Recently, a research team from Hefei Institutes of Physical Science of Chinese Academy of Sciences developed a novel algorithm using data from GF-5B Directional Polarimetric Camera (DPC). This algorithm successfully captured the three-dimensional (3-D) structure of clouds through multi-angle polarized cloud parameter inversion.
The research results were published in ISPRS Journal of Photogrammetry and Remote Sensing.
Clouds influence weather and climate by affecting water circulation and energy balance. Satellite-based multi-angle polarized detection is vital for cloud data. However, clouds have complex 3-D structures, which can affect the precision of cloud parameter inversion from multi-angle observations. Therefore, accounting for these 3-D structures is essential in the inversion process.
In this study, researchers use cloud-sensor geometry of DPC to rebuild observation path at each observation views.
"DPC captures multi-angle, multi-spectral, and polarization data simultaneously, enriching 3-D cloud property inversion, with its wide-Fields of View imaging capability enhancing cloud position detection," said Prof. SUN Xiaobing, who led the team.
They used texture of cloud to construct multi-view perspective matrix of different observation views in unified space. 3-D retrieval of cloud properties can be obtained based on 3-D positioning cloud radiation.
Compare to the traditional two-dimensional (2-D) plane hypothesis, this 3-D algorithm improved efficiency and accuracy of retrieval based on same identifying criteria of 2-D plane inversion. Real cloud retrieval samples demonstrated its superior performance in atmospheric applications.
This algorithm proposed in this study, which considers cloud structure, holds the promise of enhancing the inversion accuracy of atmospheric parameter products derived from domestically-produced satellite-based multi-directional polarized data even further.
Illustration of cloud cluster's 3-D radiation transfer simulation observation process (left) and corresponding multi-view radiation statistics (right). (Image by YU Haixiao)
