Articles about the ISMN

 

Conference Contributions

  1. Heer, E., Xaver, A., Messner, R., Himmelbauer, I., Zappa, L., and Dorigo, W.A. (September 19th-21th 2017). Recent Developments of the International Soil Moisture Network . 4th Satellite Soil Moisture Valdiation and Application Workshop, Vienna, Austria
  2. Himmelbauer, I., Xaver, A., Zappa, L., and Dorigo, W.A. (April 10th 2018). The International Soil Moisture Network and its benefits for soil moisture product validation . European Geoscience Union (EGU) General Assembly 2018, Vienna, Austria
  3. Xaver, A., Himmelbauer, I., Aberer, D., Zappa, L. and Dorigo, W.A. (November 15th 2018). The International Soil Mosture Network in support of Earth Observation service . ESA CCI Soil Moisture Workshop, in support of global climate monitoring, Vienna, Austria

 

Publications about contributing networks

  1. Bircher, S., Skou, N., Jensen, K. H., Walker, J. P., & Rasmussen, L. (2012). A soil moisture and temperature network for SMOS validation in western DenmarkHydrology and Earth System Sciences, 16(5), 1445-1463.
  2. Brocca L., Hasenauer, S., Lacava, T., Melone, F., Moramarco, T., Wagner, W., Dorigo, W., Matgen, P., Martínez-Fernández, J., Llorens, P., Latron, J., Martin, C., Bittelli, M. (2011), "Soil Moisture estimation through ASCAT ans AMSR-E sensors: An intercomparison and validation study across Europe", Remote Sensing of Environment, 115, pp.3390-3408
  3. dall'Amico, J.T., Schlenz, F., Loew, A., Mauser, W., Kainulainen, J., Balling, J.E., Bouzinac, C. The SMOS Validation Campaign 2010 in the Upper Danube Catchment: A Data Set for Studies of Soil Moisture, Brightness Temperature, and Their Spatial Variability Over a Heterogeneous Land SurfaceIEEE Transactions on Geoscience and Remote Sensing, 51 (1), 364-377
  4. Mattar, C., Santamaría-Artigas, A., Durán-Alarcón, C., Olivera-Guerra, L., Fuster, R., & Borvarán, D. (2016). The LAB-Net Soil Moisture Network: Application to Thermal Remote Sensing and Surface Energy Balance. Data, 1(1), 6. doi.org/10.3390/data1010006
  5. Peischl, S., Walker, J. P., Rüdiger, C., Ye, N., Kerr, Y. H., Kim, E., . . . Allahmoradi, M. (2012). The AACES field experiments: SMOS calibration and validation across the murrumbidgee river catchmentHydrology and Earth System Sciences, 16(6), 1697-1708.
  6. Schlenz, F., Dall'Amico, J. T., Mauser, W., & Loew, A. (2012). Analysis of SMOS brightness temperature and vegetation optical depth data with coupled land surface and radiative transfer models in southern GermanyHydrology and Earth System Sciences, 16(10), 3517-3533. 
  7. Skierucha, W., Wilczek, A., Szypłowska, A., Sławiński, C., & Lamorski, K. (2012). A TDR-based soil moisture monitoring system with simultaneous measurement of soil temperature and electrical conductivitySensors, 12(10), 13545-13566.
  8. Smith, A. B., Walker, J. P., Western, A. W., Young, R. I., Ellett, K. M., Pipunic, R. C., . . . Richter, H. (2012). The murrumbidgee soil moisture monitoring network data setWater Resources Research, 48(7)
  9. Zreda, M., W.J. Shuttleworth, X. Zeng, C. Zweck, D. Desilets, T. Franz, and R. Rosolem, 2012. COSMOS: the COsmic-ray Soil Moisture Observing System. Hydrology and Earth System Sciences 16, 4079-4099,  doi.org/10.5194/hess-16-4079-2012.

 

Articles making use of the ISMN

 

2018

  1. 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. https://doi.org/10.1016/j.jag.2018.05.026.
  2. 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. https://doi.org/10.3390/geosciences8020072.
  3. 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. https://doi.org/10.5194/essd-10-61-2018.
  4. 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. https://doi.org/10.2136/vzj2018.03.0055.
  5. 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.
  6. 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. https://doi.org/10.1175/JHM-D-17-0152.1.
  7. 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. https://doi.org/10.1016/j.jhydrol.2017.12.051.
  8. Esposito, G., Matano, F., & Scepi, G. (2018). Analysis of Increasing Flash Flood Frequency in the Densely Urbanized Coastline of the Campi Flegrei Volcanic Area, Italy. Frontiers in Earth Science, 6. https://doi.org/10.3389/feart.2018.00063.
  9. 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. https://doi.org/10.1016/j.rse.2018.02.010.
  10. 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. https://doi.org/10.1002/2017wr021277.
  11. Gumbricht, T. (2018). Detecting Trends in Wetland Extent from MODIS Derived Soil Moisture Estimates. Remote Sensing, 10. https://doi.org/10.3390/rs10040611.
  12. 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. https://doi.org/10.3390/rs10020192.
  13. 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. https://doi.org/10.1016/j.rse.2017.10.026.
  14. 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. https://doi.org/10.1016/j.rse.2017.10.045.
  15. 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. https://doi.org/10.1016/j.rse.2017.11.002.
  16. 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.
  17. 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. https://doi.org/10.5194/hess-22-2269-2018.
  18. 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. https://doi.org/10.5194/gmd-11-2009-2018.
  19. 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. https://doi.org/10.1016/j.jhydrol.2018.06.023.
  20. 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. https://doi.org/10.1016/j.rse.2018.04.003.
  21. 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. https://doi.org/10.1016/j.rse.2018.04.039.
  22. 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. https://doi.org/10.1016/j.jag.2017.08.016.
  23. 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 (pp. 293-307)
  24. 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. https://doi.org/10.3390/su10062100.
  25. 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. https://doi.org/10.3390/rs10010107.
  26. 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.
  27. 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. https://doi.org/10.1111/gcb.14335.
  28. 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. https://doi.org/10.1080/2150704x.2017.1422872.
  29. Ye, K., & Lau, N.-C. (2018). Characteristics of Eurasian snowmelt and its impacts on the land surface and surface climate. Climate Dynamics. https://doi.org/10.1007/s00382-018-4180-9.
  30. 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. https://doi.org/10.5194/hess-22-1931-2018.
  31. 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. https://doi.org/10.1016/j.rse.2018.06.033.
 
2017
  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. 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.
  10. 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.
  11. 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 (pp. 561-594): Academic Press
  12. Emery, C. (2017). Contribution de la future mission altim´etrique `a large fauch´ee SWOT pour la mod´elisation hydrologique `a grande ´echelle.
  13. 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.
  14. 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.
  15. 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.
  16. 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.
  17. 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.
  18. 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.
  19. 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.
  20. 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.
  21. 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.
  22. 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.
  23. 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.
  24. 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.
  25. 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.
  26. 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.
  27. Liangjing, Z. (2017). Terrestrial water storage from GRACE gravity data for hydrometeorological applications.
  28. Lievens, H., Martens, B., Verhoest, N.E.C., Hahn, S., Reichle, R.H., & Miralles, D.G. (2017a). 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.
  29. 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. (2017b). Joint Sentinel-1 and SMAP data assimilation to improve soil moisture estimates. Geophysical Research Letters, 44, 6145-6153. https://doi.org/10.1002/2017gl073904.
  30. Liu, Q., Hao, Y., Stebler, E., Tanaka, N., & Zou, C.B. (2017a). Impact of plant functional types on coherence between precipitation and soil moisture - a wavelet analysis. Geophysical Research Letters. https://doi.org/10.1002/2017gl075542.
  31. Liu, Z., Li, P., & Yang, J. (2017b). Soil Moisture Retrieval and Spatiotemporal Pattern Analysis Using Sentinel-1 Data of Dahra, Senegal. Remote Sensing, 9. https://doi.org/10.3390/rs9111197.
  32. 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.
  33. 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.
  34. 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.
  35. 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.
  36. 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.
  37. 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.
  38. 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.
  39. 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.
  40. 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.
  41. 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.
  42. 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.
  43. 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.
  44. Park, S., Im, J., Park, S., & Rhee, J. (2017a). 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.
  45. Park, S., Park, S., Im, J., Rhee, J., Shin, J., & Park, J. (2017b). 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.
  46. 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.
  47. 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.
  48. 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.
  49. 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.
  50. 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.
  51. 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.
  52. 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.
  53. 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.
  54. 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.
  55. 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.
  56. 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.
  57. Sun, G., Peng, F., & Mu, M. (2017a). 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.
  58. Sun, Y., Huang, S., Ma, J., Li, J., Li, X., Wang, H., Chen, S., & Zang, W. (2017b). Preliminary Evaluation of the SMAP Radiometer Soil Moisture Product over China Using In Situ Data. Remote Sensing, 9. https://doi.org/10.3390/rs9030292.
  59. 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.
  60. 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.
  61. 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.
  62. 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.
  63. 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.
  64. 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.
  65. 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.
  66. Zhao, W., Li, A., Jin, H., Zhang, Z., Bian, J., & Yin, G. (2017a). 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.
  67. Zhao, W., Li, A., & Zhao, T. (2017b). 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.
  68. 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.
 
2016
  1. Al-Yaari, A., Wigneron, J. P., Kerr, Y., de Jeu, R., Rodriguez-Fernandez, N., van der Schalie, R., … Ducharne, A. (2016). Testing regression equations to derive long-term global soil moisture datasets from passive microwave observations. Remote Sensing of Environment, 180, 453–464. https://doi.org/10.1016/j.rse.2015.11.022
  2. An, R., Zhang, L., Wang, Z., Quaye-Ballard, J. A., You, J., Shen, X., … Ke, Z. (2016). Validation of the ESA CCI soil moisture product in China. International Journal of Applied Earth Observation and Geoinformation, 48, 28–36. https://doi.org/10.1016/j.jag.2015.09.009
  3. Bi, H., Ma, J., Zheng, W., & Zeng, J. (2016). Comparison of soil moisture in GLDAS model simulations and in situ observations over the Tibetan Plateau: EVALUATE GLDAS SOIL MOISTURE OVER TP. Journal of Geophysical Research: Atmospheres, 121(6), 2658–2678. https://doi.org/10.1002/2015JD024131
  4. Chen, X., Su, Y., Liao, J., Shang, J., Dong, T., Wang, C., … Liu, L. (2016). Detecting significant decreasing trends of land surface soil moisture in eastern China during the past three decades (1979-2010): China’s 32 Year Soil Moisture. Journal of Geophysical Research: Atmospheres, 121(10), 5177–5192. https://doi.org/10.1002/2015JD024676
  5. Cissé, S., Eymard, L., Ottlé, C., Ndione, J., Gaye, A., & Pinsard, F. (2016). Rainfall Intra-Seasonal Variability and Vegetation Growth in the Ferlo Basin (Senegal). Remote Sensing, 8(1), 66. https://doi.org/10.3390/rs8010066
  6. Du, J., Kimball, J. S., & Jones, L. A. (2016). Passive Microwave Remote Sensing of Soil Moisture Based on Dynamic Vegetation Scattering Properties for AMSR-E. IEEE Transactions on Geoscience and Remote Sensing, 54(1), 597–608. https://doi.org/10.1109/TGRS.2015.2462758
  7. Enenkel, M., Reimer, C., Dorigo, W., Wagner, W., Pfeil, I., Parinussa, R., & De Jeu, R. (2016). Combining satellite observations to develop a global soil moisture product for near-real-time applications. Hydrology and Earth System Sciences, 20(10), 4191–4208. https://doi.org/10.5194/hess-20-4191-2016
  8. Faridani, F., Farid, A., Ansari, H., & Manfreda, S. (2016). Estimation of the Root-Zone Soil Moisture Using Passive Microwave Remote Sensing and SMAR Model. Journal of Irrigation and Drainage Engineering, 04016070. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001115
  9. Fascetti, F., Pierdicca, N., Crapolicchio, R., Pulvirenti, L., & Muoz-Sabater, J. (2016). An assessment of SMOS version 6.20 products through Triple and Quadruple Collocation techniques considering ASCAT, ERA/Interim LAND, ISMNand SMAP soil moisture data (pp. 91–94). IEEE. https://doi.org/10.1109/MICRORAD.2016.7530511
  10. Fascetti, F., Pierdicca, N., Pulvirenti, L., Crapolicchio, R., & Muñoz-Sabater, J. (2016). A comparison of ASCAT and SMOS soil moisture retrievals over Europe and Northern Africa from 2010 to 2013. International Journal of Applied Earth Observation and Geoinformation, 45, 135–142. https://doi.org/10.1016/j.jag.2015.09.008
  11. Fernandez-Moran, R., Wigneron, J.-P., De Lannoy, G., Lopez-Baeza, E., Mialon, A., Mahmoodi, A., … Kerr, Y. (2016). Calibrating the effective scattering albedo in the SMOS algorithm: Some first results (pp. 826–829). IEEE. https://doi.org/10.1109/IGARSS.2016.7729209
  12. Gonzalez-Zamora, A., Sanchez, N., & Martinez-Fernandez, J. (2016). Validation of Aquarius Soil Moisture Products Over the Northwest of Spain: A Comparison With SMOS. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(6), 2763–2769. https://doi.org/10.1109/JSTARS.2016.2517401
  13. González-Zamora, Á., Sánchez, N., Martínez-Fernández, J., & Wagner, W. (2016). Root-zone plant available water estimation using the SMOS-derived soil water index. Advances in Water Resources, 96, 339–353. https://doi.org/10.1016/j.advwatres.2016.08.001
  14. Griesfeller, A., Lahoz, W. A., Jeu, R. A. M. d., Dorigo, W., Haugen, L. E., Svendby, T. M., & Wagner, W. (2016). Evaluation of satellite soil moisture products over Norway using ground-based observations. International Journal of Applied Earth Observation and Geoinformation, 45, 155–164. https://doi.org/10.1016/j.jag.2015.04.016
  15. Gruber, A., Su, C.-H., Crow, W. T., Zwieback, S., Dorigo, W. A., & Wagner, W. (2016). Estimating error cross-correlations in soil moisture data sets using extended collocation analysis: EXTENDED COLLOCATION ANALYSIS. Journal of Geophysical Research: Atmospheres, 121(3), 1208–1219. https://doi.org/10.1002/2015JD024027
  16. Han, M., Lu, H., & Yang, K. (2016). Development of passive microwave retrieval algorithm for estimation of surface soil temperature from AMSR-E data (pp. 1671–1674). IEEE. https://doi.org/10.1109/IGARSS.2016.7729427
  17. Kędzior, M., & Zawadzki, J. (2016). Comparative study of soil moisture estimations from SMOS satellite mission, GLDAS database, and cosmic-ray neutrons measurements at COSMOS station in Eastern Poland. Geoderma, 283, 21–31. https://doi.org/10.1016/j.geoderma.2016.07.023
  18. Kerr, Y. H., Al-Yaari, A., Rodriguez-Fernandez, N., Parrens, M., Molero, B., Leroux, D., … Wigneron, J.-P. (2016). Overview of SMOS performance in terms of global soil moisture monitoring after six years in operation. Remote Sensing of Environment, 180, 40–63. https://doi.org/10.1016/j.rse.2016.02.042
  19. Kim, S., Parinussa, R., Liu, Y., Johnson, F., & Sharma, A. (2016). Merging Alternate Remotely-Sensed Soil Moisture Retrievals Using a Non-Static Model Combination Approach. Remote Sensing, 8(6), 518. https://doi.org/10.3390/rs8060518
  20. Koch, F., Schlenz, F., Prasch, M., Appel, F., Ruf, T., & Mauser, W. (2016). Soil Moisture Retrieval Based on GPS Signal Strength Attenuation. Water, 8(7), 276. https://doi.org/10.3390/w8070276
  21. Lee, J. H. (2016). The consecutive dry days to trigger rainfall over West Africa. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2016.06.003
  22. Leng, P., Song, X., Duan, S.-B., & Li, Z.-L. (2016). Preliminary validation of two temporal parameter-based soil moisture retrieval models using a satellite product and in situ soil moisture measurements over the REMEDHUS network. International Journal of Remote Sensing, 37(24), 5902–5917. https://doi.org/10.1080/01431161.2016.1253896
  23. Martens, B., Miralles, D. G., Lievens, H., van der Schalie, R., de Jeu, R. A. M., Férnandez-Prieto, D., … Verhoest, N. E. C. (2016). GLEAM v3: satellite-based land evaporation and root-zone soil moisture. Geoscientific Model Development Discussions, 1–36. https://doi.org/10.5194/gmd-2016-162
  24. Martens, B., Miralles, D., Lievens, H., Fernández-Prieto, D., & Verhoest, N. E. C. (2016). Improving terrestrial evaporation estimates over continental Australia through assimilation of SMOS soil moisture. International Journal of Applied Earth Observation and Geoinformation, 48, 146–162. https://doi.org/10.1016/j.jag.2015.09.012
  25. McNally, A., Shukla, S., Arsenault, K. R., Wang, S., Peters-Lidard, C. D., & Verdin, J. P. (2016). Evaluating ESA CCI soil moisture in East Africa. International Journal of Applied Earth Observation and Geoinformation, 48, 96–109. https://doi.org/10.1016/j.jag.2016.01.001
  26. Nair, A., & Indu, J. (2016). Enhancing Noah Land Surface Model Prediction Skill over Indian Subcontinent by Assimilating SMOPS Blended Soil Moisture. Remote Sensing, 8(12), 976. https://doi.org/10.3390/rs8120976
  27. Orth, R., Dutra, E., & Pappenberger, F. (2016). Improving Weather Predictability by Including Land Surface Model Parameter Uncertainty. Monthly Weather Review, 144(4), 1551–1569. https://doi.org/10.1175/MWR-D-15-0283.1
  28. Pablos, M., Martínez-Fernández, J., Piles, M., Sánchez, N., Vall-llossera, M., & Camps, A. (2016). Multi-Temporal Evaluation of Soil Moisture and Land Surface Temperature Dynamics Using in Situ and Satellite Observations. Remote Sensing, 8(7), 587. https://doi.org/10.3390/rs8070587
  29. Pal, M., Maity, R., & Dey, S. (2016). Statistical Modelling of Vertical Soil Moisture Profile: Coupling of Memory and Forcing. Water Resources Management, 30(6), 1973–1986. https://doi.org/10.1007/s11269-016-1263-4
  30. Parinussa, R., de Jeu, R., van der Schalie, R., Crow, W., Lei, F., & Holmes, T. (2016). A Quasi-Global Approach to Improve Day-Time Satellite Surface Soil Moisture Anomalies through the Land Surface Temperature Input. Climate, 4(4), 50. https://doi.org/10.3390/cli4040050
  31. Piles, M., Petropoulos, G. P., Sánchez, N., González-Zamora, Á., & Ireland, G. (2016). Towards improved spatio-temporal resolution soil moisture retrievals from the synergy of SMOS and MSG SEVIRI spaceborne observations. Remote Sensing of Environment, 180, 403–417. https://doi.org/10.1016/j.rse.2016.02.048
  32. Rautiainen, K., Parkkinen, T., Lemmetyinen, J., Schwank, M., Wiesmann, A., Ikonen, J., … Pulliainen, J. (2016). SMOS prototype algorithm for detecting autumn soil freezing. Remote Sensing of Environment, 180, 346–360. https://doi.org/10.1016/j.rse.2016.01.012
  33. Santi, E., Paloscia, S., Pettinato, S., Brocca, L., & Ciabatta, L. (2016). Robust Assessment of an Operational Algorithm for the Retrieval of Soil Moisture From AMSR-E Data in Central Italy. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(6), 2478–2492. https://doi.org/10.1109/JSTARS.2016.2575361
  34. Santi, E., Paloscia, S., Pettinato, S., & Fontanelli, G. (2016). Application of artificial neural networks for the soil moisture retrieval from active and passive microwave spaceborne sensors. International Journal of Applied Earth Observation and Geoinformation, 48, 61–73. https://doi.org/10.1016/j.jag.2015.08.002
  35. Schalie, R. va. der, Kerr, Y. H., Wigneron, J. P., Rodríguez-Fernández, N. J., Al-Yaari, A., & Jeu, R. A. M. d. (2016). Global SMOS Soil Moisture Retrievals from The Land Parameter Retrieval Model. International Journal of Applied Earth Observation and Geoinformation, 45, 125–134. https://doi.org/10.1016/j.jag.2015.08.005
  36. Scholze, M., Kaminski, T., Knorr, W., Blessing, S., Vossbeck, M., Grant, J. P., & Scipal, K. (2016). Simultaneous assimilation of SMOS soil moisture and atmospheric CO2 in-situ observations to constrain the global terrestrial carbon cycle. Remote Sensing of Environment, 180, 334–345. https://doi.org/10.1016/j.rse.2016.02.058
  37. Shin, Y., Lim, K., Park, K., & Jung, Y. (2016). Development of Dynamic Ground Water Data Assimilation for Quantifying Soil Hydraulic Properties from Remotely Sensed Soil Moisture. Water, 8(8), 311. https://doi.org/10.3390/w8070311
  38. Su, C.-H., Ryu, D., Dorigo, W., Zwieback, S., Gruber, A., Albergel, C., … Wagner, W. (2016). Homogeneity of a global multisatellite soil moisture climate data record: HOMOGENEITY OF SOIL MOISTURE CDR. Geophysical Research Letters. https://doi.org/10.1002/2016GL070458
  39. Wang, L., Li, X., Chen, Y., Yang, K., Chen, D., Zhou, J., … Huang, J. (2016). Validation of the global land data assimilation system based on measurements of soil temperature profiles. Agricultural and Forest Meteorology, 218–219, 288–297. https://doi.org/10.1016/j.agrformet.2016.01.003
  40. Wu, Q., Liu, H., Wang, L., & Deng, C. (2016). Evaluation of AMSR2 soil moisture products over the contiguous United States using in situ data from the International Soil Moisture Network. International Journal of Applied Earth Observation and Geoinformation, 45, 187–199. https://doi.org/10.1016/j.jag.2015.10.011
  41. Zawadzki, J., & Kędzior, M. (2016). Soil moisture variability over Odra watershed: Comparison between SMOS and GLDAS data. International Journal of Applied Earth Observation and Geoinformation, 45, 110–124. https://doi.org/10.1016/j.jag.2015.03.005
  42. Zeng, J., Chen, K.-S., Bi, H., & Chen, Q. (2016). A Preliminary Evaluation of the SMAP Radiometer Soil Moisture Product Over United States and Europe Using Ground-Based Measurements. IEEE Transactions on Geoscience and Remote Sensing, 54(8), 4929–4940. https://doi.org/10.1109/TGRS.2016.2553085
  43. Zhang, D., Madsen, H., Ridler, M. E., Kidmose, J., Jensen, K. H., & Refsgaard, J. C. (2016). Multivariate hydrological data assimilation of soil moisture and groundwater head. Hydrology and Earth System Sciences, 20(10), 4341–4357. https://doi.org/10.5194/hess-20-4341-2016
  44. Zhao, L., Yang, Z.-L., & Hoar, T. J. (2016). Global Soil Moisture Estimation by Assimilating AMSR-E Brightness Temperatures in a Coupled CLM4–RTM–DART System. Journal of Hydrometeorology, 17(9), 2431–2454. https://doi.org/10.1175/JHM-D-15-0218.1
  45. Zwieback, S., Su, C.-H., Gruber, A., Dorigo, W. A., & Wagner, W. (2016). The Impact of Quadratic Nonlinear Relations between Soil Moisture Products on Uncertainty Estimates from Triple Collocation Analysis and Two Quadratic Extensions. Journal of Hydrometeorology, 17(6), 1725–1743. https://doi.org/10.1175/JHM-D-15-0213.1

 

2015 
  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, 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 (pp. 271–277). IEEE. 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 (pp. 5182–5185). IEEE. 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 (pp. 4738–4741). IEEE. 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. Kerr, Y. H., Al-Yaari, A., Rodriguez-Fernandez, N., Parrens, M., Molero, B., Leroux, D., … Wigneron, J.-P. (2016). Overview of SMOS performance in terms of global soil moisture monitoring after six years in operation. Remote Sensing of Environment, 180, 40–63. http://doi.org/10.1016/j.rse.2016.02.042
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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
  18. 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
  19. Pal, M., Maity, R., & Dey, S. (2016). Statistical Modelling of Vertical Soil Moisture Profile: Coupling of Memory and Forcing. Water Resources Management, 30(6), 1973–1986. http://doi.org/10.1007/s11269-016-1263-4
  20. 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
  21. 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
  22. 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
  23. 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
  24. 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
  25. 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
 
2014
  1. Angevine, W. M., Bazile, E., Legain, D., and Pino, D. Land surface spinup for episodic modelingAtmos. Chem. Phys., 14, 8165-8172, doi:10.5194/acp-14-8165-2014
 
2013
  1. Albergel, C., Dorigo, W.,Balsamo, G., Muñoz-Sabater, J., de Rosnay, P., Isaksen, L., Brocca, L., de Jeu,R., Wagner, W. Monitoring multi-decadal satellite earth observation of soil moisture products through land surface reanalysesRemote Sensing of Environment, 138, 77-89,doi: 10.1016/j.rse.2013.07.009
  2. Albergel, C., Dorigo, W., Reichle, R.H.,Balsamo, G., de Rosnay, P., Muñoz-Sabater, J., Isaksen, L., de Jeu, R., Wagner,W. Skill and global trend analysis ofsoil moisture from reanalyses and microwave remote sensingJournal of Hydrometeorology, 14, 1259-1277, doi:10.1175/JHM-D-12-0161.1
 
2012
  1. Albergel, C., de Rosnay, P., Gruhier, C., Muñoz-Sabater, J., Hasenauer, S., Isaksen, L., Kerr, Y., & Wagner, W. (2012). Evaluation of remotely sensed and modelled soil moisture products using global ground-based in situ observationsRemote Sensing of Environment, 118, 215-226
  2. Albergel, C., De Rosnay, P., Balsamo, G., Isaksen, L., & Muñoz-Sabater, J. (2012). Soil Moisture Analyses at ECMWF: Evaluation Using Global Ground-Based In Situ ObservationsJournal of Hydrometeorology, 13, 1442-1460
  3. Albergel, C.., G. Balsamo, P. de Rosnay, J. Muñoz-Sabater, and S. Boussetta, (2012). A bare ground evaporation revision in the ECMWF land-surface scheme: evaluation of its impact using ground soil moisture and satellite microwave data. Hydrol. Earth Syst. Sci., 16, 3607-3620
  4. Collow, T.W., Robock, A., Basara, J.B., Illston, B.G. Evaluation of SMOS retrievals of soil moisture over the central United States with currently available in situ observationsJournal of Geophysical Research D: Atmospheres, 117 (9), art. no. D09113
  5. dall''Amico, J.T.; Schlenz, F.; Loew, A.; Mauser, W., First Results of SMOS Soil Moisture Validation in the Upper Danube CatchmentIEEE Transactions on Geoscience and Remote Sensing, 50 (5), 1507-1516
  6. Liu, Y. Y., Dorigo, W. A., Parinussa, R. M., De Jeu, R. A. M., Wagner, W., McCabe, M. F., . . . Van Dijk, A. I. J. M. Trend-preserving blending of passive and active microwave soil moisture retrievalsRemote Sensing of Environment, 123, 280-297.
  7. Luo, Y. Q., Randerson, J. T., Abramowitz, G., Bacour, C., Blyth, E., Carvalhais, N., . . . Zhou, X. H.  A framework for benchmarking land models. Biogeosciences. 9(10), 3857-3874.
  8. Mecklenburg, S., Drusch, M., Kerr, Y. H., Font, J., Martin-Neira, M., Delwart, S., . . . Crapolicchio, R. ESA's soil moisture and ocean salinity mission: Mission performance and operations. IEEE Transactions on Geoscience and Remote Sensing, 50(5 PART 1), 1354-1366.
  9. Pan, M., Sahoo, A.K., Wood, E.F., Al Bitar, A., Leroux, D., Kerr, Y.H. An initial assessment of SMOS derived soil moisture over the continental United StatesIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5 (5), art. no. 6211458, pp. 1448-1457
  10. Parrens, M., Zakharova, E., Lafont, S., Calvet, J.-C., Kerr, Y., Wagner, W., and Wigneron, J.-P.: Comparing soil moisture retrievals from SMOS and ASCAT over FranceHydrol. Earth Syst. Sci., 16, 423-440, doi:10.5194/hess-16-423-2012
  11. Schlenz, F.; dall''Amico, J.T.; Loew, A.; Mauser, W., Uncertainty Assessment of the SMOS Validation in the Upper Danube CatchmentIEEE Transactions on Geoscience and Remote Sensing, 50 (5), 1517-1529
  12. Skierucha, W., Wilczek, A., & Szypłowska, A. (2012). Dielectric spectroscopy in agrophysics.International Agrophysics, 26(2), 187-197
  13. Van doninck, J., Peters, J., Lievens, H., De Baets, B., and Verhoest, N. E. C. (2012). Accounting for seasonality in a soil moisture change detection algorithm for ASAR Wide Swath time seriesHydrol. Earth Syst. Sci., 16, 773-786
  14. Wanders, N., Karssenberg, D., Bierkens, M., Parinussa, R., de Jeu, R., van Dam, J., & de Jong, S. (2012). Observation uncertainty of satellite soil moisture products determined with physically-based modelingRemote Sensing of Environment, 127, 341-356
 
2011
  1. Brocca L., Hasenauer, S., Lacava, T., Melone, F., Moramarco, T., Wagner, W., Dorigo, W., Matgen, P., Martínez-Fernández, J., Llorens, P., Latron, J., Martin, C., Bittelli, M. Soil Moisture estimation through ASCAT ans AMSR-E sensors: An intercomparison and validation study across EuropeRemote Sensing of Environment, 115, pp.3390-3408
  2. Liu, Y. Y., Parinussa, R. M., Dorigo, W. A., De Jeu, R. A. M., Wagner, W., van Dijk, A. I. J. M., McCabe, M. F., Evans, J. P. Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievalsHydrology and Earth System Sciences, 15, 425-436, doi:10.5194/hess-15-425-2011