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  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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
  18. 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
  19. 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
  20. 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
  21. 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
  22. 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
  23. 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
  24. 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
  25. 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
  26. 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
  27. 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
  28. 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
  29. 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
  30. 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
  31. 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
  32. 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
  33. 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
  34. 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
  35. 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
  36. 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
  37. 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
  38. 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
  39. 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
  40. 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
  41. 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
  42. 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
  43. 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
  44. 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
  45. Adamolekun, O. (2019). Field validation of proximal sensors on a typical Prairie field. https://hdl.handle.net/1993/33950
  46. 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
  47. 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
  48. 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
  49. 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
  50. 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
  51. 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
  52. 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
  53. 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
  54. 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
  55. 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
  56. 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
  57. 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
  58. 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
  59. 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
  60. 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
  61. 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
  62. 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
  63. 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
  64. 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
  65. 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
  66. 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
  67. 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
  68. 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
  69. Chipade, R. A. (2019). Soil moisture retrieval using indigenously developed NavIC-GPS-SBAS receiver. Coordinates. https://www.researchgate.net/publication/333641239
  70. 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
  71. 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
  72. 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
  73. 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
  74. 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
  75. 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
  76. 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
  77. 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
  78. 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
  79. 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.
  80. 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
  81. 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
  82. 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
  83. 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.
  84. 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
  85. 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
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