Gruber, Alexander

Bulgin, Claire

Dorigo, Wouter Arnoud

Embury, Owen

Formanek, Maud

Merchant, Christopher

Mittaz, Jonathan

Muñoz-Sabater, Joaquin

Pöppl, Florian

Povey, Adam C.

Wagner, Wolfgang

2025-08-21T13:15:50Z

2025-08-21T13:15:50Z

2025-06-27

<div class="csl-bib-body"> <div class="csl-entry">Gruber, A., Bulgin, C., Dorigo, W. A., Embury, O., Formanek, M., Merchant, C., Mittaz, J., Muñoz-Sabater, J., Pöppl, F., Povey, A. C., &#38; Wagner, W. (2025, June 27). <i>Making sense of uncertainties: Ask the right question</i> [Conference Presentation]. Living Planet Symposium (LPS 2025), Wien, Austria. http://hdl.handle.net/20.500.12708/218342</div> </div>

http://hdl.handle.net/20.500.12708/218342

Earth observation data should help make decisions. However, all Earth observation data have an associated uncertainty arising from a number of error effects in the measurement process that vary in time and space, including limitations of the measurement and calibration process, uncertainties in auxiliary data, approximations in the data processing, and inhomogeneities in the observed field. Data producers thus strive to provide reliable uncertainty estimates alongside their products that should help inform decisions that are based on these products. However, data users often struggle to make sense of uncertainty information, because it is usually expressed as the statistical spread in the observations (for example, as random error standard deviation), which does not relate to an intended use of the data. That is, expressing data and their uncertainty as “x plus/minus y” does not answer the really important question: How much can I trust “x”, or any use of or decision based upon “x”? In this talk, we present a Bayesian framework that can be used to transform Earth observation product uncertainties into more meaningful, actionable information by explicitly quantifying the resulting probabilities of events of interest. We demonstrate the merit of this framework using two case examples: (i) monitoring drought severity based on soil moisture; and (ii) estimating coral bleaching risk based on sea surface temperature. We first show how looking at deterministic state estimates from Earth observations alone can be misleading, and that any decisions based on these estimates are unlikely to be the best course of action. We then show how typical data representations like “the state of this variable is “x plus/minus y” can be transformed into more informative statements such as “the data and their uncertainties suggest that we can be “z” % confident that…” and discuss how such an approach can help data users make better decisions, thus maximizing the socioeconomic merit of Earth observation data.

en

remote sensing

uncertainty

earth observation

Making sense of uncertainties: Ask the right question

Presentation

Vortrag

University of Reading, United Kingdom of Great Britain and Northern Ireland (the)

University of Reading, United Kingdom of Great Britain and Northern Ireland (the)

University of Reading, United Kingdom of Great Britain and Northern Ireland (the)

University of Reading, United Kingdom of Great Britain and Northern Ireland (the)

European Centre for Medium-Range Weather Forecasts, United Kingdom of Great Britain and Northern Ireland (the)

University of Leicester, United Kingdom of Great Britain and Northern Ireland (the)

Conference Presentation

E4

Environmental Monitoring and Climate Adaptation

100

E120-01 - Forschungsbereich Fernerkundung

E120-08 - Forschungsbereich Klima- und Umweltfernerkundung

0000-0003-4368-7386

0000-0001-8054-7572

0000-0002-1661-7828

0000-0003-4687-9850

0000-0001-9915-711X

0000-0001-7704-6857

Living Planet Symposium (LPS 2025)

23-06-2025

27-06-2025

On Site

Event for scientific audience

Wien

AT

ESA

Gruber, Alexander

Geodäsie, Vermessungswesen

Informatik

Physische Geographie

2074

1020

1054

70

15

15

conference paper not in proceedings

http://purl.org/coar/resource_type/c_18cp

none

en

no Fulltext

Publications

E120-08 - Forschungsbereich Klima- und Umweltfernerkundung

University of Reading, United Kingdom of Great Britain and Northern Ireland (the)

E120-08 - Forschungsbereich Klima- und Umweltfernerkundung

University of Reading, United Kingdom of Great Britain and Northern Ireland (the)

E120-08 - Forschungsbereich Klima- und Umweltfernerkundung

University of Reading, United Kingdom of Great Britain and Northern Ireland (the)

University of Reading, United Kingdom of Great Britain and Northern Ireland (the)

European Centre for Medium-Range Weather Forecasts, United Kingdom of Great Britain and Northern Ireland (the)

E120-07 - Forschungsbereich Photogrammetrie

University of Leicester, United Kingdom of Great Britain and Northern Ireland (the)

E120-01 - Forschungsbereich Fernerkundung

0000-0003-4368-7386

0000-0001-8054-7572

0000-0002-1661-7828

0000-0003-4687-9850

0000-0001-9915-711X

0000-0001-7704-6857

E120 - Department für Geodäsie und Geoinformation

E120 - Department für Geodäsie und Geoinformation

E120 - Department für Geodäsie und Geoinformation

E120 - Department für Geodäsie und Geoinformation

E120 - Department für Geodäsie und Geoinformation