摘要
从霾污染遥感监测业务化流程出发,选取EOS/MODIS为主要数据源,MODIS气溶胶产品及气象数据为辅,在数据预处理的基础上,利用LM-BP人工神经网络模型算法反演区域大气颗粒物浓度,分析了可获取的遥感监测指标及气象指标对霾污染的贡献率,筛选出可业务化的霾污染遥感评价指标。对2013年1月江苏省2次典型的霾污染进行了星地同步分析,从分析结果来看,霾污染遥感监测结果与地面实测结果基本一致,霾污染遥感监测可以作为地面监测的有效补充,宏观反映区域霾污染空间分布,为大气污染防治提供有力的技术支撑。
On the basis of the remote sensing monitoring operational processes of haze pollution, EOS/ MODIS and MODO4_L2 product were chosen as the main data and the secondary data, respectively. The LM - BP artificial neural network was used to retrieve the mass concentration of regional atmospheric particles. The contribution rate of the remote sensing monitoring index and the meteorological index on the haze pollution were analyzed. The operational remote sensing evaluation index of haze pollution was se lected. Based on the synchronously analysis of two typical haze pollution cases in Jiangsu Province in January 2013, the remote sensing result was basically consistent with the ground measured result. Haze pollution remote sensing monitoring can be used as an effective supplement to the ground monitoring, macroscopically reflecting the spatial regional distribution of haze pollution.
出处
《环境监控与预警》
2014年第5期15-18,共4页
Environmental Monitoring and Forewarning
基金
江苏省环保科研课题项目(201130)
江苏省环境监测科研基金项目(1217)
关键词
江苏省
霾
遥感
监测
业务化
Jiangsu Province
Haze
Remote sensing
Monitoring
Operational application
