Foundation AI Models for Aerosol Optical Depth Estimation from PACE Satellite Data
For Earth observation researchers, this work demonstrates the first application of foundation AI models to AOD retrieval, significantly improving accuracy and spatial coherence over existing methods.
This paper introduces ViTCG, a Vision Transformer with Channel-wise Grouping for AOD retrieval from PACE satellite data, achieving a 62% reduction in mean squared error compared to state-of-the-art foundation models like Prithvi.
Aerosol Optical Depth (AOD) retrieval is essential for Earth observation, supporting applications from air quality monitoring to climate studies. Conventional physics-based AOD retrieval methods formulate the problem as a pixel-wise inversion, relying on radiative transfer modeling, memory-intensive look-up tables, and auxiliary meteorological data. While recent data-driven approaches have shown promise, many fail to exploit the spatial-spectral coherence of hyperspectral imagery, leading to spatially inconsistent and noise-sensitive retrievals. We present the first study exploring Foundation AI models for AOD retrieval and propose ViTCG, a Vision Transformer with Channel-wise Grouping-based spatial regression framework that reduces retrieval bias and error. ViTCG uses hyperspectral top-of-atmosphere radiance as input and jointly models spatial context and spectral information. Validation with PACE radiance observations demonstrates a 62% reduction in mean squared error compared to state-of-the-art foundation models, including Prithvi, and produces spatially coherent AOD fields.