MISR Cloud Detection over Ice and Snow Based on Linear Correlation Matching
Abstract
Cloud detection is a crucial step in any climate modelling or prediction. Multi-angle Imaging SpectroRadiometer "MISR" was launched in 1999 by NASA to provide 9 angle and 4 band data to retrieve or estimate the cloud height and hence cloud detection. However, cloud detection even with MISR data has been proven very difficult over ice and snow. In this paper, we bypass the cloud height estimation step to directly tackle cloud detection by using features of ice and snow "no cloud" pixels from different MISR angles. We propose the linear correlation matching classification "LCMC" algorithm based on Fisher linear correlation tests. We compare LCMC with the Steroscopically-Derived Cloud Mask "SDCM", which is the cloud mask from MISR Level 2 Top-of-the atmosphere Cloud algorithm "known as L2TC", and find that LCMC gives more coverage and more robust results judged by visual inspection of finer resolution images. LCMC can also detect the very thin clouds most of the time. Moreover, LCMC is computationally much faster than L2TC and easier to implement. We hope to combine LCMC with L2TC in the future to improve the accuracy of the L2TC cloud height retrieval.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 01, 2003
- Accession Number
- ADA473013
Entities
People
- Amy Braverman
- Bin Yu
- Tao Shi
Organizations
- University of California, Berkeley