Detection of daytime arctic clouds using MISR and MODIS data

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

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

Expert labels were used to evaluate arctic cloud detection accuracies of several methods based on MISR angular radiances and MODIS spectral radiances. The accuracy of cloud detections was evaluated relative to 5.086 million expert labels applied to 7.114 million 1.1-km resolution pixels with valid radiances from 57 scenes. The accuracy of the MODIS operational cloud mask was 90.72% for the 32 partly cloudy scenes and 93.37% for the 25 completely clear and overcast scenes. An automated, simple threshold algorithm based on three features extracted from MISR radiances and the MODIS operational cloud mask agreed with each other for 74.91% of the pixels in the 32 partly cloudy scenes and 78.44% of the pixels in the 25 completely clear and overcast scenes. These subsets of pixels had, relative to the expert labels, classification accuracies of 96.53% for the 32 partly cloudy scenes and 99.05% for the 25 completely clear and overcast scenes. Fisher's quadratic discriminate analysis (QDA) trained on expert labels from the 32 partly cloud scenes was applied to MISR radiances, three features based on MISR radiances, MODIS radiances, and the six features of the MODIS operational cloud mask with accuracies ranging from 87.51% to 96.43%. Accuracies increased to about 97% when QDA with expert labels was applied to combined radiances or features from both MISR and MODIS. Operational QDA-based classifiers were developed using as training labels those pixels for which the MISR automated, simple threshold and MODIS operational cloud mask algorithms agreed. Training the QDA classifier on these automatic labels using MISR radiances, three features based on MISR radiances, MODIS radiances, and the six features of the MODIS operational cloud mask led to accuracies ranging from 85.23% to 93.62% for the 32 partly cloudy scenes. Classification accuracies increased to 93.74% (93.40%) when combined MISR and MODIS radiances (features) were used. The highest accuracy attained with any operational algorithm tested on all 57 scenes was 94.51%. These results suggest that both MISR and MODIS radiances are sufficient for cloud detection in daytime polar regions.

Original languageEnglish (US)
Pages (from-to)172-184
Number of pages13
JournalRemote Sensing of Environment
Volume107
Issue number1-2
DOIs
StatePublished - Mar 15 2007

All Science Journal Classification (ASJC) codes

  • Soil Science
  • Geology
  • Computers in Earth Sciences

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