Spectral normalization and fusion of optical sensors for the retrieval of BRDF and albedo: Application to VEGETATION, MODIS, and MERIS data sets

This paper aims at demonstrating the possibility of merging data from various medium-resolution spaceborne sensors to produce a consistent time series of surface bidirectional reflectance distribution function (BRDF) and albedo products. The spectral, directional, temporal, and spatial aspects of the multisensor fusion are presented. Emphasis is then given on the spectral normalization for the fusion of Medium Resolution Imaging Spectrometer Instrument (MERIS) data with Moderate Resolution Imaging Spectroradiometer (MODIS) and VEGETATION (VGT) data. Two methods are evaluated: a simple statistical method, which relies on a linear regression using all the available spectral bands, and a more innovative method called the spectral mode method, which is based on the restitution of the surface spectral signature by a combination of universal spectral functions. Analysis with Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral data and satellite products reveals that the spectral mode method is more efficient. This approach is used to merge top-of-canopy bidirectional reflectances from MERIS and VGT for the restitution of BRDF and albedo over a subset of West Africa. Compared to the products obtained with MERIS alone, the fusion with VGT demonstrates an improvement of the spatial coverage and a reduction of product uncertainty by about a third.