A Robust Supervised Method to Estimate Chlorophyll Ab Content from Spectral Reflectance

Abstract:

Leaf chlorophyll ab content is an important indicator of vegetation physiological status and is generally obtained from spectral reflectance. For non-destructive estimation of chlorophyll ab content, physical leaf reflectance models, such as the PROSPECT model and supervised methods have been applied. While the former generally does not perform optimal, the latter only performs well when trained on similar data. In this work, we developed a robust supervised method that overcomes this problem. The method derives a proxy for chlorophyll ab content as the relative position of a leaf reflectance spectrum on the arc spanned by the two extremes, containing high and low chlorophyll ab content. This proxy is found to be unaffected by spectral variability, caused by environmental and acquisition conditions. The relation between this proxy and the actual chlorophyll ab content is obtained by a supervised regression model, that is trained on a single leaf reflectance dataset, and that is transferable to other datasets. The proposed method is validated on seven real hyperspectral datasets.

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