A global land aerosol fine-mode fraction dataset (2001–2020) retrieved from MODIS using hybrid physical and deep learning approaches
<p>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 synergizin...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2022-03-01
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| Series: | Earth System Science Data |
| Online Access: | https://essd.copernicus.org/articles/14/1193/2022/essd-14-1193-2022.pdf |
| _version_ | 1818335230958764032 |
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| author | X. Yan Z. Zang Z. Li N. Luo C. Zuo Y. Jiang D. Li Y. Guo W. Zhao W. Shi M. Cribb |
| author_facet | X. Yan Z. Zang Z. Li N. Luo C. Zuo Y. Jiang D. Li Y. Guo W. Zhao W. Shi M. Cribb |
| author_sort | X. Yan |
| collection | DOAJ |
| description | <p>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<span class="inline-formula"><sup>∘</sup></span> 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 <span class="inline-formula">±</span>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 <span class="inline-formula">±</span>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 <a href="https://doi.org/10.5281/zenodo.5105617">https://doi.org/10.5281/zenodo.5105617</a>
(Yan, 2021).</p> |
| first_indexed | 2024-12-13T14:20:08Z |
| format | Article |
| id | doaj.art-b56d334ea6e342929a514e5f2bbafae1 |
| institution | Directory Open Access Journal |
| issn | 1866-3508 1866-3516 |
| language | English |
| last_indexed | 2024-12-13T14:20:08Z |
| publishDate | 2022-03-01 |
| publisher | Copernicus Publications |
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| series | Earth System Science Data |
| spelling | doaj.art-b56d334ea6e342929a514e5f2bbafae12022-12-21T23:42:07ZengCopernicus PublicationsEarth System Science Data1866-35081866-35162022-03-01141193121310.5194/essd-14-1193-2022A global land aerosol fine-mode fraction dataset (2001–2020) retrieved from MODIS using hybrid physical and deep learning approachesX. Yan0Z. Zang1Z. Li2N. Luo3C. Zuo4Y. Jiang5D. Li6Y. Guo7W. Zhao8W. Shi9M. Cribb10State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, 100875, ChinaState Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, 100875, ChinaDepartment of Atmospheric and Oceanic Science and ESSIC, University of Maryland, College Park, MD, 20740, USASchool of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102612, ChinaState Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, 100875, ChinaState Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, 100875, ChinaState Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, 100875, ChinaState Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, 100875, ChinaCollege of Resource Environment and Tourism, Capital Normal University, Beijing, ChinaDepartment of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, ChinaDepartment of Atmospheric and Oceanic Science and ESSIC, University of Maryland, College Park, MD, 20740, USA<p>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<span class="inline-formula"><sup>∘</sup></span> 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 <span class="inline-formula">±</span>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 <span class="inline-formula">±</span>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 <a href="https://doi.org/10.5281/zenodo.5105617">https://doi.org/10.5281/zenodo.5105617</a> (Yan, 2021).</p>https://essd.copernicus.org/articles/14/1193/2022/essd-14-1193-2022.pdf |
| spellingShingle | X. Yan Z. Zang Z. Li N. Luo C. Zuo Y. Jiang D. Li Y. Guo W. Zhao W. Shi M. Cribb A global land aerosol fine-mode fraction dataset (2001–2020) retrieved from MODIS using hybrid physical and deep learning approaches Earth System Science Data |
| title | 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_short | 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 |
| url | https://essd.copernicus.org/articles/14/1193/2022/essd-14-1193-2022.pdf |
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