Fan, Dong and Wu, Hua and Dong, Guotao and Jiang, Xiaoguang and Xue, Huazhu (2019). A Temporal Disaggregation Approach for TRMM Monthly Precipitation Products Using AMSR2 Soil Moisture Data. Remote Sensing, 11, 24, 2962. 10.3390/rs11242962
  111. Fang, Bin and Lakshmi, Venkat and Bindlish, Rajat and Jackson, Thomas J and Liu, Pang-Wei (2019). Downscaling and Validation of SMAP Radiometer Soil Moisture in CONUS. IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 6194-6197. 10.1109/IGARSS.2019.8897943
  112. Ford, Trent W. and Quiring, Steven M. (2019). Comparison of Contemporary In Situ, Model, and Satellite Remote Sensing Soil Moisture With a Focus on Drought Monitoring. Water Resources Research, 55, 2, 1565-1582. 10.1029/2018WR024039
  113. Fu, Haoyang and Zhou, Tingting and Sun, Chenglin (2019). Evaluation and Analysis of AMSR2 and FY3B Soil Moisture Products by an In Situ Network in Cropland on Pixel Scale in the Northeast of China. Remote Sensing, 11, 7, 868. 10.3390/rs11070868
  114. Ghilain, Nicolas and Arboleda, Alirio and Batelaan, Okke and Ardö, Jonas and Trigo, Isabel and Barrios, Jose-Miguel and Gellens-Meulenberghs, Francoise (2019). A New Retrieval Algorithm for Soil Moisture Index from Thermal Infrared Sensor On-Board Geostationary Satellites over Europe and Africa and Its Validation. Remote Sensing, 11, 17, 1968. 10.3390/rs11171968
  115. Gruber, A., De Lannoy, G., & Crow, W. (2019). A Monte Carlo based adaptive Kalman filtering framework for soil moisture data assimilation. Remote Sensing of Environment, 228, 105-114. https://doi.org/10.1016/j.rse.2019.04.003
  116. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., & Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology. Earth System Science Data, 11, 717-739. https://doi.org/10.5194/essd-11-717-2019
  117. Gu, Y., Gao, M., & Zhao, G. (2019). Earth Observation Payloads and Data Applications of Tiangong-2 Space Laboratory: Technology, Method and Application. Springer. https://doi.org/10.1007/978-981-13-3501-3_1
  118. Himmelbauer, I., Aberer, D., Schremmer, L., Xaver, A., Zappa, L., Dorigo, W., Preimesberger, W., Scanlon, T. (2019). The International Soil Moisture Network (ISMN) in support of Earth Observation services. GTN-H Panel Meeting 9th Session, Koblenz, Germany.
  119. Himmelbauer, I., Aberer, D., Zappa, L., Xaver, A., Bauer-Marschallinger, B., Sabia, R., Dorigo, W. A. (2019). The International Soil Moisture Network (ISMN) in support of high-resolution soil moisture product validation. Living Planet Symposium - ESA, Milan, Italy.
  120. Himmelbauer, I., Aberer, D., Zappa, L., Xaver, A., Dorigo, W. A., Sabia, R. (2019). The International Soil Moisture Network (ISMN) in support of Satellite Soil Moisture Validation. European Geoscience Union (EGU) General Assembly 2019, Vienna, Austria.
  121. Hongtao, J., Huanfeng, S., Xinghua, L., Chao, Z., Huiqin, L., & Fangni, L. (2019). Extending the SMAP 9-km soil moisture product using a spatio-temporal fusion model. Remote Sensing of Environment, 231. https://doi.org/10.1016/j.rse.2019.111224
  122. Hu, T., Zhao, T., Zhao, K., & Shi, J. (2019). A continuous global record of near-surface soil freeze/thaw status from AMSR-E and AMSR2 data. International Journal of Remote Sensing, 40, 6993-7016. https://doi.org/10.1080/01431161.2019.1597307
  123. Kang, C.S., Kanniah, K.D., & Kerr, Y.H. (2019). Calibration of SMOS Soil Moisture Retrieval Algorithm: A Case of Tropical Site in Malaysia. IEEE Transactions on Geoscience and Remote Sensing, 57, 3827-3839. https://doi.org/10.1109/tgrs.2018.2888535
  124. Kiyoung, K., Sungwon, J., & Yeongil, L. (2019). A Study for establishment of soil moisture station in mountain terrain (1): the representative analysis of soil moisture for construction of Cosmic-ray verification system. Journal of Korea Water Resources Association, 52, 51-60. https://doi.org/10.3741/JKWRA.2019.52.1.51
  125. Kovács, K.Z., Hemment, D., Woods, M., van der Velden, N.K., Xaver, A., Giesen, R.H., Burton, V.J., Garrett, N.L., Zappa, L., Long, D., Dobos, E., & Skalsky, R. (2019). Citizen observatory based soil moisture monitoring – the GROW example. Hungarian Geographical Bulletin, 68, 119-139. https://doi.org/10.15201/hungeobull.68.2.2
  126. Kumar, S., Newman, M., Wang, Y., & Livneh, B. (2019). Potential Reemergence of Seasonal Soil Moisture Anomalies in North America. Journal of Climate, 32, 2707-2734. https://doi.org/10.1175/jcli-d-18-0540.1
  127. Liao, W., Wang, D., Wang, G., Xia, Y., & Liu, X. (2019). Quality Control and Evaluation of the Observed Daily Data in the North American Soil Moisture Database. Journal of Meteorological Research, 33, 501-518. https://doi.org/10.1007/s13351-019-8121-2
  128. Luo, W., Xu, X., Liu, W., Liu, M., Li, Z., Peng, T., Xu, C., Zhang, Y., & Zhang, R. (2019). UAV based soil moisture remote sensing in a karst mountainous catchment. Catena, 174, 478-489. https://doi.org/10.1016/j.catena.2018.11.017
  129. Ma, H., Zeng, J., Chen, N., Zhang, X., Cosh, M.H., & Wang, W. (2019). Satellite surface soil moisture from SMAP, SMOS, AMSR2 and ESA CCI: A comprehensive assessment using global ground-based observations. Remote Sensing of Environment, 231. https://doi.org/10.1016/j.rse.2019.111215
  130. Myeni, L., Moeletsi, M.E., & Clulow, A.D. (2019). Present status of soil moisture estimation over the African continent. Journal of Hydrology: Regional Studies, 21, 14-24. https://doi.org/10.1016/j.ejrh.2018.11.004
  131. Nguyen, H.H., Jeong, J., & Choi, M. (2019). Extension of cosmic-ray neutron probe measurement depth for improving field scale root-zone soil moisture estimation by coupling with representative in-situ sensors. Journal of Hydrology, 571, 679-696. https://doi.org/10.1016/j.jhydrol.2019.02.018
  132. Ochsner, T.E., Linde, E., Haffner, M., & Dong, J. (2019). Mesoscale Soil Moisture Patterns Revealed Using a Sparse In Situ Network and Regression Kriging. Water Resources Research. https://doi.org/10.1029/2018wr024535
  133. Osenga, E. C., Arnott, J. C., Endsley, K. A., & Katzenberger, J. W. (2019). Bioclimatic and soil moisture monitoring across elevation in a mountain watershed: Opportunities for research and resource management. Water Resources Research, 55
  134. Pal, M., & Maity, R. (2019). Development of a spatially-varying Statistical Soil Moisture Profile model by coupling memory and forcing using hydrologic soil groups. Journal of Hydrology, 570, 141-155. https://doi.org/10.1016/j.jhydrol.2018.12.042
  135. Quintana Seguí, Pere and Barella-Ortiz, Anaïs and Regueiro-Sanfiz, Sabela and Miguez-Macho, Gonzalo (2019). The Utility of Land-Surface Model Simulations to Provide Drought Information in a Water Management Context Using Global and Local Forcing Datasets. Water Resources Management. 10.1007/s11269-018-2160-9
  136. Rodríguez-Fernández, N., de Rosnay, P., Albergel, C., Richaume, P., Aires, F., Prigent, C., & Kerr, Y. (2019). SMOS Neural Network Soil Moisture Data Assimilation in a Land Surface Model and Atmospheric Impact. Remote Sensing, 11. https://doi.org/10.3390/rs11111334
  137. Ropelewski, C. F., Arkin, P. A.: (2019). Climate Analysis. Cambridge University Press, ISBN 978-0-521-89616. https://doi.org/10.1017/9781139034746
  138. Sadeghi, M., Tuller, M., Warrick, A.W., Babaeian, E., Parajuli, K., Gohardoust, M.R., & Jones, S.B. (2019). An analytical model for estimation of land surface net water flux from near-surface soil moisture observations. Journal of Hydrology, 570, 26-37. https://doi.org/10.1016/j.jhydrol.2018.12.038
  139. Sun, H., Cai, C., Liu, H., & Yang, B. (2019). Microwave and Meteorological Fusion: A method of Spatial Downscaling of Remotely Sensed Soil Moisture. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12, 1107-1119. https://doi.org/10.1109/jstars.2019.2901921
  140. Tian, J., Zhang, B., He, C., Han, Z., Bogena, H.R., & Huisman, J.A. (2019). Dynamic response patterns of profile soil moisture wetting events under different land covers in the Mountainous area of the Heihe River Watershed. Northwest China. Agricultural and Forest Meteorology, 271, 225-239. https://doi.org/10.1016/j.agrformet.2019.03.006
  141. Tian, S., Renzullo, L.J., van Dijk, A.I.J.M., Tregoning, P., & Walker, J.P. (2019). Global joint assimilation of GRACE and SMOS for improved estimation of root-zone soil moisture and vegetation response. Hydrology and Earth System Sciences, 23, 1067-1081. https://doi.org/10.5194/hess-23-1067-2019
  142. Wang, C., Wang, Z., Kong, Y., Zhang, F., Yang, K., & Zhang, T. (2019). Most of the Northern Hemisphere Permafrost Remains under Climate Change. Sci Rep, 9, 3295. https://doi.org/10.1038/s41598-019-39942-4
  143. Wang, L., He, B., Bai, X., Xing, M. (2019). Assessment of Different Vegetation Parameters for Parameterizing the Coupled Water Cloud Model and Advanced Integral Equation Model for Soil Moisture Retrieval Using Time Series Sentinel-1A Data. Photogrammetric Engineering & Remote Sensing, 85, 7, 43-54(12). https://doi.org/10.14358/PERS.85.1.43
  144. Wang, Q., van der Velde, R., Ferrazzoli, P., Chen, X., Bai, X., & Su, Z. (2019). Mapping soil moisture across the Tibetan Plateau plains using Aquarius active and passive L-band microwave observations. International Journal of Applied Earth Observation and Geoinformation, 77, 108-118. https://doi.org/10.1016/j.jag.2019.01.005
  145. Wang, Y., Yang, J., Chen, Y., Fang, G., Duan, W., Li, Y., & De Maeyer, P. (2019). Quantifying the Effects of Climate and Vegetation on Soil Moisture in an Arid Area. China. Water, 11. https://doi.org/10.3390/w11040767
  146. Xia, Y., Hao, Z., Shi, C., Li, Y., Meng, J., Xu, T., Wu, X., & Zhang, B. (2019). Regional and Global Land Data Assimilation Systems Innovations, Challenges, and Prospects. Journal of Meteorological Research, 33, 159-189. https://doi.org/10.1007/s13351-019-8172-4
  147. Zaussinger, F., Dorigo, W., Gruber, A., Tarpanelli, A., Filippucci, P., & Brocca, L. (2019). Estimating irrigation water use over the contiguous United States by combining satellite and reanalysis soil moisture data. Hydrology and Earth System Sciences, 23, 897-923. https://doi.org/10.5194/hess-23-897-2019
  148. Zeng, L., Hu, S., Xiang, D., Zhang, X., Li, D., Li, L., & Zhang, T. (2019). Multilayer Soil Moisture Mapping at a Regional Scale from Multisource Data via a Machine Learning Method. Remote Sensing, 11. https://doi.org/10.3390/rs11030284
  149. Zhang, Q., Fan, K., Singh, V.P., Song, C., Xu, C.Y., & Sun, P. (2019). Is Himalayan-Tibetan Plateau "drying"? Historical estimations and future trends of surface soil moisture. Sci Total Environ, 658, 374-384. https://doi.org/10.1016/j.scitotenv.2018.12.209
  150. Zhang, R., Kim, S., & Sharma, A. (2019). A comprehensive validation of the SMAP Enhanced Level-3 Soil Moisture product using ground measurements over varied climates and landscapes. Remote Sensing of Environment, 223, 82-94. https://doi.org/10.1016/j.rse.2019.01.015
  151. Zhang, S., Meurey, C., & Calvet, J.-C. (2019). Identification of soil-cooling rains in southern France from soil temperature and soil moisture observations. Atmospheric Chemistry and Physics, 19, 5005-5020. https://doi.org/10.5194/acp-19-5005-2019
  152. Zhu, L., Wang, H., Tong, C., Liu, W., & Du, B. (2019). Evaluation of ESA Active, Passive and Combined Soil Moisture Products Using Upscaled Ground Measurements. Sensors (Basel), 19. https://doi.org/10.3390/s19122718
  153. Al-Yaari, A., Dayau, S., Chipeaux, C., Aluome, C., Kruszewski, A., Loustau, D., & Wigneron, J.P. (2018). The AQUI Soil Moisture Network for Satellite Microwave Remote Sensing Validation in South-Western France. Remote Sensing, 10. https://doi.org/10.3390/rs10111839
  154. Bao, Y., Lin, L., Wu, S., Kwal Deng, K.A., & Petropoulos, G.P. (2018). Surface soil moisture retrievals over partially vegetated areas from the synergy of Sentinel-1 and Landsat 8 data using a modified water-cloud model. International Journal of Applied Earth Observation and Geoinformation, 72, 76-85. https://doi.org/10.1016/j.jag.2018.05.026
  155. Belfort, B., Toloni, I., Ackerer, P., Cotel, S., Viville, D., & Lehmann, F. (2018). Vadose Zone Modeling in a Small Forested Catchment: Impact of Water Pressure Head Sampling Frequency on 1D-Model Calibration. Geosciences, 8. https://doi.org/10.3390/geosciences8020072
  156. Benninga, H.-J.F., Carranza, C.D.U., Pezij, M., van Santen, P., van der Ploeg, M.J., Augustijn, D.C.M., & van der Velde, R. (2018). The Raam regional soil moisture monitoring network in the Netherlands. Earth System Science Data, 10, 61-79. https://doi.org/10.5194/essd-10-61-2018
  157. Bogena, H.R., Montzka, C., Huisman, J.A., Graf, A., Schmidt, M., Stockinger, M., von Hebel, C., Hendricks-Franssen, H.J., van der Kruk, J., Tappe, W., Lücke, A., Baatz, R., Bol, R., Groh, J., Pütz, T., Jakobi, J., Kunkel, R., Sorg, J., & Vereecken, H. (2018). The TERENO-Rur Hydrological Observatory: A Multiscale Multi-Compartment Research Platform for the Advancement of Hydrological Science. Vadose Zone Journal, 17. https://doi.org/10.2136/vzj2018.03.0055
  158. Cassardo, C., Park, S., O, S., & Galli, M. (2018). Projected Changes in Soil Temperature and Surface Energy Budget Components over the Alps and Northern Italy. Water, 10. https://doi.org/10.3390/w10070954
  159. Dabrowska-Zielinska, K., Musial, J., Malinska, A., Budzynska, M., Gurdak, R., Kiryla, W., Bartold, M., & Grzybowski, P. (2018). Soil Moisture in the Biebrza Wetlands Retrieved from Sentinel-1 Imagery. Remote Sensing, 10. https://doi.org/10.3390/rs10121979
  160. Dirmeyer, P.A., Chen, L., Wu, J., Shin, C.S., Huang, B., Cash, B.A., Bosilovich, M.G., Mahanama, S., Koster, R.D., Santanello, J.A., Ek, M.B., Balsamo, G., Dutra, E., & Lawrence, D.M. (2018). Verification of land-atmosphere coupling in forecast models, reanalyses and land surface models using flux site observations. J Hydrometeorol, 19, 375-392. https://doi.org/10.1175/JHM-D-17-0152.1
  161. Ebrahimi, M., Alavipanah, S.K., Hamzeh, S., Amiraslani, F., Neysani Samany, N., & Wigneron, J.-P. (2018). Exploiting the synergy between SMAP and SMOS to improve brightness temperature simulations and soil moisture retrievals in arid regions. Journal of Hydrology, 557, 740-752. https://doi.org/10.1016/j.jhydrol.2017.12.051
  162. Esposito, G., Matano, F., & Scepi, G. (2018). Analysis of Increasing Flash Flood Frequency in the Densely Urbanized Coastline of the Campi Flegrei Volcanic Area. Frontiers in Earth Science, 6, Italy.. https://doi.org/10.3389/feart.2018.00063
  163. Fang, B., Lakshmi, V., Bindlish, R., & Jackson, T. (2018). AMSR2 Soil Moisture Downscaling Using Temperature and Vegetation Data. Remote Sensing, 10. https://doi.org/10.3390/rs10101575
  164. Fersch, B., Jagdhuber, T., Schrön, M., Völksch, I., & Jäger, M. (2018). Synergies for Soil Moisture Retrieval Across Scales From Airborne Polarimetric SAR, Cosmic Ray Neutron Roving, and an In Situ Sensor Network. Water Resources Research, 54, 9364-9383. https://doi.org/10.1029/2018wr023337
  165. Franz, T., Mengistu, M., Everson, C., & Vather, T. (2018). Cosmic ray neutrons provide an innovative technique for estimating intermediate scale soil moisture. South African Journal of Science, 114. https://doi.org/10.17159/sajs.2018/20170422
  166. González-Zamora, Á., Sánchez, N., Pablos, M., & Martínez-Fernández, J. (2018). CCI soil moisture assessment with SMOS soil moisture and in situ data under different environmental conditions and spatial scales in Spain. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2018.02.010
  167. Greifeneder, F., Khamala, E., Sendabo, D., Wagner, W., Zebisch, M., Farah, H., & Notarnicola, C. (2018). Detection of soil moisture anomalies based on Sentinel-1. Physics and Chemistry of the Earth Parts A/B/C. https://doi.org/10.1016/j.pce.2018.11.009
  168. Gruber, A., Crow, W.T., & Dorigo, W.A. (2018). Assimilation of Spatially Sparse In Situ Soil Moisture Networks into a Continuous Model Domain. Water Resources Research, 54, 1353-1367. https://doi.org/10.1002/2017wr021277
  169. Gumbricht, T. (2018). Detecting Trends in Wetland Extent from MODIS Derived Soil Moisture Estimates. Remote Sensing, 10. https://doi.org/10.3390/rs10040611
  170. Himmelbauer, I., Dorigo, W.A., Xaver, A., Zappa, L., Aberer, D., Preimesberger, W., Scanlon, T., Buttinger, P. (2018). The International Soil Moisture Validation Network (ISMN): Status and Update. IDEAS+ CAL/VAL Workshop #6, Davos, Switzerland.
  171. Himmelbauer, I., Xaver, A., Zappa, L., and Dorigo, W.A. (2018). The International Soil Moisture Network and its benefits for soil moisture product validation . European Geoscience Union (EGU) General Assembly 2018, Vienna, Austria.
  172. Himmelbauer, I., Zappa, L., Xaver, A., Scanlon, T., Aberer, D., Sabia, R., Dorigo, W.A. (2018). The Interantional Soil Moisture Network in support of EO services. The 5th Satellite Soil Moisture Validation and Application Workshop, Washington DC area, USA.
  173. Himmelbauer, I., Zappa, L., Xaver, A., Scanlon, T., Aberer, D., Sabia, R., Dorigo, W.A. (2018). The International Soil Moisture Network in support of SMAP calibration and validation. Soil Moisture Active Passive (SMAP) Workshop, Washington DC area, USA.
  174. Högström, E., Heim, B., Bartsch, A., Bergstedt, H., & Pointner, G. (2018). Evaluation of a MetOp ASCAT-Derived Surface Soil Moisture Product in Tundra Environments. Journal of Geophysical Research: Earth Surface, 123, 3190-3205. https://doi.org/10.1029/2018jf004658
  175. Jeong, J., Cho, S., Baik, J., & Choi, M. (2018). A Study on the Establishment of a Korean Soil Moisture Network (2): Measurement of Intermediate-Scale Soil Moisture Using a Cosmic-Ray Sensor. Journal of the Korean Society of Hazard Mitigation, 18, 83-91. https://doi.org/10.9798/kosham.2018.18.7.83
  176. Kang, J., Jin, R., Li, X., Zhang, Y., & Zhu, Z. (2018). Spatial Upscaling of Sparse Soil Moisture Observations Based on Ridge Regression. Remote Sensing, 10. https://doi.org/10.3390/rs10020192
  177. Kim, H., Parinussa, R., Konings, A.G., Wagner, W., Cosh, M.H., Lakshmi, V., Zohaib, M., & Choi, M. (2018). Global-scale assessment and combination of SMAP with ASCAT (active) and AMSR2 (passive) soil moisture products. Remote Sensing of Environment, 204, 260-275. https://doi.org/10.1016/j.rse.2017.10.026
  178. Kim, S., Jeong, J., Zohaib, M., & Choi, M. (2018). Spatial disaggregation of ASCAT soil moisture under all sky condition using support vector machine. Stochastic Environmental Research and Risk Assessment, 32, 3455-3473. https://doi.org/10.1007/s00477-018-1620-3
  179. Kolassa, J., Reichle, R.H., Liu, Q., Alemohammad, S.H., Gentine, P., Aida, K., Asanuma, J., Bircher, S., Caldwell, T., Colliander, A., Cosh, M., Collins, C.H., Jackson, T.J., Martinez-Fernandez, J., McNairn, H., Pacheco, A., Thibeault, M., & Walker, J.P. (2018). Estimating surface soil moisture from SMAP observations using a Neural Network technique. Remote Sens Environ, 204, 43-59. https://doi.org/10.1016/j.rse.2017.10.045
  180. Lei, F., Crow, W.T., Holmes, T.R.H., Hain, C., & Anderson, M.C. (2018). Global Investigation of Soil Moisture and Latent Heat Flux Coupling Strength. Water Resources Research, 54, 8196-8215. https://doi.org/10.1029/2018wr023469
  181. Lei, F., Crow, W.T., Shen, H., Su, C.-H., Holmes, T.R.H., Parinussa, R.M., & Wang, G. (2018). Assessment of the impact of spatial heterogeneity on microwave satellite soil moisture periodic error. Remote Sensing of Environment, 205, 85-99. https://doi.org/10.1016/j.rse.2017.11.002
  182. Lembrechts, J.J., Nijs, I., & Lenoir, J. (2018). Incorporating microclimate into species distribution models. Ecography. https://doi.org/10.1111/ecog.03947
  183. Li, Y., Li, Y., Yuan, X., Zhang, L., & Sha, S. (2018). Evaluation of Model-Based Soil Moisture Drought Monitoring over Three Key Regions in China. Journal of Applied Meteorology and Climatology, 57, 1989-2004. https://doi.org/10.1175/jamc-d-17-0118.1
  184. Martens, B., de Jeu, R., Verhoest, N., Schuurmans, H., Kleijer, J., & Miralles, D. (2018). Towards Estimating Land Evaporation at Field Scales Using GLEAM. Remote Sensing, 10. https://doi.org/10.3390/rs10111720
  185. Meng, Q., Zhang, L., Xie, Q., Yao, S., Chen, X., & Zhang, Y. (2018). Combined Use of GF-3 and Landsat-8 Satellite Data for Soil Moisture Retrieval over Agricultural Areas Using Artificial Neural Network. Advances in Meteorology, 1-11. https://doi.org/10.1155/2018/9315132
  186. Mishra, V., Shah, R., Azhar, S., Shah, H., Modi, P., & Kumar, R. (2018). Reconstruction of droughts in India using multiple land-surface models (1951–2015). Hydrology and Earth System Sciences, 22, 2269-2284. https://doi.org/10.5194/hess-22-2269-2018
  187. Murguia-Flores, F., Arndt, S., Ganesan, A.L., Murray-Tortarolo, G., & Hornibrook, E.R.C. (2018). Soil Methanotrophy Model (MeMo v1.0): a process-based model to quantify global uptake of atmospheric methane by soil. Geoscientific Model Development, 11, 2009-2032. https://doi.org/10.5194/gmd-11-2009-2018
  188. Parinussa, R.M., Wang, G., Liu, Y., Lou, D., Hagan, D.F.T., Zhan, M., Su, B., & Jiang, T. (2018). Improved surface soil moisture anomalies from Fengyun-3B over the Jiangxi province of the People’s Republic of China. International Journal of Remote Sensing, 1-13. https://doi.org/10.1080/01431161.2018.1500729
  189. Qin, M., Giménez, D., & Miskewitz, R. (2018). Temporal dynamics of subsurface soil water content estimated from surface measurement using wavelet transform. Journal of Hydrology, 563, 834-850. https://doi.org/10.1016/j.jhydrol.2018.06.023
  190. Rowlandson, T.L., Berg, A.A., Roy, A., Kim, E., Pardo Lara, R., Powers, J., Lewis, K., Houser, P., McDonald, K., Toose, P., Wu, A., De Marco, E., Derksen, C., Entin, J., Colliander, A., Xu, X., & Mavrovic, A. (2018). Capturing agricultural soil freeze/thaw state through remote sensing and ground observations: A soil freeze/thaw validation campaign. Remote Sensing of Environment, 211, 59-70. https://doi.org/10.1016/j.rse.2018.04.003
  191. Santi, E., Paloscia, S., Pettinato, S., Brocca, L., Ciabatta, L., & Entekhabi, D. (2018). Integration of microwave data from SMAP and AMSR2 for soil moisture monitoring in Italy. Remote Sensing of Environment, 212, 21-30. https://doi.org/10.1016/j.rse.2018.04.039
  192. Spennemann, P.C., Salvia, M., Ruscica, R.C., Sörensson, A.A., Grings, F., & Karszenbaum, H. (2018). Land-atmosphere interaction patterns in southeastern South America using satellite products and climate models. International Journal of Applied Earth Observation and Geoinformation, 64, 96-103. https://doi.org/10.1016/j.jag.2017.08.016
  193. Stein, S., Eberhard, E., Grosse, M., Helming, K., Hierold, W., Hoffmann, C., Kühnert, T., Liess, M., Russel, D., & S., S. (2018). Report on available soil data for German agricultural areas. -
  194. Tóth, E., Gelybó, G., Dencső, M., Kása, I., Birkás, M., & Horel, Á. (2018). Soil CO 2 Emissions in a Long-Term Tillage Treatment Experiment. Soil Management and Climate Change, 293-307
  195. Um, M.-J., Kim, M., Kim, Y., & Park, D. (2018). Drought Assessment with the Community Land Model for 1951–2010 in East Asia. Sustainability, 10. https://doi.org/10.3390/su10062100
  196. van der Schalie, R., de Jeu, R., Parinussa, R., Rodríguez-Fernández, N., Kerr, Y., Al-Yaari, A., Wigneron, J.-P., & Drusch, M. (2018). The Effect of Three Different Data Fusion Approaches on the Quality of Soil Moisture Retrievals from Multiple Passive Microwave Sensors. Remote Sensing, 10. https://doi.org/10.3390/rs10010107
  197. Wang, X., Ciais, P., Wang, Y., & Zhu, D. (2018). Divergent response of seasonally dry tropical vegetation to climatic variations in dry and wet seasons. Glob Chang Biol. https://doi.org/10.1111/gcb.14335
  198. Williamson, M., Rowlandson, T.L., Berg, A.A., Roy, A., Toose, P., Derksen, C., Arnold, L., & Tetlock, E. (2018). L-band radiometry freeze/ thaw validation using air temperature and ground measurements. Remote Sensing Letters, 9, 403-410. https://doi.org/10.1080/2150704x.2017.1422872
  199. Wu, M., Scholze, M., Voßbeck, M., Kaminski, T., & Hoffmann, G. (2018). Simultaneous Assimilation of Remotely Sensed Soil Moisture and FAPAR for Improving Terrestrial Carbon Fluxes at Multiple Sites Using CCDAS. Remote Sensing, 11. https://doi.org/10.3390/rs11010027
  200. Wu, M., Scholze, M., Voßbeck, M., Kaminski, T., & Hoffmann, G. (2018). Simultaneous Assimilation of Remotely Sensed Soil Moisture and FAPAR for Improving Terrestrial Carbon Fluxes at Multiple Sites Using CCDAS. Remote Sensing, 11. https://doi.org/10.3390/rs11010027
  201. Xaver, A. (2018). The International Soil Moisture Network. BMon KO meeting (Soil moisture monitoring in Austria) Vienna;, Austria.
  202. Xaver, A., Himmelbauer, I., Aberer, D., Zappa, L. and Dorigo, W.A. (2018). The International Soil Mosture Network in support of Earth Observation service, in support of global climate monitoring. ESA CCI Soil Moisture Workshop, Vienna, Austria.
  203. Xu, H., Yuan, Q., Li, T., Shen, H., Zhang, L., & Jiang, H. (2018). Quality Improvement of Satellite Soil Moisture Products by Fusing with In-Situ Measurements and GNSS-R Estimates in the Western Continental U.S. Remote Sensing, 10. https://doi.org/10.3390/rs10091351
  204. Ye, K., & Lau, N.-C. (2018). Characteristics of Eurasian snowmelt and its impacts on the land surface and surface climate. Climate Dynamics. https://doi.org/10.1007/s00382-018-4180-9
  205. Zhang, S., Calvet, J.-C., Darrozes, J., Roussel, N., Frappart, F., & Bouhours, G. (2018). Deriving surface soil moisture from reflected GNSS signal observations from a grassland site in southwestern France. Hydrology and Earth System Sciences, 22, 1931-1946. https://doi.org/10.5194/hess-22-1931-2018
  206. Zhao, L., & Yang, Z.-L. (2018). Multi-sensor land data assimilation: Toward a robust global soil moisture and snow estimation. Remote Sensing of Environment, 216, 13-27. https://doi.org/10.1016/j.rse.2018.06.033
  207. Abdi, A., Boke-Olén, N., Tenenbaum, D., Tagesson, T., Cappelaere, B., & Ardö, J. (2017). Evaluating Water Controls on Vegetation Growth in the Semi-Arid Sahel Using Field and Earth Observation Data. Remote Sensing, 9. https://doi.org/10.3390/rs9030294
  208. Afshar, M.H., & Yilmaz, M.T. (2017). The added utility of nonlinear methods compared to linear methods in rescaling soil moisture products. Remote Sensing of Environment, 196, 224-237. https://doi.org/10.1016/j.rse.2017.05.017
  209. Al-Yaari, A., Wigneron, J.P., Kerr, Y., Rodriguez-Fernandez, N., O'Neill, P.E., Jackson, T.J., De Lannoy, G.J.M., Al Bitar, A., Mialon, A., Richaume, P., Walker, J.P., Mahmoodi, A., & Yueh, S. (2017). Evaluating soil moisture retrievals from ESA's SMOS and NASA's SMAP brightness temperature datasets. Remote Sensing of Environment, 193, 257-273. https://doi.org/10.1016/j.rse.2017.03.010
  210. Anoop, S., Maurya, D.K., Rao, P.V.N., & Sekhar, M. (2017). Validation and Comparison of LPRM Retrieved Soil Moisture Using AMSR2 Brightness Temperature at Two Spatial Resolutions in the Indian Region. IEEE Geoscience and Remote Sensing Letters, 14, 1561-1564. https://doi.org/10.1109/lgrs.2017.2722542
  211. Baguis Pierre, & Emmanuel, R. (2017). Soil Moisture Data Assimilation in a Hydrological Model: A Case Study in Belgium Using Large-Scale Satellite Data. Remote Sensing, 9. https://doi.org/10.3390/rs9080820
  212. Brocca, L., Crow, W.T., Ciabatta, L., Massari, C., de Rosnay, P., Enenkel, M., Hahn, S., Amarnath, G., Camici, S., Tarpanelli, A., & Wagner, W. (2017). A Review of the Applications of ASCAT Soil Moisture Products. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10, 2285-2306. https://doi.org/10.1109/jstars.2017.2651140
  213. Candy, B., Saunders, R.W., Ghent, D., & Bulgin, C.E. (2017). The Impact of Satellite-Derived Land Surface Temperatures on Numerical Weather Prediction Analyses and Forecasts. Journal of Geophysical Research: Atmospheres, 122, 9783-9802. https://doi.org/10.1002/2016jd026417
  214. Cerlini, P.B., Meniconi, S., & Brunone, B. (2017). Groundwater Supply and Climate Change Management by Means of Global Atmospheric Datasets. Preliminary Results. Procedia Engineering, 186, 420-427. https://doi.org/10.1016/j.proeng.2017.03.245
  215. De Santis, D., & Biondi, D. (2017). A quality assessment of the soil water index by the propagation of ASCAT soil moisture error estimates through an exponential filter. International Journal of Remote Sensing, 39, 232-257. https://doi.org/10.1080/01431161.2017.1382745
  216. Dong, J., & Crow, W. (2017). An improved triple collocation analysis algorithm for decomposing auto-correlated and white soil moisture retrieval errors. Journal of Geophysical Research: Atmospheres. https://doi.org/10.1002/2017jd027387
  217. Dorigo, W., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, P.D., Hirschi, M., Ikonen, J., de Jeu, R., Kidd, R., Lahoz, W., Liu, Y.Y., Miralles, D., Mistelbauer, T., Nicolai-Shaw, N., Parinussa, R., Pratola, C., Reimer, C., van der Schalie, R., Seneviratne, S.I., Smolander, T., & Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. Remote Sensing of Environment, 203, 185-215. https://doi.org/10.1016/j.rse.2017.07.001
  218. Dwevedi, A., Kumar, P., Kumar, P., Kumar, Y., Sharma, Y.K., & Kayastha, A.M. (2017). 15 - Soil sensors: detailed insight into research updates, significance, and future prospects A2 - Grumezescu, Alexandru Mihai. New Pesticides and Soil Sensors. Academic Press, 561-594
  219. Emery, C. (2017). Contribution de la future mission altim´etrique `a large fauch´ee SWOT pour la mod´elisation hydrologique `a grande ´echelle. -
  220. Fernandez-Moran, R., Wigneron, J.P., De Lannoy, G., Lopez-Baeza, E., Parrens, M., Mialon, A., Mahmoodi, A., Al-Yaari, A., Bircher, S., Al Bitar, A., Richaume, P., & Kerr, Y. (2017). A new calibration of the effective scattering albedo and soil roughness parameters in the SMOS SM retrieval algorithm. International Journal of Applied Earth Observation and Geoinformation, 62, 27-38. https://doi.org/10.1016/j.jag.2017.05.013
  221. Gasch, C.K., Brown, D.J., Brooks, E.S., Yourek, M., Poggio, M., Cobos, D.R., & Campbell, C.S. (2017). A pragmatic, automated approach for retroactive calibration of soil moisture sensors using a two-step, soil-specific correction. Computers and Electronics in Agriculture, 137, 29-40. https://doi.org/10.1016/j.compag.2017.03.018
  222. Gruber, A., Dorigo, W.A., Crow, W., & Wagner, W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing, 55, 6780-6792. https://doi.org/10.1109/TGRS.2017.2734070
  223. Hartmann, A., Gleeson, T., Wada, Y., & Wagener, T. (2017). Enhanced groundwater recharge rates and altered recharge sensitivity to climate variability through subsurface heterogeneity. Proc Natl Acad Sci U S A, 114, 2842-2847. https://doi.org/10.1073/pnas.1614941114
  224. Heer, E., Xaver, A., Dorigo, W.A. and Messner R. (2017). Enhancement of the Automated Quality Control Procedures for the International Soil Moisture Network. European Geoscience Union (EGU) General Assembly 2017, Vienna, Austria.
  225. Heer, E., Xaver, A., Messner, R., Himmelbauer, I., Zappa, L., and Dorigo, W.A. (2017). Recent Developments of the International Soil Moisture Network . 4th Satellite Soil Moisture Valdiation and Application Workshop, Vienna, Austria.
  226. Ji, P., Yuan, X., & Liang, X.-Z. (2017). Do Lateral Flows Matter for the Hyperresolution Land Surface Modeling?. Journal of Geophysical Research: Atmospheres. https://doi.org/10.1002/2017jd027366
  227. Jung, C., Lee, Y., Cho, Y., & Kim, S. (2017). A Study of Spatial Soil Moisture Estimation Using a Multiple Linear Regression Model and MODIS Land Surface Temperature Data Corrected by Conditional Merging. Remote Sensing, 9. https://doi.org/10.3390/rs9080870
  228. Kapilaratne, R.G.C.J., & Lu, M. (2017). Automated general temperature correction method for dielectric soil moisture sensors. Journal of Hydrology, 551, 203-216. https://doi.org/10.1016/j.jhydrol.2017.05.050
  229. Karthikeyan, L., Pan, M., Wanders, N., Kumar, D.N., & Wood, E.F. (2017). Four decades of microwave satellite soil moisture observations: Part 2. Product validation and inter-satellite comparisons. Advances in Water Resources, 109, 236-252. https://doi.org/10.1016/j.advwatres.2017.09.010
  230. Kim, H., Parinussa, R., Konings, A.G., Wagner, W., Cosh, M.H., Lakshmi, V., Zohaib, M., Choi, M. (2017). Global-scale assessment and combination of SMAP with ASCAT (active) and AMSR2 (passive) soil moisture products. Remote Sensing of Environment, 204, 260-275. dx.doi.org/10.1016/j.rse.2017.10.026
  231. Kim, S., Balakrishnan, K., Liu, Y., Johnson, F., & Sharma, A. (2017). Spatial Disaggregation of Coarse Soil Moisture Data by Using High-Resolution Remotely Sensed Vegetation Products. IEEE Geoscience and Remote Sensing Letters, 14, 1604-1608. https://doi.org/10.1109/lgrs.2017.2725945
  232. Kolassa, J., Gentine, P., Prigent, C., Aires, F., & Alemohammad, S.H. (2017). Soil moisture retrieval from AMSR-E and ASCAT microwave observation synergy. Part 2: Product evaluation. Remote Sensing of Environment, 195, 202-217. https://doi.org/10.1016/j.rse.2017.04.020
  233. Lauer, A., Eyring, V., Righi, M., Buchwitz, M., Defourny, P., Evaldsson, M., Friedlingstein, P., de Jeu, R., de Leeuw, G., Loew, A., Merchant, C.J., Müller, B., Popp, T., Reuter, M., Sandven, S., Senftleben, D., Stengel, M., Van Roozendael, M., Wenzel, S., & Willén, U. (2017). Benchmarking CMIP5 models with a subset of ESA CCI Phase 2 data using the ESMValTool. Remote Sensing of Environment, 203, 9-39. https://doi.org/10.1016/j.rse.2017.01.007
  234. Lee, J.H., Zhao, C., & Kerr, Y. (2017). Stochastic Bias Correction and Uncertainty Estimation of Satellite-Retrieved Soil Moisture Products. Remote Sensing, 9. https://doi.org/10.3390/rs9080847
  235. Leeper, R.D., Bell, J.E., Vines, C., & Palecki, M. (2017). An Evaluation of the North American Regional Reanalysis Simulated Soil Moisture Conditions during the 2011–13 Drought Period. Journal of Hydrometeorology, 18, 515-527. https://doi.org/10.1175/jhm-d-16-0132.1
  236. Liangjing, Z. (2017). Terrestrial water storage from GRACE gravity data for hydrometeorological applications. -
  237. Lievens, H., Martens, B., Verhoest, N.E.C., Hahn, S., Reichle, R.H., & Miralles, D.G. (2017). Assimilation of global radar backscatter and radiometer brightness temperature observations to improve soil moisture and land evaporation estimates. Remote Sensing of Environment, 189, 194-210. https://doi.org/10.1016/j.rse.2016.11.022
  238. Lievens, H., Reichle, R.H., Liu, Q., De Lannoy, G.J.M., Dunbar, R.S., Kim, S.B., Das, N.N., Cosh, M., Walker, J.P., & Wagner, W. (2017). Joint Sentinel-1 and SMAP data assimilation to improve soil moisture estimates. Geophysical Research Letters, 44, 6145-6153. https://doi.org/10.1002/2017gl073904
  239. Lin, X., Wen, J., Tang, Y., Ma, M., You, D., Dou, B., Wu, X., Zhu, X., Xiao, Q., & Liu, Q. (2017). A web-based land surface remote sensing products validation system (LAPVAS): application to albedo product. International Journal of Digital Earth, 11, 308-328. https://doi.org/10.1080/17538947.2017.1320593
  240. Liu, Q., Hao, Y., Stebler, E., Tanaka, N., & Zou, C.B. (2017). Impact of plant functional types on coherence between precipitation and soil moisture - a wavelet analysis. Geophysical Research Letters.. https://doi.org/10.1002/2017gl075542
  241. Liu, Z., Li, P., & Yang, J. (2017). Soil Moisture Retrieval and Spatiotemporal Pattern Analysis Using Sentinel-1 Data of Dahra, Senegal. Remote Sensing, 9. https://doi.org/10.3390/rs9111197
  242. Mahecha, M.D., Gans, F., Sippel, S., Donges, J.F., Kaminski, T., Metzger, S., Migliavacca, M., Papale, D., Rammig, A., & Zscheischler, J. (2017). Detecting impacts of extreme events with ecological in-situ monitoring networks. Biogeosciences Discussions, 1-33. https://doi.org/10.5194/bg-2017-130
  243. Martens, B., Miralles, D.G., Lievens, H., van der Schalie, R., de Jeu, R.A.M., Fernández-Prieto, D., Beck, H.E., Dorigo, W.A., & Verhoest, N.E.C. (2017). GLEAM v3: satellite-based land evaporation and root-zone soil moisture. Geoscientific Model Development, 10, 1903-1925. https://doi.org/10.5194/gmd-10-1903-2017
  244. Martinez, G., Brocca, L., Gerke, H.H., & Pachepsky, Y.A. (2017). Soil Variability and Biogeochemical Fluxes: Toward a Better Understanding of Soil Processes at the Land Surface. Vadose Zone Journal, 16. https://doi.org/10.2136/vzj2017.07.0145
  245. Massari, C., Su, C.-H., Brocca, L., Sang, Y.-F., Ciabatta, L., Ryu, D., & Wagner, W. (2017). Near real time de-noising of satellite-based soil moisture retrievals: An intercomparison among three different techniques. Remote Sensing of Environment, 198, 17-29. https://doi.org/10.1016/j.rse.2017.05.037
  246. McCabe, M.F., Rodell, M., Alsdorf, D.E., Miralles, D.G., Uijlenhoet, R., Wagner, W., Lucieer, A., Houborg, R., Verhoest, N.E.C., Franz, T.E., Shi, J., Gao, H., & Wood, E.F. (2017). The Future of Earth Observation in Hydrology. Hydrology and Earth System Sciences Discussions, 1-55. https://doi.org/10.5194/hess-2017-54
  247. Miyaoka, K., Gruber, A., Ticconi, F., Hahn, S., Wagner, W., Figa-Saldana, J., & Anderson, C. (2017). Triple Collocation Analysis of Soil Moisture From Metop-A ASCAT and SMOS Against JRA-55 and ERA-Interim. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10, 2274-2284. https://doi.org/10.1109/jstars.2016.2632306
  248. Mohanty, B.P., Cosh, M.H., Lakshmi, V., & Montzka, C. (2017). Soil Moisture Remote Sensing: State-of-the-Science. Vadose Zone Journal, 16. https://doi.org/10.2136/vzj2016.10.0105
  249. Montzka, C., Bogena, H., Zreda, M., Monerris, A., Morrison, R., Muddu, S., & Vereecken, H. (2017). Validation of Spaceborne and Modelled Surface Soil Moisture Products with Cosmic-Ray Neutron Probes. Remote Sensing, 9. https://doi.org/10.3390/rs9020103
  250. Murguia-Flores, F., Arndt, S., Ganesan, A.L., Murray-Tortarolo, G.N., & Hornibrook, E.R.C. (2017). Soil Methanotrophy Model (MeMo v1.0): a process-based model to quantify global uptake of atmospheric methane by soil. Geoscientific Model Development Discussions, 1-38. https://doi.org/10.5194/gmd-2017-124
  251. Nguyen, H.H., Kim, H., & Choi, M. (2017). Evaluation of the soil water content using cosmic-ray neutron probe in a heterogeneous monsoon climate-dominated region. Advances in Water Resources, 108, 125-138. https://doi.org/10.1016/j.advwatres.2017.07.020
  252. Nilawar, A., Calderella, C., Lakhankar, T., Waikar, M., & Munoz, J. (2017). Satellite Soil Moisture Validation Using Hydrological SWAT Model: A Case Study of Puerto Rico, USA. Hydrology, 4. https://doi.org/10.3390/hydrology4040045
  253. Pan, X., Kornelsen, K.C., & Coulibaly, P. (2017). Estimating Root Zone Soil Moisture at Continental Scale Using Neural Networks. JAWRA Journal of the American Water Resources Association, 53, 220-237. https://doi.org/10.1111/1752-1688.12491
  254. Park, S., Im, J., Park, S., & Rhee, J. (2017). Drought monitoring using high resolution soil moisture through multi-sensor satellite data fusion over the Korean peninsula. Agricultural and Forest Meteorology, 237-238, 257-269. https://doi.org/10.1016/j.agrformet.2017.02.022
  255. Park, S., Park, S., Im, J., Rhee, J., Shin, J., & Park, J. (2017). Downscaling GLDAS Soil Moisture Data in East Asia through Fusion of Multi-Sensors by Optimizing Modified Regression Trees. Water, 9. https://doi.org/10.3390/w9050332
  256. Peng, J., Loew, A., Merlin, O., & Verhoest, N.E.C. (2017). A review of spatial downscaling of satellite remotely sensed soil moisture. Reviews of Geophysics, 55, 341-366. https://doi.org/10.1002/2016RG000543
  257. Petropoulos, G.P., & McCalmont, J.P. (2017). An Operational In Situ Soil Moisture & Soil Temperature Monitoring Network for West Wales, UK: The WSMN Network. Sensors (Basel), 17. https://doi.org/10.3390/s17071481
  258. Phillips, T.J., Klein, S.A., Ma, H.-Y., Tang, Q., Xie, S., Williams, I.N., Santanello, J.A., Cook, D.R., & Torn, M.S. (2017). Using ARM Observations to Evaluate Climate Model Simulations of Land-Atmosphere Coupling on the U.S. Southern Great Plains. Journal of Geophysical Research: Atmospheres. https://doi.org/10.1002/2017jd027141
  259. Pierdicca, N., Fascetti, F., Pulvirenti, L., & Crapolicchio, R. (2017). Error Characterization of Soil Moisture Satellite Products: Retrieving Error Cross-Correlation Through Extended Quadruple Collocation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10, 4522-4530. https://doi.org/10.1109/jstars.2017.2714025
  260. Przeździecki, K., Zawadzki, J., Cieszewski, C., & Bettinger, P. (2017). Estimation of soil moisture across broad landscapes of Georgia and South Carolina using the triangle method applied to MODIS satellite imagery. Silva Fennica, 51. https://doi.org/10.14214/sf.1683
  261. Rains, D., Han, X., Lievens, H., Montzka, C., & Verhoest, N.E.C. (2017). SMOS brightness temperature assimilation into the Community Land Model. Hydrology and Earth System Sciences, 21, 5929-5951. https://doi.org/10.5194/hess-21-5929-2017
  262. Ran, Y., Li, X., Jin, R., Kang, J., & Cosh, M.H. (2017). Strengths and weaknesses of temporal stability analysis for monitoring and estimating grid-mean soil moisture in a high-intensity irrigated agricultural landscape. Water Resources Research, 53, 283-301. https://doi.org/10.1002/2015wr018182
  263. Ray, R., Fares, A., He, Y., & Temimi, M. (2017). Evaluation and Inter-Comparison of Satellite Soil Moisture Products Using In Situ Observations over Texas, U.S. Water, 9. https://doi.org/10.3390/w9060372
  264. Reichle, R.H., Draper, C.S., Liu, Q., Girotto, M., Mahanama, S.P.P., Koster, R.D., & Lannoy, G.J.M.D. (2017). Assessment of MERRA-2 Land Surface Hydrology Estimates. Journal of Climate, 30, 2937-2960. https://doi.org/10.1175/jcli-d-16-0720.1
  265. Rodríguez-Fernández, N.J., Muñoz Sabater, J., Richaume, P., de Rosnay, P., Kerr, Y.H., Albergel, C., Drusch, M., & Mecklenburg, S. (2017). SMOS near-real-time soil moisture product: processor overview and first validation results. Hydrology and Earth System Sciences, 21, 5201-5216. https://doi.org/10.5194/hess-21-5201-2017
  266. Scholze, M., Buchwitz, M., Dorigo, W., Guanter, L., & Quegan, S. (2017). Reviews and syntheses: Systematic Earth observations for use in terrestrial carbon cycle data assimilation systems. Biogeosciences Discussions, 1-49. https://doi.org/10.5194/bg-2016-557
  267. Sun, G., Peng, F., & Mu, M. (2017). Uncertainty assessment and sensitivity analysis of soil moisture based on model parameter errors – Results from four regions in China. Journal of Hydrology, 555, 347-360. https://doi.org/10.1016/j.jhydrol.2017.09.059
  268. Sun, Y., Huang, S., Ma, J., Li, J., Li, X., Wang, H., Chen, S., & Zang, W. (2017). Preliminary Evaluation of the SMAP Radiometer Soil Moisture Product over China Using In Situ Data. Remote Sensing, 9. https://doi.org/10.3390/rs9030292
  269. Tobin, K.J., Torres, R., Crow, W.T., & Bennett, M.E. (2017). Multi-decadal analysis of root-zone soil moisture applying the exponential filter across CONUS. Hydrology and Earth System Sciences, 21, 4403-4417. https://doi.org/10.5194/hess-21-4403-2017
  270. van der Schalie, R., de Jeu, R.A.M., Kerr, Y.H., Wigneron, J.P., Rodríguez-Fernández, N.J., Al-Yaari, A., Parinussa, R.M., Mecklenburg, S., & Drusch, M. (2017). The merging of radiative transfer based surface soil moisture data from SMOS and AMSR-E. Remote Sensing of Environment, 189, 180-193. https://doi.org/10.1016/j.rse.2016.11.026
  271. Varikoden, H., & Revadekar, J.V. (2017). Relation Between the Rainfall and Soil Moisture During Different Phases of Indian Monsoon. Pure and Applied Geophysics, 175, 1187-1196. https://doi.org/10.1007/s00024-017-1740-6
  272. Williamson, M., Adams, J.R., Berg, A.A., Derksen, C., Toose, P., & Walker, A. (2017). Plot-scale assessment of soil freeze/thaw detection and variability with impedance probes: implications for remote sensing validation networks. Hydrology Research. https://doi.org/10.2166/nh.2017.183
  273. Xaver, A. (2017). The International Soil Moisture Network – Recent activities and outlook. GTN-H 8th Panel Session, Koblenz, Germany.
  274. Xing, C., Chen, N., Zhang, X., & Gong, J. (2017). A Machine Learning Based Reconstruction Method for Satellite Remote Sensing of Soil Moisture Images with In Situ Observations. Remote Sensing, 9. https://doi.org/10.3390/rs9050484
  275. Yao, P., Shi, J., Zhao, T., Lu, H., & Al-Yaari, A. (2017). Rebuilding Long Time Series Global Soil Moisture Products Using the Neural Network Adopting the Microwave Vegetation Index. Remote Sensing, 9. https://doi.org/10.3390/rs9010035
  276. Yuan, S., & Quiring, S.M. (2017). Evaluation of soil moisture in CMIP5 simulations over the contiguous United States using in situ and satellite observations. Hydrology and Earth System Sciences, 21, 2203-2218. https://doi.org/10.5194/hess-21-2203-2017
  277. Zhang, X., Zhang, T., Zhou, P., Shao, Y., & Gao, S. (2017). Validation Analysis of SMAP and AMSR2 Soil Moisture Products over the United States Using Ground-Based Measurements. Remote Sensing, 9. https://doi.org/10.3390/rs9020104
  278. Zhao, W., Li, A., Jin, H., Zhang, Z., Bian, J., & Yin, G. (2017). Performance Evaluation of the Triangle-Based Empirical Soil Moisture Relationship Models Based on Landsat-5 TM Data and In Situ Measurements. IEEE Transactions on Geoscience and Remote Sensing, 55, 2632-2645. https://doi.org/10.1109/tgrs.2017.2649522
  279. Zhao, W., Li, A., & Zhao, T. (2017). Potential of Estimating Surface Soil Moisture With the Triangle-Based Empirical Relationship Model. IEEE Transactions on Geoscience and Remote Sensing, 55, 6494-6504. https://doi.org/10.1109/tgrs.2017.2728815
  280. Zhou, H., Chang, J., Sun, J., Shang, C., Han, F., & Hu, D. (2017). Spatial variation of temperature of surface soil layer adjacent to constructions: A theoretical framework for atmosphere-building-soil energy flow systems. Building and Environment, 124, 143-152. https://doi.org/10.1016/j.buildenv.2017.08.002
  281. Al-Yaari, A., Wigneron, J. P., Kerr, Y., de Jeu, R., Rodriguez-Fernandez, N., van der Schalie, R., … Ducharne, A. (2016). Testing regression equations to derive long-term global soil moisture datasets from passive microwave observations. Remote Sensing of Environment, 180, 453–464. https://doi.org/10.1016/j.rse.2015.11.022
  282. An, R., Zhang, L., Wang, Z., Quaye-Ballard, J. A., You, J., Shen, X., … Ke, Z. (2016). Validation of the ESA CCI soil moisture product in China. International Journal of Applied Earth Observation and Geoinformation, 48, 28–36. https://doi.org/10.1016/j.jag.2015.09.009
  283. Bi, H., Ma, J., Zheng, W., & Zeng, J. (2016). Comparison of soil moisture in GLDAS model simulations and in situ observations over the Tibetan Plateau: EVALUATE GLDAS SOIL MOISTURE OVER TP. Journal of Geophysical Research: Atmospheres, 121, 6, 2658–2678. https://doi.org/10.1002/2015JD024131
  284. Chen, X., Su, Y., Liao, J., Shang, J., Dong, T., Wang, C., … Liu, L. (2016). Detecting significant decreasing trends of land surface soil moisture in eastern China during the past three decades (1979-2010): China’s 32 Year Soil Moisture. Journal of Geophysical Research: Atmospheres, 121, 10, 5177–5192. https://doi.org/10.1002/2015JD024676
  285. Cissé, S., Eymard, L., Ottlé, C., Ndione, J., Gaye, A., & Pinsard, F. (2016). Rainfall Intra-Seasonal Variability and Vegetation Growth in the Ferlo Basin (Senegal). Remote Sensing, 8, 1, 66. https://doi.org/10.3390/rs8010066
  286. Du, J., Kimball, J. S., & Jones, L. A. (2016). Passive Microwave Remote Sensing of Soil Moisture Based on Dynamic Vegetation Scattering Properties for AMSR-E. IEEE Transactions on Geoscience and Remote Sensing, 54, 1, 597–608. https://doi.org/10.1109/TGRS.2015.2462758
  287. Enenkel, M., Reimer, C., Dorigo, W., Wagner, W., Pfeil, I., Parinussa, R., & De Jeu, R. (2016). Combining satellite observations to develop a global soil moisture product for near-real-time applications. Hydrology and Earth System Sciences, 20, 10, 4191–4208. https://doi.org/10.5194/hess-20-4191-2016
  288. Faridani, F., Farid, A., Ansari, H., & Manfreda, S. (2016). Estimation of the Root-Zone Soil Moisture Using Passive Microwave Remote Sensing and SMAR Model. Journal of Irrigation and Drainage Engineering, 4016070. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001115
  289. Fascetti, F., Pierdicca, N., Crapolicchio, R., Pulvirenti, L., & Muoz-Sabater, J. (2016). An assessment of SMOS version 6.20 products through Triple and Quadruple Collocation techniques considering ASCAT, ERA/Interim LAND, ISMNand SMAP soil moisture data. IEEE, 91–94. https://doi.org/10.1109/MICRORAD.2016.7530511
  290. Fascetti, F., Pierdicca, N., Pulvirenti, L., Crapolicchio, R., & Muñoz-Sabater, J. (2016). A comparison of ASCAT and SMOS soil moisture retrievals over Europe and Northern Africa from 2010 to 2013. International Journal of Applied Earth Observation and Geoinformation, 45, 135–142. https://doi.org/10.1016/j.jag.2015.09.008
  291. Fernandez-Moran, R., Wigneron, J.-P., De Lannoy, G., Lopez-Baeza, E., Mialon, A., Mahmoodi, A., … Kerr, Y. (2016). Calibrating the effective scattering albedo in the SMOS algorithm: Some first results. IEEE, 826–829. https://doi.org/10.1109/IGARSS.2016.7729209
  292. Gonzalez-Zamora, A., Sanchez, N., & Martinez-Fernandez, J. (2016). Validation of Aquarius Soil Moisture Products Over the Northwest of Spain: A Comparison With SMOS. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9, 6, 2763–2769. https://doi.org/10.1109/JSTARS.2016.2517401
  293. González-Zamora, Á., Sánchez, N., Martínez-Fernández, J., & Wagner, W. (2016). Root-zone plant available water estimation using the SMOS-derived soil water index. Advances in Water Resources, 96, 339–353. https://doi.org/10.1016/j.advwatres.2016.08.001
  294. Griesfeller, A., Lahoz, W. A., Jeu, R. A. M. d., Dorigo, W., Haugen, L. E., Svendby, T. M., & Wagner, W. (2016). Evaluation of satellite soil moisture products over Norway using ground-based observations. International Journal of Applied Earth Observation and Geoinformation, 45, 155–164. https://doi.org/10.1016/j.jag.2015.04.016
  295. Gruber, A., Su, C.-H., Crow, W. T., Zwieback, S., Dorigo, W. A., & Wagner, W. (2016). Estimating error cross-correlations in soil moisture data sets using extended collocation analysis: EXTENDED COLLOCATION ANALYSIS. Journal of Geophysical Research: Atmospheres, 121, 3, 1208–1219. https://doi.org/10.1002/2015JD024027
  296. Han, M., Lu, H., & Yang, K. (2016). Development of passive microwave retrieval algorithm for estimation of surface soil temperature from AMSR-E data. IEEE, 1671–1674. https://doi.org/10.1109/IGARSS.2016.7729427
  297. Kędzior, M., & Zawadzki, J. (2016). Comparative study of soil moisture estimations from SMOS satellite mission, GLDAS database, and cosmic-ray neutrons measurements at COSMOS station in Eastern Poland. Geoderma, 283, 21–31. https://doi.org/10.1016/j.geoderma.2016.07.023
  298. Kerr, Y. H., Al-Yaari, A., Rodriguez-Fernandez, N., Parrens, M., Molero, B., Leroux, D., … Wigneron, J.-P. (2016). Overview of SMOS performance in terms of global soil moisture monitoring after six years in operation. Remote Sensing of Environment, 180, 40–63. http://doi.org/10.1016/j.rse.2016.02.042
  299. Kerr, Y. H., Al-Yaari, A., Rodriguez-Fernandez, N., Parrens, M., Molero, B., Leroux, D., … Wigneron, J.-P. (2016). Overview of SMOS performance in terms of global soil moisture monitoring after six years in operation. Remote Sensing of Environment, 180, 40–63. https://doi.org/10.1016/j.rse.2016.02.042
  300. Kim, S., Parinussa, R., Liu, Y., Johnson, F., & Sharma, A. (2016). Merging Alternate Remotely-Sensed Soil Moisture Retrievals Using a Non-Static Model Combination Approach. Remote Sensing, 8, 6, 518. https://doi.org/10.3390/rs8060518
  301. Koch, F., Schlenz, F., Prasch, M., Appel, F., Ruf, T., & Mauser, W. (2016). Soil Moisture Retrieval Based on GPS Signal Strength Attenuation. Water, 8, 7, 276. https://doi.org/10.3390/w8070276
  302. Lee, J. H. (2016). The consecutive dry days to trigger rainfall over West Africa. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2016.06.003
  303. Leng, P., Song, X., Duan, S.-B., & Li, Z.-L. (2016). Preliminary validation of two temporal parameter-based soil moisture retrieval models using a satellite product and in situ soil moisture measurements over the REMEDHUS network. International Journal of Remote Sensing, 37, 24, 5902–5917. https://doi.org/10.1080/01431161.2016.1253896
  304. Martens, B., Miralles, D. G., Lievens, H., van der Schalie, R., de Jeu, R. A. M., Férnandez-Prieto, D., … Verhoest, N. E. C. (2016). GLEAM v3: satellite-based land evaporation and root-zone soil moisture. Geoscientific Model Development Discussions, 1–36. https://doi.org/10.5194/gmd-2016-162
  305. Martens, B., Miralles, D., Lievens, H., Fernández-Prieto, D., & Verhoest, N. E. C. (2016). Improving terrestrial evaporation estimates over continental Australia through assimilation of SMOS soil moisture. International Journal of Applied Earth Observation and Geoinformation, 48, 146–162. https://doi.org/10.1016/j.jag.2015.09.012
  306. Mattar, C., Santamaría-Artigas, A., Durán-Alarcón, C., Olivera-Guerra, L., Fuster, R., & Borvarán, D. (2016). The LAB-Net Soil Moisture Network: Application to Thermal Remote Sensing and Surface Energy Balance. Data, 1, 1, 6. doi.org/10.3390/data1010006
  307. McNally, A., Shukla, S., Arsenault, K. R., Wang, S., Peters-Lidard, C. D., & Verdin, J. P. (2016). Evaluating ESA CCI soil moisture in East Africa. International Journal of Applied Earth Observation and Geoinformation, 48, 96–109. https://doi.org/10.1016/j.jag.2016.01.001
  308. Nair, A., & Indu, J. (2016). Enhancing Noah Land Surface Model Prediction Skill over Indian Subcontinent by Assimilating SMOPS Blended Soil Moisture. Remote Sensing, 8, 12, 976. https://doi.org/10.3390/rs8120976
  309. Orth, R., Dutra, E., & Pappenberger, F. (2016). Improving Weather Predictability by Including Land Surface Model Parameter Uncertainty. Monthly Weather Review, 144, 4, 1551–1569. https://doi.org/10.1175/MWR-D-15-0283.1
  310. Pablos, M., Martínez-Fernández, J., Piles, M., Sánchez, N., Vall-llossera, M., & Camps, A. (2016). Multi-Temporal Evaluation of Soil Moisture and Land Surface Temperature Dynamics Using in Situ and Satellite Observations. Remote Sensing, 8, 7, 587. https://doi.org/10.3390/rs8070587
  311. Pal, M., Maity, R., & Dey, S. (2016). Statistical Modelling of Vertical Soil Moisture Profile: Coupling of Memory and Forcing. Water Resources Management, 30, 6, 1973–1986. http://doi.org/10.1007/s11269-016-1263-4
  312. Pal, M., Maity, R., & Dey, S. (2016). Statistical Modelling of Vertical Soil Moisture Profile: Coupling of Memory and Forcing. Water Resources Management, 30, 6, 1973–1986. https://doi.org/10.1007/s11269-016-1263-4
  313. Parinussa, R., de Jeu, R., van der Schalie, R., Crow, W., Lei, F., & Holmes, T. (2016). A Quasi-Global Approach to Improve Day-Time Satellite Surface Soil Moisture Anomalies through the Land Surface Temperature Input. Climate, 4, 4, 50. https://doi.org/10.3390/cli4040050
  314. Piles, M., Petropoulos, G. P., Sánchez, N., González-Zamora, Á., & Ireland, G. (2016). Towards improved spatio-temporal resolution soil moisture retrievals from the synergy of SMOS and MSG SEVIRI spaceborne observations. Remote Sensing of Environment, 180, 403–417. https://doi.org/10.1016/j.rse.2016.02.048
  315. Rautiainen, K., Parkkinen, T., Lemmetyinen, J., Schwank, M., Wiesmann, A., Ikonen, J., … Pulliainen, J. (2016). SMOS prototype algorithm for detecting autumn soil freezing. Remote Sensing of Environment, 180, 346–360. https://doi.org/10.1016/j.rse.2016.01.012
  316. Santi, E., Paloscia, S., Pettinato, S., Brocca, L., & Ciabatta, L. (2016). Robust Assessment of an Operational Algorithm for the Retrieval of Soil Moisture From AMSR-E Data in Central Italy. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9, 6, 2478–2492. https://doi.org/10.1109/JSTARS.2016.2575361
  317. Santi, E., Paloscia, S., Pettinato, S., & Fontanelli, G. (2016). Application of artificial neural networks for the soil moisture retrieval from active and passive microwave spaceborne sensors. International Journal of Applied Earth Observation and Geoinformation, 48, 61–73. https://doi.org/10.1016/j.jag.2015.08.002
  318. Schalie, R. va. der, Kerr, Y. H., Wigneron, J. P., Rodríguez-Fernández, N. J., Al-Yaari, A., & Jeu, R. A. M. d. (2016). Global SMOS Soil Moisture Retrievals from The Land Parameter Retrieval Model. International Journal of Applied Earth Observation and Geoinformation, 45, 125–134. https://doi.org/10.1016/j.jag.2015.08.005
  319. Scholze, M., Kaminski, T., Knorr, W., Blessing, S., Vossbeck, M., Grant, J. P., & Scipal, K. (2016). Simultaneous assimilation of SMOS soil moisture and atmospheric CO2 in-situ observations to constrain the global terrestrial carbon cycle. Remote Sensing of Environment, 180, 334–345. https://doi.org/10.1016/j.rse.2016.02.058
  320. Shin, Y., Lim, K., Park, K., & Jung, Y. (2016). Development of Dynamic Ground Water Data Assimilation for Quantifying Soil Hydraulic Properties from Remotely Sensed Soil Moisture. Water, 8, 8, 311. https://doi.org/10.3390/w8070311
  321. Su, C.-H., Ryu, D., Dorigo, W., Zwieback, S., Gruber, A., Albergel, C., … Wagner, W. (2016). Homogeneity of a global multisatellite soil moisture climate data record: HOMOGENEITY OF SOIL MOISTURE CDR. Geophysical Research Letters.. https://doi.org/10.1002/2016GL070458
  322. Wang, L., Li, X., Chen, Y., Yang, K., Chen, D., Zhou, J., … Huang, J. (2016). Validation of the global land data assimilation system based on measurements of soil temperature profiles. Agricultural and Forest Meteorology, 218–219, 288–297. https://doi.org/10.1016/j.agrformet.2016.01.003
  323. Wu, Q., Liu, H., Wang, L., & Deng, C. (2016). Evaluation of AMSR2 soil moisture products over the contiguous United States using in situ data from the International Soil Moisture Network. International Journal of Applied Earth Observation and Geoinformation, 45, 187–199. https://doi.org/10.1016/j.jag.2015.10.011
  324. Xaver, A. (2016). The International Soil Moisture Network – recent activities and future planning. 12th annual IGWCO CoP meeting, Koblenz, Germany.
  325. Xaver, A. (2016). Automated Quality Control Procedures for the International Soil Moisture Network. DGPF-OVG-SGPF Dreiländertagung 2016, Bern, Switzerland.
  326. Zawadzki, J., & Kędzior, M. (2016). Soil moisture variability over Odra watershed: Comparison between SMOS and GLDAS data. International Journal of Applied Earth Observation and Geoinformation, 45, 110–124. https://doi.org/10.1016/j.jag.2015.03.005
  327. Zeng, J., Chen, K.-S., Bi, H., & Chen, Q. (2016). A Preliminary Evaluation of the SMAP Radiometer Soil Moisture Product Over United States and Europe Using Ground-Based Measurements. IEEE Transactions on Geoscience and Remote Sensing, 54, 8, 4929–4940. https://doi.org/10.1109/TGRS.2016.2553085
  328. Zhang, D., Madsen, H., Ridler, M. E., Kidmose, J., Jensen, K. H., & Refsgaard, J. C. (2016). Multivariate hydrological data assimilation of soil moisture and groundwater head. Hydrology and Earth System Sciences, 20, 10, 4341–4357. https://doi.org/10.5194/hess-20-4341-2016
  329. Zhao, L., Yang, Z.-L., & Hoar, T. J. (2016). Global Soil Moisture Estimation by Assimilating AMSR-E Brightness Temperatures in a Coupled CLM4–RTM–DART System. Journal of Hydrometeorology, 17, 9, 2431–2454. https://doi.org/10.1175/JHM-D-15-0218.1
  330. Zwieback, S., Su, C.-H., Gruber, A., Dorigo, W. A., & Wagner, W. (2016). The Impact of Quadratic Nonlinear Relations between Soil Moisture Products on Uncertainty Estimates from Triple Collocation Analysis and Two Quadratic Extensions. Journal of Hydrometeorology, 17, 6, 1725–1743. https://doi.org/10.1175/JHM-D-15-0213.1
  331. Balsamo, G., Albergel, C., Beljaars, A., Boussetta, S., Brun, E., Cloke, H., … Vitart, F. (2015). ERA-Interim/Land: a global land surface reanalysis data set. Hydrology and Earth System Sciences, 19, 1, 389–407. http://doi.org/10.5194/hess-19-389-2015
  332. Boussetta, S., Balsamo, G., Dutra, E., Beljaars, A., & Albergel, C. (2015). Assimilation of surface albedo and vegetation states from satellite observations and their impact on numerical weather prediction. Remote Sensing of Environment, 163, -8, 111–126. http://doi.org/10.1016/j.rse.2015.03.009
  333. Brocca, L., Massari, C., Ciabatta, L., Moramarco, T., Penna, D., Zuecco, G., … Martínez-Fernández, J. (2015). Rainfall estimation from in situ soil moisture observations at several sites in Europe: an evaluation of the SM2RAIN algorithm. Journal of Hydrology and Hydromechanics, 63, 3. http://doi.org/10.1515/johh-2015-0016
  334. Calvet, J.-C., Fritz, N., Berne, C., Piguet, B., Maurel, W., & Meurey, C. (2015). Impact of gravels and organic matter on the thermal properties of grassland soils in southern France. SOIL Discussions, 2, 1, 737–765. http://doi.org/10.5194/soild-2-737-2015
  335. Cammalleri, C., Micale, F., & Vogt, J. (2015). On the value of combining different modelled soil moisture products for European drought monitoring. Journal of Hydrology, 525, 547–558. http://doi.org/10.1016/j.jhydrol.2015.04.021
  336. Chappell, A., Weaver, J., Purohit, S., Smith, W., Schuchardt, K., West, P., … Fox, P. (2015). Enhancing the impact of science data toward data discovery and reuse. IEEE, 271–277. http://doi.org/10.1109/ICIS.2015.7166605
  337. Coopersmith, E. J., Cosh, M. H., Bindlish, R., & Bell, J. (2015). Comparing AMSR-E soil moisture estimates to the extended record of the U.S. Climate Reference Network (USCRN). Advances in Water Resources, 85, 79–85. http://doi.org/10.1016/j.advwatres.2015.09.003
  338. Dorigo, W. A., Gruber, A., De Jeu, R. A. M., Wagner, W., Stacke, T., Loew, A., … Kidd, R. (2015). Evaluation of the ESA CCI soil moisture product using ground-based observations. Remote Sensing of Environment, 162, 380–395. http://doi.org/10.1016/j.rse.2014.07.023
  339. Dorigo, W., Wagner, W., Drusch M., Mecklenburg, S. and Van Oevelen, P. Xaver, A. (2015). The International Soil Moisture Network – Updates and recent progress. GTN-H Panel, Koblenz.
  340. Fernandez-Moran, R., Wigneron, J.-P., Lopez-Baeza, E., Al-Yaari, A., Bircher, S., Coll-Pajaron, A., … Kerr, Y. (2015). Analyzing the impact of using the SRP (Simplified roughness parameterization) method on soil moisture retrieval over different regions of the globe . IEEE, 5182–5185. http://doi.org/10.1109/IGARSS.2015.7327001
  341. Gonzalez-Zamora, A., Sanchez, N., Martinez-Fernandez, J., & Gumuzzio, A. (2015). Validation of SMOS and Aquarius soil moisture using two in situ networks in Spain. IEEE, 4738–4741. http://doi.org/10.1109/IGARSS.2015.7326888
  342. Hottenstein, J. D., Ponce-Campos, G. E., Moguel-Yanes, J., & Moran, M. S. (2015). Impact of Varying Storm Intensity and Consecutive Dry Days on Grassland Soil Moisture. Journal of Hydrometeorology, 16, 1, 106–117. http://doi.org/10.1175/JHM-D-14-0057.1
  343. Kim, S., Liu, Y. Y., Johnson, F. M., Parinussa, R. M., & Sharma, A. (2015). A global comparison of alternate AMSR2 soil moisture products: Why do they differ?. Remote Sensing of Environment, 161, 43–62. http://doi.org/10.1016/j.rse.2015.02.002
  344. Kim, S., Parinussa, R. M., Liu, Y. Y., Johnson, F. M., & Sharma, A. (2015). A framework for combining multiple soil moisture retrievals based on maximizing temporal correlation: IMPROVING AMSR2 SOIL MOISTURE RETRIEVALS. Geophysical Research Letters, 42, 16, 6662–6670. http://doi.org/10.1002/2015GL064981
  345. Kornelsen, K. C., & Coulibaly, P. (2015). Reducing multiplicative bias of satellite soil moisture retrievals. Remote Sensing of Environment, 165, 109–122. doi.org/10.1016/j.rse.2015.04.031
  346. Lee, J., & Im, J. (2015). A Novel Bias Correction Method for Soil Moisture and Ocean Salinity (SMOS) Soil Moisture: Retrieval Ensembles. Remote Sensing, 7, 12, 16045–16061. http://doi.org/10.3390/rs71215824
  347. Leng, P., Song, X., Li, Z.-L., Wang, Y., & Wang, R. (2015). Toward the Estimation of Surface Soil Moisture Content Using Geostationary Satellite Data over Sparsely Vegetated Area. Remote Sensing, 7, 4, 4112–4138. http://doi.org/10.3390/rs70404112
  348. Nicolai-Shaw, N., Hirschi, M., Mittelbach, H., & Seneviratne, S. I. (2015). Spatial representativeness of soil moisture using in situ, remote sensing, and land reanalysis data: SPATIAL REPRESENTATIVENESS OF SOIL MOISTURE. Journal of Geophysical Research: Atmospheres, 120, 19, 9955–9964. http://doi.org/10.1002/2015JD023305
  349. Parinussa, R. M., Holmes, T. R. H., Wanders, N., Dorigo, W. A., & de Jeu, R. A. M. (2015). A Preliminary Study toward Consistent Soil Moisture from AMSR2. Journal of Hydrometeorology, 16, 2, 932–947. http://doi.org/10.1175/JHM-D-13-0200.1
  350. Pierdicca, N., Fascetti, F., Pulvirenti, L., Crapolicchio, R., & Munoz-Sabater, J. (2015). Quadruple Collocation Analysis for Soil Moisture Product Assessment. IEEE Geoscience and Remote Sensing Letters, 12, 8, 1595–1599. http://doi.org/10.1109/LGRS.2015.2414654
  351. Pierdicca, N., Fascetti, F., Pulvirenti, L., Crapolicchio, R., & Muñoz-Sabater, J. (2015). Analysis of ASCAT, SMOS, in-situ and land model soil moisture as a regionalized variable over Europe and North Africa. Remote Sensing of Environment, 170, 280–289. http://doi.org/10.1016/j.rse.2015.09.005
  352. Spennemann, P. C., Rivera, J. A., Saulo, A. C., & Penalba, O. C. (2015). A Comparison of GLDAS Soil Moisture Anomalies against Standardized Precipitation Index and Multisatellite Estimations over South America. Journal of Hydrometeorology, 16, 1, 158–171. http://doi.org/10.1175/JHM-D-13-0190.1
  353. Su, C.-H., Narsey, S. Y., Gruber, A., Xaver, A., Chung, D., Ryu, D., & Wagner, W. (2015). Evaluation of post-retrieval de-noising of active and passive microwave satellite soil moisture. Remote Sensing of Environment, 163, 127–139. http://doi.org/10.1016/j.rse.2015.03.010
  354. Xaver, A. (2015). The International Soil Moisture Network – Background and experiences. GROW Workshop, Edinburgh.
  355. Zwieback, S., Paulik, C., & Wagner, W. (2015). Frozen Soil Detection Based on Advanced Scatterometer Observations and Air Temperature Data as Part of Soil Moisture Retrieval. Remote Sensing, 7, 3, 3206–3231. http://doi.org/10.3390/rs70303206
  356. Angevine, W. M., Bazile, E., Legain, D., and Pino, D. (2014). Land surface spinup for episodic modeling. Atmos. Chem. Phys., 14, 8165-8172. doi:10.5194/acp-14-8165-2014
  357. Albergel, C., Dorigo, W.,Balsamo, G., Muñoz-Sabater, J., de Rosnay, P., Isaksen, L., Brocca, L., de Jeu,R., Wagner, W. (2013). Monitoring multi-decadal satellite earth observation of soil moisture products through land surface reanalyses. Remote Sensing of Environment, 138, 77-89. doi: 10.1016/j.rse.2013.07.009
  358. Albergel, C., Dorigo, W., Reichle, R.H.,Balsamo, G., de Rosnay, P., Muñoz-Sabater, J., Isaksen, L., de Jeu, R., Wagner,W. (2013). Skill and global trend analysis ofsoil moisture from reanalyses and microwave remote sensing. Journal of Hydrometeorology, 14, 1259-1277. doi:10.1175/JHM-D-12-0161.1
  359. Dorigo, W. (2013). The International Soil Moisture Network – Background, experiences, outlook. GTN-H Panel, Koblenz.
  360. Dorigo, W. A., A. Gruber, A. Xaver, D. Zamojski, C. Paulik, C. Cordes, M. Vreugdenhil, W. Wagner, K. Scipal, P. van Oevelen, M. Drusch, S. Mecklenburg (2013). The International Soil Moisture Network – Latest Advancements and Future Prospects. Satellite Soil Moisture Validation and Application Workshop, Frascati, Italy.
  361. Dorigo, W.A., Xaver, A., Vreugdenhil, M., Gruber, A., Hegyiová, A., Sanchis-Dufau, A.D., Zamojski, D., Cordes, C., Wagner, W., and Drusch, M. (2013). Global Automated Quality Control of In situ Soil Moisture data from the International Soil Moisture Network. Vadose Zone Journal. doi:10.2136/vzj2012.0097
  362. Gruber, A., Dorigo, W.A., Zwieback, S., Xaver, A., Wagner, W. (2013). Characterizing coarse-scale representativeness of in-situ soil moisture measurements from the International Soil Moisture Network. Vadose Zone Journal, 12, 2. doi:10.2136/vzj2012.0170
  363. Ochsner, T., Cosh, M.,Cuenca, R., Dorigo, W., Draper, C., Hagimoto, Y., Kerr, Y., Larson, K.,Njoku, E., Small, E., Zreda, M. (2013). The state-of-the-art in large scale monitoring of soil moisture. Soil Science Society of America Journal, 77, 6, 1888-1919. doi:10.2136/sssaj2013.03.0093
  364. Xaver, A., Dorigo, W. A., Gruber, A., Hegyiova, A., Sanchis-Dufau, A.D. (2013). The International Soil Moisture Network – Recent Progress and Its Benefit for Soil Moisture Product Validation. 35th International Symposium on Remote Sensing of Environment – Earth Observation and Global Environmental Change, Beijing, China.
  365. Albergel, C., De Rosnay, P., Balsamo, G., Isaksen, L., & Muñoz-Sabater, J. (2012). Soil Moisture Analyses at ECMWF: Evaluation Using Global Ground-Based In Situ Observations. Journal of Hydrometeorology, 13, 1442-1460
  366. Albergel, C., de Rosnay, P., Gruhier, C., Muñoz-Sabater, J., Hasenauer, S., Isaksen, L., Kerr, Y., & Wagner, W. (2012). Evaluation of remotely sensed and modelled soil moisture products using global ground-based in situ observations. Remote Sensing of Environment, 118, 215-226
  367. Albergel, C.., G. Balsamo, P. de Rosnay, J. Muñoz-Sabater, and S. Boussetta, (2012). A bare ground evaporation revision in the ECMWF land-surface scheme: evaluation of its impact using ground soil moisture and satellite microwave data. Hydrol. Earth Syst. Sci., 16, 3607-3620
  368. Bircher, S., Skou, N., Jensen, K. H., Walker, J. P., & Rasmussen, L. (2012). A soil moisture and temperature network for SMOS validation in western Denmark. Hydrology and Earth System Sciences, 16, 5, 1445-1463
  369. Collow, T.W., Robock, A., Basara, J.B., Illston, B.G. (2012). Evaluation of SMOS retrievals of soil moisture over the central United States with currently available in situ observations. Journal of Geophysical Research D: Atmospheres, 117, 9
  370. dall''Amico, J.T.; Schlenz, F.; Loew, A.; Mauser, W. (2012). First Results of SMOS Soil Moisture Validation in the Upper Danube Catchment. IEEE Transactions on Geoscience and Remote Sensing, 50, 5, 1507-1516
  371. dall'Amico, J.T., Schlenz, F., Loew, A., Mauser, W., Kainulainen, J., Balling, J.E., Bouzinac, C. (2012). The SMOS Validation Campaign 2010 in the Upper Danube Catchment: A Data Set for Studies of Soil Moisture, Brightness Temperature, and Their Spatial Variability Over a Heterogeneous Land Surface. IEEE Transactions on Geoscience and Remote Sensing, 51, 1, 364-377
  372. Dorigo, W. A., P. van Oevelen, M. Drusch, W. Wagner, K. Scipal, S. Mecklenburg (2012). Advances, experiences, and prospects of the International Soil Moisture Network. American Geophysical Union (AGU) Fall Meeting, San Francisco, CA, USA.
  373. Gruber, A., Dorigo, W., Xaver, A., Drusch, M (2012). Quality characterization of the in-situ soil moisture observations within the International Soil Moisture Network. Joint Meeting of the Second International Soil Sensing Technology Conference the Soil Physics Technical Committee Annual Meeting, and the ASA Sensor-based Water Management Community, Honolulu, Hawaii, USA.
  374. Hegyiová, A., W. A. Dorigo, A. Gruber, A. Xaver, A. D. Sanchis-Dufau, M. Drusch. (2012). The International Soil Moisture Network: A database of in-situ soil moisture measurements to support Metop/ASCAT soil moisture product validation. 2012 EUMETSAT Meteorological Satellite Conference, Sopot, Poland.
  375. Liu, Y. Y., Dorigo, W. A., Parinussa, R. M., De Jeu, R. A. M., Wagner, W., McCabe, M. F., . . . Van Dijk, A. I. J. M. (2012). Trend-preserving blending of passive and active microwave soil moisture retrievals. Remote Sensing of Environment, 123, 280-297
  376. Luo, Y. Q., Randerson, J. T., Abramowitz, G., Bacour, C., Blyth, E., Carvalhais, N., . . . Zhou, X. H. (2012). A framework for benchmarking land models. Biogeosciences, 9, 10, 3857-3874
  377. Mecklenburg, S., Drusch, M., Kerr, Y. H., Font, J., Martin-Neira, M., Delwart, S., . . . Crapolicchio, R. (2012). ESA's soil moisture and ocean salinity mission: Mission performance and operations. IEEE Transactions on Geoscience and Remote Sensing, 50, 5, 1354-1366
  378. Pan, M., Sahoo, A.K., Wood, E.F., Al Bitar, A., Leroux, D., Kerr, Y.H. (2012). An initial assessment of SMOS derived soil moisture over the continental United States. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5, 6211458, 1448-1457
  379. Parrens, M., Zakharova, E., Lafont, S., Calvet, J.-C., Kerr, Y., Wagner, W., and Wigneron, J.-P. (2012). Comparing soil moisture retrievals from SMOS and ASCAT over France. Hydrol. Earth Syst. Sci., 16, 423-440. doi:10.5194/hess-16-423-2012
  380. Paulik, C., Naeimi, V., Dorigo, W., Wagner, W., Kidd, R (2012). A global validation of the ASCAT Soil Water Index (SWI) with in situ data from the International Soil Moisture Network. Geophysical Research Abstracts, EGU2012-10189, EGU General Assembly, Vienna, Austria.
  381. Peischl, S., Walker, J. P., Rüdiger, C., Ye, N., Kerr, Y. H., Kim, E., . . . Allahmoradi, M. (2012). The AACES field experiments: SMOS calibration and validation across the murrumbidgee river catchment. Hydrology and Earth System Sciences, 16, 6, 1697-1708
  382. Schlenz, F.; dall''Amico, J.T.; Loew, A.; Mauser, W. (2012). Uncertainty Assessment of the SMOS Validation in the Upper Danube Catchment. IEEE Transactions on Geoscience and Remote Sensing, 50, 5, 1517-1529
  383. Schlenz, F., Dall'Amico, J. T., Mauser, W., & Loew, A. (2012). Analysis of SMOS brightness temperature and vegetation optical depth data with coupled land surface and radiative transfer models in southern Germany. Hydrology and Earth System Sciences, 16, 10, 3517-3533
  384. Skierucha, W., Wilczek, A., & Szypłowska, A. (2012). Dielectric spectroscopy in agrophysics. International Agrophysics, 26, 2, 187-197
  385. Skierucha, W., Wilczek, A., Szypłowska, A., Sławiński, C., & Lamorski, K. (2012). A TDR-based soil moisture monitoring system with simultaneous measurement of soil temperature and electrical conductivity. Sensors, 12, 10, 13545-13566
  386. Smith, A. B., Walker, J. P., Western, A. W., Young, R. I., Ellett, K. M., Pipunic, R. C., . . . Richter, H. (2012). The murrumbidgee soil moisture monitoring network data set. Water Resources Research, 48, 7
  387. Van doninck, J., Peters, J., Lievens, H., De Baets, B., and Verhoest, N. E. C. (2012). Accounting for seasonality in a soil moisture change detection algorithm for ASAR Wide Swath time series. Hydrol. Earth Syst. Sci., 16, 773-786
  388. Wanders, N., Karssenberg, D., Bierkens, M., Parinussa, R., de Jeu, R., van Dam, J., & de Jong, S. (2012). Observation uncertainty of satellite soil moisture products determined with physically-based modeling. Remote Sensing of Environment, 127, 341-356
  389. Xaver, A., Gruber, A., Dorigo, W.A., Hegyiova, A., Sanchis-Dufau, A.D. (2012). The International Soil Moisture Network: a global soil moisture monitoring platform. EUROSOIL 2012 Soil Science for the Benefit of Mankind and Environment, Bari, Italy.
  390. Xaver, A., Gruber, A., Hegiova, A., Sanchis-Dufau, A.D. and Dorigo, W.A. (2012). The advanced quality control techniques planned for the Interantion Soil Moisture Network. European Geoscience Union (EGU) General Assembly 2012, Vienna, Austria.
  391. Xaver, A., Gruber, A., Hegyiova, A. Dorigo, W.A., Drusch, M. (2012). Recent progress on the International Soil Moisture Network. AGU Chapman Conference on Remote Sensing of the Terrestrial Water Cycle, Kona, Hawaii.
  392. Zreda, M., W.J. Shuttleworth, X. Zeng, C. Zweck, D. Desilets, T. Franz, and R. Rosolem, (2012). COSMOS: the COsmic-ray Soil Moisture Observing System. Hydrology and Earth System Sciences 16, 4079-4099. doi.org/10.5194/hess-16-4079-2012
  393. Brocca L., Hasenauer, S., Lacava, T., Melone, F., Moramarco, T., Wagner, W., Dorigo, W., Matgen, P., Martínez-Fernández, J., Llorens, P., Latron, J., Martin, C., Bittelli, M. (2011). Soil Moisture estimation through ASCAT ans AMSR-E sensors: An intercomparison and validation study across Europe. Remote Sensing of Environment, 115, 3390-3408
  394. Brocca L., Hasenauer, S., Lacava, T., Melone, F., Moramarco, T., Wagner, W., Dorigo, W., Matgen, P., Martínez-Fernández, J., Llorens, P., Latron, J., Martin, C., Bittelli, M. (2011). Soil Moisture estimation through ASCAT ans AMSR-E sensors: An intercomparison and validation study across Europe. Remote Sensing of Environment, 115, 3390-3408
  395. Dorigo, W. A. (2011). The International Soil Moisture Network. SMAP cal/val workshop, Oxnard, USA.
  396. Dorigo, W.A., Wagner, W., Hohensinn, R., Hahn, S., Paulik, C., Xaver, A., Gruber, A., Drusch, M., Mecklenburg, S., van Oevelen, P., Robock, A., and Jackson, T., Jackson, T. (2011). "The International Soil Moisture Network: A data hosting facility for global in situ soil moisture measurements". Hydrology and Earth System Sciences 15, 15, 5, 1675-1698. doi:10.5194/hess-15-1675-2011
  397. Dorigo, W., Gruber, A., Van Oevelen, P., Wagner, W., Drusch, M., Mecklenburg, S., Robock, A., Jackson, T. (2011). The International Soil Moisture Network - An observational network for soil moisture product validations. 34th International Symposium on Remote Sensing of Environment. The GEOSS Era: Towards Operational Environmental Monitoring., Sydney, Australia.
  398. Dorigo, W., Van Oevelen, P., Wagner, W., Drusch, M., Mecklenburg, S., Robock, A., Jackson, T. (2011). A new international network for in situ soil moisture data. Eos, 92, 17, 141-142. doi:10.1029/2011EO170001
  399. Dorigo, W., Van Oevelen, P., Wagner, W., jackson, T, De Jeu, R., Robock, A. (2011). Towards a soil moisture climate data record in support of GCOS: where are we?. WCRP Open Science Conference, Denver, CO, USA.
  400. Liu, Y. Y., Parinussa, R. M., Dorigo, W. A., De Jeu, R. A. M., Wagner, W., van Dijk, A. I. J. M., McCabe, M. F., Evans, J. P. (2011). Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals. Hydrology and Earth System Sciences, 15, 425-436. doi:10.5194/hess-15-425-2011
  401. Van Oevelen, P.J., Dorigo, W., Jackson, T.J., Wagner, W., Drusch, M., Mecklenburg, S. (2011). The Establishment of the International Soil Moisture Network. 91st American Meteorological Society Annual Meeting, Seattle, USA.
  402. Dorigo, W., Hahn, S., Hohensinn, R., Paulik, C., Wagner,W., Drusch, M., van Oevelen, P. (2010). The International Soil Moisture Network - A data hosting facility for in situ soil moisture measurements in support of SMOS cal/val. Geophysical Research Abstracts, EGU2010-12063 EGU General Assembly, 12, Vienna, Austria.
  403. Dorigo, W., Wagner, W., Drusch M., Mecklenburg, S. and Van Oevelen, P. (2010). The International Soil Moisture Network - A data hosting facility for in situ soil moisture measurements in support of SMOS cal/val. SMOS Validation and Retrieval Team Workshop, ESRIN, Frascati, Italy.