This repository contains Python examples and tutorials for working with Google Earth Engine (GEE). The examples range from basic introductory tutorials to more advanced time series analysis.

Google Earth Engine is a cloud-based platform for planetary-scale geospatial analysis. It provides access to a vast catalog of satellite imagery and geospatial datasets.

• Python 3.7 or higher
• A Google Earth Engine account (sign up here)
• Basic knowledge of Python and remote sensing concepts

1. Clone this repository:

git clone https://github.com/ChaoEcohydroRS/GEE_Example.git
cd GEE_Example

1. Install required packages:

pip install -r requirements.txt

1. Authenticate with Google Earth Engine:

earthengine authenticate

GEE_Example/
├── README.md
├── requirements.txt
├── basic_tutorials/
│   ├── 01_getting_started.py
│   ├── 02_image_visualization.py
│   ├── 03_filtering_collections.py
│   └── 04_basic_calculations.py
└── time_series_analysis/
    ├── 01_ndvi_time_series.py
    ├── 02_temperature_analysis.py
    └── 03_precipitation_analysis.py

1. Getting Started - Initialize GEE, load your first image, and print metadata
2. Image Visualization - Visualize satellite imagery with different band combinations
3. Filtering Collections - Filter image collections by date, location, and cloud cover
4. Basic Calculations - Perform simple calculations like NDVI

1. NDVI Time Series - Extract and analyze vegetation index trends over time
2. Temperature Analysis - Analyze land surface temperature patterns and trends
3. Precipitation Analysis - Examine precipitation patterns using CHIRPS dataset

Each example is a standalone Python script that can be run independently:

python basic_tutorials/01_getting_started.py

• Image and ImageCollection handling
• Spatial and temporal filtering
• Band math and spectral indices
• Reducers for temporal aggregation
• Chart generation and data export
• Time series extraction and visualization

• GEE Python API Documentation
• GEE Datasets Catalog
• GEE Code Editor

Feel free to contribute additional examples or improvements via pull requests.

MIT License - feel free to use these examples for learning and research.

For questions or suggestions, please open an issue in this repository.