Reconstruction Methods in Oceanographic Satellite Data Observation—A Survey
Oceanographic parameters, such as sea surface temperature, surface chlorophyll-a concentration, sea surface ice concentration, sea surface height, etc., are listed as Essential Climate Variables. Therefore, there is a crucial need for persistent and accurate measurements on a global scale. While in...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/11/2/340 |
| _version_ | 1827756956662628352 |
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| author | Leon Ćatipović Frano Matić Hrvoje Kalinić |
| author_facet | Leon Ćatipović Frano Matić Hrvoje Kalinić |
| author_sort | Leon Ćatipović |
| collection | DOAJ |
| description | Oceanographic parameters, such as sea surface temperature, surface chlorophyll-a concentration, sea surface ice concentration, sea surface height, etc., are listed as Essential Climate Variables. Therefore, there is a crucial need for persistent and accurate measurements on a global scale. While in situ methods tend to be accurate and continuous, these qualities are difficult to scale spatially, leaving a significant portion of Earth’s oceans and seas unmonitored. To tackle this, various remote sensing techniques have been developed. One of the more prominent ways to measure the aforementioned parameters is via satellite spacecraft-mounted remote sensors. This way, spatial coverage is considerably increased while retaining significant accuracy and resolution. Unfortunately, due to the nature of electromagnetic signals, the atmosphere itself and its content (such as clouds, rain, etc.) frequently obstruct the signals, preventing the satellite-mounted sensors from measuring, resulting in gaps—missing data—in satellite recordings. One way to deal with these gaps is via various reconstruction methods developed through the past two decades. However, there seems to be a lack of review papers on reconstruction methods for satellite-derived oceanographic variables. To rectify the lack, this paper surveyed more than 130 articles dealing with the issue of data reconstruction. Articles were chosen according to two criteria: (a) the article has to feature satellite-derived oceanographic data (b) gaps in satellite data have to be reconstructed. As an additional result of the survey, a novel categorising system based on the type of input data and the usage of time series in reconstruction efforts is proposed. |
| first_indexed | 2024-03-11T08:35:52Z |
| format | Article |
| id | doaj.art-963d4da3b28a4eb8b44478484784b1c0 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-11T08:35:52Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-963d4da3b28a4eb8b44478484784b1c02023-11-16T21:27:48ZengMDPI AGJournal of Marine Science and Engineering2077-13122023-02-0111234010.3390/jmse11020340Reconstruction Methods in Oceanographic Satellite Data Observation—A SurveyLeon Ćatipović0Frano Matić1Hrvoje Kalinić2Environmental Data Analysis Laboratory, Faculty of Science, University of Split, 21000 Split, CroatiaUniversity Department of Marine Studies, University of Split, 21000 Split, CroatiaEnvironmental Data Analysis Laboratory, Faculty of Science, University of Split, 21000 Split, CroatiaOceanographic parameters, such as sea surface temperature, surface chlorophyll-a concentration, sea surface ice concentration, sea surface height, etc., are listed as Essential Climate Variables. Therefore, there is a crucial need for persistent and accurate measurements on a global scale. While in situ methods tend to be accurate and continuous, these qualities are difficult to scale spatially, leaving a significant portion of Earth’s oceans and seas unmonitored. To tackle this, various remote sensing techniques have been developed. One of the more prominent ways to measure the aforementioned parameters is via satellite spacecraft-mounted remote sensors. This way, spatial coverage is considerably increased while retaining significant accuracy and resolution. Unfortunately, due to the nature of electromagnetic signals, the atmosphere itself and its content (such as clouds, rain, etc.) frequently obstruct the signals, preventing the satellite-mounted sensors from measuring, resulting in gaps—missing data—in satellite recordings. One way to deal with these gaps is via various reconstruction methods developed through the past two decades. However, there seems to be a lack of review papers on reconstruction methods for satellite-derived oceanographic variables. To rectify the lack, this paper surveyed more than 130 articles dealing with the issue of data reconstruction. Articles were chosen according to two criteria: (a) the article has to feature satellite-derived oceanographic data (b) gaps in satellite data have to be reconstructed. As an additional result of the survey, a novel categorising system based on the type of input data and the usage of time series in reconstruction efforts is proposed.https://www.mdpi.com/2077-1312/11/2/340data reconstructiongap fillingmissing datagapssatellite oceanography |
| spellingShingle | Leon Ćatipović Frano Matić Hrvoje Kalinić Reconstruction Methods in Oceanographic Satellite Data Observation—A Survey Journal of Marine Science and Engineering data reconstruction gap filling missing data gaps satellite oceanography |
| title | Reconstruction Methods in Oceanographic Satellite Data Observation—A Survey |
| title_full | Reconstruction Methods in Oceanographic Satellite Data Observation—A Survey |
| title_fullStr | Reconstruction Methods in Oceanographic Satellite Data Observation—A Survey |
| title_full_unstemmed | Reconstruction Methods in Oceanographic Satellite Data Observation—A Survey |
| title_short | Reconstruction Methods in Oceanographic Satellite Data Observation—A Survey |
| title_sort | reconstruction methods in oceanographic satellite data observation a survey |
| topic | data reconstruction gap filling missing data gaps satellite oceanography |
| url | https://www.mdpi.com/2077-1312/11/2/340 |
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