检索规则说明:AND代表“并且”;OR代表“或者”;NOT代表“不包含”;(注意必须大写,运算符两边需空一格)
检 索 范 例 :范例一: (K=图书馆学 OR K=情报学) AND A=范并思 范例二:J=计算机应用与软件 AND (U=C++ OR U=Basic) NOT M=Visual
作 者:王晶杰[1,2] 李琦[1] 冯逍[1] 朱亚杰[1]
机构地区:[1]北京大学遥感与地理信息系统研究所 [2]68029部队
出 处:《测绘科学》2015年第2期78-83,共6页Science of Surveying and Mapping
摘 要:针对地面监测站覆盖范围有限且成本高的不足,该文采用遥感卫星与地面站点数据相结合的方式,利用简化的气溶胶反演算法反演京津冀地区2013年秋、冬季节两次污染过程的气溶胶光学厚度,分辨率为500m。以气溶胶自动观测网在北京站点的监测数据作为简化的气溶胶反演算法的输入参数,分析气溶胶光学厚度与气溶胶自动观测网对应地面站点的相关性;将所反演气溶胶光学厚度与京津冀地区空气质量监测站点的细颗粒物浓度24h均值进行相关性分析,发现除近海城市外,相关性均较高。结果表明,简化的气溶胶反演算法适用于区域污染过程中的气溶胶光学厚度反演,反演精度高,对空气质量具有较好的监测能力。This paper analyzed two air pollution events during autumn and winter in 2013 based on satellite and surface observations.The study using SARA algorithm inversed AOD with resolution of 500 meters,and validated it using AERONET data.The 500 m AOD showed high consistency with ground-based AOD measurements,with correlation co-efficients are 0.937 and 0.866,root mean square errors(RMSE)are 0.115 and 0.262 in Beijing_RADI and XiangHe sites,respectively.The results showed high accuracy of SARA AOD during pollution events.Then the paper analyzed the correlation between AOD and PM2.5(Fine Particulate Matter),which was 24 hour averaged value.It showed that they had high correlation(R2-0.7)except Tianjin and Qinhuangdao,which are near Bohai Sea.The results proved that SARA AOD had high capability in representing air quality,which would have more effective application in the future.
关 键 词:京津冀地区 简化的气溶胶反演算法 气溶胶光学厚度 细颗粒物
分 类 号:P237[天文地球—摄影测量与遥感]
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在链接到云南高校图书馆文献保障联盟下载...
云南高校图书馆联盟文献共享服务平台 版权所有©
您的IP:216.73.216.229