Detection of Daytime Arctic Clouds using MISR and MODIS Data

• Main Authors: Shi, Tao, Clothiaux, Eugene E., Yu, Bin, Braverman, Amy J., Groff, David N.
• Other Authors: OHIO STATE UNIV COLUMBUS DEPT OF STATISTICS
• Format: Text
• Language: English
• Published: 2006
• Subjects: Meteorology; Statistics and Probability; Optical Detection and Detectors; Test Facilities; Equipment and Methods; *CLOUDS; *SPECTRORADIOMETERS; ALGORITHMS; SPECTRAL EMITTANCE; SCIENTIFIC SATELLITES; EXPERIMENTAL DESIGN; ARCTIC REGIONS; CLOUD DETECTION; MODIS(MODERATE RESOLUTION IMAGING SPECTRORADIOMETER); MISR(MULTI-ANGLE IMAGING SPECTRORADIOMETER); SPATIAL CORRELATION; FISCHER'S QDA(QUADRATIC DISCRIMINATE ANALYSIS); Arctic
• Online Access: http://www.dtic.mil/docs/citations/ADA473001; http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA473001

Amongst the spectral radiances available on the Moderate Resolution Imaging Spectroradiometer (MODIS) 7 are used operationally for detection of clouds in daytime polar regions. While the information content of clouds inherent in spectral radiances is familiar, the information content of clouds contained in angular radiances (i.e., radiances emanating to space from the same object but in different directions) is not. The Multi-angle Imaging Spectroradiometer (MISR) measures angular radiances to space and its collocation on the NASA Terra satellite with MODIS allows for a comparative analysis of its cloud detection capabilities with those of MODIS. Expert labels are used to compare arctic cloud detection efficiencies of several methods based on MISR radiances and radiance-based features, MODIS radiances and radiance-based features, and their combinations. Fisher's quadratic discriminate analysis (QDA) with expert labels is applied to MISR radiances, MISR radiance-based features, MODIS radiances, and MODIS radiance-based features. Accuracies increase when QDA with expert labels is applied to combined radiances (features) from both MISR and MODIS. These results are indicative of the information content inherent in spectral and angular radiances, but these classifiers are impossible to obtain in practice due to their reliance on expert labels. A second group of classifiers, also QDA-based, used automatic training labels from thresholding on combined MISR and MODIS radiance-based features. Training a QDA classifier on the MODIS mask did not improve classification accuracy. These results suggest that both MISR and MODIS radiances have sufficient information content for cloud detection in daytime polar regions. These results imply that further analysis of daytime cloud masks obtained from MISR and MODIS radiances over much larger spatial and temporal scales is a worthwhile endeavor. Sponsored in part by National Aeronautic and Space Administration Grant no. NNG04GL93G and National Science Foundation Grants nos. CCR-0106656 and DMS-03036508. Prepared in collaboration with the Department of Meteorology, Pennsylvania State University, University Park, PA; the Department of Statistics, University of California, Berkeley, CA; the Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA; and the Department of Energy, Southern Great Plains Site, Billings, OK.