Max Planck Gesellschaft


Modelling Responses of Dehesas with Hyperspectral Remote Sensing

Start date 01/2017
End date 12/2019
BMBF Förderprogramm „nationales Erdbeobachtungsprogramm“ funded by DLR

introduction | methods | publications


Biogeochemical modeling relies on remote sensing information to represent mass and energy transfers between the biosphere and the atmosphere. These remote sensing products are so far related to land use, structural and/or biochemical variables of vegetation; some of which are as well Essential Biodiversity Variables (EBV). However, products describing key physiological variables are still missing. Consequently, rough estimates are used at global scale instead, leading to large uncertainties. The estimation of these variables requires can be achieved coupling different models describing radiation transfer and vegetation functioning; however, due to the large number of variables involved overdetermined information is needed for the inversion problem.
MoReDEHESHyReS develop methodologies to exploit hyperspectral information from the Environmental Mapping and Analysis Program (EnMAP) mission in order to generate new products mapping new EBV describing plant physiology and vegetation traits and structure.


MoReDEHESHyReS methodology will be tested using hyperspectral ground and airborne data acquired in a Mediterranean tree-grass ecosystem located in Majadas del Tiétar, Cáceres, Spain. I this site, a complete set of replicated Eddy covariance ecosystem and subcanopy towers support a large scale fertilization experiment (MANIP). Spectral data were acquired from ground (SMANIE) and airborne (DEHESHyRe, FLUXPEC) platforms simultaneously to surface fluxes.

Figure 1. CASI sensor overpass in Majadas del Tiétar research site, Cáceres, Spain. April 2014

MoReDEHESHyReS will start assessing the impact of EnMAP sensor features on the relationships existing between spectral information and fluxes. Then, the retrieval algorithm of physiological EBV will be developed using these high-resolution datasets via radiative transfer and global vegetation models coupling with different degrees of complexity. Model inversion will be supported by Machine Learning techniques. Finally, the model will be tested using synthetic EnMAP imagery developed from the DEHESHyRe airborne datasets.

Figure 2. Vegetation spectra resampled to EnMAP spectral bands.


Pacheco-Labrador J., El-Madany T.S., Martín P.M., Migliavacca M., Rossini M., Carrara A. and Zarco-Tejada P.J. (2017) Spatio-Temporal Relationships between Optical Information and Carbon Fluxes in a Mediterranean Tree-Grass Ecosystem. Remote Sens. 2017, 9, 608; doi:10.3390/rs9060608

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