Abstract. The aerosol fine-mode fraction (FMF) is potentially valuable for discriminating natural aerosols from anthropogenic ones. However, most current satellite-based FMF products are highly unreliable. Here, we developed a new satellite-based global land daily FMF dataset (Phy-DL FMF) by synergizing the advantages of physical and deep learning methods at a 1° spatial resolution by covering the period from 2001 to 2020. The Phy-DL FMF dataset is comparable to Aerosol Robotic Network (AERONET) measurements, based on the analysis of 361,089 data samples from 1170 AERONET sites around the world. Overall, Phy-DL FMF showed a root-mean-square error of 0.136 and correlation coefficient of 0.68, and the proportion of results that fell within the ±20 % expected error window was 79.15 %. Phy-DL FMF showed superior performance over alternate deep learning or physical approaches (such as the spectral deconvolution algorithm presented in our previous studies), particularly for forests, grasslands, croplands, and urban and barren land types. As a long-term dataset, Phy-DL FMF is able to show an overall significant decreasing trend (at a 95 % significance level) over global land areas. Based on the trend analysis of Phy-DL FMF for different countries, the upward trend in the FMFs was particularly strong over India and the western USA. Overall, this study provides a new FMF dataset for global land areas that can help improve our understanding of spatiotemporal fine- and coarse-mode aerosol changes. The datasets can be downloaded from https://doi.org/10.5281/zenodo.5105617 (Yan, 2021).

中文翻译：

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使用混合物理和深度学习方法从 MODIS 检索的全球陆地气溶胶精细模式分数数据集（2001-2020）

摘要。气溶胶精细模式分数（FMF）对于区分天然气溶胶和人为气溶胶具有潜在价值。然而，目前大多数基于卫星的 FMF 产品非常不可靠。在这里，我们通过在 2001 年至 2020 年期间以 1° 空间分辨率协同物理和深度学习方法的优势，开发了一个新的基于卫星的全球陆地每日 FMF 数据集（Phy-DL FMF）。 Phy-DL FMF基于对来自全球 1170 个 AERONET 站点的 361,089 个数据样本的分析，数据集与气溶胶机器人网络 (AERONET) 测量结果相当。总体而言，Phy-DL FMF 的均方根误差为 0.136，相关系数为 0.68，落在 ±20% 预期误差窗口内的结果比例为 79.15%。Phy-DL FMF 表现出优于替代深度学习或物理方法（例如我们之前研究中介绍的光谱反卷积算法）的性能，特别是对于森林、草原、农田以及城市和贫瘠土地类型。作为一个长期数据集，Phy-DL FMF 能够显示全球陆地区域的整体显着下降趋势（显着性水平为 95%）。根据不同国家 Phy-DL FMF 的趋势分析，FMF 的上升趋势在印度和美国西部尤为强劲。总的来说，这项研究为全球陆地区域提供了一个新的 FMF 数据集，有助于提高我们对时空细模式和粗模式气溶胶变化的理解。数据集可以从 https://doi.org/10.5281/zenodo.5105617 (Yan, 2021) 下载。