摘要
卫星遥感在沙尘气溶胶的监测中具有独特优势,是沙尘判识研究的重要技术支撑。为提高沙尘暴识别精度,利用FY-4A气象卫星14个通道的反射率及亮温数据,通过目标物多通道阈值综合分析方法识别沙尘天气现象,确立并优化沙尘暴判识阈值,为沙尘暴天气现象智能分析判识模型提供支持。结果表明:可见光与近红外通道在有云时变化比较明显,在沙尘区域可见光与近红外通道和短波红外通道亮温值差距较小,并且短波红外通道值大于可见光与近红外通道值;中波红外通道在沙尘区表现出相同的变化趋势,并且在沙尘区亮温值明显高。经过阈值优化,针对大范围的沙尘天气识别结果与观测实况基本吻合,所以多通道阈值法可以有效提升沙尘区的判识精度。
Satellite remote sensing has unique advantages in monitoring sand and dust aerosols,and is a key technical support for identification research of sand and dust.To improve the accuracy of dust storm identification,the reflectivity and bright temperature data of 14 channels of FY-4A meteorological satellites are utilized to identify sand and dust weather phenomena through the comprehensive analysis method of multi-channel thresholds of targets,and to establish and optimize the identification thresholds of sand and dust storms,to provide support for the intelligent analysis of identi-fication models of sand and dust storms weather phenomena.The results show that visible and near-infrared channels change significantly when there are clouds,the difference between the bright temperature values of visible and near-in-frared channels and short-wave infrared channels in the dust area is small,and the value of short-wave infrared channel is greater than that of visible and near-infrared channel.The mid-wave infrared channels showed the same trend in the dust area,and the bright temperature value in the dust area was significantly higher.After multiple threshold optimiza-tions,the recognition effect of sand and dust weather is getting better and better,especially for large-scale sand and dust weather,and the recognition results are consistent with the observation reality.The multi-channel threshold method has a good effect on the identification of sand and dust weather and can be used for the identification of sand and dust areas.
作者
范彦芳
杨有林
武雅珍
杨晓旻
程晓龙
马宏
FAN Yanfang;YANG Youlin;WU Yazhen;YANG Xiaomin;CHENG Xiaolong;MA Hong(Key Laboratory for Meteorological Disaster Monitoring and Early Warning and Risk Management of Characteristic Agriculturein in Arid Regions,CMA,Yinchuan 750002,China;Ningxia Key Lab of Meteorological Disaster Prevention and reduction,Yinchuan 750002,China;Ningxia Meteorological Information Center,Yinchuan 750002,China)
出处
《成都信息工程大学学报》
2025年第1期100-106,共7页
Journal of Chengdu University of Information Technology
基金
中国气象局旱区特色农业气象灾害监测预警与风险管理重点实验室开放研究资助项目(CAMF-202304)。
