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

doi:10.1007/s11707-015-0538-z

Front. Earth Sci.

2015, Vol. 9

Issue (4) : 722-731 https://doi.org/10.1007/s11707-015-0538-z

RESEARCH ARTICLE

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

Shaolei TANG^1,², Xiaofeng YANG¹(

), Di DONG^1,², Ziwei LI¹

¹. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
². University of Chinese Academy of Sciences, Beijing 100049, China

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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

Corresponding Author(s): Xiaofeng YANG

Just Accepted Date: 20 July 2015 Online First Date: 22 October 2015 Issue Date: 30 October 2015

Cite this article:

Shaolei TANG,Xiaofeng YANG,Di DONG, et al. Merging daily sea surface temperature data from multiple satellites using a Bayesian maximum entropy method[J]. Front. Earth Sci., 2015, 9(4): 722-731.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-015-0538-z
https://academic.hep.com.cn/fesci/EN/Y2015/V9/I4/722

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