Google Earth Engine (GEE) is a vital tool for climate scientists, offering cloud-based processing capabilities for satellite imagery. It supports both Javascript and Python APIs, though Python resources are less abundant. A recent project on water balance and drought in Ecuador’s water basin highlighted five key lessons for GEE beginners. Firstly, GEE’s optimized functions for satellite image processing are efficient, especially when dealing with large datasets. For instance, a clustering algorithm implemented in GEE was shorter and more efficient than custom Python code. Secondly, understanding GEE’s limitations, like processing constraints, is crucial for large-scale projects. For example, reducing the resolution from 10m to 500m allowed GEE to process Sentinel-2 data effectively. Thirdly, GEE’s catalogue provides not just raw data but also derived products, saving time on preprocessing. A global ET dataset was readily available, avoiding complex calculations. Fourthly, maintaining workflow within GEE can prevent integration issues with external data formats. Lastly, tools like Geemap enhance GEE’s visualization capabilities, making it easier to create both interactive and static plots.
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