Sentinel2 Crop Trait Time Series Reconstruction

This paper contains code and data required to reproduce the results of our paper "Probabilistic assimilation of optical satellite data with physiologically based growth functions improves crop trait time series reconstruction".

Citation

@article{graf_reconstruction_2023
    title = {Probabilistic assimilation of optical satellite data with physiologically based growth functions improves crop trait time series reconstruction},
    year = {2023},
    author = {Graf, Lukas Valentin and Tschurr, Flavian and Aasen, Helge and Walter, Achim},
    journal = {under review}
}

Content

The Python and R source code can be found in src.

scripts_dose_response contains the R scripts required to fit the dose-response curves based on in-situ Green Leaf Area Index data. The fitted function parameters can be found here.

The main Python scripts to reconstruct the Green Leaf Area Index time series from Sentinel-2 observations at the validation sites are:

• 01_extract_s2_data.py to fetch the Sentinel-2 data from Microsoft Planetary Computer and generate the PROSAIL lookup-tables.
• 02_extract_s2_traits.py to retrieve the traits by lookup-table inversion
• 03_generate_raw_s2_trait_trajectories.py to extract the "raw" Sentinel-2 Green Leaf Area Index trajectories.
• 04_reconstruct_s2_traits.py to reconstruct the time series using dose-response response functions and Ensemble Kalman filtering.
• validation to validate the reconstructed time series against in-situ observations.

The results of the scripts will be written to results

Data

The in-situ data used in this work is the result of hard work by a lot of people in the field and laboratory conducted by teams at ETH Zurich Crop Science, the School of Agircultural, Forest and Food Sciences, HAFL and the Division of Agroecology and Environment at Agroscope Reckenholz. A list of contributors is provided.

We therefore kindly ask you to acknowledge our work by

• citing our research properly whenever you use the data and/or methods presented here
• leave a star on GitHub and/or fork our repository

This helps us to continue the labor and cost-intensive process of data acquisition, preparation and, ultimately, publication to benefit science and society.

If your work relies substantially on our data please also get in touch with us and consider offering co-authorship.

Known Issues

You must use Pandas 2.x, older versions of Pandas (Pandas 1.x) will cause errors.