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Quality assessment of S-NPP VIIRS land surface temperature product

Liu, Yuling; Yu, Yunyue; Yu, Peng; Goettsche, Frank M. ORCID iD icon 1; Trigo, Isabel F.
1 Karlsruher Institut für Technologie (KIT)

Abstract:

The VIIRS Land Surface Temperature (LST) Environmental Data Record (EDR) has reached validated (V1 stage) maturity in December 2014. This study compares VIIRS v1 LST with the ground in situ observations and with heritage LST product from MODIS Aqua and AATSR. Comparisons against U.S. SURFRAD ground observations indicate a similar accuracy among VIIRS, MODIS and AATSR LST, in which VIIRS LST presents an overall accuracy of −0.41 K and precision of 2.35 K. The result over arid regions in Africa suggests that VIIRS and MODIS underestimate the LST about 1.57 K and 2.97 K, respectively. The cross comparison indicates an overall close LST estimation between VIIRS and MODIS. In addition, a statistical method is used to quantify the VIIRS LST retrieval uncertainty taking into account the uncertainty from the surface type input. Some issues have been found as follows: (1) Cloud contamination, particularly the cloud detection error over a snow/ice surface, shows significant impacts on LST validation; (2) Performance of the VIIRS LST algorithm is strongly dependent on a correct classification of the surface type; (3) The VIIRS LST quality can be degraded when significant brightness temperature difference between the two split window channels is observed; (4) Surface type dependent algorithm exhibits deficiency in correcting the large emissivity variations within a surface type.


Volltext §
DOI: 10.5445/IR/1000055190
Originalveröffentlichung
DOI: 10.3390/rs70912215
Scopus
Zitationen: 63
Web of Science
Zitationen: 54
Dimensions
Zitationen: 61
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Meteorologie und Klimaforschung – Atmosphärische Spurenstoffe und Fernerkundung (IMK-ASF)
KIT-Zentrum Klima und Umwelt (ZKU)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2015
Sprache Englisch
Identifikator ISSN: 2072-4292
urn:nbn:de:swb:90-551908
KITopen-ID: 1000055190
HGF-Programm 12.01.01 (POF III, LK 01) Clouds in a pertubed atmosphere
Erschienen in Remote sensing
Verlag MDPI
Band 7
Heft 9
Seiten 12215-12241
Schlagwörter VIIRS LST DER, split window algorithm, surface type dependency, LST uncertainty
Nachgewiesen in Scopus
Dimensions
Web of Science
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