Developing a confidence metric for the Landsat land surface temperature product

Kelly G. Laraby; John R. Schott; Nina Raqueno

doi:10.1117/12.2222582

17 May 2016 Developing a confidence metric for the Landsat land surface temperature product

Kelly G. Laraby, John R. Schott, Nina Raqueno

Proceedings Volume 9840, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII; 98400C (2016) https://doi.org/10.1117/12.2222582
Event: SPIE Defense + Security, 2016, Baltimore, MD, United States

Abstract

Land Surface Temperature (LST) is an important Earth system data record that is useful to ﬁelds such as change detection, climate research, environmental monitoring, and smaller scale applications such as agriculture. Certain Earth-observing satellites can be used to derive this metric, and it would be extremely useful if such imagery could be used to develop a global product. Through the support of the National Aeronautics and Space Administration (NASA) and the United States Geological Survey (USGS), a LST product for the Landsat series of satellites has been developed. Currently, it has been validated for scenes in North America, with plans to expand to a trusted global product. For ideal atmospheric conditions (e.g. stable atmosphere with no clouds nearby), the LST product underestimates the surface temperature by an average of 0.26 K. When clouds are directly above or near the pixel of interest, however, errors can extend to several Kelvin. As the product approaches public release, our major goal is to develop a quality metric that will provide the user with a per-pixel map of estimated LST errors. There are several sources of error that are involved in the LST calculation process, but performing standard error propagation is a diﬃcult task due to the complexity of the atmospheric propagation component. To circumvent this diﬃculty, we propose to utilize the relationship between cloud proximity and the error seen in the LST process to help develop a quality metric. This method involves calculating the distance to the nearest cloud from a pixel of interest in a scene, and recording the LST error at that location. Performing this calculation for hundreds of scenes allows us to observe the average LST error for diﬀerent ranges of distances to the nearest cloud. This paper describes this process in full, and presents results for a large set of Landsat scenes.

Citation Download Citation

Kelly G. Laraby, John R. Schott, and Nina Raqueno "Developing a confidence metric for the Landsat land surface temperature product", Proc. SPIE 9840, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII, 98400C (17 May 2016); https://doi.org/10.1117/12.2222582

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