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2019-01-28Zeitschriftenartikel DOI: 10.3390/rs11030257
A Global MODIS Water Vapor Database for the Operational Atmospheric Correction of Historic and Recent Landsat Imagery
dc.contributor.authorFrantz, David
dc.contributor.authorStellmes, Marion
dc.contributor.authorHostert, Patrick
dc.date.accessioned2019-09-11T11:56:09Z
dc.date.available2019-09-11T11:56:09Z
dc.date.issued2019-01-28none
dc.date.updated2019-08-25T23:37:26Z
dc.identifier.urihttp://edoc.hu-berlin.de/18452/21270
dc.description.abstractAnalysis Ready Data (ARD) have undergone the most relevant pre-processing steps to satisfy most user demands. The freely available software FORCE (Framework for Operational Radiometric Correction for Environmental monitoring) is capable of generating Landsat ARD. An essential step of generating ARD is atmospheric correction, which requires water vapor data. FORCE relies on a water vapor database obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). However, two major drawbacks arise from this strategy: (1) The database has to be compiled for each study area prior to generating ARD; and (2) MODIS and Landsat commissioning dates are not well aligned. We have therefore compiled an application-ready global water vapor database to significantly increase the operational readiness of ARD production. The free dataset comprises daily water vapor data for February 2000 to July 2018 as well as a monthly climatology that is used if no daily value is available. We systematically assessed the impact of using this climatology on surface reflectance outputs. A global random sample of Landsat 5/7/8 imagery was processed twice (i) using daily water vapor (reference) and (ii) using the climatology (estimate), followed by computing accuracy, precision, and uncertainty (APU) metrics. All APU measures were well below specification, thus the fallback usage of the climatology is generally a sound strategy. Still, the tests revealed that some considerations need to be taken into account to help quantify which sensor, band, climate, and season are most or least affected by using a fallback climatology. The highest uncertainty and bias is found for Landsat 5, with progressive improvements towards newer sensors. The bias increases from dry to humid climates, whereas uncertainty increases from dry and tropic to temperate climates. Uncertainty is smallest during seasons with low variability, and is highest when atmospheric conditions progress from a dry to a wet season (and vice versa).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.subjectatmospheric correctioneng
dc.subjectanalysis ready dataeng
dc.subjectglobaleng
dc.subjectLandsateng
dc.subjectwater vaporeng
dc.subject.ddc620 Ingenieurwissenschaften und zugeordnete Tätigkeitennone
dc.titleA Global MODIS Water Vapor Database for the Operational Atmospheric Correction of Historic and Recent Landsat Imagerynone
dc.typearticle
dc.identifier.urnurn:nbn:de:kobv:11-110-18452/21270-7
dc.identifier.doi10.3390/rs11030257none
dc.identifier.doihttp://dx.doi.org/10.18452/20539
dc.type.versionpublishedVersionnone
local.edoc.container-titleRemote Sensingnone
local.edoc.pages18none
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-volume11none
local.edoc.container-issue3none
dc.description.versionPeer Reviewednone
local.edoc.container-articlenumber257none
dc.identifier.eissn2072-4292
local.edoc.affiliationFrantz, David; Geomatics Lab, Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany,none
local.edoc.affiliationStellmes, Marion; Remote Sensing and Geoinformatics, Freie Universität Berlin, Malteserstr. 74-100, 12249 Berlin, Germany,none
local.edoc.affiliationHostert, Patrick; Geomatics Lab, Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany, Integrated Research Institute on Transformations of Human Environment Systems (IRI THESys), Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany,none

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