A Cloud Detection Algorithm Over Land Based on the Polarized Characteristics Difference Between Cloudless and Cloud Targets. Issue 9 (14th September 2019) - Gao, Xin; Hu, Xiuqing; Fang, Wei; Yin, Dekui
Abstract: The accurate identification of cloud over land is one of the key issues of the satellite data processing and the product retrievals. This paper describes a new cloud detection algorithm based on Level 1 data of Polarization and Directionality of Earth's Reflectance. The simulation of multiangular normalized polarized reflectance is done for cloudless targets over land before the cloud identification processing. First, the Normalized Difference Vegetation Index over land and reflectance of 670 nm are used as two initial criterions for the cloud mask. Then, the difference between the simulation and Polarization and Directionality of Earth's Reflectance observation of polarized reflectance is used as the third criterion to distinguish cloudless pixels from cloud ones. And this algorithm is proved to be more convenient and effective. This algorithm is also applied to cloud mask processing of the Multi‐Angular Polarization Imager (onboard the Tiangong‐2) observation. The results show that this algorithm can effectively detect cloud targets over land, and its consistency with Polarization and Directionality of Earth's Reflectance official cloud mask products is about 90%. This algorithm can provide reliable cloud mask products for the retrieval of optical and physical properties of land aerosol using Multi‐Angular Polarization Imager data. Plain Language Summary: Cloud detection is a crucial step to study clouds and other products generation for the satellite application, the result of which affects further researches. This study uses the multiangular polarized data to achieve cloud detection. A simulation was done for cloudless pixels with the scattering phase function theory before the cloud identification. And this paper shows the cloud detection results with this new algorithm, as well as comparisons with reference cloud mask from the Polarization and Directionality of Earth's Reflectance operational products. The consistency is about 90%. Key Points: A new cloud detection algorithm is described using multiangular polarized remote sensing data over land This algorithm is based on the scattering polarized phase function theory, and has a good consistency with a POLDER reference cloud mask product