Abstract. The ability to make repeated measurements of the changing Earth's surface is the principal advantage of satellite remote sensing. To realize its potential, it is necessary that true surface changes be isolated in the satellite signal from other effects which also influence the signal. In this study, we explore the magnitude of such effects in composite NOAA advanced very high resolution radiometer (AVHRR) images with a pixel spacing of 1 km. A compositing procedure is frequently used in the preparation of data sets for land biosphere studies to minimize the effect of clouds. However, the composite images contain residual artifacts which make it difficult to compare measurements at various times. We have employed a 4-year (1993-1996) AVHRR data set from NOAA 11 and 14 coveting the Canadian landmass and corrected these data for the influence of the remaining clouds (full pixel or subpixel), The impact of the uncertainty of channel 1 and 2 measurements is also significantly diminished by using ratio indices such as the NDVI. It is concluded that interannual variability exceeding 0.015-0.038 in NDVI (averaged over multiple pixels) can be detected for similar data sets and conditions, provided that sensor calibration does not introduce additional errors. Since such errors can be large for some conditions and applications, the importance of accurate sensor calibration cannot be overemphasized.