Welcome to geeagri¶
A Python package for agricultural monitoring and analysis using Google Earth Engine
- GitHub repo: https://github.com/geonextgis/geeagri
- Documentation: https://geonextgis.github.io/geeagri
- PyPI: https://pypi.org/project/geeagri
- Notebooks: https://github.com/geonextgis/geeagri/tree/main/docs/examples
- License: MIT
Introduction¶
geeagri is a Python package that integrates the power of Google Earth Engine (GEE) with domain-specific agricultural analysis. It enables scalable processing, downloading, and visualization of satellite data for crop monitoring, yield estimation, and agro-environmental assessment.
This package builds upon geospatial tools like geemap and simplifies workflows for scientists, researchers, and policymakers working in agriculture. Whether you're interested in vegetation monitoring, drought assessment, phenology extraction, or productivity mapping, geeagri offers tools and prebuilt pipelines to make your analysis easier and faster.
geeagri is ideal for: - Researchers working with satellite-derived agricultural indicators. - Practitioners and analysts from development, environmental, and governmental organizations. - Students and educators looking to learn remote sensing for agriculture.
For a complete list of examples and use cases, visit the notebooks section.
Key Features¶
- Extracting long-term time series data from GEE for both point and polygon geometries.
- Extract image patches from satellite imagery in GEE to support local-scale computer vision model training.
- Quickly transform complex multivariate datasets into a few principal components while preserving critical information.
- Easy implementation of Harmonic Regression on vegetation or climate indices.