| DING Youzhuan,WEI Zhihui,MAO Zhihua,WANG Xiaofei,PAN Delu. 2009. Reconstruction of incomplete satellite SST data sets based on EOF method. Acta Oceanologica Sinica, (2):36－44 |
| Reconstruction of incomplete satellite SST data sets based on EOF method |
| Reconstruction of incomplete satellite SST data sets based on EOF method |
| Received:February 21, 2008 Revised:December 07, 2008 |
| DOI： |
| Key words:EOF SST Changjiang River estuary Missing data sets |
| 中文关键词: EOF SST Changjiang River estuary Missing data sets |
| 基金项目:The National Natural Science Foundation of China under contract Nos 40576080 and 40506036; the National "863" Project of China under contract No. 2007AA12Z182. |
| Author Name | Affiliation | E-mail | | DING Youzhuan | Department of Computer, Nanjing University of Science and Technology, Nanjing 210094, China
State Key Laboratory of Satellite Ocean Envionment Dynamics, Second Institute of Oceanography, State Oceanic Administration, Hangzhou 310012, China | dingyzh1982@163.com | | WEI Zhihui | Department of Computer, Nanjing University of Science and Technology, Nanjing 210094, China | | | MAO Zhihua | State Key Laboratory of Satellite Ocean Envionment Dynamics, Second Institute of Oceanography, State Oceanic Administration, Hangzhou 310012, China | | | WANG Xiaofei | State Key Laboratory of Satellite Ocean Envionment Dynamics, Second Institute of Oceanography, State Oceanic Administration, Hangzhou 310012, China | | | PAN Delu | State Key Laboratory of Satellite Ocean Envionment Dynamics, Second Institute of Oceanography, State Oceanic Administration, Hangzhou 310012, China | |
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| Abstract: |
| As for the satellite remote sensing data obtained by the visible and infrared bands inversion, the clouds coverage in the sky over the ocean often results in missing data of inversion products on a large scale, and thin clouds difficult to be detected would cause the data of the inversion products to be abnormal. Alvera et al.(2005) proposed a method for the reconstruction of missing data based on an Empirical Orthogonal Functions (EOF) decomposition, but his method couldn't process these images presenting extreme cloud coverage(more than 95%), and required a long time for reconstruction. Besides, the abnormal data in the images had a great effect on the reconstruction result. Therefore, this paper tries to improve the study result. It has reconstructed missing data sets by twice applying EOF decomposition method. Firstly, the abnormity time has been detected by analyzing the temporal modes of EOF decomposition, and the abnormal data have been eliminated. Secondly, the data sets, excluding the abnormal data, are analyzed by using EOF decomposition, and then the temporal modes undergo a filtering process so as to enhance the ability of reconstructing the images which are of no or just a little data, by using EOF. At last, this method has been applied to a large data set, i.e. 43 Sea Surface Temperature (SST) satellite images of the Changjiang River (Yangtze River) estuary and its adjacent areas, and the total reconstruction root mean square error (RMSE) is 0.82℃. And it has been proved that this improved EOF reconstruction method is robust for reconstructing satellite missing data and unreliable data. |
| 中文摘要: |
| As for the satellite remote sensing data obtained by the visible and infrared bands inversion, the clouds coverage in the sky over the ocean often results in missing data of inversion products on a large scale, and thin clouds difficult to be detected would cause the data of the inversion products to be abnormal. Alvera et al.(2005) proposed a method for the reconstruction of missing data based on an Empirical Orthogonal Functions (EOF) decomposition, but his method couldn't process these images presenting extreme cloud coverage(more than 95%), and required a long time for reconstruction. Besides, the abnormal data in the images had a great effect on the reconstruction result. Therefore, this paper tries to improve the study result. It has reconstructed missing data sets by twice applying EOF decomposition method. Firstly, the abnormity time has been detected by analyzing the temporal modes of EOF decomposition, and the abnormal data have been eliminated. Secondly, the data sets, excluding the abnormal data, are analyzed by using EOF decomposition, and then the temporal modes undergo a filtering process so as to enhance the ability of reconstructing the images which are of no or just a little data, by using EOF. At last, this method has been applied to a large data set, i.e. 43 Sea Surface Temperature (SST) satellite images of the Changjiang River (Yangtze River) estuary and its adjacent areas, and the total reconstruction root mean square error (RMSE) is 0.82℃. And it has been proved that this improved EOF reconstruction method is robust for reconstructing satellite missing data and unreliable data. |
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