Python based machine learning library to use Earth Observation data to map biophysical traits using Gaussian Process Regression (GPR) models. Works with Google Earth Engine and openEO cloud back-ends.
- Access to GEE/openEO is required. Works best with the Copernicus Data Space Ecosystem. Register here or here
- Hybrid retrieval methods were used: the Gaussian Process Regression retrieval algorithms were trained on biophysical trait specific radiative transfer model (RTM) simulations
- Built-in gap-filling to avoid cloud covers
- Runs "in the cloud" with the GEE/openEO Python API. No local processing is needed.
- Resulting maps in .tiff or netCDF format
Refer to the Documentation for instructions and examples.
You can select from a list of trained variables developed for the following satellites:
- Kovács DD, Reyes-Muñoz P, Salinero-Delgado M, Mészáros VI, Berger K, Verrelst J. Cloud-Free Global Maps of Essential Vegetation Traits Processed from the TOA Sentinel-3 Catalogue in Google Earth Engine. Remote Sensing. 2023; 15(13):3404. https://doi.org/10.3390/rs15133404
or
- Dávid D.Kovács. (2024). pyeogpr (zenodo). Zenodo. https://doi.org/10.5281/zenodo.13373838
- david.kovacs@uv.es
Supported by the European Union (European Research Council, FLEXINEL, 101086622) project.
