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2018-10-01Zeitschriftenartikel DOI: 10.3390/rs10101570
Implications of Pixel Quality Flags on the Observation Density of a Continental Landsat Archive
dc.contributor.authorErnst, Stefan
dc.contributor.authorLymburner, Leo
dc.contributor.authorSixsmith, Josh
dc.date.accessioned2019-08-22T09:13:08Z
dc.date.available2019-08-22T09:13:08Z
dc.date.issued2018-10-01none
dc.date.updated2019-08-01T05:32:46Z
dc.identifier.urihttp://edoc.hu-berlin.de/18452/21185
dc.description.abstractPixel quality (PQ) products delivered with Analysis Ready Data (ARD) provide users with information about the conditions of the surface, atmosphere, and sensor at the time of acquisition. Knowing whether an observation was affected by clouds or sensor saturation is crucial when selecting data to include in automated analysis, as imperfect or erroneous observations are undesirable for most applications. There is, however, a certain rate of commission error in cloud detection, and saturation may not affect all spectral bands at a time, which can lead to suitable observations being excluded. This can have a substantial impact on the amount of data available for analysis. To understand how different surface types can affect cloud commission and saturation, we analyzed cloud and per-band saturation PQ flags for 31 years of Landsat data within Digital Earth Australia. Areas showing substantial reduction in observation density compared to their surroundings were investigated to characterize how specific surface types impact on the temporal density of observations deemed desirable. Using Fmask 3.2 by way of example, our approach demonstrates a method that can be applied to summarize the characteristics of cloud-screening algorithms and sensor saturation. Results indicate that cloud commission and sensor saturation rates show specific characteristics depending on the targets under observation. This potentially leads to an imbalance in data availability driven by surface type in a given study area. Based on our findings, the level of detail in PQ flags delivered with ARD is pivotal in maximizing the potential of EO data.eng
dc.language.isoengnone
dc.publisherHumboldt-Universität zu Berlin
dc.rights(CC BY 4.0) Attribution 4.0 Internationalger
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectpixel qualityeng
dc.subjectAnalysis Ready Dataeng
dc.subjectcloud maskingeng
dc.subjectsaturationeng
dc.subjectFmaskeng
dc.subjectLandsateng
dc.subject.ddc620 Ingenieurwissenschaften und zugeordnete Tätigkeitennone
dc.titleImplications of Pixel Quality Flags on the Observation Density of a Continental Landsat Archivenone
dc.typearticle
dc.identifier.urnurn:nbn:de:kobv:11-110-18452/21185-6
dc.identifier.doi10.3390/rs10101570none
dc.identifier.doihttp://dx.doi.org/10.18452/20416
dc.type.versionpublishedVersionnone
local.edoc.container-titleRemote Sensingnone
local.edoc.pages15none
local.edoc.type-nameZeitschriftenartikel
local.edoc.institutionMathematisch-Naturwissenschaftliche Fakultätnone
local.edoc.container-typeperiodical
local.edoc.container-type-nameZeitschrift
local.edoc.container-publisher-nameMDPInone
local.edoc.container-publisher-placeBaselnone
local.edoc.container-volume10none
local.edoc.container-issue10none
dc.description.versionPeer Reviewednone
local.edoc.container-articlenumber1570none
dc.identifier.eissn2072-4292
local.edoc.affiliationErnst, Stefan; Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany,none
local.edoc.affiliationLymburner, Leo; Geoscience Australia, GPO Box 378, Canberra ACT 2601, Australia,none
local.edoc.affiliationSixsmith, Josh; Geoscience Australia, GPO Box 378, Canberra ACT 2601, Australia,none

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