Articles | Volume 14, issue 5
https://doi.org/10.5194/amt-14-3449-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-3449-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
12 May 2021
Research article | Highlight paper |
| 12 May 2021
| 12 May 2021
A Dark Target research aerosol algorithm for MODIS observations over eastern China: increasing coverage while maintaining accuracy at high aerosol loading
NASA Goddard Space Flight Center, Greenbelt, MD, USA
UMBC/JCET, Baltimore, MD, USA
Robert C. Levy
NASA Goddard Space Flight Center, Greenbelt, MD, USA
School of Surveying and Land Information Engineering, Henan
Polytechnic University, Jiaozuo 454003, China
Lorraine A. Remer
UMBC/JCET, Baltimore, MD, USA
Shana Mattoo
NASA Goddard Space Flight Center, Greenbelt, MD, USA
SSAI, Lanham, MD, USA
Oleg Dubovik
French National Centre for Scientific Research, Unviersity of Lille, Lille, France
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Cited
19 citations as recorded by crossref.
- Accuracy, stability, and continuity of AVHRR, SeaWiFS, MODIS, and VIIRS deep blue long-term land aerosol retrieval in Asia X. Su et al. 10.1016/j.scitotenv.2022.155048
- Investigating the Spatial and Temporal Limitations for Remote Sensing of Wildfire Smoke Using Satellite and Airborne Imagers During FIREX‐AQ Y. Shi et al. 10.1029/2023JD039085
- A Novel Algorithm of Haze Identification Based on FY3D/MERSI-II Remote Sensing Data Y. Si et al. 10.3390/rs15020438
- A New Cloud and Haze Mask Algorithm From Radiative Transfer Simulations Coupled With Machine Learning Y. Jiao et al. 10.1109/TGRS.2023.3252264
- Spatial analysis of particulate matter (PM10) using MODIS aerosol optical thickness observations and GIS over East Malaysia A. Najim et al. 10.1016/j.ejrs.2023.03.001
- Validation, inter-comparison, and usage recommendation of six latest VIIRS and MODIS aerosol products over the ocean and land on the global and regional scales X. Su et al. 10.1016/j.scitotenv.2023.163794
- Aerosol optical depth climatology from the high-resolution MAIAC product over Europe: differences between major European cities and their surrounding environments L. Di Antonio et al. 10.5194/acp-23-12455-2023
- On the influence of aerosol extinction vertical profile in modeling of night sky radiance J. Petržala 10.1016/j.jqsrt.2023.108676
- Retrieval of total and fine mode aerosol optical depth by an improved MODIS Dark Target algorithm X. Su et al. 10.1016/j.envint.2022.107343
- An Improved Aerosol Retrieval Algorithm Based on Nonlinear Surface Model From FY-3D/MERSI-II Remote Sensing Data Y. Si et al. 10.1109/TGRS.2024.3367883
- A Regional Aerosol Model for the Oceanic Area around Eastern China Based on Aerosol Robotic Network (AERONET) S. Chen et al. 10.3390/rs16061106
- A Two-Stage Machine Learning Algorithm for Retrieving Multiple Aerosol Properties Over Land: Development and Validation M. Cao et al. 10.1109/TGRS.2023.3307934
- A Generalized Aerosol Algorithm for Multi‐Spectral Satellite Measurement With Physics‐Informed Deep Learning Method J. Jiang et al. 10.1029/2023GL106806
- A high-precision aerosol retrieval algorithm for FY-3D MERSI-II images Q. Wang et al. 10.1016/j.envint.2023.107841
- The extreme forest fires in California/Oregon in 2020: Aerosol optical and physical properties and comparisons of aged versus fresh smoke T. Eck et al. 10.1016/j.atmosenv.2023.119798
- Assessment of severe aerosol events from NASA MODIS and VIIRS aerosol products for data assimilation and climate continuity A. Gumber et al. 10.5194/amt-16-2547-2023
- Geographical coverage analysis and usage suggestions of temporal averaged aerosol optical depth product from GOES-R satellite data X. Jiang et al. 10.1080/01431161.2024.2331978
- Fengyun 4A Land Aerosol Retrieval: Algorithm Development, Validation, and Comparison With Other Datasets X. Su et al. 10.1109/TGRS.2023.3330544
- Parameterizing spectral surface reflectance relationships for the Dark Target aerosol algorithm applied to a geostationary imager M. Kim et al. 10.5194/amt-17-1913-2024
19 citations as recorded by crossref.
- Accuracy, stability, and continuity of AVHRR, SeaWiFS, MODIS, and VIIRS deep blue long-term land aerosol retrieval in Asia X. Su et al. 10.1016/j.scitotenv.2022.155048
- Investigating the Spatial and Temporal Limitations for Remote Sensing of Wildfire Smoke Using Satellite and Airborne Imagers During FIREX‐AQ Y. Shi et al. 10.1029/2023JD039085
- A Novel Algorithm of Haze Identification Based on FY3D/MERSI-II Remote Sensing Data Y. Si et al. 10.3390/rs15020438
- A New Cloud and Haze Mask Algorithm From Radiative Transfer Simulations Coupled With Machine Learning Y. Jiao et al. 10.1109/TGRS.2023.3252264
- Spatial analysis of particulate matter (PM10) using MODIS aerosol optical thickness observations and GIS over East Malaysia A. Najim et al. 10.1016/j.ejrs.2023.03.001
- Validation, inter-comparison, and usage recommendation of six latest VIIRS and MODIS aerosol products over the ocean and land on the global and regional scales X. Su et al. 10.1016/j.scitotenv.2023.163794
- Aerosol optical depth climatology from the high-resolution MAIAC product over Europe: differences between major European cities and their surrounding environments L. Di Antonio et al. 10.5194/acp-23-12455-2023
- On the influence of aerosol extinction vertical profile in modeling of night sky radiance J. Petržala 10.1016/j.jqsrt.2023.108676
- Retrieval of total and fine mode aerosol optical depth by an improved MODIS Dark Target algorithm X. Su et al. 10.1016/j.envint.2022.107343
- An Improved Aerosol Retrieval Algorithm Based on Nonlinear Surface Model From FY-3D/MERSI-II Remote Sensing Data Y. Si et al. 10.1109/TGRS.2024.3367883
- A Regional Aerosol Model for the Oceanic Area around Eastern China Based on Aerosol Robotic Network (AERONET) S. Chen et al. 10.3390/rs16061106
- A Two-Stage Machine Learning Algorithm for Retrieving Multiple Aerosol Properties Over Land: Development and Validation M. Cao et al. 10.1109/TGRS.2023.3307934
- A Generalized Aerosol Algorithm for Multi‐Spectral Satellite Measurement With Physics‐Informed Deep Learning Method J. Jiang et al. 10.1029/2023GL106806
- A high-precision aerosol retrieval algorithm for FY-3D MERSI-II images Q. Wang et al. 10.1016/j.envint.2023.107841
- The extreme forest fires in California/Oregon in 2020: Aerosol optical and physical properties and comparisons of aged versus fresh smoke T. Eck et al. 10.1016/j.atmosenv.2023.119798
- Assessment of severe aerosol events from NASA MODIS and VIIRS aerosol products for data assimilation and climate continuity A. Gumber et al. 10.5194/amt-16-2547-2023
- Geographical coverage analysis and usage suggestions of temporal averaged aerosol optical depth product from GOES-R satellite data X. Jiang et al. 10.1080/01431161.2024.2331978
- Fengyun 4A Land Aerosol Retrieval: Algorithm Development, Validation, and Comparison With Other Datasets X. Su et al. 10.1109/TGRS.2023.3330544
- Parameterizing spectral surface reflectance relationships for the Dark Target aerosol algorithm applied to a geostationary imager M. Kim et al. 10.5194/amt-17-1913-2024
Latest update: 24 Apr 2024
Short summary
Due to fast industrialization and development, China has been experiencing haze pollution episodes with both high frequencies and severity over the last 3 decades. This study improves the accuracy and data coverage of measured aerosol from satellites, which help quantify, characterize, and understand the impact of the haze phenomena over the entire East Asia region.
Due to fast industrialization and development, China has been experiencing haze pollution...
