China launched its first mini-satellite dedicated to carbon dioxide (CO2) detection and monitoring on Dec. 22, 2016. The Chinese Carbon Dioxide Observation Satellite or TanSat was designed to focus on the global observation of CO2.
For retrieving CO2 from TanSat observations, cloud detection is an essential preprocessing step. CO2 is one of the major greenhouse gases, and is of great concern due to the rapid increase in its atmospheric concentrations.
The TanSat project is one of the National High-tech Research and Development Programs funded by the Ministry of Science and Technology of the People's Republic of China and the Chinese Academy of Sciences.
During the pre-launch study of TanSat, a cloud-screening scheme for the Cloud and Aerosol Polarization Imager (CAPI) was proposed by a team at the Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University.
The team noticed that previous cloud-screening algorithms were basically designed to provide comprehensive utilization for sensors that contain multiple channels over a wide spectral range. For TanSat /CAPI, however, the channels available for cloud screening cover only five spectral bands, which is why such sensors need a more effective method to regroup results from few threshold tests.
Their work relies upon the radiance data from the Visible and Infrared Radiometer (VIRR) onboard the Chinese FengYun-3A Polar-orbiting Meteorological Satellite (FY-3A). VIRR uses four wavebands similar to that of CAPI and can serve as a proxy for its measurements.
Based on previous cloud-screening algorithms, the cloud-screening scheme for TanSat /CAPI defines a method to regroup individual threshold tests on a pixel-by-pixel basis according to the derived clear confidence level (CCL).
The scheme has been applied to a number of the FY3A/VIRR scenes over four target areas (desert, snow, ocean, forest) in China for all seasons.
Comparisons against the cloud-screening product from MODIS suggest the proposed scheme inherits the advantages of schemes described in previous publications and shows improved cloud-screening results. This scheme is proven to be more efficient for sensors with few channels or frequencies available for cloud screening.