To satisfy the increasing demand for ready-to use Earth Observation (EO) data, an intensified global cooperation is needed. Especially given the growing scientific and commercial exploitation of EO data on global scale, a unified confidence of the data characteristics, quality and reliability must be defined and communicated. For EO data, Calibration and Validation and sensor intercomparison are, amongst others, fundamental pillars of upcoming scientific optical imaging missions to enable quantitative application of imaging spectroscopy techniques across different missions. Hence, it is of great importance to have commonly accepted protocols on how best to measure and most importantly how to report on the data quality metrics.

Operators of space-borne optical Earth observation sensors often use a variety of definitions and practices and procedures to get from radiances received by the sensor in the form of digital counts to higher level data products, which are not necessarily standardised between different spacecraft operators, space agencies and communities of practice.

The main objective of this Forum is to establish a common strategy for handling scientific space-based sensor artefacts and understand the reasons for disparity between sensors (in this case, for the planning of high-precision optical sensors such as imaging spectrometers) as a source of further impacts on the scientific data products. Considering the current ‘zero-term’ policy, the Forum shall seek to outline the key requirements/concepts/recommendations for a reference to which operators of scientific space-based imaging spectroscopy missions can adhere to. While some space agencies are starting to initiate a quantitative analysis of these uncertainty terms, this is still is often outside the scope of satellite mission projects.  Therefore, an assessment driven by reasonable assumptions on which of these terms contribute more to the uncertainty budget will be relevant to drive further investigations with a view to gaining confidence in error assumptions of higher-level scientific data products.

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(Last update: May 1, 2024)