Reconstruction of High-Resolution Solar Spectral Irradiance Based on Residual Channel Attention Networks
The accurate measurement of high-resolution solar spectral irradiance (SSI) and its variations at the top of the atmosphere is crucial for solar physics, the Earth’s climate, and the in-orbit calibration of optical satellites. However, existing space-based solar spectral irradiance instruments achie...
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MDPI AG
2024-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/24/4698 |
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| author | Peng Zhang Jianwen Weng Qing Kang Jianjun Li |
| author_facet | Peng Zhang Jianwen Weng Qing Kang Jianjun Li |
| author_sort | Peng Zhang |
| collection | DOAJ |
| description | The accurate measurement of high-resolution solar spectral irradiance (SSI) and its variations at the top of the atmosphere is crucial for solar physics, the Earth’s climate, and the in-orbit calibration of optical satellites. However, existing space-based solar spectral irradiance instruments achieve high-precision SSI measurements at the cost of spectral resolution, which falls short of meeting the requirements for identifying fine solar spectral features. Therefore, this paper proposes a new method for reconstructing high-resolution solar spectral irradiance based on a residual channel attention network. This method considers the stability of SSI spectral features and employs residual channel attention blocks to enhance the expression ability of key features, achieving the high-accuracy reconstruction of spectral features. Additionally, to address the issue of excessively large output features from the residual channel attention blocks, a scaling coefficient adjustment network block is introduced to achieve the high-accuracy reconstruction of spectral absolute values. Finally, the proposed method is validated using the measured SSI dataset from SCIAMACHY on Envisat-1 and the simulated dataset from TSIS-1 SIM. The validation results show that, compared to existing scaling coefficient adjustment algorithms, the proposed method achieves single-spectrum super-resolution reconstruction without relying on external data, with a Mean Absolute Percentage Error (MAPE) of 0.0302% for the reconstructed spectra based on the dataset. The proposed method achieves higher-resolution reconstruction results while ensuring the accuracy of SSI. |
| format | Article |
| id | doaj-art-adc69cfac4e4415ab6b495954d115e8b |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-adc69cfac4e4415ab6b495954d115e8b2025-08-20T02:01:29ZengMDPI AGRemote Sensing2072-42922024-12-011624469810.3390/rs16244698Reconstruction of High-Resolution Solar Spectral Irradiance Based on Residual Channel Attention NetworksPeng Zhang0Jianwen Weng1Qing Kang2Jianjun Li3School of Physical Sciences, University of Science and Technology of China, Hefei 230026, ChinaKey Laboratory of Optical Calibration and Characterization, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, ChinaKey Laboratory of Optical Calibration and Characterization, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, ChinaKey Laboratory of Optical Calibration and Characterization, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, ChinaThe accurate measurement of high-resolution solar spectral irradiance (SSI) and its variations at the top of the atmosphere is crucial for solar physics, the Earth’s climate, and the in-orbit calibration of optical satellites. However, existing space-based solar spectral irradiance instruments achieve high-precision SSI measurements at the cost of spectral resolution, which falls short of meeting the requirements for identifying fine solar spectral features. Therefore, this paper proposes a new method for reconstructing high-resolution solar spectral irradiance based on a residual channel attention network. This method considers the stability of SSI spectral features and employs residual channel attention blocks to enhance the expression ability of key features, achieving the high-accuracy reconstruction of spectral features. Additionally, to address the issue of excessively large output features from the residual channel attention blocks, a scaling coefficient adjustment network block is introduced to achieve the high-accuracy reconstruction of spectral absolute values. Finally, the proposed method is validated using the measured SSI dataset from SCIAMACHY on Envisat-1 and the simulated dataset from TSIS-1 SIM. The validation results show that, compared to existing scaling coefficient adjustment algorithms, the proposed method achieves single-spectrum super-resolution reconstruction without relying on external data, with a Mean Absolute Percentage Error (MAPE) of 0.0302% for the reconstructed spectra based on the dataset. The proposed method achieves higher-resolution reconstruction results while ensuring the accuracy of SSI.https://www.mdpi.com/2072-4292/16/24/4698high-resolutionsolar spectral irradianceconvolutional neural networkresidual networkchannel attentionspectral super-resolution reconstruction |
| spellingShingle | Peng Zhang Jianwen Weng Qing Kang Jianjun Li Reconstruction of High-Resolution Solar Spectral Irradiance Based on Residual Channel Attention Networks Remote Sensing high-resolution solar spectral irradiance convolutional neural network residual network channel attention spectral super-resolution reconstruction |
| title | Reconstruction of High-Resolution Solar Spectral Irradiance Based on Residual Channel Attention Networks |
| title_full | Reconstruction of High-Resolution Solar Spectral Irradiance Based on Residual Channel Attention Networks |
| title_fullStr | Reconstruction of High-Resolution Solar Spectral Irradiance Based on Residual Channel Attention Networks |
| title_full_unstemmed | Reconstruction of High-Resolution Solar Spectral Irradiance Based on Residual Channel Attention Networks |
| title_short | Reconstruction of High-Resolution Solar Spectral Irradiance Based on Residual Channel Attention Networks |
| title_sort | reconstruction of high resolution solar spectral irradiance based on residual channel attention networks |
| topic | high-resolution solar spectral irradiance convolutional neural network residual network channel attention spectral super-resolution reconstruction |
| url | https://www.mdpi.com/2072-4292/16/24/4698 |
| work_keys_str_mv | AT pengzhang reconstructionofhighresolutionsolarspectralirradiancebasedonresidualchannelattentionnetworks AT jianwenweng reconstructionofhighresolutionsolarspectralirradiancebasedonresidualchannelattentionnetworks AT qingkang reconstructionofhighresolutionsolarspectralirradiancebasedonresidualchannelattentionnetworks AT jianjunli reconstructionofhighresolutionsolarspectralirradiancebasedonresidualchannelattentionnetworks |