Abstract Geostationary Ocean Color Imager (GOCI) sensor onboard the COMS (Communication, Ocean and Meteorological Satellite) launched in 2010 was primarily designed to provide high-frequency observations in and around the Korean Peninsula to ensure the thorough monitoring of ocean properties. Owing to its pixel resolution of 500 m and large set of spectral solar channels, GOCI can also be considered for applications related to the characterization of vegetation and the retrieval of aerosol properties over land. However, to apply it for the full characterization of land, it is mandatory to properly remove clouds from the images. Such a procedure has limitations when there is a lack of thermal bands, as is the case with GOCI. However, GOCI data are impacted by shadows and radiation scattering effects during the daily course of the sun. Although this yields strong directional effects, the bidirectional reflectance distribution function (BRDF) can be determined to a high level of accuracy. This information is used as a reference to detect clouds over land because surface BRDF varies slowly with time compared to that of clouds. The proposed algorithm relies on knowledge of the BRDF field derived from the application of a semi-empirical model that simulates the minimum difference between top and bottom of atmosphere reflectance values as the baseline of clear atmosphere. This step also serves to estimate background surface reflectance underneath clouds. Accuracy assessment of the new GOCI cloud mask product is appraised through a comparison with high-resolution vertical profiles of lidar data from the polar orbiting Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO). The results for the Probability Of Detection (POD) of all cloud types was found to be 0.831 for GOCI; this is comparable to that of MODIS (0.772). For the case of only thin cirrus, GOCI POD value was assessed to be 0.849, similar to that of MODIS, underlining the improved efficiency of determining thin cloud pixels.

中文翻译：

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基于应用于地球静止海洋彩色成像仪 (GOCI) 卫星数据集的 BRDF 模型，使用背景表面反射率检测陆地上的薄云

摘要 2010 年发射的 COMS（通信、海洋和气象卫星）搭载的地球同步海洋彩色成像仪 (GOCI) 传感器主要用于提供朝鲜半岛及其周边地区的高频观测，以确保对海洋特性的彻底监测。由于 500 m 的像素分辨率和大量的光谱太阳通道，GOCI 也可以考虑用于与植被特征和陆地上气溶胶特性检索相关的应用。但是，要将其应用于土地的完整表征，必须从图像中正确去除云。当缺少热带时，这种程序有局限性，就像 GOCI 的情况一样。然而，GOCI 数据受到太阳日常运行过程中阴影和辐射散射效应的影响。虽然这会产生很强的方向效应，但可以高精度地确定双向反射分布函数 (BRDF)。此信息用作检测陆地上云的参考，因为与云相比，表面 BRDF 随时间变化缓慢。所提出的算法依赖于从应用半经验模型得出的 BRDF 场知识，该模型模拟大气顶部和底部反射值之间的最小差异作为晴朗大气的基线。此步骤还用于估计云层下方的背景表面反射率。通过与极地轨道云气溶胶激光雷达和红外探路者卫星观测 (CALIPSO) 的激光雷达数据的高分辨率垂直剖面进行比较，对新的 GOCI 云罩产品的准确性进行了评估。发现所有云类型的检测概率 (POD) 的结果对于 GOCI 为 0.831；这与 MODIS (0.772) 相当。对于只有薄卷云的情况，GOCI POD 值被评估为 0.849，与 MODIS 的值相似，强调了确定薄云像素的效率的提高。