Hyperspectral versus multispectral data for estimating leaf area index in four different biomes

Motivated by the increasing importance of hyperspectral remote sensing data, this study sought to determine whether current-generation narrow-band hyperspectral remote sensing data could better track vegetation leaf area index (LAI) than traditional broad-band multispectral data. The study takes advantage of a unique dataset, wherein field measurements of LAI were acquired at the same general time and grain size as both Landsat ETM+ and AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) imagery in four different biomes. Biome types sampled included row-crop agriculture, tallgrass prairie, mixed hardwood-conifer forest, and boreal conifer forest. The effects of bandwidth, band placement, and number of bands were isolated from radiometric quality by comparing regression models derived from individual AVIRIS channels with those derived from simulated ETM+ and MODIS channels using the AVIRIS data. Models with selected subsets of individual AVIRIS channels performed better to predict LAI than those based on the broadband datasets, although the potential to overfit models using the large number of available AVIRIS bands is a concern. Models based on actual ETM+ data were generally stronger than those based on simulated ETM+ data, suggesting that, for predicting LAI, ETM+ data suffer no penalty for having lower radiometric quality. NDVI was generally not sensitive to LAI at the four sites. Band placement of broad-band sensors (e.g., simulated ETM+ and MODIS) did not affect relationships with LAI, suggesting that there is no inherent advantage to MODIS spectral properties over those of ETM+ for estimating LAI. Spectral channels in the red-edge and shortwave-infrared regions were generally more important than those in the near-infrared for predicting LAI. Published by Elsevier B.V.