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

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 82?kg/ha and 2116?kg/ha, respectively. Furthermore, data assimilation enabled the production of biomass maps showing detailed spatial variability. Data assimilation using a particle filter for biomass estimation was conducted.Proof of concept with synthetic case studies.Multispectral satellite data (visible and near infrared) was found to be suitable for data assimilation.Assimilation of satellite data allowed biomass prediction on a pixel basis.

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