Enhancing global aerosol retrieval from satellite data via deep learning with mutual information estimation
Satellite-based data can provide continuous aerosol observations but suffer from significant uncertainties across various regions. Transfer learning improves model generalization, yet its application in atmospheric research remains limited. Here, we introduce an innovative framework for retrieving g...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-05-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843225001815 |
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| author | Xiaohu Sun Yong Xue Lin Sun |
| author_facet | Xiaohu Sun Yong Xue Lin Sun |
| author_sort | Xiaohu Sun |
| collection | DOAJ |
| description | Satellite-based data can provide continuous aerosol observations but suffer from significant uncertainties across various regions. Transfer learning improves model generalization, yet its application in atmospheric research remains limited. Here, we introduce an innovative framework for retrieving global aerosol optical depth (AOD) which named the Aerosol domain-Adaptive Network (AAdaN). The framework utilizes a neural network to estimate mutual information, and aligns spatial covariate shift via a transfer loss term. Then, we assess the retrieval potential in unknown scenarios using independent land cover type, and the proposed model demonstrates satisfactory results. The cross-validation shows strong agreement with in-situ measurements, both in sample-based and site-based evaluations. Specifically, the site-based ten-fold cross-validation of our AOD retrievals indicate that all accuracy metrics are satisfactory, with a Pearson correlation of 0.766 and a Root-Mean-Square Error of 0.118, and that about 76.05 % of the retrievals meet the expected error criteria [±(0.05 + 20 %)]. Additionally, the proposed AAdaN achieves stable, high-accuracy aerosol retrievals across various surface and atmospheric conditions, and can generate spatially continuous AOD distributions. This study significantly improves spatial generalization and offers valuable insights for future model development. |
| format | Article |
| id | doaj-art-03d1aa224c134c4ba4ccd3d41b684baa |
| institution | OA Journals |
| issn | 1569-8432 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-03d1aa224c134c4ba4ccd3d41b684baa2025-08-20T02:31:22ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-05-0113910453410.1016/j.jag.2025.104534Enhancing global aerosol retrieval from satellite data via deep learning with mutual information estimationXiaohu Sun0Yong Xue1Lin Sun2School of Atmospheric Physics, Nanjing University of Information Science & Technology, 210044 Nanjing, ChinaSchool of Emergency Management, Nanjing University of Information Science & Technology, Nanjing 210044, China; Corresponding author.College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, Shandong 266590, ChinaSatellite-based data can provide continuous aerosol observations but suffer from significant uncertainties across various regions. Transfer learning improves model generalization, yet its application in atmospheric research remains limited. Here, we introduce an innovative framework for retrieving global aerosol optical depth (AOD) which named the Aerosol domain-Adaptive Network (AAdaN). The framework utilizes a neural network to estimate mutual information, and aligns spatial covariate shift via a transfer loss term. Then, we assess the retrieval potential in unknown scenarios using independent land cover type, and the proposed model demonstrates satisfactory results. The cross-validation shows strong agreement with in-situ measurements, both in sample-based and site-based evaluations. Specifically, the site-based ten-fold cross-validation of our AOD retrievals indicate that all accuracy metrics are satisfactory, with a Pearson correlation of 0.766 and a Root-Mean-Square Error of 0.118, and that about 76.05 % of the retrievals meet the expected error criteria [±(0.05 + 20 %)]. Additionally, the proposed AAdaN achieves stable, high-accuracy aerosol retrievals across various surface and atmospheric conditions, and can generate spatially continuous AOD distributions. This study significantly improves spatial generalization and offers valuable insights for future model development.http://www.sciencedirect.com/science/article/pii/S1569843225001815Aerosol optical depthTransfer learningMutual informationModerate resolution imaging spectroradiometer |
| spellingShingle | Xiaohu Sun Yong Xue Lin Sun Enhancing global aerosol retrieval from satellite data via deep learning with mutual information estimation International Journal of Applied Earth Observations and Geoinformation Aerosol optical depth Transfer learning Mutual information Moderate resolution imaging spectroradiometer |
| title | Enhancing global aerosol retrieval from satellite data via deep learning with mutual information estimation |
| title_full | Enhancing global aerosol retrieval from satellite data via deep learning with mutual information estimation |
| title_fullStr | Enhancing global aerosol retrieval from satellite data via deep learning with mutual information estimation |
| title_full_unstemmed | Enhancing global aerosol retrieval from satellite data via deep learning with mutual information estimation |
| title_short | Enhancing global aerosol retrieval from satellite data via deep learning with mutual information estimation |
| title_sort | enhancing global aerosol retrieval from satellite data via deep learning with mutual information estimation |
| topic | Aerosol optical depth Transfer learning Mutual information Moderate resolution imaging spectroradiometer |
| url | http://www.sciencedirect.com/science/article/pii/S1569843225001815 |
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