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Remote Sens. 2017, 9(1), 38; doi:10.3390/rs9010038

Improving Land Surface Temperature Retrievals over Mountainous Regions

1
Instituto Dom Luiz, University of Lisbon, IDL, Campo Grande, Ed. C1, 1749-016 Lisbon, Portugal
2
Instituto Português do Mar e da Atmosfera, I.P., Rua C do Aeroporto, 1749-077 Lisbon, Portugal
3
Federal Office of Meteorology and Climatology MeteoSwiss, Operation Center 1, CH-8058 Zurich-Airport, Switzerland
*
Author to whom correspondence should be addressed.
Academic Editors: Zhaoliang Li and Prasad S. Thenkabail
Received: 3 November 2016 / Revised: 16 December 2016 / Accepted: 1 January 2017 / Published: 5 January 2017
View Full-Text   |   Download PDF [3406 KB, uploaded 5 January 2017]   |  

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. View Full-Text
Keywords: LST; Split-Windows; Mono-Window; retrieval algorithms; thermal infrared; Meteosat; water vapor; orographic correction LST; Split-Windows; Mono-Window; retrieval algorithms; thermal infrared; Meteosat; water vapor; orographic correction
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Bento, V.A.; DaCamara, C.C.; Trigo, I.F.; Martins, J.P.A.; Duguay-Tetzlaff, A. Improving Land Surface Temperature Retrievals over Mountainous Regions. Remote Sens. 2017, 9, 38.

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