Filter Set

    Using ISMN data:

  1. 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
  2. Adamolekun, O. (2019). Field validation of proximal sensors on a typical Prairie field. https://hdl.handle.net/1993/33950
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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
  18. 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
  19. 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
  20. 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
  21. 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
  22. 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
  23. 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
  24. 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
  25. 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
  26. Chipade, R. A. (2019). Soil moisture retrieval using indigenously developed NavIC-GPS-SBAS receiver. Coordinates. https://www.researchgate.net/publication/333641239
  27. 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
  28. 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
  29. 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
  30. 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
  31. 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
  32. 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
  33. 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
  34. 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
  35. 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
  36. 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
  37. 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
  38. 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
  39. 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.
  40. 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
  41. 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
  42. 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
  43. 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
  44. 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
  45. 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
  46. 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
  47. 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
  48. 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
  49. 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
  50. 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
  51. 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
  52. 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
  53. 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
  54. 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
  55. 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
  56. 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
  57. 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
  58. 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
  59. 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
  60. 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
  61. 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
  62. 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
  63. Ropelewski, C. F., Arkin, P. A.: (2019). Climate Analysis. Cambridge University Press, ISBN 978-0-521-89616. https://doi.org/10.1017/9781139034746
  64. 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
  65. 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
  66. 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
  67. 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
  68. 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
  69. 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
  70. 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
  71. 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
  72. 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
  73. 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
  74. 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
  75. 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
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  77. 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
  78. 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
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  83. 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
  84. 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
  85. 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
  86. 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
  87. 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
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