Functional Data Analysis of Multi-Angular Hyperspectral Data on Vegetation

Abstract

Abstract - The surface reflectance anisotropy can be estimated by directional reflectance analysis through the collection of multi-angular spectral data. Proper characterization of the surface anisotropy is an important element in the successful interpretation of remotely sensed signals. A signal received by a sensor from a vegetation canopy is affected by several factors. One of them is the sensor zenith angle. Functional data analysis can be used to assess the distribution and variation of spectral reflectance due to sensor zenith angle. This paper examines the effect of sensor zenith angles on the spectral reflectance of vegetation, example on cotton leaves. The spectra were acquired in a green house trial in order to address the question ‘how much information can be obtained from multi-angular hyperspectral remote sensing on vegetation?’ The goals of the functional data analysis applied in this paper is to examine the Functional Data Analysis approach was applied to analysis multi-angular hyperspectral data on cotton, highlighting various characteristics of cotton spectra due to sensor view angles, and to infer directional variation in an outcome or dependent variable with different zenith angles.