Imaging Spectroscopy BRDF Correction for Mapping Louisiana’s Coastal Ecosystems

Abstract

This paper presents the adaptive reflectance geometric correction (ARGC), a bidirectional reflectance distribution function (BRDF) correction algorithm to address intensity gradients across remotely sensed images. The ARGC is developed and tested on data from the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) collected over Louisiana’s Atchafalaya River Delta, an area of complex wetland vegetation and waterbodies suited to AVIRIS-NG’s fine spatial and spectral resolutions. Changing view and solar geometry, in conjunction with surfaces’ anisotropic properties, impact a scene’s observed reflectance. As traditional BRDF corrections may not be appropriate for wetland environments that have distinctive vegetation and hydrologic structures, more flexible functional corrections are shown to improve results. We compared two existing methods and the ARGC. The first method fits a quadratic function over image column averages, and the second is based on the inversion of the Ross Thick and Li Sparse kernels. Building upon the principles of these methods, the ARGC uses a multiple regression-based BRDF correction whereby the image’s solar and view geometric descriptors form the independent variables. Each BRDF correction method was applied to the set of six partially overlapping AVIRIS-NG scenes. Assuming the actual surface reflectance of a given land cover type is independent of geometry, we used adjacent images’ overlapping regions to quantitatively assess each correction method’s efficacy. The ARGC produced the lowest overall root-mean-square difference and the lowest overlap mean absolute difference across the vast majority of bands. The ARGC is proposed as a practical new BRDF correction option for investigators using AVIRIS-NG data.