Fusion of Multisensor SSTs Based on the Spatiotemporal Hierarchical Bayesian Model
文献类型：期刊论文
| 作者 | Zhu, Yuxin1,2; Bo, Yanchen3; Zhang, Jinzong4; Wang, Yuexiang4 |
| 刊名 | JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY
 |
| 出版日期 | 2018 |
| 卷号 | 35期号:1页码:91-109 |
| ISSN号 | 0739-0572 |
| DOI | 10.1175/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. |
| WOS关键词 | SEA-SURFACE TEMPERATURE ; PARTICULATE MATTER DISTRIBUTIONS ; VALIDATION ; OCEAN ; RADIOMETER ; PRODUCTS ; SYSTEMS |
| 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研究方向 | Engineering ; Meteorology & Atmospheric Sciences |
| 语种 | 英语 |
| WOS记录号 | WOS:000425445600006 |
| 出版者 | AMER METEOROLOGICAL SOC |
| 资助机构 | Natural Science Foundation of China ; China Postdoctoral Science Foundation ; Statistics Bureau of China ; Natural Science Foundation of Shandong Province |
| 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. |
入库方式： OAI收割
来源：地理科学与资源研究所
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