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%.
","abstract_html":"<p>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%.</p>","abstract_has_math":false,"creators":["Chittimalli, Sandeep Kumar"],"institution":null,"degree_name":"Master of Science (MS)","degree_level":"Thesis - Open Access","degree_discipline":"Electrical Engineering and Computer Science","degree_department":null,"school":null,"contributors":["Larry Leigh","Dennis Helder"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-01-01T08:00:00Z","date_published":"2016-01-01T08:00:00Z","updated_at":"2026-07-24T04:28:36Z","subjects":["Electrical and Computer Engineering"],"languages":["en"],"rights":[],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://openprairie.sdstate.edu/etd/1104","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Larry Leigh","Dennis Helder"]},{"key":"dc:creator","label":"Author","values":["Chittimalli, Sandeep Kumar"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2016-12-09T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering and Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MS)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Electrical and Computer Engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://openprairie.sdstate.edu/etd/1104"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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%.
"]},{"key":"dc:title","label":"Title","values":["Reflectance-based Calibration and Validation of the Landsat Satellite Archive"]}]}],"canonical_facts":{"dc:contributor":["Larry Leigh","Dennis Helder"],"dc:creator":["Chittimalli, Sandeep Kumar"],"dc:date.available":["2016-12-09T08:00:00Z"],"dc:description.abstract":["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%.
"],"dc:identifier":["https://openprairie.sdstate.edu/etd/1104"],"dc:language":["en"],"dc:rights":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Electrical and Computer Engineering"],"dc:title":["Reflectance-based Calibration and Validation of the Landsat Satellite Archive"],"thesis:degree_discipline":["Electrical Engineering and Computer Science"],"thesis:degree_level":["Thesis - Open Access"],"thesis:degree_name":["Master of Science (MS)"]},"updated_at":"2026-07-24T04:28:36Z"}