用视觉变压器模型提升卫星气溶胶反演精度，更准更连贯。
Foundation AI Models for Aerosol Optical Depth Estimation from PACE Satellite Data
- 基于通道分组的视觉变压器，融合光谱与空间信息。
- 相比顶尖模型，均方误差降低62%，结果更稳定。
- 适合遥感、气候研究等需要高精度气溶胶数据的场景。
气溶胶光学厚度（AOD）反演对地球观测至关重要，支持空气质量监测与气候研究。传统物理方法依赖辐射传输建模和查表，计算成本高。近年数据驱动方法虽有进展，但常忽略高光谱影像的空间-光谱一致性，导致结果不连贯且易受噪声干扰。本文首次探索基础型AI模型用于AOD反演，提出ViTCG——一种基于通道分组的视觉变压器框架，联合建模空间上下文与光谱特征，有效降低反演偏差与误差。以PACE卫星顶层大气辐照度为输入，验证表明，相比当前最优基础模型（如Prithvi），均方误差降低62%，且生成空间一致的AOD场。
原文摘要 · Abstract (English)
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.
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