We, the Earth Observation of Agroecosystems (EOA) team at Agroscope, contribute to improving the sustainability of our land management practices by providing necessary information for targeted decision making.

We use Earth Observation (EO) and Geographic Information Systems (GIS) and develop empirical and mechanistic models to interpreted EO data from multiple sources across several scales in order to understand the ecophysiological processes happening in our agroecosystems.

Visit our website: https://www.eoa-team.net/

• EOdal - Earth Observation Data Analysis Library, a Python library enabling the acquisition, organization, and analysis of EO data in a completely open-source manner within a unified framework.
• EOdal notebooks - Collection of example notebooks showcasting the capabilities of EOdal.

• interactive_plots - Generate interactive subplots for exploratory analysis in Python.
• python-dem-shadows - A Python library for projecting solar shadows on digital elevation models.

• rtm_inv - Forward run of ProSAIL and SPART RTMs generating look-up tables (LUTs) in Python. LUT-based inversion of RTMs to be used with Sentinel2A and B, Landsat 8 and 9, and PlanetScope SuperDove.
• PyProSAIL- Python interface to the ProSAIL combined leaf and canopy radiative transfer model.

• sentinel2_crop_traits - Extract Sentinel-2 data, run PROSAIL simulations, perform the inversion for trait retrieval and implements a phenology model to constrain the inversion.
• s2toarp - Study the impact of radiometric uncertainty in Sentinel-2 Top-of-Atmosphere data on the retrieval of land surface metrics.
• sentinel2_crop_trait_timeseries - Retrieve traits from look-up tables generated by ProSAIL forward runs and reconstruct the Green Leaf Area Index time series from Sentinel-2 observations.

You can find our publications below:

• Graf, L.V., Merz, Q.M, Walter, A., Aasen, H. (2023) "Insights from field phenotyping improve satellite remote sensing based in-season estimation of winter wheat growth and phenology".Remote Sensing of Environment. DOI:https://doi.org/10.1016/j.rse.2023.113860.
• Graf, L.V., Tschurr, F., Aasen, H., Walter, A. (2023) "Probabilistic assimilation of optical satellite data with physiologically based growth functions improves crop trait time series reconstruction". IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. DOI:https://doi.org/10.31223/X5596B
• Graf, L.V., Perich, G., Aasen, H. (2022) "EOdal: An open-source Python package for large-scale agroecological research using Earth Observation and gridded environmental data". Computers and Electronics in Agriculture. DOI:https://doi.org/10.1016/j.compag.2022.107487
• Graf, L.V., Gorroño, J., Hueni, A., Walter, A., Aasen, H. (2023) "Propagating Sentinel-2 Top-of-Atmosphere Radiometric Uncertainty into Land Surface Phenology Metrics Using a Monte Carlo Framework". IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. DOI:10.1109/JSTARS.2023.3297713