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| Format: | Preprint |
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2026
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| Online-Zugang: | https://arxiv.org/abs/2603.10856 |
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| _version_ | 1866914386403131392 |
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| author | Cañas, Gabriel Caballero Arranz, Bárbara Alvado Sòria-Perpinyà, Xavier Ruiz-Verdú, Antonio Delegido, Jesús Moreno, José |
| author_facet | Cañas, Gabriel Caballero Arranz, Bárbara Alvado Sòria-Perpinyà, Xavier Ruiz-Verdú, Antonio Delegido, Jesús Moreno, José |
| contents | The Environmental Mapping and Analysis Program (EnMAP) mission has opened new frontiers in the monitoring of optically complex environments. However, the accurate retrieval of surface reflectance over water bodies remains a significant challenge, as the water-leaving signal typically accounts for only a small fraction of the total radiance, being easily obscured by atmospheric scattering and surface reflection effects. This paper introduces 6ABOS (6S-based Atmospheric Background Offset Subtraction), a novel open-source Python framework designed to automate the atmospheric correction (AC) of EnMAP hyperspectral imagery. By leveraging the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) radiative transfer model, 6ABOS implements a physically-based inversion scheme that accounts for Rayleigh scattering, aerosol interactions, and gaseous absorption. The framework integrates automated EnMAP metadata parsing with dynamic atmospheric parameter retrieval via the Google Earth Engine (GEE) Application Programming Interface (API). Validation was conducted over two Mediterranean inland water reservoirs with contrasting trophic states: the oligotrophic Benag{'e}ber and the hypertrophic Bell{'u}s. Results demonstrate a high degree of spectral similarity between in situ measurements and EnMAP-derived water-leaving reflectances. The Spectral Angle Mapper (SAM) values remained consistently low (SAM $<$ 10$^\circ$) across both study sites. 6ABOS is distributed via conda-forge, providing the scientific community with a scalable, transparent, and reproducible open-science tool for advancing hyperspectral aquatic research in the cloud-computing era. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_10856 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | 6ABOS: An Open-Source Atmospheric Correction Framework for the EnMAP Hyperspectral Mission Based on 6S Cañas, Gabriel Caballero Arranz, Bárbara Alvado Sòria-Perpinyà, Xavier Ruiz-Verdú, Antonio Delegido, Jesús Moreno, José Machine Learning The Environmental Mapping and Analysis Program (EnMAP) mission has opened new frontiers in the monitoring of optically complex environments. However, the accurate retrieval of surface reflectance over water bodies remains a significant challenge, as the water-leaving signal typically accounts for only a small fraction of the total radiance, being easily obscured by atmospheric scattering and surface reflection effects. This paper introduces 6ABOS (6S-based Atmospheric Background Offset Subtraction), a novel open-source Python framework designed to automate the atmospheric correction (AC) of EnMAP hyperspectral imagery. By leveraging the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) radiative transfer model, 6ABOS implements a physically-based inversion scheme that accounts for Rayleigh scattering, aerosol interactions, and gaseous absorption. The framework integrates automated EnMAP metadata parsing with dynamic atmospheric parameter retrieval via the Google Earth Engine (GEE) Application Programming Interface (API). Validation was conducted over two Mediterranean inland water reservoirs with contrasting trophic states: the oligotrophic Benag{'e}ber and the hypertrophic Bell{'u}s. Results demonstrate a high degree of spectral similarity between in situ measurements and EnMAP-derived water-leaving reflectances. The Spectral Angle Mapper (SAM) values remained consistently low (SAM $<$ 10$^\circ$) across both study sites. 6ABOS is distributed via conda-forge, providing the scientific community with a scalable, transparent, and reproducible open-science tool for advancing hyperspectral aquatic research in the cloud-computing era. |
| title | 6ABOS: An Open-Source Atmospheric Correction Framework for the EnMAP Hyperspectral Mission Based on 6S |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2603.10856 |