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Remote Sensing
Volume 18
Issue 2
10.3390/rs18020311
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Trans-AODnet for Aerosol Optical Depth Retrieval and Atmospheric Correction of Moderate to High-Spatial-Resolution Satellite Imagery

by
He Cai
1,
Bo Zhong
2,
Huilin Liu
1,*,
Yao Li
3,
Bailin Du
2,4,
Yang Qiao
2,4,
Xiaoya Wang
2,4,
Shanlong Wu
2,
Junjun Wu
2 and
Qinhuo Liu
2
1
College of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
2
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100081, China
3
College of Artificial Intelligence, Shenyang University of Technology, Shenyang 110870, China
4
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2026, 18(2), 311; https://doi.org/10.3390/rs18020311
Submission received: 29 November 2025 / Revised: 13 January 2026 / Accepted: 14 January 2026 / Published: 16 January 2026
(This article belongs to the Section Atmospheric Remote Sensing)
Versions Notes

Abstract

High accuracy and time synchronous aerosol optical depth (AOD) is essential for atmospheric correction (AC) of medium and high spatial resolution (MHSR) remote sensing data. However, existing high-resolution AOD retrieval methods often rely on sparsely distributed ground-based measurements, which limits their capacity to resolve fine-scale spatial heterogeneity and consequently constrains retrieval performance. To address this limitation, we propose a framework that takes GF-1 top-of-atmosphere (TOA) reflectance as input, where the model is first pre-trained using MCD19A2 as Pseudo-labels, with high-confidence samples weighted according to their spatial consistency and temporal stability, and then fine-tuned using Aerosol Robotic Network (AERONET) observations. This approach enables improved retrieval accuracy while better capturing surface variability. Validation across multiple regions demonstrates strong agreement with AOD measurements, achieving the correlation coefficient (R) of 0.941 and RMSE of 0.113. Compared to models without pretraining, the proportion of AOD retrievals within EE improves by 13%. While applied to AC, the corrected surface reflectance also shows strong consistency with in situ observations (R > 0.93, RMSE < 0.04). The proposed Trans-AODnet significantly enhances the accuracy and reliability of AOD inputs for AC of high-resolution wide-field sensors (e.g., GF-WFV), offering robust support for regional environmental monitoring and exhibiting strong potential for broader remote sensing applications.
Keywords: AERONET; satellite-based AOD retrieval; aerosol optical depth; deep learning; transfer learning AERONET; satellite-based AOD retrieval; aerosol optical depth; deep learning; transfer learning

Share and Cite

MDPI and ACS Style

Cai, H.; Zhong, B.; Liu, H.; Li, Y.; Du, B.; Qiao, Y.; Wang, X.; Wu, S.; Wu, J.; Liu, Q. Trans-AODnet for Aerosol Optical Depth Retrieval and Atmospheric Correction of Moderate to High-Spatial-Resolution Satellite Imagery. Remote Sens. 2026, 18, 311. https://doi.org/10.3390/rs18020311

AMA Style

Cai H, Zhong B, Liu H, Li Y, Du B, Qiao Y, Wang X, Wu S, Wu J, Liu Q. Trans-AODnet for Aerosol Optical Depth Retrieval and Atmospheric Correction of Moderate to High-Spatial-Resolution Satellite Imagery. Remote Sensing. 2026; 18(2):311. https://doi.org/10.3390/rs18020311

Chicago/Turabian Style

Cai, He, Bo Zhong, Huilin Liu, Yao Li, Bailin Du, Yang Qiao, Xiaoya Wang, Shanlong Wu, Junjun Wu, and Qinhuo Liu. 2026. "Trans-AODnet for Aerosol Optical Depth Retrieval and Atmospheric Correction of Moderate to High-Spatial-Resolution Satellite Imagery" Remote Sensing 18, no. 2: 311. https://doi.org/10.3390/rs18020311

APA Style

Cai, H., Zhong, B., Liu, H., Li, Y., Du, B., Qiao, Y., Wang, X., Wu, S., Wu, J., & Liu, Q. (2026). Trans-AODnet for Aerosol Optical Depth Retrieval and Atmospheric Correction of Moderate to High-Spatial-Resolution Satellite Imagery. Remote Sensing, 18(2), 311. https://doi.org/10.3390/rs18020311

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