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Sunlit vegetation and shaded vegetation are inseparable parts for most remotely sensed images, and the presence of shadows affects high spatial resolution remote sensing and multiangle remote sensing data. Shadows can lead to either a reduction in or a total loss of information in an image. This can potentially lead to the corruption of biophysical parameters derived from pixel values, such as vegetation indexes (VIs). VIs are widely used in remote sensing inversion applications. If the effects of shadows are not properly accounted for, retrieval may be uncertain when using a VI to calculate vegetation parameters. One of the major reasons that the effects of shadows are easy to be ignored in remote sensing is the spatial resolution of the measurement. High spatial and spectral resolutions are typically difficult to achieve simultaneously, and images that have one tend to not have the other. A ground-based imaging spectrometer brings a turning point to solve this problem as it can obtain both high spatial and high spectral resolutions to obtain feature and shadow images simultaneously. The resolution of the system used here was 1 mm at a height of 1 m, and the spectral resolution was better than 5 nm. For each pixel, the spectral curve of the image was almost a pure-pixel spectral curve, which allowed the differentiation of sunlit and shaded areas. To investigate the effects of shadows on different indexes, 14 hyperspectral VIs were calculated. Moreover, the vegetation fractional coverage calculated using the same 14 VIs was compared. The results show that shadows affect not only each narrowband of a VI but also vegetation parameters.

An Analysis of Shadow Effects on Spectral Vegetation Indexes Using a Ground-Based Imaging Spectrometer