eeharvest 1.0.3

A simple harvester for Google Earth Engine

eeharvest

The eeharvest package was designed to simplify access to Google Earth Engine's data catalog through a trio of convenient methods to collect, process and download data:

• preprocess(): server-side processing, cloud and shadow masking, image reduction and calculation of spectral indices
• aggregate(): (work-in-progress) perform additional temporal and/or spatial aggregaton on data
• download(): download data collection(s) to disk without limits on size or number of files

Example

from eeharvest import harvester
harvester.initialise()
# specify collection, coordinates and date range
img = harvester.collect(
        collection="LANDSAT/LC08/C02/T1_L2",
        coords=[149.799, -30.31, 149.80, -30.309],
        date_min="2019-01-01",
        date_max="2019-02-01",
    )

# cloud and shadow masking, spatial aggregation, NDVI calculation
img.preprocess(mask_clouds=True, reduce="median", spectral="NDVI")

# download to disk
img.download(bands="NDVI")

Installation

Installing dependencies from conda

Before installing the package you may need to install the following packages manually:

• GDAL: to manipulate raster and vector geospatial data
• gcloud CLI: needed to authenticate to Google servers

In most cases, these can be installed through conda-forge (but see alternatives below if not):

conda install -c conda-forge gdal google-cloud-sdk

Installing dependencies from binaries

If conda is somehow not an option, you can install the dependencies from binaries. For GDAL, use apt-get or brew (macOS). Clear instructions have been written on the rasterio website, so we won't repeat these here. For the Google Cloud SDK, follow the instructions on the gcloud CLI page.

Pip

pip install eeharvest

Conda

# conda install -c conda-forge eeharvest # WORK IN PROGRESS

Attribution and Acknowledgments

This software was developed by the Sydney Informatics Hub, a core research facility of the University of Sydney, as part of the Data Harvesting project for the Agricultural Research Federation (AgReFed). AgReFed is supported by the Australian Research Data Commons (ARDC) and the Australian Government through the National Collaborative Research Infrastructure Strategy (NCRIS).

Acknowledgments are an important way for us to demonstrate the value we bring to your research. Your research outcomes are vital for ongoing funding of the Sydney Informatics Hub. If you make use of this software for your research project, please include the following acknowledgment:

This research was supported by the Sydney Informatics Hub, a Core Research Facility of the University of Sydney, and the Agricultural Research Federation (AgReFed).

Note

This project has been set up using PyScaffold 4.3.1 and the dsproject extension 0.7.2. Developers will also need to install pre-commit. For more information see CONTRIBUTING.md in this repository.