Cluster Low-Streams Regression Method for Hyperspectral Radiative Transfer Computations: Cases of O<sub>2</sub> A- and CO<sub>2</sub> Bands
Current atmospheric composition sensors provide a large amount of high spectral resolution data. The accurate processing of this data employs time-consuming line-by-line (LBL) radiative transfer models (RTMs). In this paper, we describe a method to accelerate hyperspectral radiative transfer models...
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MDPI AG
2020-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/8/1250 |
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| author | Ana del Águila Dmitry S. Efremenko Víctor Molina García Michael Yu. Kataev |
| author_facet | Ana del Águila Dmitry S. Efremenko Víctor Molina García Michael Yu. Kataev |
| author_sort | Ana del Águila |
| collection | DOAJ |
| description | Current atmospheric composition sensors provide a large amount of high spectral resolution data. The accurate processing of this data employs time-consuming line-by-line (LBL) radiative transfer models (RTMs). In this paper, we describe a method to accelerate hyperspectral radiative transfer models based on the clustering of the spectral radiances computed with a low-stream RTM and the regression analysis performed for the low-stream and multi-stream RTMs within each cluster. This approach, which we refer to as the Cluster Low-Streams Regression (CLSR) method, is applied for computing the radiance spectra in the O<sub>2</sub> A-band at 760 nm and the CO<sub>2</sub> band at 1610 nm for five atmospheric scenarios. The CLSR method is also compared with the principal component analysis (PCA)-based RTM, showing an improvement in terms of accuracy and computational performance over PCA-based RTMs. As low-stream models, the two-stream and the single-scattering RTMs are considered. We show that the error of this approach is modulated by the optical thickness of the atmosphere. Nevertheless, the CLSR method provides a performance enhancement of almost two orders of magnitude compared to the LBL model, while the error of the technique is below 0.1% for both bands. |
| first_indexed | 2024-03-10T20:26:42Z |
| format | Article |
| id | doaj.art-008051eb585147a6b16dff7a3fb6f33d |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T20:26:42Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-008051eb585147a6b16dff7a3fb6f33d2023-11-19T21:40:53ZengMDPI AGRemote Sensing2072-42922020-04-01128125010.3390/rs12081250Cluster Low-Streams Regression Method for Hyperspectral Radiative Transfer Computations: Cases of O<sub>2</sub> A- and CO<sub>2</sub> BandsAna del Águila0Dmitry S. Efremenko1Víctor Molina García2Michael Yu. Kataev3Remote Sensing Technology Institute, German Aerospace Center (DLR), 82234 Oberpfaffenhofen, GermanyRemote Sensing Technology Institute, German Aerospace Center (DLR), 82234 Oberpfaffenhofen, GermanyRemote Sensing Technology Institute, German Aerospace Center (DLR), 82234 Oberpfaffenhofen, GermanyDepartment of Control Systems, Tomsk State University of Control Systems and Radioelectronics, Tomsk 634050, RussiaCurrent atmospheric composition sensors provide a large amount of high spectral resolution data. The accurate processing of this data employs time-consuming line-by-line (LBL) radiative transfer models (RTMs). In this paper, we describe a method to accelerate hyperspectral radiative transfer models based on the clustering of the spectral radiances computed with a low-stream RTM and the regression analysis performed for the low-stream and multi-stream RTMs within each cluster. This approach, which we refer to as the Cluster Low-Streams Regression (CLSR) method, is applied for computing the radiance spectra in the O<sub>2</sub> A-band at 760 nm and the CO<sub>2</sub> band at 1610 nm for five atmospheric scenarios. The CLSR method is also compared with the principal component analysis (PCA)-based RTM, showing an improvement in terms of accuracy and computational performance over PCA-based RTMs. As low-stream models, the two-stream and the single-scattering RTMs are considered. We show that the error of this approach is modulated by the optical thickness of the atmosphere. Nevertheless, the CLSR method provides a performance enhancement of almost two orders of magnitude compared to the LBL model, while the error of the technique is below 0.1% for both bands.https://www.mdpi.com/2072-4292/12/8/1250hyperspectral datafast radiative transfer modelsacceleration techniquesregressionO<sub>2</sub> A-bandCO<sub>2</sub> band |
| spellingShingle | Ana del Águila Dmitry S. Efremenko Víctor Molina García Michael Yu. Kataev Cluster Low-Streams Regression Method for Hyperspectral Radiative Transfer Computations: Cases of O<sub>2</sub> A- and CO<sub>2</sub> Bands Remote Sensing hyperspectral data fast radiative transfer models acceleration techniques regression O<sub>2</sub> A-band CO<sub>2</sub> band |
| title | Cluster Low-Streams Regression Method for Hyperspectral Radiative Transfer Computations: Cases of O<sub>2</sub> A- and CO<sub>2</sub> Bands |
| title_full | Cluster Low-Streams Regression Method for Hyperspectral Radiative Transfer Computations: Cases of O<sub>2</sub> A- and CO<sub>2</sub> Bands |
| title_fullStr | Cluster Low-Streams Regression Method for Hyperspectral Radiative Transfer Computations: Cases of O<sub>2</sub> A- and CO<sub>2</sub> Bands |
| title_full_unstemmed | Cluster Low-Streams Regression Method for Hyperspectral Radiative Transfer Computations: Cases of O<sub>2</sub> A- and CO<sub>2</sub> Bands |
| title_short | Cluster Low-Streams Regression Method for Hyperspectral Radiative Transfer Computations: Cases of O<sub>2</sub> A- and CO<sub>2</sub> Bands |
| title_sort | cluster low streams regression method for hyperspectral radiative transfer computations cases of o sub 2 sub a and co sub 2 sub bands |
| topic | hyperspectral data fast radiative transfer models acceleration techniques regression O<sub>2</sub> A-band CO<sub>2</sub> band |
| url | https://www.mdpi.com/2072-4292/12/8/1250 |
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