Filter Set


  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  18. Ji, P., Yuan, X., & Liang, X.-Z. (2017). Do Lateral Flows Matter for the Hyperresolution Land Surface Modeling?. Journal of Geophysical Research: Atmospheres. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  26. Lee, J.H., Zhao, C., & Kerr, Y. (2017). Stochastic Bias Correction and Uncertainty Estimation of Satellite-Retrieved Soil Moisture Products. Remote Sensing, 9. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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.. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  40. Mohanty, B.P., Cosh, M.H., Lakshmi, V., & Montzka, C. (2017). Soil Moisture Remote Sensing: State-of-the-Science. Vadose Zone Journal, 16. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI:
  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. DOI: