Inversion of True Leaf Reflectance from Very High Spatial Resolution Hyperspectral Images

The spectral reflectance of vegetation obtained from optical sensors provides information on their biophysical and biochemical properties. However, in remote sensing, reflectance is typically computed with respect to the top-of canopy (TOC) surface, resulting in an apparent reflectance due to the differences between the illumination conditions between the observed vegetation elements and the TOC surface. While the TOC reflectance is useful for data with coarse spatial resolution, it leads to erroneous estimates of the vegetation properties when applied to very high spatial resolution (VHR) data where individual leaves are visible. An illumination correction is required to retrieve the true leaf reflectance from the TOC reflectance. The present work investigates an illumination correction method for retrieving the true leaf reflectance from VHR hyperspectral TOC reflectance images based on the spectral invariant theory and a simple mathematical model for the leaf reflectance. The method is tested on simulated and measured data. The results show that the leaf reflectance can be accurately estimated from both data (average RMSD between 0.02 and < 0.12).