349 
SPECTRAL VARIABILITY AND ANALYSIS PROCEDURES FOR HIGH 
RESOLUTION REFLECTANCE DATA 
JOHN C. PRICE 
USDA Agricultural Research Service 
Beltsville Agricultural Research Center 
Beltsville, Maryland 20705 
ABSTRACT: 
Hyperspectral data (0.4 - 2.5 /¿m reflectance data at 0.01 fim resolution) are 
considered for both surface and aircraft data sets representing soils, 
vegetation, and other common surfaces. Both types of data are well described 
by 20-30 spectral shapes, although minerals may require a larger number due to 
sharp absorption features at the longer wavelengths. This suggests that, in 
contrast with research studies, operational applications do not require 200 or 
more spectral measurements at 0.01 //m resolution to obtain the useful 
information in reflectance data. Comparison of surface and aircraft 
observations shows that the types of shapes are similar, except for known 
atmospheric water vapor features in the aircraft spectra. It thus should be 
possible to estimate water vapor corrections in remotely sensed reflectance 
data using relatively broad band (0.04/um) spectral observations. 
KEYWORDS: Hyperspectral, Spectral Collections, AVIRIS, Basis functions 
1. INTRODUCTION 
Recent advances in instrumentation, combined with interest in global 
environmental assessment, have prompted the development of high spectral 
resolution sensors which provide imagery with large numbers of spectral bands. 
The AVIRIS instrument (Vane, 1987) is a good example, obtaining data in 224 
spectral bands in the range 0.4-2.5 ¿im for an image swath more than 600 pixels 
wide. This discussion follows closely that of the previous meeting of this 
symposium (Price, 1991), applying a newer methodology which has been developed 
to address the expansion of spectra in basis functions (Price, 1993) when the 
number of such functions becomes relatively large, i.e. >15. We first review 
the description by spectral basis functions, then apply the formalism to 
collections of surface/laboratory spectra, then to AVIRIS imagery, and then 
describe the relationship between the two types of spectra, where the 
systemmatic difference (atmospheric water vapor) is readily identified. 2 
2. DESCRIPTION OF HYPERSPECTRAL DATA BY BASIS FUNCTIONS 
Let x a (A) = (x^, x°, ...x^) represent a measured spectrum for the set of n 
wavelength values A = (A^, A^, A^, ...A^), with superscript a denoting the 
individual sample. Throughout we shall work with reflectance spectra, i. e. 
the ratio of reflected to incident radiation, as this eliminates effects of 
local illumination conditions and facilitates comparison of spectral 
collections from many laboratories. For remote sensing applications the 
illumination source is usually the sun. We shall describe visible to near 
infrared spectra (0.4 to 2.5 ¿¿m) by a set of spectral basis functions: