Articles | Volume 8, issue 11
Research article
06 Nov 2015
Research article |  | 06 Nov 2015

Known and unknown unknowns: uncertainty estimation in satellite remote sensing

A. C. Povey and R. G. Grainger

Abstract. This paper discusses a best-practice representation of uncertainty in satellite remote sensing data. An estimate of uncertainty is necessary to make appropriate use of the information conveyed by a measurement. Traditional error propagation quantifies the uncertainty in a measurement due to well-understood perturbations in a measurement and in auxiliary data – known, quantified "unknowns". The under-constrained nature of most satellite remote sensing observations requires the use of various approximations and assumptions that produce non-linear systematic errors that are not readily assessed – known, unquantifiable "unknowns". Additional errors result from the inability to resolve all scales of variation in the measured quantity – unknown "unknowns". The latter two categories of error are dominant in under-constrained remote sensing retrievals, and the difficulty of their quantification limits the utility of existing uncertainty estimates, degrading confidence in such data.

This paper proposes the use of ensemble techniques to present multiple self-consistent realisations of a data set as a means of depicting unquantified uncertainties. These are generated using various systems (different algorithms or forward models) believed to be appropriate to the conditions observed. Benefiting from the experience of the climate modelling community, an ensemble provides a user with a more complete representation of the uncertainty as understood by the data producer and greater freedom to consider different realisations of the data.

Short summary
Clear communication of the uncertainty on data is necessary for users to make appropriate use of it. This paper discusses the representation of uncertainty in satellite observations of the environment, arguing that the dominant sources of error are assumptions made during data analysis. The resulting uncertainty may be more usefully represented using ensemble techniques (a set of analyses using different assumptions to illustrate their impact) than with traditional statistical metrics.