Articles about the ISMN

Publications about contributing networks

Articles making use of the ISMN

2017

  • 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. https://doi.org/10.1111/1752-1688.12491

2016

  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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 

  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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

2013

2012


2011