云阴影检测在遥感科学领域是一个重要研究方向，在许多方面有重要应用。单纯采用光谱阈值法识别云阴影会面临较多困难，难以准确识别，而采用时间序列法，则需耗费较多机时，过程较为繁复。在准确识别云像元的基础上，应用几何学方法识别云阴影是较为准确、实用的方法。目前，应用几何学方法计算起伏地形条件下的云阴影分布和变化特征的研究较少，本文提出了一种基于几何学方法的复杂地形条件下的云阴影识别方法，可以计算云像元对应阴影在复杂地形上的位置。模拟和实验表明，所提出的算法在识别复杂地形条件的云阴影方面具有较好效果，克服了几何法识别云阴影模型的原有缺陷。

Many methods for cloud shadow detection in remote sensing have been proposed, such as utilizing the threshold and time series. Adopting the spectrum threshold can encounter many difficulties, such as the selected threshold having significant uncertainty and can be influenced by other factors. The time series method requires much time, and its process is complicated. The geometric method is particularly more accurate and practical. However, few studies have investigated the geometry method to calculate the cloud shadows on complex terrains. Therefore, this paper reports on a method that identifies cloud shadows on complex topography based on the principle of geometrical optics.
When a straight line crosses a plane, it intersects with the plane at a point. However, when a straight line crosses a curved surface, it can intersect with the surface at many points. We can abstract the light through the cloud pixels as a straight line, whereas the complex terrain can be treated as a curved surface. When the light arrives on the surface by a cloud, it inevitably intersects with the surface at a certain point. This point is the shadow position casted by the cloud. We combined the previous formula to calculate the cloud shadows on the horizontal surface with DEM, which is based on the above principle of the proposed algorithm.
A series of simulations and experiments determined that the proposed algorithm could properly describe the cloud shadows on the complex surface. The actual calculated and visual interpretation results had high consistency in the shadow position, and these had better fitting with the coverage by comprehensive interpretation. The γ² value was 0.78, whereas the RMSE was 3.49, which were much better than the results that did not consider the terrain effect. Our algorithm had a clearer advantage in terms of comprehensively identifying cloud shadows compared with the results of the threshold method based on images.
An algorithm was proposed in this study based on geometry method to calculate the cloud shadows on complex terrain and overcome the existing defects of the geometry method in identifying cloud shadows. This algorithm could obtain the shadow positions of corresponding cloud pixels on a complex terrain, and the distortion of cloud shadows on complex surfaces could be properly described. However, the accuracy of the calculated results could be influenced by many factors, such as the accuracy of cloud detection and thin clouds that cannot be easily detected. However, these clouds can also cast shadows on the ground. Cloud height is an important parameter in the calculation process that largely influences the result, but the acquisition of accurate cloud heights has a certain difficulty level in remote sensing. The DEM and visible band data also have a matching problem.