Improving AOD Algorithm Evaluation: A Spatial Matching Method for Minimizing Quality Control Bias
Satellite-derived aerosol optical depth (AOD) products from MODIS and VIIRS sensors are vital for monitoring global aerosol distributions. However, inconsistencies in quality control algorithms and spatial resolution introduce errors that complicate validation processes and reduce the accuracy of sa...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/7/1235 |
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| author | Bailin Du Bo Zhong He Cai Shanlong Wu Yang Qiao Xiaoya Wang Aixia Yang Junjun Wu Qinhuo Liu Jinxiong Jiang Haizhen Zhang |
| author_facet | Bailin Du Bo Zhong He Cai Shanlong Wu Yang Qiao Xiaoya Wang Aixia Yang Junjun Wu Qinhuo Liu Jinxiong Jiang Haizhen Zhang |
| author_sort | Bailin Du |
| collection | DOAJ |
| description | Satellite-derived aerosol optical depth (AOD) products from MODIS and VIIRS sensors are vital for monitoring global aerosol distributions. However, inconsistencies in quality control algorithms and spatial resolution introduce errors that complicate validation processes and reduce the accuracy of satellite-to-ground comparisons. This study proposes the “optimal” spatial matching method to minimize these errors and enable a more accurate evaluation of retrieval algorithm performance. Using AERONET ground observations from 2012 to 2021, MODIS and VIIRS AOD products were systematically validated with three spatial matching methods—“direct”, “average”, and “optimal”. Results demonstrate that the “optimal” method consistently outperformed the other methods by selecting pixel values. The study highlights significant quality control disparities across AOD products and demonstrates that high-resolution products, with purer pixels, achieve superior accuracy under the “optimal” method. These insights provide valuable guidance for optimizing dataset applications and refining aerosol retrieval algorithms. |
| format | Article |
| id | doaj-art-9ccd827c381a4ff2be922f5bed61d5dc |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-9ccd827c381a4ff2be922f5bed61d5dc2025-08-20T02:09:17ZengMDPI AGRemote Sensing2072-42922025-03-01177123510.3390/rs17071235Improving AOD Algorithm Evaluation: A Spatial Matching Method for Minimizing Quality Control BiasBailin Du0Bo Zhong1He Cai2Shanlong Wu3Yang Qiao4Xiaoya Wang5Aixia Yang6Junjun Wu7Qinhuo Liu8Jinxiong Jiang9Haizhen Zhang10Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaSpace Star Technology Co., Ltd., Beijing 101399, ChinaKey Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center (National Center for Space Weather), China Meteorological Administration, Beijing 100081, ChinaSatellite-derived aerosol optical depth (AOD) products from MODIS and VIIRS sensors are vital for monitoring global aerosol distributions. However, inconsistencies in quality control algorithms and spatial resolution introduce errors that complicate validation processes and reduce the accuracy of satellite-to-ground comparisons. This study proposes the “optimal” spatial matching method to minimize these errors and enable a more accurate evaluation of retrieval algorithm performance. Using AERONET ground observations from 2012 to 2021, MODIS and VIIRS AOD products were systematically validated with three spatial matching methods—“direct”, “average”, and “optimal”. Results demonstrate that the “optimal” method consistently outperformed the other methods by selecting pixel values. The study highlights significant quality control disparities across AOD products and demonstrates that high-resolution products, with purer pixels, achieve superior accuracy under the “optimal” method. These insights provide valuable guidance for optimizing dataset applications and refining aerosol retrieval algorithms.https://www.mdpi.com/2072-4292/17/7/1235aerosol optical depthproduct validationspatial matching methodquality control |
| spellingShingle | Bailin Du Bo Zhong He Cai Shanlong Wu Yang Qiao Xiaoya Wang Aixia Yang Junjun Wu Qinhuo Liu Jinxiong Jiang Haizhen Zhang Improving AOD Algorithm Evaluation: A Spatial Matching Method for Minimizing Quality Control Bias Remote Sensing aerosol optical depth product validation spatial matching method quality control |
| title | Improving AOD Algorithm Evaluation: A Spatial Matching Method for Minimizing Quality Control Bias |
| title_full | Improving AOD Algorithm Evaluation: A Spatial Matching Method for Minimizing Quality Control Bias |
| title_fullStr | Improving AOD Algorithm Evaluation: A Spatial Matching Method for Minimizing Quality Control Bias |
| title_full_unstemmed | Improving AOD Algorithm Evaluation: A Spatial Matching Method for Minimizing Quality Control Bias |
| title_short | Improving AOD Algorithm Evaluation: A Spatial Matching Method for Minimizing Quality Control Bias |
| title_sort | improving aod algorithm evaluation a spatial matching method for minimizing quality control bias |
| topic | aerosol optical depth product validation spatial matching method quality control |
| url | https://www.mdpi.com/2072-4292/17/7/1235 |
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