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    Articles making use of the ISMN:

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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
  18. 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
  19. 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
  20. 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
  21. 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
  22. 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
  23. 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