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

International Journal of Digital Earth 2022年第1期15卷 2355-2375页

作者： Donghyun Jin Kyeong-Sang Lee Sungwon Choi Noh-Hun Seong Daeseong Jung Suyoung Sim Jongho Woo uujin jeon Yugyeong Byeon Kyung-Soo Han Division of Earth Environmental System Science (Major of Spatial Information System Engineering) Pukyong National UniversityBusanKorea Korea Institute of Ocean Science and Technology Korea Ocean Satellite CenterBusanKorea BK21 FOUR Project of the School of Integrated Science for Sustainable Earth Environmental Disaster Pukyong National UniversityBusanKorea

Snow and cloud discrimination is a main factor contributing to errors in satellite-based snow *** address the error,satellite-based snow cover performs snow reclassification tests on the cloud pixels of the cloud mask,but the error still *** Learning(ML)has recently been applied to remote sensing to calculate satellite-based meteorological data,and its utility has been *** 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 *** ML-based snow reclassified was integrated with the GK-2A/AMI snow cover through *** 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 *** ML-based integrated snow cover detected 33–53%more snow compared to the GK-2A/AMI snow *** 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.

关键词： Geostationary satellite GK-2A/AMI snow cover product snow/cloud discrimination machine learning remote sensing

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