Enhanced biomass prediction by assimilating satellite data into a crop growth model

Miriam Machwitz, Laura Giustarini, Christian Bossung, David Frantz, Martin Schlerf, Holger Lilienthal, L. Wandera, Patrick Matgen, Lucien Hoffmann, Thomas Udelhoven

Research output: Contribution to journalArticleAcademicpeer-review

46 Citations (Scopus)


Complex crop growth models (CGM) require a large number of input parameters, which can cause large errors if they are uncertain. Furthermore, they often lack spatial information. The coupling of a CGM with a radiative transfer model offers the possibility to assimilate remote sensing data while taking into account uncertainties in input parameters. A particle filter was used to assimilate satellite data into a CGM coupled with a leaf-canopy radiative transfer model to update biomass simulations of maize. The synthetic experiment set up to test the reliability of the procedure, highlighted the importance of the acquisition time. The real case study with RapidEye observations confirmed these findings. Data assimilation increased the accuracy of biomass predictions in the majority of the six maize fields where biomass validation data was available, with improvements of up to 15%. The smallest and largest errors in biomass prediction after assimilation were 82kg/ha and 2116kg/ha, respectively. Furthermore, data assimilation enabled the production of biomass maps showing detailed spatial variability.
Original languageEnglish
Pages (from-to)437-453
Number of pages17
JournalEnvironmental modelling & software
Publication statusPublished - 1 Dec 2014
Externally publishedYes


  • Crop grow modelling
  • Data assimilation
  • Maize
  • Particle filter
  • RapidEye
  • Remote sensing
  • n/a OA procedure


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