Web of Science: 3 citas, Scopus: 3 citas, Google Scholar: citas,
Improving mean minimum and maximum month-to-month air temperature surfaces using satellite-derived land surface temperature
Mira Sarrió, Maria (Universitat Autònoma de Barcelona. Departament de Geografia)
Ninyerola i Casals, Miquel (Universitat Autònoma de Barcelona. Departament de Biologia Animal, de Biologia Vegetal i d'Ecologia)
Batalla, Meritxell (Centre de Recerca Ecològica i Aplicacions Forestals)
Pesquer Mayos, Lluís (Centre de Recerca Ecològica i Aplicacions Forestals)
Pons, Xavier (Universitat Autònoma de Barcelona. Departament de Geografia)

Fecha: 2017
Resumen: Month-to-month air temperature (T) surfaces are increasingly demanded to feed quantitative models related to a wide range of fields, such as hydrology, ecology or climate change studies. Geostatistical interpolation techniques provide such continuous and objective surfaces of climate variables, while the use of remote sensing data may improve the estimates, especially when temporal resolution is detailed enough. The main goal of this study is to propose an empirical methodology for improving the month-to-month T mapping (minimum and maximum) using satellite land surface temperatures (LST) besides of meteorological data and geographic information. The methodology consists on multiple regression analysis combined with the spatial interpolation of residual errors using the inverse distance weighting. A leave-one-out cross-validation procedure has been included in order to compare predicted with observed values. Different operational daytime and nighttime LST products corresponding to the four months more characteristic of the seasonal dynamics of a Mediterranean climate have been considered for a thirteen-year period. The results can be considered operational given the feasibility of the models employed (linear dependence on predictors that are nowadays easily available), the robustness of the leave-one-out cross-validation procedure and the improvement in accuracy achieved when compared to classical T modeling results. Unlike what is considered by most studies, it is shown that nighttime LST provides a good proxy not only for minimum T, but also for maximum T. The improvement achieved by the inclusion of remote sensing LST products was higher for minimum T (up to 0. 35 K on December), especially over forests and rugged lands. Results are really encouraging, as there are generally few meteorological stations in zones with these characteristics, clearly showing the usefulness of remote sensing to improve information about areas that are difficult to access or simply with a poor availability of conventional meteorological data.
Nota: Número d'acord de subvenció MINECO/CGL2015-69888-P
Nota: Número d'acord de subvenció AGAUR/2014/SGR-1491
Derechos: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original. Creative Commons
Lengua: Anglès.
Documento: article ; recerca ; publishedVersion
Materia: Air surface temperature ; Climatological modeling ; Land surface temperature ; Remote sensing ; Spatial interpolation
Publicado en: Remote sensing, Vol. 9, Issue 12 (December 2017) , art. 1313, ISSN 2072-4292

DOI: 10.3390/rs9121313


24 p, 5.4 MB

El registro aparece en las colecciones:
Documentos de investigación > Documentos de los grupos de investigación de la UAB > Centros y grupos de investigación (producción científica) > Ciencias > Methods and Applications in Remote Sensing and Geographic Information Systems Research Group (GRUMETS)
Documentos de investigación > Documentos de los grupos de investigación de la UAB > Centros y grupos de investigación (producción científica) > Ciencias > CREAF (Centre de Recerca Ecològica i d'Aplicacions Forestals)
Artículos > Artículos de investigación
Artículos > Artículos publicados

 Registro creado el 2019-01-30, última modificación el 2019-11-06



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