SSAT: Sensor-Satellite Auto-Correlation Transformer for Enhanced Aerosol Optical Depth Prediction

SSAT: Sensor-Satellite Auto-Correlation Transformer for Enhanced Aerosol Optical Depth Prediction

Satellite-derived aerosol optical depth (AOD) observations are highly valuable to describe the horizontal distribution of aerosols, but are hampered by spatial data gaps and limited temporal coverage (typically once a day). The synergism of these measurements with chemistry-transport models (CTM) ma...

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Bibliographic Details
Main Authors: Maoquan Zhang, Bisser Raytchev, Juan Cuesta, Farouk Lemmouchi, Maithili Karle, Daniel Andrade
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Aerosol optical depth (AOD) prediction
satellite remote sensing
transformer regression prediction
deep learning complementary for physical data assimilation
enhanced spatio-temporal prediction by GridMSE loss function
Online Access:https://ieeexplore.ieee.org/document/11091309/
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https://ieeexplore.ieee.org/document/11091309/

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