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dc.creatorMilitino, Ana F.es_ES
dc.creatorUgarte Martínez, María Doloreses_ES
dc.creatorPérez Goya, Unaies_ES
dc.date.accessioned2020-12-02T10:17:54Z
dc.date.available2020-12-02T10:17:54Z
dc.date.issued2018
dc.identifier.issn2072-4292
dc.identifier.urihttps://hdl.handle.net/2454/38820
dc.description.abstractMultitemporal imagery is by and large geometrically and radiometrically accurate, but the residual noise arising from removal clouds and other atmospheric and electronic effects can produce outliers that must be mitigated to properly exploit the remote sensing information. In this study, we show how ground-truth data from rain gauge stations can improve the quality of satellite imagery. To this end, a simulation study is conducted wherein different sizes of outlier outbreaks are spread and randomly introduced in the normalized difference vegetation index (NDVI) and the day and night land surface temperature (LST) of composite images from Navarre (Spain) between 2011 and 2015. To remove outliers, a new method called thin-plate splines with covariates (TpsWc) is proposed. This method consists of smoothing the median anomalies with a thin-plate spline model, whereby transformed ground-truth data are the external covariates of the model. The performance of the proposed method is measured with the square root of the mean square error (RMSE), calculated as the root of the pixel-by-pixel mean square differences between the original data and the predicted data with the TpsWc model and with a state-space model with and without covariates. The study shows that the use of ground-truth data reduces the RMSE in both the TpsWc model and the state-space model used for comparison purposes. The new method successfully removes the abnormal data while preserving the phenology of the raw data. The RMSE reduction percentage varies according to the derived variables (NDVI or LST), but reductions of up to 20% are achieved with the new proposal.en
dc.description.sponsorshipThis research was supported by the Spanish Ministry of Economy, Industry and Competitiveness (project MTM2017-82553-R) jointly financed with the European Regional Development Fund (FEDER), the Government of Navarre (PI015-2016 and PI043-2017 projects) and the Fundación CAN-Obra Social Caixa-UNED Pamplona 2016 and 2017.en
dc.format.extent16 p.
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.publisherMDPIen
dc.relation.ispartofRemote Sensing, 2018, 10(3): 398en
dc.rights© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access_x000D_ article distributed under the terms and conditions of the Creative Commons Attribution_x000D_ (CC BY) license.en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectKrigingen
dc.subjectSpatial statisticsen
dc.subjectThin-plate splinesen
dc.subjectOutliersen
dc.subjectSmoothingen
dc.titleImproving the quality of satellite imagery based on ground-truth data from rain gauge stationsen
dc.typeinfo:eu-repo/semantics/articleen
dc.typeArtículo / Artikuluaes
dc.contributor.departmentUniversidad Pública de Navarra. Departamento de Estadística e Investigación Operativaes_ES
dc.contributor.departmentNafarroako Unibertsitate Publikoa. Estatistika eta Ikerketa Operatiboa Sailaeu
dc.contributor.departmentUniversidad Pública de Navarra / Nafarroako Unibertsitate Publikoa. InaMat - Institute for Advanced Materialses_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen
dc.rights.accessRightsAcceso abierto / Sarbide irekiaes
dc.identifier.doi10.3390/rs10030398
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/2PE/MTM2017-82553-Ren
dc.relation.publisherversionhttps://doi.org/10.3390/rs10030398
dc.type.versioninfo:eu-repo/semantics/publishedVersionen
dc.type.versionVersión publicada / Argitaratu den bertsioaes
dc.contributor.funderGobierno de Navarra / Nafarroako Gobernuaes


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© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access_x000D_
article distributed under the terms and conditions of the Creative Commons Attribution_x000D_
(CC BY) license.
Except where otherwise noted, this item's license is described as © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access_x000D_ article distributed under the terms and conditions of the Creative Commons Attribution_x000D_ (CC BY) license.