Characteristics of composited AVHRR data and problems in their classification

Abstract

Unsupervised classification procedures were applied to a temporal sequence of fifteen bi-weekly composited NDVI images produced from AVHRR LAC data. Individual examination of the input images appeared to show substantial contamination due to clouds which persist through the compositing period. These apparent cloud features dominated the results of the clustering procedures. The composites also include large numbers of far off-nadir pixels. This causes severe spatial smoothing and produces a blurred image appearance. Further combining the data to monthly composites largely eliminated the cloud cover problem, but did not necessarily reduce the frequency of large view zenith angles. Preprocessing of high temporal frequency; low spatial resolution data such as that provided by AVHRR and the planned EOS MODIS instrument must more effectively remove the effects of clouds, correct for anisotropic scattering from the atmosphere and bi-directional reflectance from the surface, and should be biased towards the selection of near-nadir measurements. The design and processing procedures for MODIS will reduce problems associated with atmospheric effects and the geometric distortion of pixels, while enhancing the detection and screening of clouds.