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Vegetation trait retrieval with remote sensing data in Google Earth Engine and openEO

pyeogpr 최신버젼 다운로드

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# pyeogpr [![GitHub](https://img.shields.io/badge/GitHub-pyeogpr-purple.svg)](https://github.com/daviddkovacs/pyeogpr)   [![Documentation](https://img.shields.io/badge/docs-pyeogpr-blue.svg)](https://pyeogpr.readthedocs.io/en/latest/pyeogpr.html) [![DOI](https://img.shields.io/badge/DOI-10.5281%2Fzenodo.13373838-green)](https://doi.org/10.5281/zenodo.13373838)

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.

# Features

- Access to GEE/openEO is required. Works best with the Copernicus Data Space Ecosystem. Register [here](https://code.earthengine.google.com/register) or [here](https://docs.openeo.cloud/join/free_trial.html)
- Hybrid retrieval methods were used: the Gaussian Process Regression retrieval algorithms were trained on biophysical trait specific radiative transfer model (RTM) simulations
- Uncertainties provided! 
- Runs "in the cloud" with the GEE/openEO Python API. No local processing is needed.
- Resulting maps in .tiff or netCDF format

# Get started

Refer to the [Documentation](https://pyeogpr.readthedocs.io/en/latest/pyeogpr.html) for instructions and examples.

# Satellites and biophysical variables

You can select from a list of trained variables developed for the following satellites:

[Sentinel-2 L1C](https://pyeogpr.readthedocs.io/en/latest/sensors.html#)

[Sentinel-2 L2A](https://pyeogpr.readthedocs.io/en/latest/sensors.html#)

[Sentinel-3](https://pyeogpr.readthedocs.io/en/latest/sensors.html#sentinel-3-ocean-and-land-colour-instrument-olci)

# Cite as / Contact

- Kovács, Dávid D., Emma De Clerck, and Jochem Verrelst. "PyEOGPR: A Python package for vegetation trait mapping with Gaussian Process Regression on Earth observation cloud platforms." Ecological Informatics 92, no. 10349 (2025): 7.
  
or

- Dávid D.Kovács. (2024). pyeogpr (zenodo). Zenodo. https://doi.org/10.5281/zenodo.13373838
- david.kovacs@tuwien.ac.at

## 
Supported by the European Union (European Research Council, FLEXINEL, 101086622) project.

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