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URN: urn:nbn:de:swb:90-551908
Originalveröffentlichung
DOI: 10.3390/rs70912215
Scopus
Zitationen: 10
Web of Science
Zitationen: 9

Quality assessment of S-NPP VIIRS land surface temperature product

Liu, Yuling; Yu, Yunyue; Yu, Peng; Goettsche, Frank M.; Trigo, Isabel F.

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 ... mehr


Zugehörige Institution(en) am KIT Fakultät für Physik (PHYSIK)
Institut für Meteorologie und Klimaforschung - Atmosphärische Spurenstoffe und Fernerkundung (IMK-ASF)
Publikationstyp Zeitschriftenaufsatz
Jahr 2015
Sprache Englisch
Identifikator ISSN: 2072-4292
KITopen ID: 1000055190
HGF-Programm 12.01.01; LK 01
Erschienen in Remote sensing
Band 7
Heft 9
Seiten 12215-12241
Schlagworte VIIRS LST DER, split window algorithm, surface type dependency, LST uncertainty
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