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    Articles making use of the ISMN:

  1. Abdi, A., Boke-Olén, N., Tenenbaum, D., Tagesson, T., Cappelaere, B., & Ardö, J. (2017). Evaluating Water Controls on Vegetation Growth in the Semi-Arid Sahel Using Field and Earth Observation Data. Remote Sensing, 9. https://doi.org/10.3390/rs9030294
  2. Afshar, M.H., & Yilmaz, M.T. (2017). The added utility of nonlinear methods compared to linear methods in rescaling soil moisture products. Remote Sensing of Environment, 196, 224-237. https://doi.org/10.1016/j.rse.2017.05.017
  3. Al-Yaari, A., Wigneron, J.P., Kerr, Y., Rodriguez-Fernandez, N., O'Neill, P.E., Jackson, T.J., De Lannoy, G.J.M., Al Bitar, A., Mialon, A., Richaume, P., Walker, J.P., Mahmoodi, A., & Yueh, S. (2017). Evaluating soil moisture retrievals from ESA's SMOS and NASA's SMAP brightness temperature datasets. Remote Sensing of Environment, 193, 257-273. https://doi.org/10.1016/j.rse.2017.03.010
  4. Anoop, S., Maurya, D.K., Rao, P.V.N., & Sekhar, M. (2017). Validation and Comparison of LPRM Retrieved Soil Moisture Using AMSR2 Brightness Temperature at Two Spatial Resolutions in the Indian Region. IEEE Geoscience and Remote Sensing Letters, 14, 1561-1564. https://doi.org/10.1109/lgrs.2017.2722542
  5. Baguis Pierre, & Emmanuel, R. (2017). Soil Moisture Data Assimilation in a Hydrological Model: A Case Study in Belgium Using Large-Scale Satellite Data. Remote Sensing, 9. https://doi.org/10.3390/rs9080820
  6. Brocca, L., Crow, W.T., Ciabatta, L., Massari, C., de Rosnay, P., Enenkel, M., Hahn, S., Amarnath, G., Camici, S., Tarpanelli, A., & Wagner, W. (2017). A Review of the Applications of ASCAT Soil Moisture Products. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10, 2285-2306. https://doi.org/10.1109/jstars.2017.2651140
  7. Candy, B., Saunders, R.W., Ghent, D., & Bulgin, C.E. (2017). The Impact of Satellite-Derived Land Surface Temperatures on Numerical Weather Prediction Analyses and Forecasts. Journal of Geophysical Research: Atmospheres, 122, 9783-9802. https://doi.org/10.1002/2016jd026417
  8. Cerlini, P.B., Meniconi, S., & Brunone, B. (2017). Groundwater Supply and Climate Change Management by Means of Global Atmospheric Datasets. Preliminary Results. Procedia Engineering, 186, 420-427. https://doi.org/10.1016/j.proeng.2017.03.245
  9. De Santis, D., & Biondi, D. (2017). A quality assessment of the soil water index by the propagation of ASCAT soil moisture error estimates through an exponential filter. International Journal of Remote Sensing, 39, 232-257. https://doi.org/10.1080/01431161.2017.1382745
  10. Dong, J., & Crow, W. (2017). An improved triple collocation analysis algorithm for decomposing auto-correlated and white soil moisture retrieval errors. Journal of Geophysical Research: Atmospheres. https://doi.org/10.1002/2017jd027387
  11. Dorigo, W., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, P.D., Hirschi, M., Ikonen, J., de Jeu, R., Kidd, R., Lahoz, W., Liu, Y.Y., Miralles, D., Mistelbauer, T., Nicolai-Shaw, N., Parinussa, R., Pratola, C., Reimer, C., van der Schalie, R., Seneviratne, S.I., Smolander, T., & Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. Remote Sensing of Environment, 203, 185-215. https://doi.org/10.1016/j.rse.2017.07.001
  12. Dwevedi, A., Kumar, P., Kumar, P., Kumar, Y., Sharma, Y.K., & Kayastha, A.M. (2017). 15 - Soil sensors: detailed insight into research updates, significance, and future prospects A2 - Grumezescu, Alexandru Mihai. New Pesticides and Soil Sensors. Academic Press, 561-594
  13. Emery, C. (2017). Contribution de la future mission altim´etrique `a large fauch´ee SWOT pour la mod´elisation hydrologique `a grande ´echelle. -
  14. Fernandez-Moran, R., Wigneron, J.P., De Lannoy, G., Lopez-Baeza, E., Parrens, M., Mialon, A., Mahmoodi, A., Al-Yaari, A., Bircher, S., Al Bitar, A., Richaume, P., & Kerr, Y. (2017). A new calibration of the effective scattering albedo and soil roughness parameters in the SMOS SM retrieval algorithm. International Journal of Applied Earth Observation and Geoinformation, 62, 27-38. https://doi.org/10.1016/j.jag.2017.05.013
  15. Gasch, C.K., Brown, D.J., Brooks, E.S., Yourek, M., Poggio, M., Cobos, D.R., & Campbell, C.S. (2017). A pragmatic, automated approach for retroactive calibration of soil moisture sensors using a two-step, soil-specific correction. Computers and Electronics in Agriculture, 137, 29-40. https://doi.org/10.1016/j.compag.2017.03.018
  16. Gruber, A., Dorigo, W.A., Crow, W., & Wagner, W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing, 55, 6780-6792. https://doi.org/10.1109/TGRS.2017.2734070
  17. Hartmann, A., Gleeson, T., Wada, Y., & Wagener, T. (2017). Enhanced groundwater recharge rates and altered recharge sensitivity to climate variability through subsurface heterogeneity. Proc Natl Acad Sci U S A, 114, 2842-2847. https://doi.org/10.1073/pnas.1614941114
  18. Ji, P., Yuan, X., & Liang, X.-Z. (2017). Do Lateral Flows Matter for the Hyperresolution Land Surface Modeling?. Journal of Geophysical Research: Atmospheres. https://doi.org/10.1002/2017jd027366
  19. Jung, C., Lee, Y., Cho, Y., & Kim, S. (2017). A Study of Spatial Soil Moisture Estimation Using a Multiple Linear Regression Model and MODIS Land Surface Temperature Data Corrected by Conditional Merging. Remote Sensing, 9. https://doi.org/10.3390/rs9080870
  20. Kapilaratne, R.G.C.J., & Lu, M. (2017). Automated general temperature correction method for dielectric soil moisture sensors. Journal of Hydrology, 551, 203-216. https://doi.org/10.1016/j.jhydrol.2017.05.050
  21. Karthikeyan, L., Pan, M., Wanders, N., Kumar, D.N., & Wood, E.F. (2017). Four decades of microwave satellite soil moisture observations: Part 2. Product validation and inter-satellite comparisons. Advances in Water Resources, 109, 236-252. https://doi.org/10.1016/j.advwatres.2017.09.010
  22. Kim, H., Parinussa, R., Konings, A.G., Wagner, W., Cosh, M.H., Lakshmi, V., Zohaib, M., Choi, M. (2017). Global-scale assessment and combination of SMAP with ASCAT (active) and AMSR2 (passive) soil moisture products. Remote Sensing of Environment, 204, 260-275. dx.doi.org/10.1016/j.rse.2017.10.026
  23. Kim, S., Balakrishnan, K., Liu, Y., Johnson, F., & Sharma, A. (2017). Spatial Disaggregation of Coarse Soil Moisture Data by Using High-Resolution Remotely Sensed Vegetation Products. IEEE Geoscience and Remote Sensing Letters, 14, 1604-1608. https://doi.org/10.1109/lgrs.2017.2725945
  24. Kolassa, J., Gentine, P., Prigent, C., Aires, F., & Alemohammad, S.H. (2017). Soil moisture retrieval from AMSR-E and ASCAT microwave observation synergy. Part 2: Product evaluation. Remote Sensing of Environment, 195, 202-217. https://doi.org/10.1016/j.rse.2017.04.020
  25. Lauer, A., Eyring, V., Righi, M., Buchwitz, M., Defourny, P., Evaldsson, M., Friedlingstein, P., de Jeu, R., de Leeuw, G., Loew, A., Merchant, C.J., Müller, B., Popp, T., Reuter, M., Sandven, S., Senftleben, D., Stengel, M., Van Roozendael, M., Wenzel, S., & Willén, U. (2017). Benchmarking CMIP5 models with a subset of ESA CCI Phase 2 data using the ESMValTool. Remote Sensing of Environment, 203, 9-39. https://doi.org/10.1016/j.rse.2017.01.007
  26. Lee, J.H., Zhao, C., & Kerr, Y. (2017). Stochastic Bias Correction and Uncertainty Estimation of Satellite-Retrieved Soil Moisture Products. Remote Sensing, 9. https://doi.org/10.3390/rs9080847
  27. Leeper, R.D., Bell, J.E., Vines, C., & Palecki, M. (2017). An Evaluation of the North American Regional Reanalysis Simulated Soil Moisture Conditions during the 2011–13 Drought Period. Journal of Hydrometeorology, 18, 515-527. https://doi.org/10.1175/jhm-d-16-0132.1
  28. Liangjing, Z. (2017). Terrestrial water storage from GRACE gravity data for hydrometeorological applications. -
  29. Lievens, H., Martens, B., Verhoest, N.E.C., Hahn, S., Reichle, R.H., & Miralles, D.G. (2017). Assimilation of global radar backscatter and radiometer brightness temperature observations to improve soil moisture and land evaporation estimates. Remote Sensing of Environment, 189, 194-210. https://doi.org/10.1016/j.rse.2016.11.022
  30. Lievens, H., Reichle, R.H., Liu, Q., De Lannoy, G.J.M., Dunbar, R.S., Kim, S.B., Das, N.N., Cosh, M., Walker, J.P., & Wagner, W. (2017). Joint Sentinel-1 and SMAP data assimilation to improve soil moisture estimates. Geophysical Research Letters, 44, 6145-6153. https://doi.org/10.1002/2017gl073904
  31. Lin, X., Wen, J., Tang, Y., Ma, M., You, D., Dou, B., Wu, X., Zhu, X., Xiao, Q., & Liu, Q. (2017). A web-based land surface remote sensing products validation system (LAPVAS): application to albedo product. International Journal of Digital Earth, 11, 308-328. https://doi.org/10.1080/17538947.2017.1320593
  32. Liu, Q., Hao, Y., Stebler, E., Tanaka, N., & Zou, C.B. (2017). Impact of plant functional types on coherence between precipitation and soil moisture - a wavelet analysis. Geophysical Research Letters.. https://doi.org/10.1002/2017gl075542
  33. Liu, Z., Li, P., & Yang, J. (2017). Soil Moisture Retrieval and Spatiotemporal Pattern Analysis Using Sentinel-1 Data of Dahra, Senegal. Remote Sensing, 9. https://doi.org/10.3390/rs9111197
  34. Mahecha, M.D., Gans, F., Sippel, S., Donges, J.F., Kaminski, T., Metzger, S., Migliavacca, M., Papale, D., Rammig, A., & Zscheischler, J. (2017). Detecting impacts of extreme events with ecological in-situ monitoring networks. Biogeosciences Discussions, 1-33. https://doi.org/10.5194/bg-2017-130
  35. Martens, B., Miralles, D.G., Lievens, H., van der Schalie, R., de Jeu, R.A.M., Fernández-Prieto, D., Beck, H.E., Dorigo, W.A., & Verhoest, N.E.C. (2017). GLEAM v3: satellite-based land evaporation and root-zone soil moisture. Geoscientific Model Development, 10, 1903-1925. https://doi.org/10.5194/gmd-10-1903-2017
  36. Martinez, G., Brocca, L., Gerke, H.H., & Pachepsky, Y.A. (2017). Soil Variability and Biogeochemical Fluxes: Toward a Better Understanding of Soil Processes at the Land Surface. Vadose Zone Journal, 16. https://doi.org/10.2136/vzj2017.07.0145
  37. Massari, C., Su, C.-H., Brocca, L., Sang, Y.-F., Ciabatta, L., Ryu, D., & Wagner, W. (2017). Near real time de-noising of satellite-based soil moisture retrievals: An intercomparison among three different techniques. Remote Sensing of Environment, 198, 17-29. https://doi.org/10.1016/j.rse.2017.05.037
  38. McCabe, M.F., Rodell, M., Alsdorf, D.E., Miralles, D.G., Uijlenhoet, R., Wagner, W., Lucieer, A., Houborg, R., Verhoest, N.E.C., Franz, T.E., Shi, J., Gao, H., & Wood, E.F. (2017). The Future of Earth Observation in Hydrology. Hydrology and Earth System Sciences Discussions, 1-55. https://doi.org/10.5194/hess-2017-54
  39. Miyaoka, K., Gruber, A., Ticconi, F., Hahn, S., Wagner, W., Figa-Saldana, J., & Anderson, C. (2017). Triple Collocation Analysis of Soil Moisture From Metop-A ASCAT and SMOS Against JRA-55 and ERA-Interim. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10, 2274-2284. https://doi.org/10.1109/jstars.2016.2632306
  40. Mohanty, B.P., Cosh, M.H., Lakshmi, V., & Montzka, C. (2017). Soil Moisture Remote Sensing: State-of-the-Science. Vadose Zone Journal, 16. https://doi.org/10.2136/vzj2016.10.0105
  41. Montzka, C., Bogena, H., Zreda, M., Monerris, A., Morrison, R., Muddu, S., & Vereecken, H. (2017). Validation of Spaceborne and Modelled Surface Soil Moisture Products with Cosmic-Ray Neutron Probes. Remote Sensing, 9. https://doi.org/10.3390/rs9020103
  42. Murguia-Flores, F., Arndt, S., Ganesan, A.L., Murray-Tortarolo, G.N., & Hornibrook, E.R.C. (2017). Soil Methanotrophy Model (MeMo v1.0): a process-based model to quantify global uptake of atmospheric methane by soil. Geoscientific Model Development Discussions, 1-38. https://doi.org/10.5194/gmd-2017-124
  43. Nguyen, H.H., Kim, H., & Choi, M. (2017). Evaluation of the soil water content using cosmic-ray neutron probe in a heterogeneous monsoon climate-dominated region. Advances in Water Resources, 108, 125-138. https://doi.org/10.1016/j.advwatres.2017.07.020
  44. Nilawar, A., Calderella, C., Lakhankar, T., Waikar, M., & Munoz, J. (2017). Satellite Soil Moisture Validation Using Hydrological SWAT Model: A Case Study of Puerto Rico, USA. Hydrology, 4. https://doi.org/10.3390/hydrology4040045
  45. Pan, X., Kornelsen, K.C., & Coulibaly, P. (2017). Estimating Root Zone Soil Moisture at Continental Scale Using Neural Networks. JAWRA Journal of the American Water Resources Association, 53, 220-237. https://doi.org/10.1111/1752-1688.12491
  46. Park, S., Im, J., Park, S., & Rhee, J. (2017). Drought monitoring using high resolution soil moisture through multi-sensor satellite data fusion over the Korean peninsula. Agricultural and Forest Meteorology, 237-238, 257-269. https://doi.org/10.1016/j.agrformet.2017.02.022
  47. Park, S., Park, S., Im, J., Rhee, J., Shin, J., & Park, J. (2017). Downscaling GLDAS Soil Moisture Data in East Asia through Fusion of Multi-Sensors by Optimizing Modified Regression Trees. Water, 9. https://doi.org/10.3390/w9050332
  48. Peng, J., Loew, A., Merlin, O., & Verhoest, N.E.C. (2017). A review of spatial downscaling of satellite remotely sensed soil moisture. Reviews of Geophysics, 55, 341-366. https://doi.org/10.1002/2016RG000543
  49. Petropoulos, G.P., & McCalmont, J.P. (2017). An Operational In Situ Soil Moisture & Soil Temperature Monitoring Network for West Wales, UK: The WSMN Network. Sensors (Basel), 17. https://doi.org/10.3390/s17071481
  50. Phillips, T.J., Klein, S.A., Ma, H.-Y., Tang, Q., Xie, S., Williams, I.N., Santanello, J.A., Cook, D.R., & Torn, M.S. (2017). Using ARM Observations to Evaluate Climate Model Simulations of Land-Atmosphere Coupling on the U.S. Southern Great Plains. Journal of Geophysical Research: Atmospheres. https://doi.org/10.1002/2017jd027141
  51. Pierdicca, N., Fascetti, F., Pulvirenti, L., & Crapolicchio, R. (2017). Error Characterization of Soil Moisture Satellite Products: Retrieving Error Cross-Correlation Through Extended Quadruple Collocation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10, 4522-4530. https://doi.org/10.1109/jstars.2017.2714025
  52. Przeździecki, K., Zawadzki, J., Cieszewski, C., & Bettinger, P. (2017). Estimation of soil moisture across broad landscapes of Georgia and South Carolina using the triangle method applied to MODIS satellite imagery. Silva Fennica, 51. https://doi.org/10.14214/sf.1683
  53. Rains, D., Han, X., Lievens, H., Montzka, C., & Verhoest, N.E.C. (2017). SMOS brightness temperature assimilation into the Community Land Model. Hydrology and Earth System Sciences, 21, 5929-5951. https://doi.org/10.5194/hess-21-5929-2017
  54. Ran, Y., Li, X., Jin, R., Kang, J., & Cosh, M.H. (2017). Strengths and weaknesses of temporal stability analysis for monitoring and estimating grid-mean soil moisture in a high-intensity irrigated agricultural landscape. Water Resources Research, 53, 283-301. https://doi.org/10.1002/2015wr018182
  55. Ray, R., Fares, A., He, Y., & Temimi, M. (2017). Evaluation and Inter-Comparison of Satellite Soil Moisture Products Using In Situ Observations over Texas, U.S. Water, 9. https://doi.org/10.3390/w9060372
  56. Reichle, R.H., Draper, C.S., Liu, Q., Girotto, M., Mahanama, S.P.P., Koster, R.D., & Lannoy, G.J.M.D. (2017). Assessment of MERRA-2 Land Surface Hydrology Estimates. Journal of Climate, 30, 2937-2960. https://doi.org/10.1175/jcli-d-16-0720.1
  57. Rodríguez-Fernández, N.J., Muñoz Sabater, J., Richaume, P., de Rosnay, P., Kerr, Y.H., Albergel, C., Drusch, M., & Mecklenburg, S. (2017). SMOS near-real-time soil moisture product: processor overview and first validation results. Hydrology and Earth System Sciences, 21, 5201-5216. https://doi.org/10.5194/hess-21-5201-2017
  58. Scholze, M., Buchwitz, M., Dorigo, W., Guanter, L., & Quegan, S. (2017). Reviews and syntheses: Systematic Earth observations for use in terrestrial carbon cycle data assimilation systems. Biogeosciences Discussions, 1-49. https://doi.org/10.5194/bg-2016-557
  59. Sun, G., Peng, F., & Mu, M. (2017). Uncertainty assessment and sensitivity analysis of soil moisture based on model parameter errors – Results from four regions in China. Journal of Hydrology, 555, 347-360. https://doi.org/10.1016/j.jhydrol.2017.09.059
  60. Sun, Y., Huang, S., Ma, J., Li, J., Li, X., Wang, H., Chen, S., & Zang, W. (2017). Preliminary Evaluation of the SMAP Radiometer Soil Moisture Product over China Using In Situ Data. Remote Sensing, 9. https://doi.org/10.3390/rs9030292
  61. Tobin, K.J., Torres, R., Crow, W.T., & Bennett, M.E. (2017). Multi-decadal analysis of root-zone soil moisture applying the exponential filter across CONUS. Hydrology and Earth System Sciences, 21, 4403-4417. https://doi.org/10.5194/hess-21-4403-2017
  62. van der Schalie, R., de Jeu, R.A.M., Kerr, Y.H., Wigneron, J.P., Rodríguez-Fernández, N.J., Al-Yaari, A., Parinussa, R.M., Mecklenburg, S., & Drusch, M. (2017). The merging of radiative transfer based surface soil moisture data from SMOS and AMSR-E. Remote Sensing of Environment, 189, 180-193. https://doi.org/10.1016/j.rse.2016.11.026
  63. Varikoden, H., & Revadekar, J.V. (2017). Relation Between the Rainfall and Soil Moisture During Different Phases of Indian Monsoon. Pure and Applied Geophysics, 175, 1187-1196. https://doi.org/10.1007/s00024-017-1740-6
  64. Williamson, M., Adams, J.R., Berg, A.A., Derksen, C., Toose, P., & Walker, A. (2017). Plot-scale assessment of soil freeze/thaw detection and variability with impedance probes: implications for remote sensing validation networks. Hydrology Research. https://doi.org/10.2166/nh.2017.183
  65. Xing, C., Chen, N., Zhang, X., & Gong, J. (2017). A Machine Learning Based Reconstruction Method for Satellite Remote Sensing of Soil Moisture Images with In Situ Observations. Remote Sensing, 9. https://doi.org/10.3390/rs9050484
  66. Yao, P., Shi, J., Zhao, T., Lu, H., & Al-Yaari, A. (2017). Rebuilding Long Time Series Global Soil Moisture Products Using the Neural Network Adopting the Microwave Vegetation Index. Remote Sensing, 9. https://doi.org/10.3390/rs9010035
  67. Yuan, S., & Quiring, S.M. (2017). Evaluation of soil moisture in CMIP5 simulations over the contiguous United States using in situ and satellite observations. Hydrology and Earth System Sciences, 21, 2203-2218. https://doi.org/10.5194/hess-21-2203-2017
  68. Zhang, X., Zhang, T., Zhou, P., Shao, Y., & Gao, S. (2017). Validation Analysis of SMAP and AMSR2 Soil Moisture Products over the United States Using Ground-Based Measurements. Remote Sensing, 9. https://doi.org/10.3390/rs9020104
  69. Zhao, W., Li, A., Jin, H., Zhang, Z., Bian, J., & Yin, G. (2017). Performance Evaluation of the Triangle-Based Empirical Soil Moisture Relationship Models Based on Landsat-5 TM Data and In Situ Measurements. IEEE Transactions on Geoscience and Remote Sensing, 55, 2632-2645. https://doi.org/10.1109/tgrs.2017.2649522
  70. Zhao, W., Li, A., & Zhao, T. (2017). Potential of Estimating Surface Soil Moisture With the Triangle-Based Empirical Relationship Model. IEEE Transactions on Geoscience and Remote Sensing, 55, 6494-6504. https://doi.org/10.1109/tgrs.2017.2728815
  71. Zhou, H., Chang, J., Sun, J., Shang, C., Han, F., & Hu, D. (2017). Spatial variation of temperature of surface soil layer adjacent to constructions: A theoretical framework for atmosphere-building-soil energy flow systems. Building and Environment, 124, 143-152. https://doi.org/10.1016/j.buildenv.2017.08.002