Self-supervised AI fills Chl-a gaps
· newswise.com · popularity 17/100 · 0 similar stories today · Ocean & Marine Science (Earth, Nature & Environment)
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Summary
<img src="https://www.newswise.com/legacy/image.php?image=https://media.newswise.com/pio/articles/857692/6ae478be8069b_17_857692.jpg&width=100&height=150" alt="Newswise image" />Clouds and incomplete satellite sampling leave major gaps in ocean-color chlorophyll-a (Chl-a) observations, limiting studies of marine ecosystems and climate variability. A new self-supervised framework, GDCM-EOF, learns directly from incomplete satellite records without requiring external gap-free labels. Compared with the DINEOF baseline, reconstruction root mean square error (RMSE) is about 82% lower in a high-resolution Caribbean Sea test and 40% lower in a global test, with both results assessed in log-transformed Chl-a space.
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