A New Approach to Defining Uncertainties for MODIS Land Surface Temperature
journal contributionposted on 2019-08-21, 10:48 authored by Darren Ghent, Karen Veal, Tim Trent, Emma Dodd, Harjinder Sembhi, John Remedios
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.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 640171 with additional funding received from the European Space Agency within the framework of the GlobTemperature project under the Data User Element of ESA’s Fourth Earth Observation Envelope Programme (2013–2017), contract number 4000109452/13/I-AM. Finally, support has also been received from NERC national capability funding to the National Centre for Earth Observation (NCEO) in the UK.
CitationRemote Sensing, 2019, 11(9), 1021
Author affiliation/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Physics and Astronomy
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