An improvement of snow/cloud discrimination from machine learning using geostationary satellite data

Snow and cloud discrimination is a main factor contributing to errors in satellite-based snow cover.To address the error,satellite-based snow cover performs snow reclassification tests on the cloud pixels of the cloud mask,but the error still remains.Machine Learning(ML)has recently been applied to remote sensing to calculate satellite-based meteorological data,and its utility has been demonstrated.In this study,snow and cloud discrimination errors were analyzed for GK-2A/AMI snow cover,and ML models(Random Forest and Deep Neural Network)were applied to accurately distinguish snow and clouds.The ML-based snow reclassified was integrated with the GK-2A/AMI snow cover through post-processing.We used the S-NPP/VIIRS snow cover and ASOS in situ snow observation data,which are satellite-based snow cover and ground truth data,as validation data to evaluate whether the snow/cloud discrimination is improved.The ML-based integrated snow cover detected 33–53%more snow compared to the GK-2A/AMI snow cover.In terms of performance,the F1-score and overall accuracy of the GK-2A/AMI snow cover was 73.06%and 89.99%,respectively,and those of the integrated snow cover were 76.78–78.28%and 90.93–91.26%,respectively.