Fusing information from MISR and MODIS for polar cloud detection

Abstract

Clouds play a major role in controlling Earth's climate, and cloud detection is a crucial step in the numerical weather prediction, and global climate models. Multi-angle imaging spectroradiometer (MLSR) and moderate resolution imaging spectroradiometer (MODIS) were launched in 1999 by clouds. However, cloud detection algorithms using either MISR or MODIS data separately do not take full advantage of the data collected by both sensors. In this paper, we propose and test two schemes to combine MISR and MODIS data for cloud detection in polar regions, Both schemes are follow-ups of a two-step polar cloud detection algorithm using MISR data: enhanced linear correlation matching classification followed by quadratic discriminate analysis (ELMS-QDA) (T, Shi et al.). The first scheme is mapping the MODIS cloud detection results to the MISR grid based on a nearest neighbor method, then only reporting the agreed pixels of the ELCMC-QDA results (from MISR) and MODIS operational results. This scheme improves the classification accuracy, but reduces the coverage of the results. Instead of combining the MISR and MODIS results directly, the second scheme uses the agreed pixels of ELCMC results and MODIS operational results as the training data for the QDA on MISR features, and output the results from the QDA. Both schemes are tested over a region where expert labels show that both MISR and MODIS operational algorithms do not work well (according to expert labels, 53% and 12.72% misclassification rates for MISR and MODIS operational algorithms respectively). The first scheme only makes 0.72% of errors, but leaves 68.72% of pixels unclassified. The second scheme reaches a 2.93% of misclassification rate, which is smaller than a 4.09% rate from ELCMC-QDA, and it provides a full coverage. Hence we propose using QDA on ELCMC and MODIS agreed pixels as an algorithm to fuse the MISR and MODIS information for the polar cloud detection.