Introduction

Vegetation indices are often used as an alternative to more complex algorithms to retrieve surface properties from space. Most of the older indices suffer from various well-known defects such as undesirable dependencies to geophysical variables or processes not of interest, or to the conditions of observation. These drawbacks can be avoided by designing, for instance, optimized spectral indices (Verstraete and Pinty, 1996). The application of these principles to the MERIS, GLI and VEGETATION sensors has been discussed in Govaerts et al. (1999), Gobron et al. (1999), and Gobron et al.

Remote Sensing and Climate Modeling: Synergies and Limitations, 5-21. © 2001 Kluwer Academic Publishers. Printed in the Netherlands.

(2000a), respectively. The details of the actual implementation of these new indices are given in Gobron et al. (1998) and Verstraete et al. (1998).

New vegetation indices are optimized with the help of a training data set generated with radiation transfer models of the coupled surface-atmosphere system which simulate sensor-like observations over various representative land surface types and for a wide range of atmospheric conditions. These simulations produce a large number of radiance fields at the blue, red and near-infrared wavelengths of the given sensor, which can then be sampled in the angular domain in a way similar to what is done with actual instruments. The models used to generate these radiation fields are also suitable to estimate the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) for the various land surface types under investigation.

The design of optimal spectral indices is based on a two step procedure. First, the spectral radiances measured in the red and near-infrared bands are rectified to decontaminate them from atmospheric and angular effects. Then, the rectified red and near-infrared bands are combined, via a generic polynomial expression, to yield an index formula that optimally estimates the environmental parameter of interest. The rectification process is based on the use of simultaneous measurements in the blue band to address atmospheric effects and on a parametric bi-directional reflectance model to account for angular (anisotropy) effects.

We developed a new spectral index specifically designed to estimate FAPAR for global applications on the basis of Sea-viewing Wide Field-of-view Sensor (SeaWiFS) data. Although this sensor was originally designed for the observation of ocean color, it permits the monitoring of terrestrial land surfaces thanks to its spectral bands centered at 443 (blue), 670 (red) and 865 nm (near-infrared) and a detector and amplifier design which does not saturate over land.

The implementation and optimization of the vegetation index for SeaWiFS is described in the next section. Its robustness with respect to angular variations in viewing geometry and its performance to characterize land surface patterns are discussed later. The last section presents some of the SeaWiFS products available at SAI for global analyses.

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