Filter Set

    Articles making use of the ISMN:

  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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..
  11. Fang, B., Lakshmi, V., Bindlish, R., & Jackson, T. (2018). AMSR2 Soil Moisture Downscaling Using Temperature and Vegetation Data. Remote Sensing, 10.
  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.
  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.
  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.
  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.
  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.
  17. Gumbricht, T. (2018). Detecting Trends in Wetland Extent from MODIS Derived Soil Moisture Estimates. Remote Sensing, 10.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  26. Lembrechts, J.J., Nijs, I., & Lenoir, J. (2018). Incorporating microclimate into species distribution models. Ecography.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  46. Ye, K., & Lau, N.-C. (2018). Characteristics of Eurasian snowmelt and its impacts on the land surface and surface climate. Climate Dynamics.
  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.
  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.