Thesis - Open Access
Master of Science (MS)
Electrical Engineering and Computer Science
The primary objective of this project was to consistently calibrate the entire Landsat series to a common reflectance scale by performing cross-calibration corrections from Landsat-8 OLI to Landsat- 1 MSS. A consistent radiance-based calibration was already performed from Landsat-8 OLI through Landsat-1 MSS using bright targets and dark targets. The MSS radiance-based calibration results showed an uncertainty of about ±5%. Typically to convert from radiance to reflectance a solar model is used. Unfortunately, there are numerous solar models, all with various levels of accuracies. It was also seen that there is a data format inconsistency for different types of MSS data that impact the radiometric uncertainty of the products when compared to Landsat-8 OLI data. One of the advances Landsat-8 OLI has over to earlier missions is a solar model independent reflectance calibration. Hence, to reduce these uncertainties and remove the dependency on the solar model, direct reflectance-based calibration was performed for all previous missions using Landsat-8 OLI as the “standard”. A consistent cross-calibration of all Landsat sensors was achieved using coincident/near-coincident scene pairs. The work started from cross-calibration of Landsat-8 OLI to Landsat-7 ETM+ and continued through Landsat-1 MSS. Due to the fact each Landsat sensor measures slightly different parts of the electromagnetic spectrum, a spectral band adjustment factor (SBAF) was computed and used prior to the cross-calibration. To determine the significance of the bias derived from cross-calibration, a t-test was performed with a null hypothesis that the bias equals zero at a confidence interval of 95%. From the final calibration equations, it was found that for band 5 of Landsat-1 bias is significant. The effectiveness of these cross-calibration results is discussed by showing a significant improvement in the observed inconsistencies in the absolute calibration of all Landsat sensors for both bright and dark targets. The results show a significant improvement in reflectance calibration, and an overall uncertainty of less than ±3%.
Library of Congress Subject Headings
Landsat satellites -- Calibration.
Artificial satellites in remote sensing -- Calibration.
Reflectance -- Measurement.
Remote sensing -- Data processing.
Includes bibliographical references (pages 104-107)
Number of Pages
South Dakota State University
Copyright © 2016 Sandeep Kumar Chittimalli
Chittimalli, Sandeep Kumar, "Reflectance-based Calibration and Validation of the Landsat Satellite Archive" (2016). Electronic Theses and Dissertations. 1104.