NASA's EO-1 Retired; Project Matsu


After 17 years in orbit, NASA's Earth Observing-1 (EO-1) satellite was powered off on March 30, 2017. Expected to only last a year, the EO-1 mission developed into a NASA testbed for sensors like Advanced Land Imager (ALI), hyperspectral sensors like Hyperion, and novel self-piloting AI. The OCC has collected and redistributed ALI and Hyperion sensor data from EO-1 in a data commons since 2009 as part of a commitment to Project Matsu.


On a shoestring budget contributed by NASA, the U.S. Geological Survey, the National Oceanic and Atmospheric Administration, National Reconnaissance Office and Naval Research Laboratory, the satellite continued to operate for sixteen more years, resulting in more than 1,500 papers published on EO-1 research.

E0-1 Mission director and Project Matsu lead Dan Mandl is quoted in Wired:

"In normal operations, you never get to try new things. It’s so expensive, you don’t want to mess anything up," Mandl says. "But when you have a platform to test things, a whole area of space technology evolves faster.”

Project Matsu is a collaboration between the NASA Goddard Space Flight Center and the OCC to develop open source technology for cloud-based processing of satellite imagery to support the earth science research community as well as human assisted disaster relief.

“The data from EO-1 has been essential for prototyping a cloud-based reanalysis framework for large volumes of streaming data, called the 'Matsu Wheel’, which has been running over daily batches of EO-1 satellite data for the last few years, " says Dr. Maria Patterson, a research scientist in the LSST data management group at University of Washington and former lead for Project Matsu.

A copy of the paper describing the framework and the analytics applied to EO-1 data, including spectral anomaly detection and land coverage classification, is available here: The Matsu Wheel: a reanalysis framework for Earth satellite imagery in data commons.

Project Matsu was maintained on hardware operated by the team at the Center for Data Intensive Science at University of Chicago.

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