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Towards a novel approach for Sentinel-3 synergistic OLCI/SLSTR cloud and cloud shadow detection based on stereo cloud-top height estimation

机译:朝着Sentinel-3协同OLCI / SLSTL云和基于立体云顶部高度估计的新方法

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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.
机译:Sentinel-3是欧洲航天局推出的地球观测卫星星座。每个卫星都带有两种光学多光谱仪器:海洋和土地彩色仪器(OLCI)和海洋和陆地温度辐射计(SLST)。 OLCI和SLST传感器产生覆盖可见的和红外光谱的图像,以便产生协同产品。在地球观察中,光学传感器的特定弱点是对云和阴影的高敏感性。云和云阴影检测不正确导致生物物理参数的土地和海洋检索中的错误。为了利用OLCI和SLSTH功能,需要在地面的图像共同登记。然而,由于OLCI和SLST的不同观察角度,施加这种图像的耦合导致云定位不匹配,其使协同云检测复杂化。本研究旨在根据云顶部高度的估计来提供正确获取投影云的解决方案,以便更好地在传感器之间覆盖云并检测它们的阴影。该研究提出了前向和后向方法,以估计从现有云掩模开始的卫星图像上的云的真实NadiR位置,以及根据太阳能和传感器观察角度的表面上的相应云凸起。云顶部高度的估计是基于SLSTL Nadir和倾斜视图的云投影的差异。实验结果表明,与频谱距离和光谱角度最小化相比,基于SLSTL Nadir和倾斜谱之间的最大互相关的立体声云匹配是匹配Nadir和倾斜视图的最稳健的方法。我们在世界各地的几张图像上测试该方法,与Sentinel-3官方产品相比,在检测SLST云和OLCI云阴影(SLSTL Nadir OA = 93.6%,SLSTL Oblique OA = 88.7)中,导致整体精度更高(OA)。 %,对于立体声匹配,奥尔西云影子OA = 93.9%,分别为82.2%,81.3%和90.5%,用于官方的哨兵-3产品)。本研究还提供了即将到来的荧光勘探器(Flex)卫星使命的云筛选方法开发的起点,该方法预计将与Sentinel-3一起串联飞行。

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