Adjacency effect (AE) corrections over inland water surfaces has been a known issue in space-borne optical remote sensing over more than four decades. Here we present a novel algorithm able to simultaneously retrieve the aerosol optical depth, sun glint, AE, water reflectance, and water inherent optical properties (IOPs). The method was evaluated against an in situ data set of remote sensing reflectance (Rrs) collected in ∼100 lakes across Canada. The new algorithm is based on a genetic optimization scheme (GAAC: Genetic Algorithm for Atmospheric Correction), and was here compared to the most popular atmospheric correction algorithms available (ACOLITE, iCOR+SIMEC). The statistical metrics of the Rrs retrieval were improved by a factor of almost 2 in all wavelengths, and for all metrics (Bias, Error, Similarity Angle) relative to other algorithms. Demonstrations of GAAC on scenes of Lansdat-8 OLI, and Sentinel-2 MSI sensors demonstrate the algorithm’s robustness when applied to spatially complex small lake (∼10 km of width) surfaces.

中文翻译：

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受邻接效应影响的水体大气校正遗传算法 （GAAC）


四十多年来，内陆水域表面的邻接效应 （AE） 校正一直是星载光学遥感中的一个已知问题。在这里，我们提出了一种新颖的算法，能够同时检索气溶胶光学深度、太阳光芒、AE、水反射率和水固有光学特性 （IOP）。该方法是根据在加拿大 ∼100 个湖泊中收集的遥感反射率 （Rrs） 的原位数据集进行评估的。新算法基于遗传优化方案（GAAC：Genetic Algorithm for Atmospheric Correction），并与最流行的大气校正算法（ACOLITE、iCOR+SIMEC）进行了比较。相对于其他算法，Rrs 检索的统计指标在所有波长和所有指标（偏差、误差、相似角）中都提高了近 2 倍。在 Lansdat-8 OLI 和 Sentinel-2 MSI 传感器场景上演示 GAAC 证明了该算法在应用于空间复杂的小湖（∼10 公里宽）表面时的稳健性。