Researchers have developed a physics-guided neural network to estimate carbon dioxide levels using the GOES-East satellite. While not as precise as dedicated CO2 sensors, this approach provides high-frequency, 10-minute updates across the western hemisphere. This method allows for better observation of CO2 fluctuations over urban centers and agricultural lands. It fills a critical gap in temporal and spatial coverage, aiding the independent verification of carbon fluxes on a global scale.
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Independent verification is the biggest hurdle for carbon markets right now. If we can get reliable, high-frequency data on local fluxes, it makes reporting standards much harder to fake.
The precision part is what worries me. If it cannot rival dedicated instruments, can it actually be used for compliance or just for general trends? There is a big difference in the data quality needed for policy.
Interesting use of existing hardware. Using a neural network to turn a weather satellite into a carbon tracker is a smart way to get data without waiting years for a new launch.
The 10 minute revisit time is the real win here. Most carbon satellites only hit the same spot every few weeks. This lets us see how emissions actually move in real time.
