Merging Satellite Ocean Color Data With Bayesian Maximum Entropy Method
文献类型：期刊论文
| 作者 | Shi, Yingni1; Zhou, Xuan1; Yang, Xiaofeng1; Shi, Lijian1; Ma, Sheng1 |
| 刊名 | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
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| 出版日期 | 2015 |
| 卷号 | 8期号:7(SI)页码:722-731 |
| 关键词 | Bayesian maximum entropy (BME) merging ocean color satellite |
| 通讯作者 | Zhou, X (reprint author), Chinese Acad Sci, Inst Elect, Beijing 100190, Peoples R China. |
| 英文摘要 | Merging multiple satellite ocean color data is one of the ways to create a unified ocean color product and improve the spatial coverage. In this paper, the Bayesian maximum entropy (BME), a probabilistic method, is used to integrate chlorophyll-a (chl-a) concentration data obtained by the seaviewing wide field-of-view sensor (SeaWiFS) on Orbview-2, the medium-resolution imaging spectrometer instrument (MERIS) on ENVISAT and the moderate-resolution imaging spectroradiometer (MODIS) on Aqua. MODIS chl-a concentration on current day is considered as the accurate hard data. A probabilistic model is developed to link hard data and chl-a concentration of other sensors on previous days. The latter are processed as soft data by this probabilistic model to take into account the differences between mission-specific products. The semivariogram of chl-a concentration, which presents the spatial variability and provides a priori knowledge, is developed to improve the spatial coverage. The average daily coverage of the merged chl-a field is 74% for the 1-day temporal integration which is about six times higher than any single mission, and 95% for the 3-day temporal integration which achieves basically a complete global coverage. Root-mean-square error (RMSE) and correlation between in situ chl-a measurements and the BME-merged chl-a from 1-day data are 0.42 and 0.72, and from 3-day data are 0.44 and 0.70, respectively. Compared with the existing GSM method and the weighted averaging (AVW) method, the BME method can greatly improve the spatial coverage and preserve the high accuracy, which demonstrates the potential advantages of the BME method to merge ocean color products from multiple sensors. |
| 研究领域[WOS] | Engineering, Electrical & Electronic ; Geography, Physical ; Remote Sensing ; Imaging Science & Photographic Technology |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000359538700005 |
| 源URL | [http://ir.ceode.ac.cn/handle/183411/38414]  |
| 专题 | 遥感与数字地球研究所_SCI/EI期刊论文_期刊论文 |
| 作者单位 | 1.[Shi, Yingni] Ocean Univ China, Qingdao 266100, Peoples R China 2.[Zhou, Xuan] Chinese Acad Sci, Inst Elect, Beijing 100190, Peoples R China 3.[Yang, Xiaofeng 4.Ma, Sheng] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China 5.[Shi, Lijian] Natl Satellite Ocean Applicat Serv, Beijing 100081, Peoples R China |
| 推荐引用方式 GB/T 7714 | Shi, Yingni,Zhou, Xuan,Yang, Xiaofeng,et al. Merging Satellite Ocean Color Data With Bayesian Maximum Entropy Method[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2015,8(7(SI)):722-731. |
| APA | Shi, Yingni,Zhou, Xuan,Yang, Xiaofeng,Shi, Lijian,&Ma, Sheng.(2015).Merging Satellite Ocean Color Data With Bayesian Maximum Entropy Method.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,8(7(SI)),722-731. |
| MLA | Shi, Yingni,et al."Merging Satellite Ocean Color Data With Bayesian Maximum Entropy Method".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 8.7(SI)(2015):722-731. |
入库方式： OAI收割
来源：遥感与数字地球研究所
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