Preprints
https://doi.org/10.5194/essd-2021-326
https://doi.org/10.5194/essd-2021-326

  30 Sep 2021

30 Sep 2021

Review status: this preprint is currently under review for the journal ESSD.

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

Xing Yan1, Zhou Zang1, Zhanqing Li2, Nana Luo3, Chen Zuo1, Yize Jiang1, Dan Li1, Yushan Guo1, Wenji Zhao4, Wenzhong Shi5, and Maureen Cribb2 Xing Yan et al.
  • 1State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, 100875, China
  • 2Department of Atmospheric and Oceanic Science and ESSIC, University of Maryland, College Park, MD, 20740, USA
  • 3School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102612, China
  • 4College of Resource Environment and Tourism, Capital Normal University, Beijing, China
  • 5Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China

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).

Xing Yan et al.

Status: open (until 25 Nov 2021)

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  • RC1: 'Comment on essd-2021-326', Anonymous Referee #1, 14 Oct 2021 reply

Xing Yan et al.

Data sets

Physical and deep learning retrieved fine mode fraction (Phy-DL FMF) Xing Yan https://doi.org/10.5281/zenodo.5105617

Xing Yan et al.

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Short summary
This study 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 was extensively evaluated against ground-truth AERONET data and tested on a global scale against conventional satellite-based FMF products to demonstrate its superiority in accuracy.