Preliminary verification for application of a support vector machine-based cloud detection method to GOSAT-2 CAI-2

The Greenhouse Gases Observing Satellite (GOSAT) was launched in 2009 to measure global atmospheric CO 2 and CH 4 concentrations. GOSAT is equipped with two sensors: the thermal and near-infrared sensor for carbon observation (TANSO)-Fourier transform spectrometer (FTS) and TANSO-cloud and aerosol imager (CAI). The presence of clouds in the instantaneous field of view of the FTS leads to incorrect estimates of the concentrations. Thus, the FTS data suspected to have cloud contamination must be identified by a CAI cloud discrimination algorithm and rejected. Conversely, overestimating clouds reduces the amount of FTS data that can be used to estimate greenhouse gases concentrations. This is a serious problem in tropical rainforest regions, such as the Amazon, where the amount of useable FTS data is small because of cloud cover. Preparations are continuing for the launch of the GOSAT-2 in fiscal year 2018. To improve the accuracy of the estimates of greenhouse gases concentrations, we need to refine the existing CAI cloud discrimination algorithm: Cloud and Aerosol Unbiased Decision Intellectual Algorithm (CLAUDIA1). A new cloud discrimination algorithm using a support vector machine (CLAUDIA3) was developed and presented in another paper. Visual inspection can use the locally optimized standards for judging, although CLAUDIA1 and CLAUDIA3 use common thresholds all over the world. Thus, the accuracy of visual inspection is better than that of these algorithms in most regions, with the exception of snow and ice covered surfaces, where there is not enough spectral contrast to distinguish cloud. For the reason visual inspection can be used for the truth metric for the verification exercise. In this study, we compared between CLAUDIA1-CAI and CLAUDIA3-CAI for various land cover types, and evaluated the accuracy of CLAUDIA3-CAI by comparing the both of CLAUDIA1-CAI and CLAUDIA3-CAI against visual inspection of the same CAI images in tropical rainforests. Comparative results between CLAUDIA1-CAI and CLAUDIA3-CAI for various land cover types indicated that CLAUDIA3-CAI had tendency to identify bright surface and optically thin clouds, however, misjudge the edges of clouds as compared with CLAUDIA1-CAI. The accuracy of CLAUDIA3-CAI was approximately 89.5 % in tropical rainforests, which is greater than that of CLAUDIA1-CAI (85.9 %) for the test cases presented here.