# Welcome to geeagri

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**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](https://opensource.org/licenses/MIT)

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## Introduction

**geeagri** is a Python package that integrates the power of [Google Earth Engine (GEE)](https://earthengine.google.com/) 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`](https://github.com/gee-community/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](https://github.com/geonextgis/geeagri/tree/main/docs/examples) section.

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## 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.

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