tag: earth-observation

This post describes the fundamentals of Earth-Observation datacubes, outlines the basic Python building blocks for creating Zarr-backed datacubes, and presents a scalable serverless approach to building large-scale datacubes which is cost-effective, reliable, and performant.

This is a blog version of a webinar that took place on April 16, 2024. Here’s a video of that webinar: Earth Observation satellites generate massive volumes of data about our planet, and these data are vital for confronting global challenges. Satellite imagery is commonly distributed as individual “scenes” — a single file consisting of a single image of a tiny part of the Earth. Popular public satellite programs such such as NASA / USGS Landsat and Copernicus Sentinel produce millions of such images a year, comprising petabytes of data. Increasingly, we see organizations looking to aggregate raw satellite imagery into more analysis-ready datacubes. In contrast to millions of individual images sampled unevenly in space and time, Earth-system datacubes contain multiple variables, align…
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