Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral reflectance data

Abstract

Spectral vegetation indices (VIs) are frequently used to estimate the amount of live green canopy material. They have generally been developed as an attempt to reduce spectral effects caused by external factors such as the atmosphere and the soil background. The objective of this study is to compare the performance of selected VIs, including some hyperspectral VIs based on waveform analysis of the reflectance across the vegetation red edge. The analysis is based on biophysical data measured in a winter wheat field fertilized at three nitrogen (N) levels. The measurements were acquired continuously throughout the growing season and related to canopy spectral reflectance measured with a ground-based spectroradiometer. Green crop area index (GCAI) and canopy chlorophyll density (CCD) were the major variables determined from the biophysical data set. Canopy chlorophyll was measured using nondestructive methods based on two optical instruments. The prediction power of selected VIs calculated from spectra resampled to Landsat TM configuration was compared with narrow-band indices including some VIs based on waveform analysis techniques. It was concluded that an exponential relationship exists between the two biophysical parameters and all of the selected indices except for the simple ratio index (RVI), for which a linear relationship was formed. The results indicate that VIs based on waveform analysis using narrow bands across the red edge may improve the prediction of CCD. However, none of the hyperspectral indices that were included in the study was better at estimating GCAI than the best of the traditional VIs.