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

作者:Tang, SL (Tang, Shaolei)[ 1,2 ] ; Yang, XF (Yang, Xiaofeng)[ 1 ] ; Dong, D (Dong, Di)[ 1,2 ] ; Li, ZW (Li, Ziwei)[ 1 ]

FRONTIERS OF EARTH SCIENCE

卷: 9  期: 4  页: 722-731  特刊: SI

DOI: 10.1007/s11707-015-0538-z

出版年: DEC 2015

摘要

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 0.15A degrees C and 0.72A degrees C, respectively.

通讯作者地址: Yang, XF (通讯作者)

Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China.

地址:

[ 1 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China

[ 2 ] Univ Chinese Acad Sci, Beijing 100049, Peoples R China