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Within the EU project Cloudmap, cloud-top heights should be estimated using satellite stereo images at very high temporal resolution. Since such images are not provided by operational sensors, MOMS-2P and ATSR2 images of lower temporal resolution were used instead. The MOMS stereopair was oriented with subpixel accuracy using GCPs from 1:50,000 topographic maps and Kratky’s sensor model. Preprocessing for noise reduction, cloud stripes removal and contrast enhancement was applied. For the estimation of cloud-top heights, the images were resampled to 288 m and geometrically constrained least squares matching, using image pyramids and an interest operator, as well as varying parameters was used. The results were checked by visual inspection and comparison to semi-automatically measured points in the original resolution images. Automatic blunder detection using two tests were also applied. Matching led to large blunders in land areas between clouds or close to cloud boundaries. Excluding these blunders (error > 1100 m), matching showed an RMS of ca. 0.2 pixel, exhibiting a very high accuracy potential. A matching geometric transformation using rotations and radiometric equalization during the iterations showed slightly better results compared to the other matching versions. ATSR2 images were matched with a similar approach, however without geometric constraints, as the input images were rectified. Due to differences between the images which vary spatially, varying matching parameters are optimal for each image region. First steps in combining matching results from such varying matching versions have been performed. Both datasets showed similar matching problems due to surface discontinuities, mixing of surfaces than are neighbouring in image space but differ in height, and often large illumination differences (even with along-track stereo and small time acquisition differences).

Cloud-top height estimation from satellite stereopairs for weather forecasting and climate change analysis