Can interannual land surface signal be discerned in composite AVHRR data?

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 covering the Canadian landmass and corrected these data for the influence of the remaining clouds (full pixel or subpixel), atmospheric attenuation, and bidirectional reflectance. We have found that such corrections are essential for studies of interannual variations. The magnitude of the interannual signal varied with the AVHRR channel, land cover type, and satellite sensor but it was reduced by a factor of 2 to 8 between top of the atmosphere and the normalized surface reflectance. The remaining variations consisted of true interannual signal and the residual noise in the data (including sensor calibration) which was not removed by the correction process. Assuming that barren or sparsely vegetated land in northern Canada has not changed over the 4‐year period, the mean residual uncertainty in surface reflectance of the selected sites was 0.012 for AVHRR channel 1, 0.042 for channel 2, and 0.068 for the normalized difference vegetation index (NDVI). These values decreased to 0.011, 0.024 and 0.038, respectively, when excluding 1994 data because their atmospheric and bidirectional corrections were hampered by high solar zenith angles (mean values above 55° in all 1994 composite periods). The errors could be further reduced by more refined corrections for bidirectional and atmospheric effects. 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.