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Article: Assessment of long-term sensor radiometric degradation using time series analysis

TitleAssessment of long-term sensor radiometric degradation using time series analysis
Authors
KeywordsLandsat 5
Libyan Desert
Sonoran Desert
time series analysis
vicarious calibration
Issue Date2014
Citation
IEEE Transactions on Geoscience and Remote Sensing, 2014, v. 52, n. 5, p. 2960-2976 How to Cite?
AbstractThe monitoring of top-of-atmosphere (TOA) reflectance time series provides useful information regarding the long-term degradation of satellite sensors. For a precise assessment of sensor degradation, the TOA reflectance time series is usually corrected for surface and atmospheric anisotropy by using bidirectional reflectance models so that the angular effects do not compromise the trend estimates. However, the models sometimes fail to correct the angular effects, particularly for spectral bands that exhibit a large seasonal oscillation due to atmospheric variability. This paper investigates the use of time series algorithms to identify both the angular effects and the atmospheric variability simultaneously in the time domain using their periodical patterns within the time series. Two nonstationary time series algorithms were tested with the Landsat 5 Thematic Mapper time series data acquired over two pseudoinvariant desert sites, the Sonoran and Libyan Deserts, to compute a precise long-term trend of the time series by removing the seasonal variability. The trending results of the time series algorithms were compared to those of the original TOA reflectance time series and those normalized by a widely used bidirectional-reflectance-distribution-function model. The time series results showed an effective removal of seasonal oscillation, caused by angular and atmospheric effects, producing trending results that have a higher statistical significance than other approaches. © 1980-2012 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/321568
ISSN
2023 Impact Factor: 7.5
2023 SCImago Journal Rankings: 2.403
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorKim, Wonkook-
dc.contributor.authorHe, Tao-
dc.contributor.authorWang, Dongdong-
dc.contributor.authorCao, Changyong-
dc.contributor.authorLiang, Shunlin-
dc.date.accessioned2022-11-03T02:19:50Z-
dc.date.available2022-11-03T02:19:50Z-
dc.date.issued2014-
dc.identifier.citationIEEE Transactions on Geoscience and Remote Sensing, 2014, v. 52, n. 5, p. 2960-2976-
dc.identifier.issn0196-2892-
dc.identifier.urihttp://hdl.handle.net/10722/321568-
dc.description.abstractThe monitoring of top-of-atmosphere (TOA) reflectance time series provides useful information regarding the long-term degradation of satellite sensors. For a precise assessment of sensor degradation, the TOA reflectance time series is usually corrected for surface and atmospheric anisotropy by using bidirectional reflectance models so that the angular effects do not compromise the trend estimates. However, the models sometimes fail to correct the angular effects, particularly for spectral bands that exhibit a large seasonal oscillation due to atmospheric variability. This paper investigates the use of time series algorithms to identify both the angular effects and the atmospheric variability simultaneously in the time domain using their periodical patterns within the time series. Two nonstationary time series algorithms were tested with the Landsat 5 Thematic Mapper time series data acquired over two pseudoinvariant desert sites, the Sonoran and Libyan Deserts, to compute a precise long-term trend of the time series by removing the seasonal variability. The trending results of the time series algorithms were compared to those of the original TOA reflectance time series and those normalized by a widely used bidirectional-reflectance-distribution-function model. The time series results showed an effective removal of seasonal oscillation, caused by angular and atmospheric effects, producing trending results that have a higher statistical significance than other approaches. © 1980-2012 IEEE.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Geoscience and Remote Sensing-
dc.subjectLandsat 5-
dc.subjectLibyan Desert-
dc.subjectSonoran Desert-
dc.subjecttime series analysis-
dc.subjectvicarious calibration-
dc.titleAssessment of long-term sensor radiometric degradation using time series analysis-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TGRS.2013.2268161-
dc.identifier.scopuseid_2-s2.0-84896316466-
dc.identifier.volume52-
dc.identifier.issue5-
dc.identifier.spage2960-
dc.identifier.epage2976-
dc.identifier.isiWOS:000332484700055-

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