Algorithm of retrieving needle leaf chlorophyll content from hyperspectral remote sensing

Abstract

In this paper, we report on a process-based approach to estimate leaf chlorophyll content from hyperspectral remote sensing imagery. Extensive field and laboratory measurements were conducted for ten sites in black spruce (Picea mariana) forests near Sudbury, Ontario, Canada in 2003 and 2004. Leaf optical spectra and chlorophyll content, leaf and canopy biophysical parameters, and forest background optical properties were collected. Hyperspectral remote sensing images were acquired by the compact airborne spectrographic imager (CASI) over the study sites within one week of ground measurements. Using measured data as inputs, a geometrical- optical model 4-Scale was investigated to estimate forest canopy reflectance. The simulated canopy reflectance agrees well with the CASI measured reflectance. A look-up-table approach was developed to provide the probabilities of viewing sunlit foliage and background, and to determine a spectral multiple scattering factor as functions of leaf area index, view zenith angle, and solar zenith angle. With the look-up-tables, leaf reflectance spectra were inverted from hyperspectral remote sensing imagery. Leaf chlorophyll content was estimated from the retrieved leaf reflectance spectra using the modified leaf-level PROSPECT inversion model.