IGSNRR OpenIR
Fusion of Multisensor SSTs Based on the Spatiotemporal Hierarchical Bayesian Model
Zhu, Yuxin1,2; Bo, Yanchen3; Zhang, Jinzong4; Wang, Yuexiang4
2018
Source PublicationJOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY
ISSN0739-0572
Volume35Issue:1Pages:91-109
Corresponding AuthorBo, Yanchen(boyc@bnu.edu.cn)
AbstractThis 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.
DOI10.1175/JTECH-D-17-0116.1
WOS KeywordSEA-SURFACE TEMPERATURE ; PARTICULATE MATTER DISTRIBUTIONS ; VALIDATION ; OCEAN ; RADIOMETER ; PRODUCTS ; SYSTEMS
Indexed BySCI
Language英语
Funding ProjectNatural 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]
Funding OrganizationNatural Science Foundation of China ; China Postdoctoral Science Foundation ; Statistics Bureau of China ; Natural Science Foundation of Shandong Province
WOS Research AreaEngineering ; Meteorology & Atmospheric Sciences
WOS SubjectEngineering, Ocean ; Meteorology & Atmospheric Sciences
WOS IDWOS:000425445600006
PublisherAMER METEOROLOGICAL SOC
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/57047
Collection中国科学院地理科学与资源研究所
Corresponding AuthorBo, Yanchen
Affiliation1.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
Recommended Citation
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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