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Uncertainty Management in Satellite Remote Sensing

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmospheric Remote Sensing".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 9454

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Special Issue Editors

Dr. Adam Povey

E-Mail Website
Guest Editor

University of Oxford, Oxford, United Kingdom
Interests: optimal estimation retrieval; aerosol–cloud interactions; satellite remote sensing

Dr. Claire E. Bulgin

E-Mail Website
Guest Editor

University of Reading, Reading, United Kingdom
Interests: sea and land surface temperature retrieval; uncertainty characterisation; cloud detection for remote sensing applications

Special Issue Information

Dear Colleagues,

An estimate of uncertainty is a vital component of any scientific dataset. The accurate assessment and management of uncertainty is essential in the identification and monitoring of climate trends. Uncertainty provides a context for the intercomparison of satellite data products with ground-based observations and climate model outputs, while the propagation of error into downstream products is essential in distinguishing artefacts from new science.

To support such research, satellite data producers are moving away from diagnostic estimates of performance by developing prognostic per-pixel estimates of uncertainty. It is difficult to determine the distribution of error in this context, as it is not possible to calibrate against a fiducial reference measurement after launch. Advanced methods can decompose error estimates into different levels of correlation, as required for robust propagation into derived results. As data assimilation becomes increasingly important, satellite products face the challenge of producing unbiased estimates of their error covariance matrix.

This Special Issue seeks submissions that:

Present novel applications of uncertainty in the analysis of satellite remote sensing data;
Demonstrate the value of considering uncertainty when using satellite remote sensing products;
Introduce methods to calculate and/or validate the uncertainty in satellite remote sensing data.

Dr. Adam Povey
Dr. Claire E. Bulgin
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Uncertainty estimation and propagation
Uncertainty application
Error covariance estimation
Satellite remote sensing
Calibration and validation
Climate trend analysis
Data assimilation

Published Papers (4 papers)

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Research

18 pages, 8221 KiB

Open AccessArticle

The Influence of Underlying Land Cover on the Accuracy of MODIS C6.1 Aerosol Products—A Case Study over the Yangtze River Delta Region of China

by Kun Sun, Yang Gao, Bing Qi and Zhifeng Yu

Remote Sens. 2022, 14(4), 938; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14040938 - 15 Feb 2022

Cited by 3 | Viewed by 1396

Abstract

Due to the significant spatial variation of the performance of Moderate Resolution Imaging Spectrometer (MODIS) aerosol optical depth (AOD) retrievals, validation is very important for applications of MODIS AOD products at regional scales. This study presents a comparative analysis of Collection 6.1 MODIS AOD retrievals and ground measurements from five local sites and one Aerosol Robotic Network (AERONET) site in the Yangtze River Delta (YRD) region, which significantly complements the previous validation that utilized limited AERONET measurements. Generally, MODIS AOD retrievals showed a reasonable agreement with collocated ground measurements (R² > 0.7), with 66% of Dark Target (DT) 10 km retrievals, 56% of Deep Blue (DB) 10 km retrievals, and 69% of DT 3 km retrievals falling within the expected error (EE = ±(0.05 + 0.2 × AOD)). Nevertheless, it was found that the DT AOD retrievals tended to be overestimated over urbanized and lakeside sites, while the DB AOD retrievals tended to be underestimated over all ground sites except for lakeside sites. Such patterns appeared to be linked with the systematic biases of the single-scattering albedo estimation in the AOD retrieval algorithms. Another significant finding of this study is that the uncertainties of the MODIS AOD retrievals were highly correlated with the land cover proportions of urbanized features and water (LCP_UW) in the surrounding region, especially for the DT products. An empirical correction method based on these correlations could substantially reduce the uncertainties of DT AOD products over high LCP_UW areas. The results not only highlight the significant impacts of both urban and water areas on the MODIS AOD retrieval algorithms but also create new possibilities to correct such impacts once the universal correlations between LCP_UW and the uncertainty measures are established. Full article

(This article belongs to the Special Issue Uncertainty Management in Satellite Remote Sensing)

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23 pages, 4784 KiB

Open AccessArticle

Systematic Propagation of AVHRR AOD Uncertainties—A Case Study to Demonstrate the FIDUCEO Approach

by Thomas Popp and Jonathan Mittaz

Remote Sens. 2022, 14(4), 875; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14040875 - 12 Feb 2022

Cited by 1 | Viewed by 1798

Abstract

The AVHRR aerosol optical depth (AOD) is inverted from measured reflectances in the red band using a statistical correlation of surface reflectance with mid-infrared channel reflectances and a modelling climatology of the aerosol type. For such a sensor not specifically designed for AOD retrieval, propagating uncertainties is crucial because the sensitivity of the retrieved AOD to the measured signal varies largely with retrieval conditions (AOD itself, surface brightness, aerosol optical properties/aerosol type, observing geometry). In order to quantify the different contributions to the AOD uncertainties, we have undertaken a thorough analysis of the retrieval operator and its sensitivities to the used input and auxiliary variables. Uncertainties are then propagated from measured reflectances to geophysical retrieved AOD datasets at the super-pixel level and further to gridded daily and monthly products. The propagation uses uncertainty correlations of separate uncertainty contributions from the FIDUCEO easyFCDR level1b products (common fully correlated, independent random, and structured parts) and estimated uncertainty correlation structures of other major effects in the retrieval (surface brightness, aerosol type ensemble, cloud mask). The pixel-level uncertainties are statistically validated against true error estimates versus AERONET ground-based AOD measurements. It is shown that a 10-year time record over Europe compares well to a merged multi-satellite record and that pixel-level uncertainties provide a meaningful representation of error distributions. The study demonstrates the benefits of new recipes for uncertainty characterization from the Horizon-2020 project FIDUCEO (“Fidelity and uncertainty in climate data records from Earth Observations”) and extends them further with recent additions developed within the ESA Climate Change Initiative. Full article

(This article belongs to the Special Issue Uncertainty Management in Satellite Remote Sensing)

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23 pages, 3915 KiB

Open AccessArticle

Ancillary Data Uncertainties within the SeaDAS Uncertainty Budget for Ocean Colour Retrievals

by Pieter De Vis, Frédéric Mélin, Samuel E. Hunt, Rosalinda Morrone, Morven Sinclair and Bill Bell

Remote Sens. 2022, 14(3), 497; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14030497 - 21 Jan 2022

Cited by 6 | Viewed by 2516

Abstract

Atmospheric corrections introduce uncertainties in bottom-of-atmosphere Ocean Colour (OC) products. In this paper, we analyse the uncertainty budget of the SeaDAS atmospheric correction algorithm. A metrological approach is followed, where each of the error sources are identified in an uncertainty tree diagram and briefly discussed. Atmospheric correction algorithms depend on ancillary variables (such as meteorological properties and column densities of gases), yet the uncertainties in these variables were not studied previously in detail. To analyse these uncertainties for the first time, the spread in the ERA5 ensemble is used as an estimate for the uncertainty in the ancillary data, which is then propagated to uncertainties in remote sensing reflectances using a Monte Carlo approach and the SeaDAS atmospheric correction algorithm. In an example data set, wind speed and relative humidity are found to be the main contributors (among the ancillary parameters) to the remote sensing reflectance uncertainties. Full article

(This article belongs to the Special Issue Uncertainty Management in Satellite Remote Sensing)

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26 pages, 9582 KiB

Open AccessArticle

Traceability of the Sentinel-3 SLSTR Level-1 Infrared Radiometric Processing

by David Smith, Samuel E. Hunt, Mireya Etxaluze, Dan Peters, Tim Nightingale, Jonathan Mittaz, Emma R. Woolliams and Edward Polehampton

Remote Sens. 2021, 13(3), 374; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13030374 - 22 Jan 2021

Cited by 5 | Viewed by 2858

Abstract

Providing uncertainties in satellite datasets used for Earth observation can be a daunting prospect because of the many processing stages and input data required to convert raw detector counts to calibrated radiances. The Sea and Land Surface Temperature Radiometer (SLSTR) was designed to provide measurements of the Earth’s surface for operational and climate applications. In this paper the authors describe the traceability chain and derivation of uncertainty estimates for the thermal infrared channel radiometry. Starting from the instrument model, the contributing input quantities are identified to build up an uncertainty effects tree. The characterisation of each input effect is described, and uncertainty estimates provided which are used to derive the combined uncertainties as a function of scene temperature. The SLSTR Level-1 data products provide uncertainty estimates for fully random effects (noise) and systematic effects that can be mapped for each image pixel, examples of which are shown. Full article

(This article belongs to the Special Issue Uncertainty Management in Satellite Remote Sensing)

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