Articles | Volume 14, issue 3
https://doi.org/10.5194/essd-14-1193-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-14-1193-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
16 Mar 2022
Data description paper |
| 16 Mar 2022
| 16 Mar 2022
A global land aerosol fine-mode fraction dataset (2001–2020) retrieved from MODIS using hybrid physical and deep learning approaches
State Key Laboratory of Remote Sensing Science, College of Global
Change and Earth System Science, Beijing Normal University, Beijing, 100875,
China
Zhou Zang
State Key Laboratory of Remote Sensing Science, College of Global
Change and Earth System Science, Beijing Normal University, Beijing, 100875,
China
Department of Atmospheric and Oceanic Science and ESSIC, University of
Maryland, College Park, MD, 20740, USA
Nana Luo
School of Geomatics and Urban Spatial Informatics, Beijing University
of Civil Engineering and Architecture, Beijing 102612, China
Chen Zuo
State Key Laboratory of Remote Sensing Science, College of Global
Change and Earth System Science, Beijing Normal University, Beijing, 100875,
China
Yize Jiang
State Key Laboratory of Remote Sensing Science, College of Global
Change and Earth System Science, Beijing Normal University, Beijing, 100875,
China
Dan Li
State Key Laboratory of Remote Sensing Science, College of Global
Change and Earth System Science, Beijing Normal University, Beijing, 100875,
China
Yushan Guo
State Key Laboratory of Remote Sensing Science, College of Global
Change and Earth System Science, Beijing Normal University, Beijing, 100875,
China
Wenji Zhao
College of Resource Environment and Tourism, Capital Normal
University, Beijing, China
Wenzhong Shi
Department of Land Surveying and Geo-Informatics, The Hong Kong
Polytechnic University, Hong Kong, China
Maureen Cribb
Department of Atmospheric and Oceanic Science and ESSIC, University of
Maryland, College Park, MD, 20740, USA
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Cited
17 citations as recorded by crossref.
- Estimation of pan-European, daily total, fine-mode and coarse-mode Aerosol Optical Depth at 0.1° resolution to facilitate air quality assessments Z. Chen et al. 10.1016/j.scitotenv.2024.170593
- Deep Learning with Pretrained Framework Unleashes the Power of Satellite-Based Global Fine-Mode Aerosol Retrieval X. Yan et al. 10.1021/acs.est.4c02701
- Development of a hybrid algorithm for the simultaneous retrieval of aerosol optical thickness and fine-mode fraction from multispectral satellite observation combining radiative transfer and transfer learning approaches C. Tang et al. 10.1016/j.rse.2025.114619
- A data-driven method for aerosol FMF retrieval over land using single-view polarization satellite data Z. Shi et al. 10.1016/j.atmosenv.2025.121083
- Analysis of Long-Term Aerosol Optical Properties Combining AERONET Sunphotometer and Satellite-Based Observations in Hong Kong X. Yu et al. 10.3390/rs14205220
- Full-coverage 250 m monthly aerosol optical depth dataset (2000–2019) amended with environmental covariates by an ensemble machine learning model over arid and semi-arid areas, NW China X. Chen et al. 10.5194/essd-14-5233-2022
- Research on the distribution and influencing factors of fine mode aerosol optical depth (AODf) in China H. Xu et al. 10.1016/j.atmosenv.2024.120721
- CALIPSO-based aerosol extinction profile estimation from MODIS and MERRA-2 data using a hybrid model of Transformer and CNN Y. Zhen et al. 10.1016/j.scitotenv.2024.176423
- Spectral Deconvolution Algorithm for Global Fine-Mode Aerosol Retrieval in the 1990s Using Dual-Angle Satellite Aerosol Data X. Yan et al. 10.1109/TGRS.2023.3332316
- An optimized semi-empirical physical approach for satellite-based PM2.5 retrieval: embedding machine learning to simulate complex physical parameters C. Jin et al. 10.5194/gmd-16-4137-2023
- Exploring Global Land Coarse-Mode Aerosol Changes from 2001–2021 Using a New Spatiotemporal Coaction Deep-Learning Model Z. Zang et al. 10.1021/acs.est.3c07982
- Unveiling global land fine- and coarse-mode aerosol dynamics from 2005 to 2020 using enhanced satellite-based monthly inversion data N. Luo et al. 10.1016/j.envpol.2024.123838
- Explainable and spatial dependence deep learning model for satellite-based O3 monitoring in China N. Luo et al. 10.1016/j.atmosenv.2022.119370
- Long-term variation of aerosol optical properties associated with aerosol types over East Asia using AERONET and satellite (VIIRS, OMI) data (2012–2019) S. Eom et al. 10.1016/j.atmosres.2022.106457
- Emission Reductions Significantly Reduce the Hemispheric Contrast in Cloud Droplet Number Concentration in Recent Two Decades Y. Cao et al. 10.1029/2022JD037417
- Global 500 m seamless dataset (2000–2022) of land surface reflectance generated from MODIS products X. Liang et al. 10.5194/essd-16-177-2024
- Deep learning in airborne particulate matter sensing: a review J. Grant-Jacob & B. Mills 10.1088/2399-6528/aca45e
17 citations as recorded by crossref.
- Estimation of pan-European, daily total, fine-mode and coarse-mode Aerosol Optical Depth at 0.1° resolution to facilitate air quality assessments Z. Chen et al. 10.1016/j.scitotenv.2024.170593
- Deep Learning with Pretrained Framework Unleashes the Power of Satellite-Based Global Fine-Mode Aerosol Retrieval X. Yan et al. 10.1021/acs.est.4c02701
- Development of a hybrid algorithm for the simultaneous retrieval of aerosol optical thickness and fine-mode fraction from multispectral satellite observation combining radiative transfer and transfer learning approaches C. Tang et al. 10.1016/j.rse.2025.114619
- A data-driven method for aerosol FMF retrieval over land using single-view polarization satellite data Z. Shi et al. 10.1016/j.atmosenv.2025.121083
- Analysis of Long-Term Aerosol Optical Properties Combining AERONET Sunphotometer and Satellite-Based Observations in Hong Kong X. Yu et al. 10.3390/rs14205220
- Full-coverage 250 m monthly aerosol optical depth dataset (2000–2019) amended with environmental covariates by an ensemble machine learning model over arid and semi-arid areas, NW China X. Chen et al. 10.5194/essd-14-5233-2022
- Research on the distribution and influencing factors of fine mode aerosol optical depth (AODf) in China H. Xu et al. 10.1016/j.atmosenv.2024.120721
- CALIPSO-based aerosol extinction profile estimation from MODIS and MERRA-2 data using a hybrid model of Transformer and CNN Y. Zhen et al. 10.1016/j.scitotenv.2024.176423
- Spectral Deconvolution Algorithm for Global Fine-Mode Aerosol Retrieval in the 1990s Using Dual-Angle Satellite Aerosol Data X. Yan et al. 10.1109/TGRS.2023.3332316
- An optimized semi-empirical physical approach for satellite-based PM2.5 retrieval: embedding machine learning to simulate complex physical parameters C. Jin et al. 10.5194/gmd-16-4137-2023
- Exploring Global Land Coarse-Mode Aerosol Changes from 2001–2021 Using a New Spatiotemporal Coaction Deep-Learning Model Z. Zang et al. 10.1021/acs.est.3c07982
- Unveiling global land fine- and coarse-mode aerosol dynamics from 2005 to 2020 using enhanced satellite-based monthly inversion data N. Luo et al. 10.1016/j.envpol.2024.123838
- Explainable and spatial dependence deep learning model for satellite-based O3 monitoring in China N. Luo et al. 10.1016/j.atmosenv.2022.119370
- Long-term variation of aerosol optical properties associated with aerosol types over East Asia using AERONET and satellite (VIIRS, OMI) data (2012–2019) S. Eom et al. 10.1016/j.atmosres.2022.106457
- Emission Reductions Significantly Reduce the Hemispheric Contrast in Cloud Droplet Number Concentration in Recent Two Decades Y. Cao et al. 10.1029/2022JD037417
- Global 500 m seamless dataset (2000–2022) of land surface reflectance generated from MODIS products X. Liang et al. 10.5194/essd-16-177-2024
- Deep learning in airborne particulate matter sensing: a review J. Grant-Jacob & B. Mills 10.1088/2399-6528/aca45e
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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.
This study developed a new satellite-based global land daily FMF dataset (Phy-DL FMF) by...
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