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Assessment of atmospheric correction algorithms for the Sentinel-2A MultiSpectral Imager over coastal and inland waters

机译:沿海地区和内陆水域的Sentinel-2a MultiSpectral成像器评估大气校正算法

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摘要

The relatively high spatial resolution, short revisit time and red-edge spectral band (705 nm) of the ESA Sentinel-2 Multi Spectral Imager makes this sensor attractive for monitoring water quality of coastal and inland waters. Reliable atmospheric correction is essential to support routine retrieval of optically active substance concentration from water-leaving reflectance. In this study, six publicly available atmospheric correction algorithms (Acolite, C2RCC, ICOR, 12gen, Polymer and Sen2Cor) are evaluated against above-water optical in situ measurements, within a robust methodology, in two optically diverse coastal regions (Baltic Sea, Western Channel) and from 13 inland waterbodies from 5 European countries with a range of optical properties. The total number of match-ups identified for each algorithm ranged from 1059 to 1668 with 521 match-ups common to all algorithms. These in situ and MSI match-ups were used to generate statistics describing the performance of each algorithm for each respective region and a combined dataset. All ACs tested showed high uncertainties, in many cases > 100% in the red and > 1000% in the near-infra red bands. Polymer and C2RCC achieved the lowest root mean square differences (similar to 0.0016 sr(-1)) and mean absolute differences (similar to 40-60% in blue/green bands) across the different datasets. Retrieval of blue-green and NIR-red band ratios indicate that further work on AC algorithms is required to reproduce the spectral shape in the red and NIR bands needed to accurately retrieve the chlorophyll-a concentration in turbid waters.
机译:ESA Sentinel-2多光谱成像器的相对高的空间分辨率,短暂的重新求时间和红边谱带(705nm)使得该传感器对监测沿海和内陆水域的水质具有吸引力。可靠的大气矫正对于支持常规检索光学活性物质浓度免受留下水留反射率至关重要。在本研究中,在两个光学多样化的沿海地区(波罗的海,西部波罗的海频道)和来自5个欧洲国家的13个内陆Waterbodies,具有一系列光学性质。对于每种算法识别的匹配总数为1059到1668,与所有算法共用521个匹配。这些原位和MSI匹配用于生成描述每个相应区域和组合数据集的每个算法的性能的统计信息。所有ACS测试的所有ACS都显示出高的不确定性,在许多情况下,在近红外线带上的红色和> 1000%。聚合物和C2RCC实现了最低的根均方差异(类似于0.0016 sr(-1)),并且在不同的数据集中的平均差异(类似于蓝色/绿色带中的40-60%)。 Revertival的蓝绿色和Nir-Riqios的检索表明AC算法上的进一步工作是在准确地检索浊水中精确地检索叶绿素浓度所需的红色和NIR带中的光谱形状。

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