Development of an Algorithm to Retrieve Aerosol Optical Properties Over Water Using an Artificial Neural Network Radiative Transfer Scheme: First Result From GOSAT-2/CAI-2

In this study, we developed a fast yet flexible remote sensing algorithm to estimate the aerosol optical properties over water for the Cloud and Aerosol Imager-2 (CAI-2) onboard the Greenhouse gases Observing SATellite-2 (GOSAT-2) launched in October 2018. The CAI-2 is the successor of GOSAT/CAI by providing more spectral and finer spatial data. The algorithm uses the optimal estimation approach to simultaneously retrieve aerosol and water substances (SIRAW), combined with an artificial neural network (ANN) solver to perform the radiative transfer (RT) calculation. The ANN was well constructed based on an improved learning scheme and educated from a coupled atmosphere-ocean vector RT model over both open and coastal water. To investigate the availability of SIRAW, the retrieval was conducted using the real CAI-2 data and preliminarily validated via the ground-based observation of aerosol robotic network and maritime aerosol network over different ocean regions from March to November in 2019. Results demonstrated that the retrieved aerosol optical thickness (AOT) at 550 nm from CAI-2 had a good consistency to the
in situ
measurement, of which about 70.37% of CAI-2 AOT fell within a ±(0.05+10%) envelope. The algorithm developed by this study performed generally well for the AOTs and oceanic suspended particles over the global ocean through the intercomparison to those of MODIS products. Moreover, the ultraviolet channel of CAI-2, which produces the first application with 500-m spatial resolution, shows a promising skill in the monitoring of the smoke plume.