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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