Filter Set


  1. 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. DOI:
  2. 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. DOI:
  3. 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. DOI:
  4. 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. DOI:
  5. 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. DOI:
  6. 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. DOI:
  7. 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. DOI:
  8. 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. DOI:
  9. 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. DOI:
  10. 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.. DOI:
  11. Fang, B., Lakshmi, V., Bindlish, R., & Jackson, T. (2018). AMSR2 Soil Moisture Downscaling Using Temperature and Vegetation Data. Remote Sensing, 10. DOI:
  12. 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. DOI:
  13. 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. DOI:
  14. 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. DOI:
  15. 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. DOI:
  16. 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. DOI:
  17. Gumbricht, T. (2018). Detecting Trends in Wetland Extent from MODIS Derived Soil Moisture Estimates. Remote Sensing, 10. DOI:
  18. 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. DOI:
  19. 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. DOI:
  20. 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. DOI:
  21. 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. DOI:
  22. 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. DOI:
  23. 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. DOI:
  24. 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. DOI:
  25. 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. DOI:
  26. Lembrechts, J.J., Nijs, I., & Lenoir, J. (2018). Incorporating microclimate into species distribution models. Ecography. DOI:
  27. 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. DOI:
  28. 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. DOI:
  29. 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. DOI:
  30. 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. DOI:
  31. 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. DOI:
  32. 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. DOI:
  33. 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. DOI:
  34. 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. DOI:
  35. 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. DOI:
  36. 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. DOI:
  37. 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. -
  38. 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
  39. 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. DOI:
  40. 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. DOI:
  41. 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. DOI:
  42. 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. DOI:
  43. 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. DOI:
  44. 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. DOI:
  45. 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. DOI:
  46. Ye, K., & Lau, N.-C. (2018). Characteristics of Eurasian snowmelt and its impacts on the land surface and surface climate. Climate Dynamics. DOI:
  47. 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. DOI:
  48. 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. DOI: