Detection of Daytime Arctic Clouds using MISR and MODIS Data

Abstract

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.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2006
Accession Number
ADA473001

Entities

People

  • Amy J. Braverman
  • Bin Yu
  • David Groff
  • Eugene E. Clothiaux
  • Tao Shi

Organizations

  • Ohio State University

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Space

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Artificial Satellites
  • Automatic
  • Classification
  • Data Science
  • Data Sets
  • Detection
  • Discriminant Analysis
  • Discriminate Analysis
  • Grids
  • Information Science
  • Jet Propulsion
  • Machine Learning
  • Polar Regions
  • Supervised Machine Learning
  • World Geodetic System

Fields of Study

  • Environmental science

Readers

  • Atmospheric Remote Sensing.

Technology Areas

  • Space