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Remote Sens. 2016, 8(6), 454; doi:10.3390/rs8060454

Improved Quality of MODIS Sea Surface Temperature Retrieval and Data Coverage Using Physical Deterministic Methods

1
NOAA/NESDIS Center for Satellite Applications and Research (STAR), E/RA3, 5830 University Research Ct., College Park, MD 20740, USA
2
CICS/Earth System Science Interdisciplinary Center, University of Maryland, 5825 University Research Ct., College Park, MD 20740, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Xiaofeng Li and Prasad S. Thenkabail
Received: 10 March 2016 / Revised: 20 May 2016 / Accepted: 23 May 2016 / Published: 27 May 2016
View Full-Text   |   Download PDF [4924 KB, uploaded 31 May 2016]   |  

Abstract

Sea surface temperature (SST) retrievals from satellite imager measurements are often performed using only two or three channels, and employ a regression methodology. As there are 16 thermal infrared (IR) channels available for MODIS, we demonstrate a new SST retrieval methodology using more channels and a physically deterministic method, the modified total least squares (MTLS), to improve the quality of SST. Since cloud detection is always a part of any parameter estimation from IR satellite measurements, we hereby extend our recently-published novel cloud detection technique, which is based on both functional spectral differences and radiative transfer modeling for GOES-13. We demonstrate that the cloud detection coefficients derived for GOES-13 are working well for MODIS, while further improvements are made possible by the extra channels replacing some of the previous tests. The results are compared with available operational MODIS SST through the Group for High Resolution SST website–the data themselves are originally processed by the NASA Goddard Ocean Biology Processing Group. It is observed the data coverage can be more than doubled compared to the currently-available operational product, and at the same time the quality can be improved significantly. Two other SST retrieval methods, offline-calculated coefficients using the same form of the operational regression equation, and radiative transfer based optimal estimation, are included for comparison purposes. View Full-Text
Keywords: modified total least squares; MODIS-AQUA; sea surface temperature; cloud masking; optimal estimation method modified total least squares; MODIS-AQUA; sea surface temperature; cloud masking; optimal estimation method
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MDPI and ACS Style

Koner, P.K.; Harris, A. Improved Quality of MODIS Sea Surface Temperature Retrieval and Data Coverage Using Physical Deterministic Methods. Remote Sens. 2016, 8, 454.

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