6ABOS自动化校正EnMAP高光谱影像，精准提取水体反射率。
6ABOS: An Open-Source Atmospheric Correction Framework for the EnMAP Hyperspectral Mission Based on 6S
- 基于6S模型与GEE API自动获取大气参数，实现物理可解释的校正。
- 在两个不同富营养化水体上验证，光谱角小于10°，精度高。
- 开源框架支持云计算，适合遥感水环境研究者使用。
环境测绘与分析计划（EnMAP）任务开启了监测光学复杂环境的新篇章。然而，水体表面反射率的准确反演仍面临挑战，因水体出射信号仅占总辐射的一小部分，易被大气散射和地表反射掩盖。本文提出6ABOS（基于6S的大气背景偏移减除），一个开源的Python框架，用于自动化处理EnMAP高光谱影像的大气校正。该框架结合6S辐射传输模型，考虑瑞利散射、气溶胶相互作用及气体吸收，集成自动解析EnMAP元数据，并通过谷歌地球引擎（GEE）API动态获取大气参数。在地中海两个不同营养状态的内陆水库——贫营养的Benagéber与富营养的Bellús——进行验证，结果表明实地测量与EnMAP反演的水体出射反射率具有高度光谱相似性，光谱角映射（SAM）值均低于10°。6ABOS通过conda-forge发布，为科学界提供可扩展、透明、可复现的开放科学工具，助力云端时代的高光谱水环境研究。
原文摘要 · Abstract (English)
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.
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