Sentinel-3 is an Earth observation satellite constellation launched by the European Space Agency. Each satellite carries two optical multispectral instruments: the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Radiometer (SLSTR). OLCI and SLSTR sensors produce images covering the visible and infrared spectrum that can be collocated in order to generate synergistic products. In Earth observation, a particular weakness of optical sensors is their high sensitivity to clouds and their shadows. An incorrect cloud and cloud shadow detection leads to mistakes in both land and ocean retrievals of biophysical parameters. In order to exploit both OLCI and SLSTR capabilities, image co-registration at ground level is needed. However, applying such collocation of the images results in cloud location mismatches due to the different viewing angles of OLCI and SLSTR, which complicates the synergistic cloud detection. This study seeks to provide a solution to correctly obtain the projected clouds based on the estimation of cloud top heights in order to better collocate clouds between sensors and detect their shadows. The study presents a forward and backward method to estimate the real nadir position of a cloud on the satellite image starting from an existing cloud mask, as well as the corresponding cloud projections on the surface depending on the solar and sensor viewing angles. The estimation of cloud top heights is based on differences in the cloud projections from SLSTR nadir and oblique views. Experimental results show that the stereo cloud matching based on maximum cross-correlation between SLSTR nadir and oblique spectra was the most robust method to match SLSTR clouds for both nadir and oblique views as compared to spectral distance and spectral angle minimization. We test the method over several images around the world, leading to higher overall accuracy (OA) as compared to Sentinel-3 official products, both in detecting SLSTR clouds and OLCI cloud shadows (SLSTR nadir OA = 93.6%, SLSTR oblique OA = 88.7%, OLCI cloud shadow OA = 93.9% for the stereo matcher, against 82.2%, 81.3% and 90.5%, respectively, for the official Sentinel-3 products). This study also provides a starting point in the development of a cloud screening approach for the upcoming Fluorescence Explorer (FLEX) satellite mission, expected to fly in tandem with Sentinel-3.

Sentinel-3 是由欧洲航天局发射的地球观测卫星星座。每颗卫星都携带两个光学多光谱仪器：海洋和陆地颜色仪器 (OLCI) 和海洋和陆地表面温度辐射计 (SLSTR)。OLCI 和 SLSTR 传感器产生的图像涵盖可见光和红外光谱，可以搭配使用以生成协同产品。在地球观测中，光学传感器的一个特别弱点是它们对云及其阴影的高度敏感。不正确的云和云阴影检测会导致陆地和海洋生物物理参数检索出错。为了利用 OLCI 和 SLSTR 功能，需要在地面进行图像配准。然而，由于 OLCI 和 SLSTR 的视角不同，应用这种图像搭配会导致云位置不匹配，这使协同云检测变得复杂。本研究旨在提供一种基于云顶高度估计正确获取投影云的解决方案，以便更好地在传感器之间配置云并检测其阴影。该研究提出了一种前向和后向方法，用于从现有云掩模开始估计卫星图像上云的真实最低点位置，以及根据太阳和传感器视角在表面上的相应云投影。云顶高度的估计基于来自 SLSTR 天底和斜视图的云投影的差异。实验结果表明，与光谱距离和光谱角度最小化相比，基于 SLSTR 天底和倾斜光谱之间最大互相关的立体云匹配是最可靠的方法来匹配天底和斜视图的 SLSTR 云。我们在世界各地的多幅图像上测试该方法，与 Sentinel-3 官方产品相比，在检测 SLSTR 云和 OLCI 云阴影（SLSTR nadir OA = 93.6%，SLSTR 倾斜 OA = 88.7）方面具有更高的整体精度（OA） %，立体匹配器的 OLCI 云阴影 OA = 93.9%，而官方 Sentinel-3 产品分别为 82.2%、81.3% 和 90.5%）。这项研究还为即将到来的荧光探索者 (FLEX) 卫星任务开发云筛选方法提供了一个起点，