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Quantifying uncertainty in satellite-retrieved land surface temperature from cloud detection errors

Bulgin, C. E., Merchant, C. J. ORCID:, Ghent, D., Klueser, L., Popp, T., Poulsen, C. and Sogacheva, L. (2018) Quantifying uncertainty in satellite-retrieved land surface temperature from cloud detection errors. Remote Sensing, 10 (4). 616. ISSN 2072-4292

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To link to this item DOI: 10.3390/rs10040616


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 0.25 degrees 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 nighttime 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 (eg. 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 magnitude 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.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > National Centre for Earth Observation (NCEO)
Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:76518


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