Please use this identifier to cite or link to this item: http://hdl.handle.net/10773/26711
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dc.contributor.authorBenali, A.pt_PT
dc.contributor.authorCarvalho, A. C.pt_PT
dc.contributor.authorNunes, João Pedropt_PT
dc.contributor.authorCarvalhais, N.pt_PT
dc.contributor.authorSantos, A.pt_PT
dc.date.accessioned2019-10-09T13:45:27Z-
dc.date.available2019-10-09T13:45:27Z-
dc.date.issued2012-
dc.identifier.issn0034-4257pt_PT
dc.identifier.urihttp://hdl.handle.net/10773/26711-
dc.description.abstractAir surface temperature (Tair) is an important parameter for a wide range of applications such as vector-borne disease bionomics, hydrology and climate change studies. Air temperature data is usually obtained from measurements made in meteorological stations, providing only limited information about spatial patterns over wide areas. The use of remote sensing data can help overcome this problem, particularly in areas with low station density, having the potential to improve the estimation of Tair at both regional and global scales. Some studies have tried to derive maximum (Tmax), minimum (Tmin) and average air temperature (Tavg) using different methods, with variable estimation accuracy; errors generally fall in the 2–3 °C range while the level of precision generally considered as accurate is 1–2 °C. The main objective of this study was to accurately estimate Tmax, Tmin and Tavg for a 10 year period based on remote sensing—Land Surface Temperature (LST) data obtained from MODIS—and auxiliary data using a statistical approach. An optimization procedure with a mixed bootstrap and jackknife resampling was employed. The statistical models estimated Tavg with a MEF (Model Efficiency Index) of 0.941 and a RMSE of 1.33 °C. Regarding Tmax and Tmin, the best MEF achieved was 0.919 and 0.871, respectively, with a 1.83 and 1.74 °C RMSE. The developed datasets provided weekly 1 km estimations and accurately described both the intra and inter annual temporal and spatial patterns of Tair. Potential sources of uncertainty and error were also analyzed and identified. The most promising developments were proposed with the aim of developing accurate Tair estimations at a larger scale in the future.pt_PT
dc.language.isoengpt_PT
dc.publisherElsevierpt_PT
dc.relationinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/67910/PTpt_PT
dc.relationinfo:eu-repo/grantAgreement/FCT/SFRH/SFRH%2FBPD%2F39721%2F2007/PTpt_PT
dc.rightsrestrictedAccesspt_PT
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectAverage air temperaturept_PT
dc.subjectMODISpt_PT
dc.subjectRemote sensingpt_PT
dc.subjectLand surface temperaturept_PT
dc.subjectLSTpt_PT
dc.subjectStatistical modelingpt_PT
dc.subjectBootstrappt_PT
dc.subjectJackknifept_PT
dc.subjectPortugalpt_PT
dc.titleEstimating air surface temperature in Portugal using MODIS LST datapt_PT
dc.typearticlept_PT
dc.description.versionpublishedpt_PT
dc.peerreviewedyespt_PT
degois.publication.firstPage108pt_PT
degois.publication.lastPage121pt_PT
degois.publication.titleRemote Sensing of Environmentpt_PT
degois.publication.volume124pt_PT
dc.identifier.doi10.1016/j.rse.2012.04.024pt_PT
dc.identifier.essn1879-0704pt_PT
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