Ocean Science
Ocean Science
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Articles | Volume 20, issue 5
Articles | Volume 20, issue 5
https://doi.org/10.5194/os-20-1309-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/os-20-1309-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 20, issue 5
Research article
 | 
24 Oct 2024
Research article |  | 24 Oct 2024

MAESSTRO: Masked Autoencoders for Sea Surface Temperature Reconstruction under Occlusion

Edwin Goh, Alice Yepremyan, Jinbo Wang, and Brian Wilson

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Agabin, A., Prochaska, J. X., Cornillon, P. C., and Buckingham, C. E.: Mitigating masked pixels in a climate-critical ocean dataset, Remote Sens., 16, 2439, https://doi.org/10.3390/rs16132439, 2024. a
Alvera-Azcárate, A., Barth, A., Sirjacobs, D., Lenartz, F., and Beckers, J.-M.: Data Interpolating Empirical Orthogonal Functions (DINEOF): a tool for geophysical data analyses, Mediterr. Mar. Sci., 12, 5–11, https://doi.org/10.12681/mms.64, 2011. a
Barth, A., Alvera-Azcárate, A., Licer, M., and Beckers, J.-M.: DINCAE 1.0: a convolutional neural network with error estimates to reconstruct sea surface temperature satellite observations, Geosci. Model Dev., 13, 1609–1622, https://doi.org/10.5194/gmd-13-1609-2020, 2020. a, b
Barth, A., Alvera-Azcárate, A., Troupin, C., and Beckers, J.-M.: DINCAE 2.0: multivariate convolutional neural network with error estimates to reconstruct sea surface temperature satellite and altimetry observations, Geosci. Model Dev., 15, 2183–2196, https://doi.org/10.5194/gmd-15-2183-2022, 2022. a
Belkin, I. M.: Remote Sensing of Ocean Fronts in Marine Ecology and Fisheries, Remote Sens., 13, 883, https://doi.org/10.3390/rs13050883, 2021. a
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An AI model was used to fill in missing parts of sea temperature (SST) maps caused by cloud cover. We found masked autoencoders can recreate missing SSTs with less than 0.2 °C error, even when 80 % are missing. This is 5000 times faster than conventional methods tested on a single central processing unit. This can enhance our ability in monitoring global small-scale ocean fronts that affect heat, carbon, and nutrient exchange in the ocean. The method is promising for future research.
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