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A global land aerosol fine-mode fraction dataset (2001–2020) retrieved from MODIS using hybrid physical and deep learning approaches

Abstract. The aerosol fine-mode fraction (FMF) is valuable for
discriminating natural aerosols from anthropogenic ones. However, most
current satellite-based FMF products are highly unreliable over land. 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 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 (RMSE) of 0.136 and correlation coefficient of 0.68,
and the proportion of results that fell within the ±20 % expected
error (EE) envelopes was 79.15 %. Moreover, the out-of-site validation
from the Surface Radiation Budget (SURFRAD) observations revealed that the
RMSE of Phy-DL FMF is 0.144 (72.50 % of the results fell within the ±20 % EE). Phy-DL FMF showed superior performance over alternative 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-mode and coarse-mode aerosol changes. The
datasets can be downloaded from https://doi.org/10.5281/zenodo.5105617
(Yan, 2021).
