Cloud detection over the Arctic region using airborne imaging spectrometer data during the daytime

• Main Authors: Bo-cai Gao, Wei Han, North F. Larsen
• Other Authors: The Pennsylvania State University CiteSeerX Archives
• Format: Text
• Language: English
• Published: 1998
• Subjects: Arctic; Sea ice; Tundra; Alaska
• Online Access: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.385.7023; http://modis-atmos.gsfc.nasa.gov/_docs/Gao et al. (1998).pdf

Detection of clouds over arctic regions from current satellite radiometric measurements in the visible and IR atmospheric window regions, such as those of Advanced Very High Resolution Radiometer and Landsat, is often difficult due to the high albedos of snow- and ice-covered surfaces in the visible and the nearly isothermal temperature profiles in the lower atmosphere. In this paper the authors show that the water vapor absorption channel at 1.38 �m is effective in detecting high clouds over snow- and ice-covered surfaces in the Arctic. Low-level clouds can be detected from surface snow and sea ice using a narrow channel centered at 1.5 �m with a width of approximately 10 nm because of the dark background that results from strong absorption by snow and sea ice. Imaging data with contiguous spectral coverage between 0.4 and 2.5 �m acquired with the Airborne Visible/Infrared Imaging Spectrometer during the Arctic Radiation Measurements in Column Atmosphere-Surface System in Alaska in 1995 are analyzed. The authors have observed that as wavelength increases from 1.38 �m the atmospheric water vapor absorption becomes weaker and weaker and the low-level clouds and surface tundra are increasingly seen. It is always possible to locate a narrow channel in the spectral range of 1.38–1.50 �m with appropriate water vapor absorption strength to separate water and ice clouds from surface snow, sea ice, and tundra. The simple cloud-masking technique described here is directly applicable to cloud detection during the daytime from hyperspectral imaging data over arctic regions, which will be acquired with future satellite sensors. 1.