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Improving the quality of satellite imagery based on ground-truth data from rain gauge stations
dc.creator | Militino, Ana F. | es_ES |
dc.creator | Ugarte Martínez, María Dolores | es_ES |
dc.creator | Pérez Goya, Unai | es_ES |
dc.date.accessioned | 2020-12-02T10:17:54Z | |
dc.date.available | 2020-12-02T10:17:54Z | |
dc.date.issued | 2018 | |
dc.identifier.issn | 2072-4292 | |
dc.identifier.uri | https://hdl.handle.net/2454/38820 | |
dc.description.abstract | Multitemporal 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.sponsorship | This 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.extent | 16 p. | |
dc.format.mimetype | application/pdf | en |
dc.language.iso | eng | en |
dc.publisher | MDPI | en |
dc.relation.ispartof | Remote Sensing, 2018, 10(3): 398 | en |
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.uri | http://creativecommons.org/licenses/by/4.0/ | |
dc.subject | Kriging | en |
dc.subject | Spatial statistics | en |
dc.subject | Thin-plate splines | en |
dc.subject | Outliers | en |
dc.subject | Smoothing | en |
dc.title | Improving the quality of satellite imagery based on ground-truth data from rain gauge stations | en |
dc.type | info:eu-repo/semantics/article | en |
dc.type | Artículo / Artikulua | es |
dc.contributor.department | Estadística e Investigación Operativa | es_ES |
dc.contributor.department | Estatistika eta Ikerketa Operatiboa | eu |
dc.contributor.department | Institute for Advanced Materials and Mathematics - INAMAT2 | es_ES |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | en |
dc.rights.accessRights | Acceso abierto / Sarbide irekia | es |
dc.identifier.doi | 10.3390/rs10030398 | |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/MTM2017-82553-R/ES/ | en |
dc.relation.publisherversion | https://doi.org/10.3390/rs10030398 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | en |
dc.type.version | Versión publicada / Argitaratu den bertsioa | es |
dc.contributor.funder | Gobierno de Navarra / Nafarroako Gobernua | es |