Commercial and Government Researchers Move Toward Collaborative Satellite Calibration

On a rapidly changing Earth, space-based imaging provides one of the most critical inputs to our scientific understanding of the whole Earth system. With the rapid increase in the number of medium- to high-resolution imaging sensors, there is increasing need for standard calibration methodology. An increase in sensor fusion, in which researchers and decision makers rely on combined sensor inputs to address a particular question or problem, further drives this need for standard calibration and data interoperability.

Commercial systems often utilize ground-based calibration in combination with an on-orbit vicarious calibration process. This vicarious calibration is performed post-launch, using a variety of reference terrestrial and lunar targets. Without a standard calibration methodology, it is difficult for researchers to combine datasets reliably.  

In September of 2018, researchers from US Geological Survey Earth Resources Observation and Science Center, European Space Agency, Planet, Maxar, NASA Goddard, and ACRI-ST, gathered together to investigate the differences in calibration methodologies, with the intention of creating standard calibration practices that could help complement commercial and government systems. The workshop was held at the National Oceanic and Atmospheric Administration (NOAA) Center for Weather and Climate Prediction.

Users from the workshop subsequently established more frequent meetings to secure “standard practices that provide a consistent ability to compare the data across platforms ranging from cubesats through large government systems.” The users also agreed to “collaboration on specific cross-calibration opportunities, developing a reference sensor for all optical systems, and encouraging the coordinated development of surface reflectance products.”

The full report from the workshop was published, open access, in the journal Remote Sensing.

Plot of Normalized Difference Vegetation index (NDVI) estimates for alfalfa in Saudi Arabia, from Landsat 8, PlanetScope, and the Cubesat-enabled Spatiotemporal Enhancement Method. CESTEM synthesizes observations from Landsat 8 (red squares) and PlanetScope (black circles) to provide an accurate and temporally dense NDVI projection.