daviddkovacs/pyeogpr

GPR processing of Earth Observation data implemented with Google Earth Engine and openEO

pyeogpr

Python based machine learning library to use Earth Observation data to map biophysical traits using Gaussian Process Regression (GPR) models.

Features

• Access to 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 openEO API. No local processing is needed.
• Resulting maps in .tiff or netCDF format

Get started

You can install pyeogpr using pip. Read the documentation

pip install pyeogpr

Basic example:

import pyeogpr

# Your region of interest
bounding_box = [
         -73.98605881463239,
          40.763066527718536,
          -73.94617017216025,
          40.80083669627726
        ]

# Time window for processing Satellite observations
time_window = ["2022-07-01", "2022-07-07"]

dc = pyeogpr.Datacube(
    "SENTINEL2_L2A",  # Satellite sensor
    "FVC",            # Fractional Vegetation Cover
    bounding_box,
    time_window,
    cloudmask=True
)

dc.construct_datacube("dekad")  # Initiates openEO datacube

dc.process_map()  # Starts GPR processing

To download the GPR processed map go to the openEO portal:

You can use QGIS or Panoply to visualize. IMPORTANT: The data range is off, due to few pixels being outliers. Set the data range manually for the corresponding variable e.g. FVC--> 0 to 1.

Satellites and biophysical variables

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

Sentinel-2 L1C

Sentinel-2 L2A

Sentinel-3 OLCI L1B

Cite as

Dávid D.Kovács. (2024). pyeogpr (zenodo). Zenodo. https://doi.org/10.5281/zenodo.13373838

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