Fusion of Multisensor SSTs Based on the Spatiotemporal Hierarchical Bayesian Model
Zhu, Yuxin1,2; Bo, Yanchen3; Zhang, Jinzong4; Wang, Yuexiang4
刊名JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY
2018
卷号35期号:1页码:91-109
ISSN号0739-0572
DOI10.1175/JTECH-D-17-0116.1
通讯作者Bo, Yanchen(boyc@bnu.edu.cn)
英文摘要This study focuses on merging MODIS-mapped SSTs with 4-km spatial resolution and AMSR-E optimally interpolated SSTs at 25-km resolution. A new data fusion method was developed-the Spatiotemporal Hierarchical Bayesian Model (STHBM). This method, which is implemented through the Markov chain Monte Carlo technique utilized to extract inferential results, is specified hierarchically by decomposing the SST spatiotemporal process into three subprocesses, that is, the spatial trend process, the seasonal cycle process, and the spatiotemporal random effect process. Spatial-scale transformation and spatiotemporal variation are introduced into the fusion model through the data model and model parameters, respectively, with suitably selected link functions. Compared with two modern spatiotemporal statistical methods-the Bayesian maximum entropy and the robust fixed rank kriging-STHBM has the following strength: it can simultaneously meet the expression of uncertainties from data and model, seamless scale transformation, and SST spatiotemporal process simulation. Utilizing multisensors' complementation, merged data with complete spatial coverage, high resolution (4 km), and fine spatial pattern lying in MODIS SSTs can be obtained through STHBM. The merged data are assessed for local spatial structure, overall accuracy, and local accuracy. The evaluation results illustrate that STHBM can provide spatially complete SST fields with reasonably good data values and acceptable errors, and that the merged SSTs collect fine spatial patterns lying in MODIS SSTs with fine resolution. The accuracy of merged SSTs is between MODIS and AMSR-E SSTs. The contribution to the accuracy and the spatial pattern of the merged SSTs from the original MODIS SSTs is stronger than that of the original AMSR-E SSTs.
资助项目Natural Science Foundation of China[41401405] ; Natural Science Foundation of China[41471425] ; China Postdoctoral Science Foundation[2014M561039] ; Statistics Bureau of China[2016LY32] ; Natural Science Foundation of Shandong Province[ZR2013DL002] ; Natural Science Foundation of Shandong Province[ZR2017MD017]
WOS关键词SEA-SURFACE TEMPERATURE ; PARTICULATE MATTER DISTRIBUTIONS ; VALIDATION ; OCEAN ; RADIOMETER ; PRODUCTS ; SYSTEMS
WOS研究方向Engineering ; Meteorology & Atmospheric Sciences
语种英语
出版者AMER METEOROLOGICAL SOC
WOS记录号WOS:000425445600006
资助机构Natural Science Foundation of China ; China Postdoctoral Science Foundation ; Statistics Bureau of China ; Natural Science Foundation of Shandong Province
内容类型期刊论文
源URL[http://ir.igsnrr.ac.cn/handle/311030/57046]  
专题中国科学院地理科学与资源研究所
通讯作者Bo, Yanchen
作者单位1.Huaiyin Normal Univ, Sch Urban & Environm Sci, Jiangsu Collaborat Innovat Ctr Reg Modern Agr & E, Huaian, Jiangsu, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China
3.Beijing Normal Univ, Fac Geog Sci, State Key Lab Remote Sensing Sci, Beijing, Peoples R China
4.Huaiyin Normal Univ, Sch Urban & Environm Sci, Huaian, Jiangsu, Peoples R China
推荐引用方式
GB/T 7714
Zhu, Yuxin,Bo, Yanchen,Zhang, Jinzong,et al. Fusion of Multisensor SSTs Based on the Spatiotemporal Hierarchical Bayesian Model[J]. JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY,2018,35(1):91-109.
APA Zhu, Yuxin,Bo, Yanchen,Zhang, Jinzong,&Wang, Yuexiang.(2018).Fusion of Multisensor SSTs Based on the Spatiotemporal Hierarchical Bayesian Model.JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY,35(1),91-109.
MLA Zhu, Yuxin,et al."Fusion of Multisensor SSTs Based on the Spatiotemporal Hierarchical Bayesian Model".JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY 35.1(2018):91-109.
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