Random Forest-Based Snow Cover Mapping in China Using Fengyun-3B VIRR Data  

作　　者：Yuchen XIE Yonghong LIU Yeping ZHANG Fuzhong WENG Shanyou ZHU Zhaojun ZHENG Shihao TANG 

机构地区：[1]School of Remote Sensing&Geomatics Engineering,Nanjing University of Information Science&Technology,Nanjing 210044 [2]CMA Earth System Modeling and Prediction Centre,China Meteorological Administration(CMA),Beijing 100081 [3]State Key Laboratory of Severe Weather,Chinese Academy of Meteorological Sciences,China Meteorological Administration,Beijing 100081 [4]National Satellite Meteorological Centre,China Meteorological Administration,Beijing 100081

出　　处：《Journal of Meteorological Research》2023年第5期666-689,共24页气象学报（英文版）

基　　金：Supported by the National Natural Science Foundation of China (U2242211);Hunan Provincial Natural Science Foundation (2021JC0009);Jiangsu Provincial Natural Science Foundation (BK20201505)。

摘　　要：Currently,there is variability in the spectral band thresholds for snow cover recognition using remote sensing in different regions and for complex terrains.Using Fengyun-3B Visible and Infra-Red Radiometer(FY-3B VIRR)satellite data,we applied random forest(RF)methodology and selected 13 feature variables to obtain snow cover.A training set was generated,containing approximately 1 million snow and nonsnow samples obtained in China from the snow monitoring reports issued by the National Satellite Meteorological Centre and four snow cover products from the Interactive Multi-sensor Snow and Ice Mapping System(IMS),the FY-3B Multi-Sensor Synergy(MULSS),the Moderate Resolution Imaging Spectroradiometer(MODIS)snow cover product(MYD10A1),and the National Cryosphere Desert Data Center(NCDC).This training set contained many different samples of cloud types and snow under forest cover to help effectively distinguish snow and clouds and improve the recognition rate of snow under forest cover.Then,two RF snow cover recognition models were constructed for the snow and nonsnow seasons and they were used to conduct daily snow cover recognition in China from 2011 to 2020.The results show that the RF models constructed based on FY-3B VIRR data have good recognition performance for shallow snow,understory snow,and snow on the Qinghai–Tibetan Plateau.The recognition accuracy against weather stations and the spatial consistency with the IMS product are better than the MULSS,MYD10A1,and NCDC products.The overall accuracy of the RF product is 90.6%,and the recall rate is 93.8%.The omission and commission errors are 6.2%and11.1%,respectively.Unlike other existing snow cover algorithms,the established RF model skips the complicated atmospheric correction and cloud identification processes and does not involve external auxiliary data;thus,it is more easily popularized and operationally applicable to generating long-time series snow cover products.

关 键 词：snow identification random forest Fengyun-3B Visible and Infra-Red Radiometer feature selection 

分 类 号：P407[天文地球—大气科学及气象学]