Uncertainty as the key to interoperability: Traceable frameworks for space-based imaging spectroscopy

By the end of this decade, several space agencies and commercial operators will develop and launch the next generation of space-based Earth observation satellites. This paper focuses on imaging spectroradiometers, which measure solar radiation reflected by the Earth’s surface across the ultra-violet, visible and short-wave infrared spectral ranges using hyperspectral sensors. To enable interoperable use of datasets from this growing constellation of sensors, it is essential to understand and manage differences between the measurements of the same target under similar conditions. These differences arise from multiple factors: real variations due to instrument design (e.g., differing spectral responses or viewing geometries), differences in Level 1 processing approaches based on pre-flight and post-launch calibration strategies, differences in higher-level processing into biophysical quantities, and any residual biases between sensors caused by measurement or processing errors — some of which may not be fully captured in reported uncertainties. A consistent approach to the analysis and documentation of uncertainties supports intercomparison between datasets. By assessing whether measurements agree within the expected uncertainty associated with the comparison, or identifying and quantifying systematic deviations when they do not, it becomes possible to validate interoperability and diagnose discrepancies. This paper outlines key concepts for harmonising uncertainty analysis and reporting techniques across missions, including the use of structured uncertainty propagation frameworks such as uncertainty trees. The goal is to initiate a roadmap for understanding and quantifying uncertainties in a way that facilitates robust intercomparison of multiple space-based imaging spectroradiometer measurements.