Satellite Data Processing

The satellite data used are from the NOAA Global Vegetation Index (GVI) archive [Gutman et al., 1995] for 1989 and 1990, and with a resolution of 1/7° latitude by longitude. Subsequent data processing by CESBIO [Berthelot et al., 1994] includes conversion to reflectances, and atmospheric correction using the SMAC code [Rahman and Dedieu, 1994].

Although most of the cloud contamination in the original daily AVHRR fields have already been removed in the GVI archive by selecting maximum contrast between channel 1 and 2 raw counts, further cloud screening and monthly compositing can lead to a still significant reduction in the residual cloud amount [Gutman et al., 1996]. The usual approach is to select those dates where the normalised difference vegetation index (NDVI) derived from channel 1 and 2 reflectances is at a maximum [Holben, 1986]. However, this maximum compositing technique tends to introduce signifant angular bias of artificially high "greenness" [Meyer et al., 1995], and temporal biases at the start and end of a growing season [Gutman et al., 1996]. It has also been found that the NDVI is rather sensitive to changes in soil background colour and atmospheric composition; if those influences are considered noise, then it can be shown that more modern indices achieve a better signal-to-noise ratio [Goel and Qin, 1994; Leprieur et al., 1994].

In this study, channels 1 and 2 of AVHRR are combined to compute the Global Environment Monitoring Index [GEMI, Pinty and Verstraete, 1992], designed to be robust against changes in soil brightness [Leprieur et al., 1994] and atmospheric perturbations [Flasse and Verstraete, 1994]. To avoid temporal and angular bias, cloud-screened weekly values from the GVI data set are averaged, with no maximum compositing performed. Cloud identifcation is based on negative deviations of weekly values from a filtered GEMI time series, based on GEMI's property of assuming low to negative values over cloud scenes [Flasse and Verstraete, 1994]. In addition, data points with so called "hot-spot" viewing conditions are also eliminated, as well as those where view or sun zenith angles are larger than 60°. Hot-spot conditions are assumed whenever a function G (defined in Verstraete et al. [1990]) assumes values less than 0.25.

The data fields are then averaged over 1° latitude by longitude, after which areas of high residual atmospheric contamination are identified by comparing time-interpolated maximum composite GEMI with the monthly average according to the standard procedure. If the ratio of monthly maximum to monthly average is above 1.1, the 1° pixel is discarded. The annual average data coverage achieved with this method is 74.8% of global land areas.

As the last step of satellite data processing, values of GEMI are translated into fAPAR using the following approximation:

The relationship is based on a regression between GEMI and fAPAR computed with a radiative transfer model [Gobron et al., 1997], and accounts for increasing residual atmospheric contamination over humid, densely vegetated areas. The error in fAPAR is estimated to lie between 0.05 and 0.10.

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