Canopy chlorophyll estimation with hyperspectral remote sensing

Abstract

In this research, proximal measurements of hyperspectral reflectance were used to develop models for estimating chlorophyll content in tallgrass prairie at leaf and canopy scales. Models were generated at the leaf scale and then extended to the canopy scale. Three chlorphyll estimation models were developed, one based on reflectance spectra and two derived from derivative transformations of the reflectance spectra. The triangle chlorophyll index (TCI) model was derived from the reflectance spectrum, whereas the first and second derivative indices (FDI and SDI) models were developed from the derivative transformed spectra. The three models were found to be well- correlated with the chlorophyll content measured with solvent extraction. The result indicated that the three models were effective for the leaf scale estimates of chlorophyll content. The three chlorophyll models developed at the leaf scale were further extended to the canopy scale and fine-scale images. The three models were found to be conditionally effective for estimating canopy chlorophyll content. The TCI model was more effective in dense vegetation, and the FDI and SDI models were better in sparser vegetation. This research suggests that the extension of chlorophyll models from the leaf scale to canopy scale is complex and affected not only by soil background, but also by canopy structure and components