An atmospheric correction method for Himawari-8 imagery based on a multi-layer stacking algorithm
The effective extraction of water-leaving reflectance using atmospheric correction (AC) algorithms is essential for accurately retrieving ocean color parameters. However, existing AC approaches designed for specific water types often struggle with the varying optical properties of open and coastal w...
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Elsevier
2025-03-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S157495412500010X |
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| author | Menghui Wang Donglin Fan Hongchang He You Zeng Bolin Fu Tianlong Liang Xinyue Zhang Wenhan Hu |
| author_facet | Menghui Wang Donglin Fan Hongchang He You Zeng Bolin Fu Tianlong Liang Xinyue Zhang Wenhan Hu |
| author_sort | Menghui Wang |
| collection | DOAJ |
| description | The effective extraction of water-leaving reflectance using atmospheric correction (AC) algorithms is essential for accurately retrieving ocean color parameters. However, existing AC approaches designed for specific water types often struggle with the varying optical properties of open and coastal waters. This study proposes an efficient multi-layer stacking method for AC (MSM AC) that is suitable for both clear and turbid waters. The implementation and validation of the method were conducted using Himawari-8 imagery. To address the lack of training data, 10,000 Rayleigh-corrected reflectance samples were synthesized for six Himawari-8 bands, using simulated water-leaving, which cover different optically complex water properties through a radiative transfer, and aerosol reflectance data under different geometrical conditions. Following the principle of heterogeneous integration, various meta-learners were preselected for model training, and the preliminary model was fine-tuned using in situ data. A weighted integration strategy was then employed to develop an MSM AC tailored to Himawari-8 image data. For comparative analysis, a near-infrared–shortwave infrared AC method and a general machine learning AC method were also implemented. Model evaluation and validation were performed using a test subset of simulated data and in-situ datasets. Validation results indicate that the MSM AC exhibits strong performance in the validation bands (470 nm, 510 nm, and 640 nm) on the in-situ dataset, with R2 values of 0.64, 0.91, and 0.82 and root-mean-square logarithmic deviation (RMSLD) values of 0.007 sr−1, 0.004 sr−1, and 0.005 sr−1, respectively. Additionally, water bodies with varying optical complexities were simulated by restructuring the ocean color component content in the simulated data. The correction performances of MSM and comparative algorithms were evaluated using the median absolute error (MedAE) between the predicted and simulated water-leaving reflectance data. The results demonstrate that the MSM AC exhibits strong adaptability to the optical characteristics of complex water bodies, achieving low MedAE values across all types of optically complex water bodies. Furthermore, the spatial mapping of MSM-corrected reflectance exhibits a high agreement with the MODIS and Himawari-8 Advanced Himawari Imagery water-leaving reflectance products, further confirming its superiority. In conclusion, the algorithm developed in this study enables consistent AC for both open and near-shore waters, offering an ideal solution for the AC of Himawari-8 satellite images. |
| format | Article |
| id | doaj-art-f935366e3c0d44d3b22ba4396a4bcb04 |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
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| series | Ecological Informatics |
| spelling | doaj-art-f935366e3c0d44d3b22ba4396a4bcb042025-01-19T06:24:46ZengElsevierEcological Informatics1574-95412025-03-0185103001An atmospheric correction method for Himawari-8 imagery based on a multi-layer stacking algorithmMenghui Wang0Donglin Fan1Hongchang He2You Zeng3Bolin Fu4Tianlong Liang5Xinyue Zhang6Wenhan Hu7School of Surveying, Mapping and Geographic Information, Guilin University of Technology, Guilin, ChinaSchool of Surveying, Mapping and Geographic Information, Guilin University of Technology, Guilin, China; Key Laboratory of Spatiotemporal Big Data Perception Services, Guilin University of Technology, Guilin, China; Corresponding author at: No. 319, Yanshan Street, Yanshan Town, Yanshan District, Guangxi Zhuang Autonomous Region, Guilin, China.School of Surveying, Mapping and Geographic Information, Guilin University of Technology, Guilin, China; Key Laboratory of Spatiotemporal Big Data Perception Services, Guilin University of Technology, Guilin, ChinaSchool of Information Engineering, Guilin Institute of Information Technology, Guilin, ChinaSchool of Surveying, Mapping and Geographic Information, Guilin University of Technology, Guilin, China; Key Laboratory of Spatiotemporal Big Data Perception Services, Guilin University of Technology, Guilin, ChinaSchool of Surveying, Mapping and Geographic Information, Guilin University of Technology, Guilin, ChinaSchool of Surveying, Mapping and Geographic Information, Guilin University of Technology, Guilin, ChinaSchool of Surveying, Mapping and Geographic Information, Guilin University of Technology, Guilin, ChinaThe effective extraction of water-leaving reflectance using atmospheric correction (AC) algorithms is essential for accurately retrieving ocean color parameters. However, existing AC approaches designed for specific water types often struggle with the varying optical properties of open and coastal waters. This study proposes an efficient multi-layer stacking method for AC (MSM AC) that is suitable for both clear and turbid waters. The implementation and validation of the method were conducted using Himawari-8 imagery. To address the lack of training data, 10,000 Rayleigh-corrected reflectance samples were synthesized for six Himawari-8 bands, using simulated water-leaving, which cover different optically complex water properties through a radiative transfer, and aerosol reflectance data under different geometrical conditions. Following the principle of heterogeneous integration, various meta-learners were preselected for model training, and the preliminary model was fine-tuned using in situ data. A weighted integration strategy was then employed to develop an MSM AC tailored to Himawari-8 image data. For comparative analysis, a near-infrared–shortwave infrared AC method and a general machine learning AC method were also implemented. Model evaluation and validation were performed using a test subset of simulated data and in-situ datasets. Validation results indicate that the MSM AC exhibits strong performance in the validation bands (470 nm, 510 nm, and 640 nm) on the in-situ dataset, with R2 values of 0.64, 0.91, and 0.82 and root-mean-square logarithmic deviation (RMSLD) values of 0.007 sr−1, 0.004 sr−1, and 0.005 sr−1, respectively. Additionally, water bodies with varying optical complexities were simulated by restructuring the ocean color component content in the simulated data. The correction performances of MSM and comparative algorithms were evaluated using the median absolute error (MedAE) between the predicted and simulated water-leaving reflectance data. The results demonstrate that the MSM AC exhibits strong adaptability to the optical characteristics of complex water bodies, achieving low MedAE values across all types of optically complex water bodies. Furthermore, the spatial mapping of MSM-corrected reflectance exhibits a high agreement with the MODIS and Himawari-8 Advanced Himawari Imagery water-leaving reflectance products, further confirming its superiority. In conclusion, the algorithm developed in this study enables consistent AC for both open and near-shore waters, offering an ideal solution for the AC of Himawari-8 satellite images.http://www.sciencedirect.com/science/article/pii/S157495412500010XAtmospheric correction algorithmsHimawari-8 imageryStacking algorithmOptical water typesOcean color remote sensing |
| spellingShingle | Menghui Wang Donglin Fan Hongchang He You Zeng Bolin Fu Tianlong Liang Xinyue Zhang Wenhan Hu An atmospheric correction method for Himawari-8 imagery based on a multi-layer stacking algorithm Ecological Informatics Atmospheric correction algorithms Himawari-8 imagery Stacking algorithm Optical water types Ocean color remote sensing |
| title | An atmospheric correction method for Himawari-8 imagery based on a multi-layer stacking algorithm |
| title_full | An atmospheric correction method for Himawari-8 imagery based on a multi-layer stacking algorithm |
| title_fullStr | An atmospheric correction method for Himawari-8 imagery based on a multi-layer stacking algorithm |
| title_full_unstemmed | An atmospheric correction method for Himawari-8 imagery based on a multi-layer stacking algorithm |
| title_short | An atmospheric correction method for Himawari-8 imagery based on a multi-layer stacking algorithm |
| title_sort | atmospheric correction method for himawari 8 imagery based on a multi layer stacking algorithm |
| topic | Atmospheric correction algorithms Himawari-8 imagery Stacking algorithm Optical water types Ocean color remote sensing |
| url | http://www.sciencedirect.com/science/article/pii/S157495412500010X |
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