Abstract
Sea surface temperature (SST) is an important variable for understanding interactions between the ocean and the atmosphere. SST fusion is crucial for acquiring SST products of high spatial resolution and coverage. This study introduces a Bayesian maximum entropy (BME) method for blending daily SSTs from multiple satellite sensors. A new spatiotemporal covariance model of an SST field is built to integrate not only single-day SSTs but also time-adjacent SSTs. In addition, AVHRR 30-year SST climatology data are introduced as soft data at the estimation points to improve the accuracy of blended results within the BME framework. The merged SSTs, with a spatial resolution of 4 km and a temporal resolution of 24 hours, are produced in the Western Pacific Ocean region to demonstrate and evaluate the proposed methodology. Comparisons with in situ drifting buoy observations show that the merged SSTs are accurate and the bias and root-mean-square errors for the comparison are 0.15°C and 0.72°C, respectively.
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Alvera-Azcárate A, Barth A, Rixen M, Beckers J M (2005). Reconstruction of incomplete oceanographic data sets using empirical orthogonal functions: application to the adriatic sea surface temperature. Ocean Model, 9(4): 325–346
Bennett A F (2002). Inverse Modeling of the Ocean and Atmosphere. London: Cambridge University Press
Brown O B, Minnett P J, Evans R, Kearns E, Kilpatrick K, Kumar A, Sikorski R, Závody A (1999). MODIS Infrared Sea Surface Temperature Algorithm- Algorithm Theoretical Basis Document (Version 2.0). University of Miami
Casey K S, Brandon T B, Cornillon P, Evans R (2010). Oceanography from Space. Springer Netherlands, 273–287
Chao Y, Li Z, Farrara J D, Hung P (2009). Blending sea surface temperatures from multiple satellites and in situ observations for coastal oceans. J Atmos Ocean Technol, 26(7): 1415–1426
Christakos G, Kolovos A, Serre M L, Vukovich F (2004). Total ozone mapping by integrating databases from remote sensing instruments and empirical models. IEEE Trans Geosci Rem Sens, 42(5): 991–1008
Christakos G, Serre M L (2000). BME analysis of spatiotemporal particulate matter distributions in North Carolina. Atmos Environ, 34 (20): 3393–3406
Christakos G, Serre M L, Kovitz J L (2001). BME representation of particulate matter distributions in the state of California on the basis of uncertain measurements. Journal of Geophysical Research: Atmospheres (1984–2012), 106 (D9): 9717–9731
Cressie N (1992). Statistics for Spatial Data. Terra Nova, 4(5): 613–617
Donlon C J, Martin M, Stark J, Roberts-Jones J, Fiedler E, Wimmer W (2012). The operational sea surface temperature and sea ice analysis (Ostia) system. Remote Sens Environ, 116(2): 140–158
Gentemann C L, Donlon C J, Stuart-Menteth A, Wentz F J (2003). Diurnal signals in satellite sea surface temperature measurements. Geophys Res Lett, 30(3): 1140–1143
Gentemann C L, Wentz F J, Mears C A, Smith D K (2004). in situ validation of tropical rainfall measuring mission microwave sea surface temperatures. Journal of Geophysical Research: Oceans, 109 (C4): 249–260
Guan L, Kawamura H (2003). SST availabilities of satellite infrared and microwave measurements. J Oceanogr, 59(2): 201–209
Isern-Fontanet J, Chapron B, Lapeyre G, Klein P (2006). Potential use of microwave sea surface temperatures for the estimation of ocean currents. Geophys Res Lett, 33(24): L24608
Kawai Y, Kawamura H, Takahashi S, Hosoda K, Murakami H, Kachi M, Guan L (2006). Satellite-based high-resolution global optimum interpolation sea surface temperature data. Journal of Geophysical Research: Oceans (1978–2012), 111(C6): 285–293
Lee S L, Balling R, Gober P (2008). Bayesian maximum entropy mapping and the soft data problem in urban climate research. Ann Assoc Am Geogr, 98(2): 309–322
Li A, Bo Y, Chen L (2013a). Bayesian maximum entropy data fusion of field-observed leaf area index (LAI) and Landsat Enhanced Thematic Mapper Plus-derived LAI. Int J Remote Sens, 34(1): 227–246
Li A, Bo Y, Zhu Y, Guo P, Bi J, He Y (2013b). Blending multiresolution satellite sea surface temperature (SST) products using Bayesian maximum entropy method. Remote Sens Environ, 135(4): 52–63
Li X, Pichel W, Clemente-Colon N P, Krasnopolsky V, Sapper J (2001a). Validation of coastal sea and lake surface temperature measurements derived from NOAA/AVHRR data. International Journal of Remote Sensing, 22(7): 1285–1303
Li X, Pichel W, Maturi E, Clemente-Colon P, Sapper J (2001b). Deriving the operational nonlinear multi-channel sea surface temperature algorithm coefficients for NOAA-15 AVHRR/3. International Journal of Remote Sensing, 22(4): 699–704
Li X, Zheng Q, Pichel W G, Yan X, Timothy Liu W, Clemente-Colon P (2001c). Analysis of coastal lee waves along the coast of Texas observed in advanced very high resolution radiometer Images. J Geophys Res, 106(C4): 7017–7025
Lorenc A C (1986). Analysis methods for numerical weather prediction. Q J R Meteorol Soc, 112(474): 1177–1194
Reynolds R W, Smith T M (1994). Improved global sea surface temperature analyses using optimum interpolation. J Clim, 7(6): 929–948
Reynolds R W, Zhang H, Smith T M, Gentemann C L, Wentz F (2005). Impacts of in situ and additional satellite data on the accuracy of a sea-surface temperature analysis for climate. Int J Climatol, 25(7): 857–864
Sakaida F, Takahashi S, Shimada T, Kawai Y, Kawamura H, Hosoda K, Guan L (2005). The production of the new generation sea surface temperature (NGSST-O ver. 1.0) in Tohoku University. Geoscience and Remote Sensing Symposium, 2005. IGARSS '05. IEEE, 2005: 2602–2605
Smith T M, Reynolds RW(2003). Extended reconstruction of global sea surface temperatures based on COADS data (1854–1997). J Clim, 16 (10): 1495–1510
Spadavecchia L, Williams M (2009). Can spatio-temporal geostatistical methods improve high resolution regionalisation of meteorological variables? Agric Meteorol, 149(6–7): 1105–1117
Tandeo P, Chapron B, Ba S, Autret E, Fablet R (2014). Segmentation of mesoscale ocean surface dynamics using satellite SST and SSH observations. IEEE Trans Geosci Rem Sens, 52(7): 4227–4235
Wang W, Xie P (2007). A multiplatform-merged (MPM) SST analysis. J Clim, 20(9): 1662–1679
Wentz F J, Meissner T (2000). AMSR Ocean Algorithm Theoretical Basis Document, Version 2. Remote Sensing Systems, Santa Rosa, CA
Yamamoto M, Hirose N (2007). Impact of SST reanalyzed using OGCM on weather simulation: a case of a developing cyclone in the Japan Sea area. Geophys Res Lett, 34(5): L05808
Yu H L, Kolovos A, Christakos G, Chen J, Warmerdam S, Dev B (2007). Interactive spatiotemporal modeling of health systems: the SEKS–GUI framework. Stochastic Environ Res Risk Assess, 21(5): 555–572
Zhou X, Yang X, Cheng L, Li Z (2012). Sensitivity analysis and validation of the single channel physical method for retrieving sea surface temperature. Journal of infrared and millimeter waves, 31(1): 91–96
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Tang, S., Yang, X., Dong, D. et al. Merging daily sea surface temperature data from multiple satellites using a Bayesian maximum entropy method. Front. Earth Sci. 9, 722–731 (2015). https://doi.org/10.1007/s11707-015-0538-z
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DOI: https://doi.org/10.1007/s11707-015-0538-z