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Open AccessArticle

Quantifying Uncertainty in Satellite-Retrieved Land Surface Temperature from Cloud Detection Errors

Department of Meteorology, University of Reading, Reading, RG6 6AL, UK
National Centre for Earth Observation, Leicester, UK
Space Research Centre, University of Leicester, Leicester, LE1 7RH, UK
Deutsches Zentrum für Luft- und Raumfahrt (DLR), 82234 Oberpfaffenhofen, Germany
Now at: Adesso Insurance Solutions GmbH, 81541 Munich, Germany
Rutherford Appleton Laboratory (RAL), Harwell Campus, Didcot, OX11 0QX, UK
Finnish Meteorological Institute (FMI), F1-00560 Helsinki, Finland
Authors to whom correspondence should be addressed.
Remote Sens. 2018, 10(4), 616;
Received: 9 March 2018 / Revised: 6 April 2018 / Accepted: 6 April 2018 / Published: 17 April 2018
(This article belongs to the Section Atmosphere Remote Sensing)
Clouds remain one of the largest sources of uncertainty in remote sensing of surface temperature in the infrared, but this uncertainty has not generally been quantified. We present a new approach to do so, applied here to the Advanced Along-Track Scanning Radiometer (AATSR). We use an ensemble of cloud masks based on independent methodologies to investigate the magnitude of cloud detection uncertainties in area-average Land Surface Temperature (LST) retrieval. We find that at a grid resolution of 625 km 2 (commensurate with a 0.25 grid size at the tropics), cloud detection uncertainties are positively correlated with cloud-cover fraction in the cell and are larger during the day than at night. Daytime cloud detection uncertainties range between 2.5 K for clear-sky fractions of 10–20% and 1.03 K for clear-sky fractions of 90–100%. Corresponding night-time uncertainties are 1.6 K and 0.38 K, respectively. Cloud detection uncertainty shows a weaker positive correlation with the number of biomes present within a grid cell, used as a measure of heterogeneity in the background against which the cloud detection must operate (e.g., surface temperature, emissivity and reflectance). Uncertainty due to cloud detection errors is strongly dependent on the dominant land cover classification. We find cloud detection uncertainties of a magnitude of 1.95 K over permanent snow and ice, 1.2 K over open forest, 0.9–1 K over bare soils and 0.09 K over mosaic cropland, for a standardised clear-sky fraction of 74.2%. As the uncertainties arising from cloud detection errors are of a significant magnitude for many surface types and spatially heterogeneous where land classification varies rapidly, LST data producers are encouraged to quantify cloud-related uncertainties in gridded products. View Full-Text
Keywords: uncertainties; land surface temperature; cloud detection errors uncertainties; land surface temperature; cloud detection errors
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Bulgin, C.E.; Merchant, C.J.; Ghent, D.; Klüser, L.; Popp, T.; Poulsen, C.; Sogacheva, L. Quantifying Uncertainty in Satellite-Retrieved Land Surface Temperature from Cloud Detection Errors. Remote Sens. 2018, 10, 616.

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