Abstract With a revisit time of 1 h, spatial resolution of 500 m, and high radiometric sensitivity, the Geostationary Ocean Color Imager (GOCI) is widely used to monitor diurnal dynamics of oceanic phenomena. However, atmospheric correction (AC) of GOCI data with high solar zenith angle (>70°) is still a challenge for traditional algorithms. Here, we propose a novel neural network (NN) AC algorithm for GOCI data under high solar zenith angles. Unlike traditional NN AC algorithms trained by radiative transfer-simulated dataset, our new AC algorithm was trained by a large number of matchups between GOCI-observed Rayleigh-corrected radiance in the morning and evening and GOCI-retrieved high-quality noontime remote-sensing reflectance (Rrs). When validated using hourly GOCI data, the new NN AC algorithm yielded diurnally stable Rrs in open ocean waters from the morning to evening. Furthermore, when validated by in-situ data from three Aerosol Robotic Network-Ocean Color (AERONET-OC) stations (Socheongcho, Gageocho and Ieodo), the GOCI-retrieved Rrs at visible bands obtained using the new AC algorithm agreed well with the in-situ values, even under high solar zenith angles. Practical application of the new algorithm was further examined using diurnal GOCI observation data acquired in clear open ocean waters. Results showed that the new algorithm successfully retrieved Rrs for the morning and evening GOCI data. Moreover, the amount of Rrs data retrieved by the new algorithm was much higher than that retrieved by the standard AC algorithm in SeaDAS. Our proposed NN AC algorithm can not only be applied to process GOCI data acquired in the morning and evening, but also has the potential to be applied to process polar-orbiting satellite ocean color data at high-latitude ocean that also include satellite observation with high solar zenith angles.

中文翻译：

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公海大太阳天顶角下地球同步卫星海洋颜色数据的大气校正

摘要 地球同步海洋彩色成像仪（GOCI）重访时间1 h、空间分辨率500 m、辐射灵敏度高，被广泛应用于海洋现象的昼夜动态监测。然而，对具有高太阳天顶角（>70°）的GOCI数据进行大气校正（AC）仍然是传统算法的挑战。在这里，我们为高太阳天顶角下的 GOCI 数据提出了一种新的神经网络 (NN) AC 算法。与传统的由辐射传输模拟数据集训练的 NN AC 算法不同，我们的新 AC 算法是通过 GOCI 观测到的早晚瑞利校正辐射与 GOCI 检索的高质量中午遥感反射率之间的大量匹配来训练的(rr)。当使用每小时 GOCI 数据进行验证时，新的 NN AC 算法在开阔的海水中从早到晚产生了昼夜稳定的 Rrs。此外，当通过来自三个气溶胶机器人网络-海洋颜色 (AERONET-OC) 站（Socheongcho、Gageocho 和 Ieodo）的原位数据进行验证时，使用新的 AC 算法获得的可见波段的 GOCI 检索 Rrs 与-situ 值，即使在高太阳天顶角下。使用在清澈的公海水域获得的昼夜 GOCI 观测数据进一步检查了新算法的实际应用。结果表明，新算法成功检索了早晚GOCI数据的Rrs。而且，新算法检索到的Rrs数据量远高于SeaDAS中标准AC算法检索到的Rrs数据量。