The Landsat series of satellites is the longest set of continuously acquired moderate resolution multispectral satellite imagery collected on a single maintained family of instruments. The data are very attractive because the entire archive has been radiometrically calibrated and characterized so that sensor reaching radiance values are well known. However, these values are not easily understood or applied, so this dataset has not been utilized to its fullest potential. This work focuses on atmospheric compensation at each Landsat pixel which will later be used with ASTER derived emissivity data from JPL to perform LST retrievals.
We develop a method to automatically generate the effective in band radiative transfer param- eters transmission, upwelled radiance, and downwelled radiance for each pixel. We validate our methodology by comparing our predicted apparent temperatures to ground truth water tempera- tures derived from buoy data at a number of validation sites around the continental United States. Initial validation was performed using Landsat 5. Results show a mean error of -0.267 K and a standard deviation of 0.900 K for 259 cloud free scenes in the validation dataset. Based on the same validation dataset, our current best expectation for a confidence metric for the final product involves categorizing each pixel as cloudy, clouds in the vicinity, or cloud free based on the incorpo- ration of a Landsat cloud product. The mean and standard deviation of the errors associated with each category will be included as a quantitative basis for each category.
To support future work, we explore the extension to a global dataset and possible improvements to the atmospheric compensation by more closely exploring the column water vapor contribution to error. Finally, we acknowledge the need for a more formal incorporation of the cloud product, and possibly improvements, in order to finalize the confidence metric for the atmospheric compensation component of the product.
Library of Congress Subject Headings
Remote sensing--Data processing; Electrooptical devices--Atmospheric effects; Landsat satellites--Calibration; Artificial satellites in remote sensing
Imaging Science (Ph.D.)
Department, Program, or Center
Chester F. Carlson Center for Imaging Science (COS)
John R. Schott
Emmett J. Ientilucci
Cook, Monica J., "Atmospheric Compensation for a Landsat Land Surface Temperature Product" (2014). Thesis. Rochester Institute of Technology. Accessed from
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