The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models

Fraction of green vegetation, f(g), and green leaf area index, L-g, are needed as a regular space-time gridded input to evapotranspiration schemes in the two National Weather Service (NWS) numerical prediction models-regional Eta and global medium range forecast. This study explores the potential of deriving these two variables from the NOAA Advanced Very High Resolution Radiometer (AVHRR) normalized difference vegetation index (NDVI) data. Obviously, one NDVI measurement does not allow simultaneous derivation of both vegetation variables. Simple models of a satellite pixel are used to illustrate the ambiguity resulting from a combination of the unknown horizontal (f(g)) and vertical (L-g) densities. We argue that for NOAA AVHRR data sets based on observations with a spatial resolution of a few kilometres the most appropriate way to resolve this ambiguity is to assume that the vegetated part of a pixel is covered by dense vegetation (i.e., its leaf area index is high), and to calculate f(g) = (NDVI-NDVIo)/(NDVIinfinity-NDVIo), where NDVIo (bare soil) and NDVIinfinity (dense vegetation) are specified as global constants independent of vegetation/soil type. Global (0.15 degrees)(2) spatial resolution monthly maps of f(g) were produced from a 5-year NDVI climatology and incorporated in the NWS models. As a result, the model surface fluxes were improved.