A global land aerosol fine-mode fraction dataset (2001–2020) retrieved from MODIS using hybrid physical and deep learning approaches
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 a...
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ftcopernicus:oai:publications.copernicus.org:essdd98012 2023-05-15T13:06:33+02:00 A global land aerosol fine-mode fraction dataset (2001–2020) retrieved from MODIS using hybrid physical and deep learning approaches Yan, Xing Zang, Zhou Li, Zhanqing Luo, Nana Zuo, Chen Jiang, Yize Li, Dan Guo, Yushan Zhao, Wenji Shi, Wenzhong Cribb, Maureen 2021-09-30 application/pdf https://doi.org/10.5194/essd-2021-326 https://essd.copernicus.org/preprints/essd-2021-326/ eng eng doi:10.5194/essd-2021-326 https://essd.copernicus.org/preprints/essd-2021-326/ eISSN: 1866-3516 Text 2021 ftcopernicus https://doi.org/10.5194/essd-2021-326 2021-10-04T16:22:28Z 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). Text Aerosol Robotic Network Copernicus Publications: E-Journals |
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Copernicus Publications: E-Journals |
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ftcopernicus |
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English |
description |
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). |
format |
Text |
author |
Yan, Xing Zang, Zhou Li, Zhanqing Luo, Nana Zuo, Chen Jiang, Yize Li, Dan Guo, Yushan Zhao, Wenji Shi, Wenzhong Cribb, Maureen |
spellingShingle |
Yan, Xing Zang, Zhou Li, Zhanqing Luo, Nana Zuo, Chen Jiang, Yize Li, Dan Guo, Yushan Zhao, Wenji Shi, Wenzhong Cribb, Maureen A global land aerosol fine-mode fraction dataset (2001–2020) retrieved from MODIS using hybrid physical and deep learning approaches |
author_facet |
Yan, Xing Zang, Zhou Li, Zhanqing Luo, Nana Zuo, Chen Jiang, Yize Li, Dan Guo, Yushan Zhao, Wenji Shi, Wenzhong Cribb, Maureen |
author_sort |
Yan, Xing |
title |
A global land aerosol fine-mode fraction dataset (2001–2020) retrieved from MODIS using hybrid physical and deep learning approaches |
title_short |
A global land aerosol fine-mode fraction dataset (2001–2020) retrieved from MODIS using hybrid physical and deep learning approaches |
title_full |
A global land aerosol fine-mode fraction dataset (2001–2020) retrieved from MODIS using hybrid physical and deep learning approaches |
title_fullStr |
A global land aerosol fine-mode fraction dataset (2001–2020) retrieved from MODIS using hybrid physical and deep learning approaches |
title_full_unstemmed |
A global land aerosol fine-mode fraction dataset (2001–2020) retrieved from MODIS using hybrid physical and deep learning approaches |
title_sort |
global land aerosol fine-mode fraction dataset (2001–2020) retrieved from modis using hybrid physical and deep learning approaches |
publishDate |
2021 |
url |
https://doi.org/10.5194/essd-2021-326 https://essd.copernicus.org/preprints/essd-2021-326/ |
genre |
Aerosol Robotic Network |
genre_facet |
Aerosol Robotic Network |
op_source |
eISSN: 1866-3516 |
op_relation |
doi:10.5194/essd-2021-326 https://essd.copernicus.org/preprints/essd-2021-326/ |
op_doi |
https://doi.org/10.5194/essd-2021-326 |
_version_ |
1766010662984613888 |