Merging daily sea surface temperature data from multiple satellites using a Bayesian maximum entropy method

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.

Keywords

sea surface temperature (SST) / Bayesian maximum entropy (BME) / remote sensing / data fusion

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Acknowledgments

This study was supported by the National Natural Science Foundation of China (Grant Nos. 41201350 and 41371355). We sincerely thank the University of North Carolina Bayesian Maximum Entropy (UNC-BME) laboratory at the UNC at Chapel Hill for supplying the BME codes.

RIGHTS & PERMISSIONS

2015 Higher Education Press and Springer-Verlag Berlin Heidelberg