Emission sources from eo data for improved pollen forecasting
Currently, more than 20% of adults and 15% of children and adolescents suffer from an allergic-atopic disease. In Bavaria, this includes about 2 million adults and 300,000 children and adolescents.
Thereby, about 80 % of all allergy sufferers are pollen allergy sufferers. These diseases caused total costs of about 600 million euros in Bavaria in 2013. The goal of the Bavarian state government is therefore a valid and practicable pollen forecast, which is characterized by both the highest possible spatial resolution and the earliest possible prediction.
The ESPE project aims to optimize pollen modeling by incorporating current high-resolution forest and open-land maps of Bavaria into forecast models. For this purpose, freely available satellite-based Earth observation data of the Copernicus Sentinels and innovative “Data Cube” spatial data infrastructures are used.
The modeling of grass and tree pollen flight is currently based on a temperature sum-driven, species-specific model (“SILAM” model of the Finish Meteorological Institute), which is based on a grassland and forest map in 1 km resolution. This approach can be improved by various remote sensing-based input data.
Small-scale and always up-to-date delineation of emission sources of allergenic pollen can improve the prediction accuracy of pollen models. Data and research needs could be identified especially with respect to high spatial resolution (<30 m) forest type maps as well as temporally differentiated land use types and land use dynamics in permanent grassland in order to optimize the detection of local pollen emission sources from forests and permanent grassland.
The following three substantive objectives will be realized in the project:
 Improvement of the baseline mapping of the current distribution of grassland and forest stands using Copernicus Sentinel-2 satellite data for the Free State of Bavaria.
 Optimization of the remote sensing based coverage of forest types and permanent grassland areas including land use dynamics as emission sources of pollen.
 Potential analysis to improve pollen forecasting by integrating thematically and spatially high-resolution land cover information with in-situ measurements and pollen models.
Facts and Figures
Covering the whole state of Bavaria, this Data Cube ingested:
- > 13.000 high-resolution images, provided by the multispectral Sentinel-2 Satellites
- > 12 TerraByte of atmosphere corrected ARD (Analysis Ready Data)
- covering a timespan from 2015 until 2020, and still growing…
The Bavarian Datacube is piloted though custom Jupyter-Notebooks, making “Big Data” easy controllable.