NDLI: Improving Land Surface Temperature Retrievals over Mountainous Regions

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Improving Land Surface Temperature Retrievals over Mountainous Regions

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Improving Land Surface Temperature Retrievals over Mountainous Regions

Content Provider	MDPI
Author	Bento, Virgílio DaCamara, Carlos Trigo, Isabel Martins, João Duguay-Tetzlaff, Anke
Abstract	Algorithms for Land Surface Temperature (LST) retrieval from infrared measurements are usually sensitive to the amount of water vapor present in the atmosphere. The Satellite Application Facilities on Climate Monitoring and Land Surface Analysis (CM SAF and LSA SAF) are currently compiling a 25 year LST Climate data record (CDR), which uses water vapor information from ERA-Int reanalysis. However, its relatively coarse spatial resolution may lead to systematic errors in the humidity profiles with implications in LST, particularly over mountainous areas. The present study compares LST estimated with three different retrieval algorithms: a radiative transfer-based physical mono-window (PMW), a statistical mono-window (SMW), and a generalized split-windows (GSW). The algorithms were tested over the Alpine region using ERA-Int reanalysis data and relied on the finer spatial scale Consortium for Small-Scale Modelling (COSMO) model data as a reference. Two methods were developed to correct ERA-Int water vapor misestimation: (1) an exponential parametrization of total precipitable water (TPW) appropriate for SMW/GSW; and (2) a level reduction method to be used in PMW. When ERA-Int TPW was used, the algorithm missed the right TPW class in 87% of the cases. When the exponential parametrization was used, the missing class rate decreased to 9%, and when the level reduction method was applied, the LST corrections went up to 1.7 K over the study region. Overall, the correction for pixel orography in TPW leads to corrections in LST estimations, which are relevant to ensure that long-term LST records meet climate requirements, particularly over mountainous regions.
File Size	3487744
File Format	PDF
e-ISSN	20724292
DOI	10.3390/rs9010038
Journal	Remote Sensing
Issue Number	1
Volume Number	9
Language	English
Publisher Date	2017-01-05
Access Restriction	Open
Subject Keyword	LST Split-Windows Mono-Window retrieval algorithms thermal infrared Meteosat water vapor orographic correction
Content Type	Text
Resource Type	Article

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