Surface Temperatures at the Continental Scale: Tracking Changes with Remote Sensing at Unprecedented Detail

Temperature is a main driver for most ecological processes, and temperature time series provide key environmental indicators for various applications and research fields. High spatial and temporal resolutions are crucial for detailed analyses in various fields of research. A disadvantage of temperat...

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Main Authors: Markus Metz, Duccio Rocchini, Markus Neteler
Format: Article
Language:English
Published: MDPI AG 2014-04-01
Series:Remote Sensing
Subjects:
Online Access:http://www.mdpi.com/2072-4292/6/5/3822
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spelling doaj-580ca37d4e404dd688c5d96a0eb4f2ab2020-11-24T20:59:01ZengMDPI AGRemote Sensing2072-42922014-04-01653822384010.3390/rs6053822rs6053822Surface Temperatures at the Continental Scale: Tracking Changes with Remote Sensing at Unprecedented DetailMarkus Metz0Duccio Rocchini1Markus Neteler2GIS and Remote Sensing Unit, Biodiversity and Molecular Ecology Department, Research and Innovation Centre, Fondazione Edmund Mach, Via E. Mach 1, 38010 S. Michele all'Adige (TN), ItalyGIS and Remote Sensing Unit, Biodiversity and Molecular Ecology Department, Research and Innovation Centre, Fondazione Edmund Mach, Via E. Mach 1, 38010 S. Michele all'Adige (TN), ItalyGIS and Remote Sensing Unit, Biodiversity and Molecular Ecology Department, Research and Innovation Centre, Fondazione Edmund Mach, Via E. Mach 1, 38010 S. Michele all'Adige (TN), ItalyTemperature is a main driver for most ecological processes, and temperature time series provide key environmental indicators for various applications and research fields. High spatial and temporal resolutions are crucial for detailed analyses in various fields of research. A disadvantage of temperature data obtained by satellites is the occurrence of gaps that must be reconstructed. Here, we present a new method to reconstruct high-resolution land surface temperature (LST) time series at the continental scale gaining 250-m spatial resolution and four daily values per pixel. Our method constitutes a unique new combination of weighted temporal averaging with statistical modeling and spatial interpolation. This newly developed reconstruction method has been applied to greater Europe, resulting in complete daily coverage for eleven years. To our knowledge, this new reconstructed LST time series exceeds the level of detail of comparable reconstructed LST datasets by several orders of magnitude. Studies on emerging diseases, parasite risk assessment and temperature anomalies can now be performed on the continental scale, maintaining high spatial and temporal detail. We illustrate a series of applications in this paper. Our dataset is available online for download as time aggregated derivatives for direct usage in GIS-based applications.http://www.mdpi.com/2072-4292/6/5/3822land surface temperatureenvironmental monitoringhealth risk assessmentMODIS reconstructiontime seriesonline dataset
collection DOAJ
language English
format Article
sources DOAJ
author Markus Metz
Duccio Rocchini
Markus Neteler
spellingShingle Markus Metz
Duccio Rocchini
Markus Neteler
Surface Temperatures at the Continental Scale: Tracking Changes with Remote Sensing at Unprecedented Detail
Remote Sensing
land surface temperature
environmental monitoring
health risk assessment
MODIS reconstruction
time series
online dataset
author_facet Markus Metz
Duccio Rocchini
Markus Neteler
author_sort Markus Metz
title Surface Temperatures at the Continental Scale: Tracking Changes with Remote Sensing at Unprecedented Detail
title_short Surface Temperatures at the Continental Scale: Tracking Changes with Remote Sensing at Unprecedented Detail
title_full Surface Temperatures at the Continental Scale: Tracking Changes with Remote Sensing at Unprecedented Detail
title_fullStr Surface Temperatures at the Continental Scale: Tracking Changes with Remote Sensing at Unprecedented Detail
title_full_unstemmed Surface Temperatures at the Continental Scale: Tracking Changes with Remote Sensing at Unprecedented Detail
title_sort surface temperatures at the continental scale: tracking changes with remote sensing at unprecedented detail
publisher MDPI AG
series Remote Sensing
issn 2072-4292
publishDate 2014-04-01
description Temperature is a main driver for most ecological processes, and temperature time series provide key environmental indicators for various applications and research fields. High spatial and temporal resolutions are crucial for detailed analyses in various fields of research. A disadvantage of temperature data obtained by satellites is the occurrence of gaps that must be reconstructed. Here, we present a new method to reconstruct high-resolution land surface temperature (LST) time series at the continental scale gaining 250-m spatial resolution and four daily values per pixel. Our method constitutes a unique new combination of weighted temporal averaging with statistical modeling and spatial interpolation. This newly developed reconstruction method has been applied to greater Europe, resulting in complete daily coverage for eleven years. To our knowledge, this new reconstructed LST time series exceeds the level of detail of comparable reconstructed LST datasets by several orders of magnitude. Studies on emerging diseases, parasite risk assessment and temperature anomalies can now be performed on the continental scale, maintaining high spatial and temporal detail. We illustrate a series of applications in this paper. Our dataset is available online for download as time aggregated derivatives for direct usage in GIS-based applications.
topic land surface temperature
environmental monitoring
health risk assessment
MODIS reconstruction
time series
online dataset
url http://www.mdpi.com/2072-4292/6/5/3822
work_keys_str_mv AT markusmetz surfacetemperaturesatthecontinentalscaletrackingchangeswithremotesensingatunprecedenteddetail
AT ducciorocchini surfacetemperaturesatthecontinentalscaletrackingchangeswithremotesensingatunprecedenteddetail
AT markusneteler surfacetemperaturesatthecontinentalscaletrackingchangeswithremotesensingatunprecedenteddetail
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