Multi-information collaborative cloud identification algorithm in Gaofen-5 Directional Polarimetric Camera imagery

Abstract

The Directional Polarimetric Camera (DPC) onboard China's Gaofen-5 (GF-5) satellite has provided multi-angle (3-polarization channel), large-scale (1850 km swath width and 3.3 km spatial resolution), and high-frequency (2-day revisit period) Earth observation since May 2018. These features make DPC imagery application scenarios extensively. However, like other optical imagery, the presence of clouds is also a pervasive and unavoidable issue in DPC imagery. To leverage both radiation and polarization properties of DPC, we proposed a multi-information collaborative (MIC) method to identify clouds in the DPC imagery. Instead of a fixed single threshold, the MIC method adopts dynamic thresholds obtained by simulation in different atmosphere models, at different times, and under different underlying surfaces. Specifically, we included surface albedo and ice/snow cover distribution libraries to the MIC method, as they compensate for fewer spectral bands in the DPC imagery, thereby improving the accuracy of cloud detection results, especially in special bright surface scenarios (e.g., desert, bare soil and ice/snow). We also added an ice/snow detection algorithm to further eliminate the issue of misidentifying ice/snow pixels as clouds. Finally, after obtaining the DPC cloud mask results based on the MIC method, we calculated four cloud confidence levels for different application requirements by cloud quality evaluation criteria.We evaluated the MIC algorithm by comparing it with two other independent satellite cloud observation products, namely Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and Moderate Resolution Imaging Spectroradiometer (MODIS). We found that the MIC cloud mask is in good agreement with the other two cloud products, with agreement probabilities of 93.06% (CALIPSO) and 85% (MODIS), respectively. Furthermore, the detected high confidence cloud and clear sky results agree with the CALIPSO cloud confidence products by more than 97.35% and 96.13%, respectively. We therefore suggest that the MIC method can provide the basis for subsequent studies of atmospheric parameters, such as accurate retrieval of aerosol optical thickness (AOT), cloud optical thickness (COT), cloud droplet effective radius (CDR) and land surface reflectance.