Soil Moisture Data Assimilation

  • Gabrielle Jacinthe Maria de LannoyEmail author
  • Patricia de RosnayEmail author
  • Rolf Helmut ReichleEmail author
Living reference work entry


Accurate knowledge of soil moisture at the continental scale is important for improving predictions of weather, agricultural productivity, and natural hazards, but observations of soil moisture at such scales are limited to indirect measurements, either obtained through satellite remote sensing or from meteorological networks. Land surface models simulate soil moisture processes, using observation-based meteorological forcing data, and auxiliary information about soil, terrain, and vegetation characteristics. Enhanced estimates of soil moisture and other land surface variables, along with their uncertainty, can be obtained by assimilating observations of soil moisture into land surface models. These assimilation results are of direct relevance for the initialization of hydrometeorological ensemble forecasting systems. The success of the assimilation depends on the choice of the assimilation technique, the nature of the model and the assimilated observations, and, most importantly, the characterization of model and observation error. Systematic differences between satellite-based microwave observations or satellite-retrieved soil moisture and their simulated counterparts require special attention. Other challenges include inferring root-zone soil moisture information from observations that pertain to a shallow surface soil layer, propagating information to unobserved areas and downscaling of coarse information to finer-scale soil moisture estimates. This chapter summarizes state-of-the-art solutions to these issues with conceptual data assimilation examples, using techniques ranging from simplified optimal interpolation to spatial ensemble Kalman filtering. In addition, operational soil moisture assimilation systems are discussed that support numerical weather prediction at ECMWF and provide value-added soil moisture products for the NASA Soil Moisture Active Passive mission.


Soil moisture retrieval Microwave brightness temperature Radar backscatter Terrestrial water storage Analysis Innovation Increment Kalman filter Observation operator Numerical weather prediction Initialization State update Calibration Radiative transfer model Land surface model Screen-level observations ASCAT AMSR2 SMOS SMAP GRACE 


  1. C. Albergel, W. Dorigo, R. Reichle, G. Balsamo, P. de Rosnay, J. Muñoz-Sabater, L. Isaksen, R. de Jeu, W. Wagner, Skill and global trend analysis of soil moisture from reanalyses and microwave remote sensing. J. Hydrometeorol. 14, 1259–1277 (2013). doi:10.1175/JHM-D-12-0161.1CrossRefGoogle Scholar
  2. G. Balsamo, J.F. Mahfouf, S. Bélair, G. Deblonde, A global root-zone soil moisture analysis using simulated L-band brightness temperature in preparation for the Hydros satellite mission. J. Hydrometeorol. 7, 1126–1146 (2006)CrossRefGoogle Scholar
  3. G. Balsamo, P. Viterbo, A. Beljaars, B. van den Hurk, M. Hirschi, A.K. Betts, K. Scipal, A revised hydrology for the ECMWF model: verification from field site to terrestrial water storage and impact in the integrated forecast system. J. Hydrometeorol. 10, 623–643 (2009). doi:10.1175/2008JHM1068.1CrossRefGoogle Scholar
  4. G. Balsamo, C. Albergel, A. Beljaars, S. Boussetta, H. Cloke, D. Dee, E. Dutra, J. Muñoz-Sabater, F. Pappenberger, P. de Rosnay, T. Stockdale, F. Vitart, ERA-Interim/Land: a global land water resources dataset. Hydrol. Earth Syst. Sci. 10, 14705–14745 (2013). doi:10.5194/hessd-10-14705-2013CrossRefGoogle Scholar
  5. Z. Bartalis, W. Wagner, V. Naeimi, S. Hasenauer, K. Scipal, H. Bonekamp, J. Figa, C. Anderson, Initial soil moisture retrievals from the METOP-A advanced scatterometer (ASCAT). Geophys. Res. Lett. 34, L20401 (2007). doi:10.1029/2007GL031088CrossRefGoogle Scholar
  6. S. Bélair, L.P. Crevier, J. Mailhot, B. Bilodeau, Y. Delage, Operational implementation of the ISBA land surface scheme in the Canadian regional weather forecast model. Part I: warm season results. J. Hydrometeorol. 4, 352–370 (2003)CrossRefGoogle Scholar
  7. W.T. Crow, M.T. Yilmaz, The auto-tuned land data assimilation system (ATLAS). Water Resour. Res. 50, 371–385 (2014). doi:10.1002/2013WR014550CrossRefGoogle Scholar
  8. G.J.M. De Lannoy, P.R. Houser, V.R.N. Pauwels, N.E. Verhoest, Assessment of model uncertainty for soil moisture through ensemble verification. J. Geophys. Res. 111, D10101 (2009). doi:10.1029/2005JD006367Google Scholar
  9. G.J.M. De Lannoy, R.H. Reichle, P.R. Houser, V.R.N. Pauwels, N.E.C. Verhoest, Correcting for forecast bias in soil moisture assimilation with the ensemble Kalman filter. Water Resour. Res. 43, W09410 (2007). doi:10.1029/2006WR00544Google Scholar
  10. G.J.M. De Lannoy, R.H. Riechle, V.N.R. Pauwels, Global calibration of the GEOS-5 L-band microwave radiative transfer model over non-frozen land using SMOS observations. J. Hydrometeorol. 14, 765–785 (2013). doi:10.1175/JHM-D-12-092.1Google Scholar
  11. P. de Rosnay, M. Drusch, D. Vasiljevic, G. Balsamo, C. Albergel, L. Isaksen, A simplified extended Kalman filter for the global operational soil moisture analysis at ECMWF. Q. J. Roy. Meteorol. Soc. 139(674), 1199–1213 (2013). doi:10.1002/qj.2023CrossRefGoogle Scholar
  12. P. de Rosnay, G. Balsamo, C. Albergel, J. Muñoz-Sabater, L. Isaksen, Initialisation of land surface variables for numerical weather prediction. Surv. Geophys. 35(3), 607–621 (2014). doi:10.1007/s10712-012-9207-xCrossRefGoogle Scholar
  13. I. Dharssi, K.J. Bovis, B. Macpherson, C.P. Jones, Operational assimilation of ASCAT surface soil wetness at the Met Office. Hydrol. Earth Syst. Sci. 15, 2729–2746 (2011). doi:10.5194/hess-15-2729-2011CrossRefGoogle Scholar
  14. P. Dirmeyer, Using a global soil wetness dataset to improve seasonal climate simulation. J. Climate 13, 2900–2921 (2000)CrossRefGoogle Scholar
  15. W.A. Dorigo, A. Gruber, R.A.M. de Jeu, W. Wagner, T. Stacke, A. Loew, C. Albergel, L. Brocca, D. Chung, R. Parinussa, R. Kidd, Evaluation of the ESA CCI soil moisture product using ground-based observations. Remote Sens. Environ. 162, 380–395 (2015). doi:10.1016/j.rse.2014.07.023CrossRefGoogle Scholar
  16. C.S. Draper, R.H. Reichle, G.J.M. De Lannoy, Q. Liu, Assimilation of passive and active microwave soil moisture retrievals. Geophys. Res. Lett. 39, L04401 (2012). doi:10.1029/2011GL050655CrossRefGoogle Scholar
  17. M. Drusch, E.F. Wood, H. Gao, Observation operators for the direct assimilation of TRMM microwave imager retrieved soil moisture. Geophys. Res. Lett. 32, L15403 (2005). doi:10.1029/2005GL023623CrossRefGoogle Scholar
  18. M. Drusch, K. Scipal, P. de Rosnay, G. Balsamo, E. Andersson, P. Bougeault, P. Viterbo, Towards a Kalman filter-based soil moisture analysis system for the operational ECMWF Integrated Forecast System. Geophys. Res. Lett. 36, L10401 (2009). doi:10.1029/2009GL037716CrossRefGoogle Scholar
  19. S. Dunne, D. Entekhabi, Land surface state and flux estimation using the ensemble Kalman smoother during the Southern Great Plains 1997 field experiment. Water Resour. Res. 42, W01407 (2006)CrossRefGoogle Scholar
  20. D. Entekhabi, H. Nakamura, E.G. Njoku, Solving the inverse problems for soil moisture and temperature profiles by sequential assimilation of multifrequency remotely-sensed observations. IEEE Trans. Geosci. Remote Sens. 32, 438–448 (1994)CrossRefGoogle Scholar
  21. D. Entekhabi, R.H. Reichle, R.D. Koster, W.T. Crow, Performance metrics for soil moisture retrievals and application requirements. J. Hydrometeorol. 11, 832–840 (2010). doi:10.1175/2010JHM1223.1CrossRefGoogle Scholar
  22. D. Entekhabi, S. Yueh, P. O’Neill, K. Kellogg, SMAP Handbook, NASA/JPL Publication JPL 400-1567, Pasadena, CA, USA, p. 182 (2014).Google Scholar
  23. A.K. Fung, Z. Li, K.S. Chen, Backscattering from a randomly rough dielectric surface. IEEE Trans. Geosci. Remote Sens. 30, 356–369 (1992)CrossRefGoogle Scholar
  24. D. Giard, E. Bazile, Implementation of a new assimilation scheme for soil and surface variables in a global NWP model. Mon. Weather Rev. 128, 997–1015 (2000)CrossRefGoogle Scholar
  25. P.H. Gleick, Water resources, in Encyclopedia of climate and weather, ed. by S.H. Schneider, vol. 2 (Oxford University Press, New York, 1996), pp. 817–823Google Scholar
  26. R. Hess, M. Lange, W. Werner, Evaluation of the variational soil moisture assimilation scheme at Deutscher Wetterdienst. Hydrol. Earth Syst. Sci. 134(635), 1499–1512 (2008)Google Scholar
  27. Y. Kerr et al., The SMOS mission: new tool for monitoring key elements of the global water cycle. Proc. IEEE 98, 666–687 (2010)CrossRefGoogle Scholar
  28. R.D. Koster, M.J. Suarez, A. Ducharne, M. Stieglitz, P. Kumar, A catchment-based approach to modeling land surface processes in a general circulation model 1. Model structure. J. Geophys. Res. 105(D20), 24,809–24,822 (2000)CrossRefGoogle Scholar
  29. R.D. Koster, P.A. Dirmeyer, Z. Guo, G. Bonan, P. Cox, C. Gordon, S. Kanae, E. Kowalczyk, D. Lawrence, P. Liu, C. Lu, S. Malyshev, B. McAvaney, K. Mitchell, D. Mocko, T. Oki, K. Oleson, A. Pitman, Y. Sud, C. Taylor, D. Verseghy, R. Vasic, Y. Xue, T. Yamada, Regions of strong coupling between soil moisture and precipitation. Science 305, 1138–1140 (2004)CrossRefGoogle Scholar
  30. S. Kumar, C. Peters-Lidard, Y. Tian, R. Reichle, J. Geiger, C. Alonge, J. Eylander, P. Houser, An integrated hydrologic modeling and data assimilation framework. IEEE Comput. 41, 52–59 (2008). doi:10.1109/MC.2008.511Google Scholar
  31. Q. Liu, R.H. Reichle, R. Bindlish, M.H. Cosh, W.T. Crow, R. de Jeu, G.J.M. De Lannoy, G.J. Huffman, T.J. Jackson, The contributions of precipitation and soil moisture observations to the skill of soil moisture estimates in a land data assimilation system. J. Hydrometeorol. 12, 750–765 (2011). doi:10.1175/JHM-D-10-05000CrossRefGoogle Scholar
  32. J.F. Mahfouf, K. Bergaoui, C. Draper, F. Bouyssel, F. Taillefer, L. Taseva, A comparison of two off-line soil analysis schemes for assimilation of screen level observations. J. Geophys. Res. 114, D08105 (2009). doi:10.1029/2008JD011077CrossRefGoogle Scholar
  33. T. Mo, B.J. Choudhury, T.J. Schmugge, J.R. Wang, T.J. Jackson, A model for microwave emission from vegetation-covered fields. J. Geophys. Res. Oceans Atmos. 87(C13), 1229–1237 (1982)CrossRefGoogle Scholar
  34. C. Montzka, J.P. Grant, J. Moradkhani, H.J. Hendricks-Franssen, L. Weihermüller, M. Drusch, H. Vereecken, Estimation of radiative transfer parameters from L-band passive microwave brightness temperatures using advanced data assimilation. Vadose Zone J. 12(3), 1–17 (2013).
  35. M. Pan, E.F. Wood, R. Wojcik, M.F. McCabe, Estimation of regional terrestrial water cycle using multi-sensor remote sensing observations and data assimilation. Remote Sens. Environ. 112, 1282–1294 (2008)CrossRefGoogle Scholar
  36. V.R.N. Pauwels, G.J.M. De Lannoy, Ensemble-based assimilation of discharge into rainfall-runoff models: a comparison of approaches to mapping observational information to state space. Water Resour. Res. 45(8), W08428 (2009). doi:10.1029/2008WR007590CrossRefGoogle Scholar
  37. R.H. Reichle, R.D. Koster, Assessing the impact of horizontal error correlations in background fields on soil moisture estimation. J. Hydrometeorol. 4(6), 1229–1242 (2003)CrossRefGoogle Scholar
  38. R.H. Reichle, R.D. Koster, Bias reduction in short records of satellite soil moisture. Geophys. Res. Lett. 31, L19501 (2004). doi:10.1029/2004GL020938CrossRefGoogle Scholar
  39. R.H. Reichle, D. Entekhabi, D. McLaughlin, Downscaling of radio brightness measurements for soil moisture estimation: a four dimensional variational data assimilation approach. Water Resour. Res. 37, 2353–2364 (2001)CrossRefGoogle Scholar
  40. R.H. Reichle, D.B. McLaughlin, D. Entekhabi, Hydrologic data assimilation with the ensemble Kalman filter. Mon. Weather Rev. 120, 103–114 (2002)CrossRefGoogle Scholar
  41. R.H. Reichle, R.D. Koster, G.J.M. De Lannoy, B.A. Forman, Q. Liu, S.P.P. Mahanama, A. Toure, Assessment and enhancement of MERRA land surface hydrology estimates. J. Climate 24, 6322–6338 (2011)CrossRefGoogle Scholar
  42. R.H. Reichle, G.J.M. De Lannoy, B.A. Forman, C.S. Draper, Q. Liu, Connecting satellite observations with water cycle variables through land data assimilation: examples using the NASA GEOS-5 LDAS. Surv. Geophys. 35, 577–606 (2014). doi:10.1007/s10712-013-9220-8CrossRefGoogle Scholar
  43. M. Rodell, P.R. Houser, U. Jambor, J. Gottschalck, K. Mitchell, C.J. Meng, K. Arsenault, B. Cosgrove, J. Radakovich, M. Bosilovich, J.K. Entin, J.P. Walker, D. Lohmann, D. Toll, The global land data assimilation system. Bull. Am. Meteorol. Soc. 85(3), 381–394 (2004)CrossRefGoogle Scholar
  44. M. Rodell, J. Chen, H. Kato, J.S. Famiglietti, J. Nigro, C.R. Wilson, Estimating groundwater storage changes in the Mississippi River basin (USA) using GRACE. Hydrogeol. J. 15, 159–166 (2007)CrossRefGoogle Scholar
  45. J. Sabater, L. Jarlan, J. Calvet, F. Bouyssel, P. de Rosnay, From near-surface to root-zone soil moisture using different assimilation techniques. J. Hydrometeorol. 8(2), 194–206 (2007)CrossRefGoogle Scholar
  46. S. Saha et al., The NCEP climate forecast system reanalysis. Bull. Am. Meteorol. Soc. ES9–ES24 (2010). doi:10.1175/2010Bams3001.1Google Scholar
  47. K. Scipal, T. Holmes, R. de Jeu, V. Naeimi, W. Wagner, A possible solution for the problem of estimating the error structure of global soil moisture data sets. Geophys. Res. Lett. 35, L24403.1–L24403.4 (2008)CrossRefGoogle Scholar
  48. J.P. Wigneron et al., L-band microwave emission of the biosphere (L-MEB) model: description and calibration against experimental data sets over crop fields. Remote Sens. Environ. 107, 639–655 (2007)CrossRefGoogle Scholar
  49. Y. Xia, K. Mitchell, M. Ek, J. Sheffield, B. Cosgrove, E. Wood, L. Luo, C. Alonge, H. Wei, J. Meng, B. Livneh, D. Lettenmaier, V. Koren, Q. Duan, K. Mo, Y. Fan, D. Mocko, Continental scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 1. Intercomparison and application of model products. J. Geophys. Res. 117, D03109 (2012). doi:10.1029/2011JD016048Google Scholar
  50. B.F. Zaitchik, M. Rodell, R.H. Reichle, Assimilation of GRACE terrestrial water storage data into a land surface model: results for the Mississippi river basin. J. Hydrometeorol. 9, 535–548 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg (outside the USA) 2015

Authors and Affiliations

  1. 1.NASA Goddard Space Flight Center, Code 610.1GreenbeltUSA
  2. 2.KU Leuven, Department of Earth and Environmental SciencesLeuvenBelgium
  3. 3.Data Assimilation SectionEuropean Center for Medium-Range Weather ForecastsReadingUK

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