| Autor: |
Pauthenet, Etienne, Martinez, Elodie, Gorgues, Thomas, Roussillon, Joana, Drumetz, Lucas, Fablet, Ronan, Roux, Maïlys |
| Předmět: |
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| Zdroj: |
Geophysical Research Letters; 7/28/2024, Vol. 51 Issue 14, p1-10, 10p |
| Abstrakt: |
Phytoplankton sustains marine ecosystems and influences the global carbon cycle. This study analyzes trends in surface chlorophyll‐a concentration (Schl), a proxy for phytoplankton biomass, using six of the most widely used merged satellite products. Significant regional variations are observed, with contrasting trends observed among different products. To assess if these trends can be related to changes in the environment or to bias in radiometric products, a convolutional neural network is used to examine the relationship between physical ocean variables versus Schl. The results suggest that the merging algorithm of Globcolour‐GSM is not reliable for trend detection and that observed changes in Schl after 2012/2016 are not supported by changes of the physical ocean. These results emphasize the need for careful interpretation of satellite‐derived Schl trends and highlight the potential of machine learning in understanding the complex interactions in marine ecosystems. Plain Language Summary: This study investigates trends in surface concentration of chlorophyll‐a (Schl), a proxy of phytoplankton biomass in the ocean. Our study reveals different trends according to the considered satellite products, challenging previous assumptions relating some Schl negative trends to the anthropogenic signal. To investigate the reliability of these trends, a machine learning approach is applied, showing that the observed changes in Schl trends may not always be related to changes in some physical ocean properties but sometimes to sensor drift. These findings underscore the importance of considering multiple factors when interpreting Schl trends and highlight the potential of advanced statistical tools in understanding marine ecosystems. Key Points: An analysis of remotely sensed surface chlorophyll‐a concentration reveals contrasted trends between available merged productsA convolutional neural network can effectively reconstruct surface chlorophyll‐a concentration from the properties of the ocean surfaceRecent trends in surface chlorophyll‐a concentration merged products are not all supported by changes in properties of the ocean surface [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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