A comparative analysis of broadband and narrowband derived vegetation indices in predicting LAI and CCD of a cotton canopy

摘要

Remote sensing is a powerful tool for obtaining important agronomic information about field crops. Many spectral vegetation indices (VIs) have been developed in the past three decades to provide more sensitive measurements of plant biophysical parameters and to reduce external noise interferences such as those related to soil and the atmosphere. Some VIs were developed based on narrowband spectral data and others on broadband sensors. Therefore, although the mathematical equations defining VIs are the same, their calculated values are different, thus affecting their stability in predicting agronomic variables such as total green leaf area index. The objective of this study was to compare the ability of VIs derived from broad and narrowbands and to determine the optimum red-NIR bands for VIs used in predicting leaf area index (LAI) and canopy chlorophyll density (CCD) of cotton canopies. A completely randomized experiment was conducted in a cotton (Gossypium hirsutum L. cv. Sumian 3) field treated with four nitrogen application rates: 0%, 50%, 100% and 200% of the recommended rate. Hyperspectral reflectance was measured at 2.3 m above the cotton canopy on July 15, August 14 and October 1,2002 using a FieldSpec~® FR spectroradiometer. Corresponding leaf area index values and CCD were also measured on these dates. A large number (i.e. 22,500) of two-band combinations in the Normalized Difference Vegetation Index (λ_2-λ _1)/(λ_1+λ_2) and the Ratio Vegetation index λ_2/λ_1 was used for a linear and exponential regression analysis against LAI and CCD values. Moreover, traditional broadband vegetation indices based on simulated spectra were compared with their narrowband versions in predicting LAI and CCD. The results suggest that 640-660 nm and 800-870 nm, the centers of the red and MR channels of several multi-spectral sensors on the current generation of earth-orbiting satellites, were not always the optimum wavelength position of red-NIR bands for VIs. Although different in formula, both the NDVI (normalized difference vegetation index) and RVI (ratio vegetation index) calculated from narrowbands at 690-710 nm and 750-900 nm were closely correlated with LAI (R~2 > 0.8) and CCD (R~2 > 0.85). The red-NIR band position was more important than band width for modeling LAI and CCD. In summary, hyperspectral remotely sensed data provide more alternative red-NIR bands compared to multi-spectral data and, therefore, can provide greater flexibility in predicting LAI and CCD.