Correction of inter-mission inconsistencies in merged ocean colour satellite data
Consistency in a time series of ocean colour satellite data is essential when determining long-term trends and statistics in Essential Climate Variables. For such a long time series, it is necessary to merge ocean colour data sets from different sensors due to the finite life span of the satellites....
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-07-01
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| Series: | Frontiers in Remote Sensing |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/frsen.2022.882418/full |
| _version_ | 1828079781159108608 |
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| author | Marit van Oostende Martin Hieronymi Hajo Krasemann Burkard Baschek Rüdiger Röttgers |
| author_facet | Marit van Oostende Martin Hieronymi Hajo Krasemann Burkard Baschek Rüdiger Röttgers |
| author_sort | Marit van Oostende |
| collection | DOAJ |
| description | Consistency in a time series of ocean colour satellite data is essential when determining long-term trends and statistics in Essential Climate Variables. For such a long time series, it is necessary to merge ocean colour data sets from different sensors due to the finite life span of the satellites. Although bias corrections have been performed on merged data set products, significant inconsistencies between missions remain. These inconsistencies appear as sudden steps in the time series of these products when a satellite mission is launched into- or removed from orbit. This inter-mission inconsistency is not caused by poor correction of sensor sensitivities but by differences in the ability of a sensor to observe certain waters. This study, based on a data set compiled by the ‘Ocean Colour Climate Change Initiative’ project (OC-CCI), shows that coastal waters, high latitudes, and areas subject to changing cloud cover are most affected by coverage variability between missions. The “Temporal Gap Detection Method” is introduced, which temporally homogenises the observations per-pixel of the time series and consequently minimises the magnitude of the inter-mission inconsistencies. The method presented is suitable to be transferred to other merged satellite-derived data sets that exhibit inconsistencies due to changes in coverage over time. The results provide insights into the correct interpretation of any merged ocean colour time series. |
| first_indexed | 2024-04-11T03:07:37Z |
| format | Article |
| id | doaj.art-c6937ebbe7394f0e932d63cc42ea7b95 |
| institution | Directory Open Access Journal |
| issn | 2673-6187 |
| language | English |
| last_indexed | 2024-04-11T03:07:37Z |
| publishDate | 2022-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Remote Sensing |
| spelling | doaj.art-c6937ebbe7394f0e932d63cc42ea7b952023-01-02T12:49:11ZengFrontiers Media S.A.Frontiers in Remote Sensing2673-61872022-07-01310.3389/frsen.2022.882418882418Correction of inter-mission inconsistencies in merged ocean colour satellite dataMarit van Oostende0Martin Hieronymi1Hajo Krasemann2Burkard Baschek3Rüdiger Röttgers4Department of Optical Oceanography, Institute of Carbon Cycles, Helmholtz-Zentrum Hereon, Geesthacht, GermanyDepartment of Optical Oceanography, Institute of Carbon Cycles, Helmholtz-Zentrum Hereon, Geesthacht, GermanyDepartment of Optical Oceanography, Institute of Carbon Cycles, Helmholtz-Zentrum Hereon, Geesthacht, GermanyDeutsches Meeresmuseum, Stralsund, GermanyDepartment of Optical Oceanography, Institute of Carbon Cycles, Helmholtz-Zentrum Hereon, Geesthacht, GermanyConsistency in a time series of ocean colour satellite data is essential when determining long-term trends and statistics in Essential Climate Variables. For such a long time series, it is necessary to merge ocean colour data sets from different sensors due to the finite life span of the satellites. Although bias corrections have been performed on merged data set products, significant inconsistencies between missions remain. These inconsistencies appear as sudden steps in the time series of these products when a satellite mission is launched into- or removed from orbit. This inter-mission inconsistency is not caused by poor correction of sensor sensitivities but by differences in the ability of a sensor to observe certain waters. This study, based on a data set compiled by the ‘Ocean Colour Climate Change Initiative’ project (OC-CCI), shows that coastal waters, high latitudes, and areas subject to changing cloud cover are most affected by coverage variability between missions. The “Temporal Gap Detection Method” is introduced, which temporally homogenises the observations per-pixel of the time series and consequently minimises the magnitude of the inter-mission inconsistencies. The method presented is suitable to be transferred to other merged satellite-derived data sets that exhibit inconsistencies due to changes in coverage over time. The results provide insights into the correct interpretation of any merged ocean colour time series.https://www.frontiersin.org/articles/10.3389/frsen.2022.882418/fullremote sensingocean colourmerged satellite datatime seriesclimate change initiativeessential climate variable |
| spellingShingle | Marit van Oostende Martin Hieronymi Hajo Krasemann Burkard Baschek Rüdiger Röttgers Correction of inter-mission inconsistencies in merged ocean colour satellite data Frontiers in Remote Sensing remote sensing ocean colour merged satellite data time series climate change initiative essential climate variable |
| title | Correction of inter-mission inconsistencies in merged ocean colour satellite data |
| title_full | Correction of inter-mission inconsistencies in merged ocean colour satellite data |
| title_fullStr | Correction of inter-mission inconsistencies in merged ocean colour satellite data |
| title_full_unstemmed | Correction of inter-mission inconsistencies in merged ocean colour satellite data |
| title_short | Correction of inter-mission inconsistencies in merged ocean colour satellite data |
| title_sort | correction of inter mission inconsistencies in merged ocean colour satellite data |
| topic | remote sensing ocean colour merged satellite data time series climate change initiative essential climate variable |
| url | https://www.frontiersin.org/articles/10.3389/frsen.2022.882418/full |
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