The accuracy of NOAA AVHRR NDVI data can be poor because of interference fr
om several sources, including cloud cover. A parameter of the variogram mod
el can be used to estimate the contribution of noise from the total variati
on in an image. However, remotely sensed information over large areas incor
porates non-stationary (regional) trend and directional effects, resulting
in violation of the assumptions for noise estimation. These assumptions wer
e investigated at five sites across Africa for a range of ecological enviro
nments over several seasons. An unsupervised spectral classification of mul
ti-temporal NDVI data partially resolved the problem of non-stationarity. Q
uadratic polynomials removed the remaining regional trend and directional e
ffects. Isotropic variograms were used to estimate the noise contributing v
ariation to the image. Standardized estimates of noise ranged from a minimu
m of 18.5% in west Zambia to 68.2% in northern Congo. Cloud cover and atmos
pheric particulates (e.g. dust) explained some of the regional and seasonal
variations in noise levels. Image artifacts were also thought to contribut
e noise to image variation. The magnitude of the noise levels and its tempo
ral variation appears to seriously constrain the use of AVHRR NDVI data.