A New Approach to Defining Uncertainties for MODIS Land Surface Temperature

The accuracy of land surface temperature (LST) observations is critical to many applications. Any observation of LST is subject to incomplete knowledge, so an accurate assessment of the uncertainty budget is critical. We present a comprehensive and consistent approach to determining an uncertainty b...

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Published inRemote sensing (Basel, Switzerland) Vol. 11; no. 9; p. 1021
Main Authors Ghent, Darren, Veal, Karen, Trent, Tim, Dodd, Emma, Sembhi, Harjinder, Remedios, John
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.05.2019
Subjects
Algorithms
Budgets
consistent uncertainty model
Data assimilation
Daytime
Land surface temperature
land surface temperature (LST)
MODIS
Night
Nighttime
Remote sensing
Retrieval
Satellites
Spectroradiometers
Standard deviation
Temperature
Uncertainty
uncertainty validation
Water vapor
United Kingdom > UK
United States > US
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Summary:The accuracy of land surface temperature (LST) observations is critical to many applications. Any observation of LST is subject to incomplete knowledge, so an accurate assessment of the uncertainty budget is critical. We present a comprehensive and consistent approach to determining an uncertainty budget for LST products. We apply this approach to the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on-board the Aqua satellite. In order to generate the uncertainty model, a new implementation of the generalised split-window algorithm is applied, in which retrieval coefficients are categorised by viewing angle and water vapour. Validation of the LST against in situ data shows a mean absolute bias of 0.37 K for daytime and 0.73 K for nighttime. The average standard deviation per site is 1.53 K for daytime and 1.21 K for nighttime. Uncertainties from the implemented model are estimates in their own right and are also validated. We do this by comparing the standard deviation of the differences between the satellite and in situ LSTs, and the total uncertainties of the validation matchups. We show that the uncertainty model provides a good fit. Our approach offers a framework for quantifying uncertainties for LST that is equally applicable across different sensors and different retrieval approaches.
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ISSN:2072-4292
2072-4292
DOI:10.3390/rs11091021