An analysis of shadow effects on spectral vegetation indices using a ground-based imaging spectrometer

Sunlit vegetation and shaded vegetation are inseparable parts of remote sensing images. Shadows can lead to either a reduction or total loss of information in an image. This can potentially lead to corruption of biophysical parameters derived from pixels values, such as vegetation indices. One of the major reasons that the effects of shadows easy to be ignored in remote sensing is the spatial resolution of the measurement. High spatial resolution and spectral resolution are typically difficult to achieve simultaneously, and images that have one tend not to 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 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 indices, 14 hyperspectral vegetation indices were calculated. The results show that shadows affect not only each narrow band of a vegetation index, but also vegetation parameters.