An Improved DINEOF Algorithm Based on Optimized Validation Points Selection Method
Ocean remote-sensing satellite data have been widely applied in the areas of oceanography, meteorology, the environment, and many more fields in science and engineering. However, missing data due to cloud cover, equipment failure, etc., limit its application. Therefore, reconstruction of the missing...
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| Format: | Article |
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MDPI AG
2023-01-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/15/3/392 |
| _version_ | 1797623091682082816 |
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| author | Zhenteng Yang Xinchen Xia Fang-Yenn Teo Sin-Poh Lim Dekui Yuan |
| author_facet | Zhenteng Yang Xinchen Xia Fang-Yenn Teo Sin-Poh Lim Dekui Yuan |
| author_sort | Zhenteng Yang |
| collection | DOAJ |
| description | Ocean remote-sensing satellite data have been widely applied in the areas of oceanography, meteorology, the environment, and many more fields in science and engineering. However, missing data due to cloud cover, equipment failure, etc., limit its application. Therefore, reconstruction of the missing data through an appropriate method is essential. The data-interpolating empirical orthogonal function (DINEOF) algorithm proposed by Beckers and Rixen (2003) is currently the most commonly used method for the reconstruction of missing data in large areas. However, the existing DINEOF algorithm adopts a random method to select the cross−validation points, which may underutilize effective information around the missing value points. In addition, the cross-validation points may be too concentrated in an area, thus being unable to reflect the overall characteristics of the data. This paper optimizes the method to select the cross-validation points so that the information around the missing values can be effectively utilized and to avoid the cross-validation points being too concentrated. On this basis, an improved validation-point DINEOF algorithm (IV−DINEOF) is proposed. An ideal dataset and a reanalysis dataset based on sea surface temperature (SST) are used to test the performance of the improved algorithm. Statistical analysis of the results shows that the data reconstruction performance of the IV−DINEOF algorithm is better than that of the DINEOF algorithm, and the computational efficiency is also improved. The VE−DINEOF algorithm has the highest computing efficiency, but its reconstruction accuracy is lower than that of IV−DINEOF. |
| first_indexed | 2024-03-11T09:19:28Z |
| format | Article |
| id | doaj.art-005ae5633965481482d74b776c9830ed |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-11T09:19:28Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
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| series | Water |
| spelling | doaj.art-005ae5633965481482d74b776c9830ed2023-11-16T18:22:00ZengMDPI AGWater2073-44412023-01-0115339210.3390/w15030392An Improved DINEOF Algorithm Based on Optimized Validation Points Selection MethodZhenteng Yang0Xinchen Xia1Fang-Yenn Teo2Sin-Poh Lim3Dekui Yuan4Department of Mechanics, School of Mechanical Engineering, Tianjin University, Tianjin 300072, ChinaDepartment of Mechanics, School of Mechanical Engineering, Tianjin University, Tianjin 300072, ChinaFaculty of Science and Engineering, University of Nottingham Malaysia, Semenyih 43500, MalaysiaFaculty of Science and Engineering, University of Nottingham Malaysia, Semenyih 43500, MalaysiaState Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300072, ChinaOcean remote-sensing satellite data have been widely applied in the areas of oceanography, meteorology, the environment, and many more fields in science and engineering. However, missing data due to cloud cover, equipment failure, etc., limit its application. Therefore, reconstruction of the missing data through an appropriate method is essential. The data-interpolating empirical orthogonal function (DINEOF) algorithm proposed by Beckers and Rixen (2003) is currently the most commonly used method for the reconstruction of missing data in large areas. However, the existing DINEOF algorithm adopts a random method to select the cross−validation points, which may underutilize effective information around the missing value points. In addition, the cross-validation points may be too concentrated in an area, thus being unable to reflect the overall characteristics of the data. This paper optimizes the method to select the cross-validation points so that the information around the missing values can be effectively utilized and to avoid the cross-validation points being too concentrated. On this basis, an improved validation-point DINEOF algorithm (IV−DINEOF) is proposed. An ideal dataset and a reanalysis dataset based on sea surface temperature (SST) are used to test the performance of the improved algorithm. Statistical analysis of the results shows that the data reconstruction performance of the IV−DINEOF algorithm is better than that of the DINEOF algorithm, and the computational efficiency is also improved. The VE−DINEOF algorithm has the highest computing efficiency, but its reconstruction accuracy is lower than that of IV−DINEOF.https://www.mdpi.com/2073-4441/15/3/392cross-validation pointsDINEOF algorithmmissing data fillingsatellite remote sensing |
| spellingShingle | Zhenteng Yang Xinchen Xia Fang-Yenn Teo Sin-Poh Lim Dekui Yuan An Improved DINEOF Algorithm Based on Optimized Validation Points Selection Method Water cross-validation points DINEOF algorithm missing data filling satellite remote sensing |
| title | An Improved DINEOF Algorithm Based on Optimized Validation Points Selection Method |
| title_full | An Improved DINEOF Algorithm Based on Optimized Validation Points Selection Method |
| title_fullStr | An Improved DINEOF Algorithm Based on Optimized Validation Points Selection Method |
| title_full_unstemmed | An Improved DINEOF Algorithm Based on Optimized Validation Points Selection Method |
| title_short | An Improved DINEOF Algorithm Based on Optimized Validation Points Selection Method |
| title_sort | improved dineof algorithm based on optimized validation points selection method |
| topic | cross-validation points DINEOF algorithm missing data filling satellite remote sensing |
| url | https://www.mdpi.com/2073-4441/15/3/392 |
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