摘要
传感器大气顶层波段平均太阳辐照度(Band-Averaged Extraterrestrial Solar Irradiance,BAESI)是表观反射率和星上辐亮度之间转换的重要因子,为了针对FY-4A/AGRI有关通道较为准确地计算这一数值,分别以韩国COMS/GOCI、美国GEOS-16/ABI、日本Himawari-8/9/AHI传感器为例,利用国际上常用的6条太阳光谱曲线与传感器光谱响应函数参与计算BAESI,并与各传感器BAESI标准值进行比较。结果表明,Kurucz计算结果平均绝对误差与均方根误差均最小,是最理想的太阳光谱曲线,此外,Kurucz计算值与各传感器各波段BAESI标准值的最大偏差均小于4.5%,最大偏差百分比及对应波段分别为-2.39%(GOCI-band3)、1.49%(ABI-band6)、4.28%(AHI-08-band6)、4.42%(AHI-09-band6)。因此选择Kurucz太阳光谱曲线参与计算FY-4A/AGRI传感器各波段BAESI值,给出相应结果,并分析了计算值的不确定性,解决了将表观反射率转换为辐射亮度的困难。
The Band-averaged Extraterrestrial Solar Irradiance(BAESI)is the significant factor to convert apparent reflectance to radiance.In order to calculate precisely the BAESI value of FY-4A/AGRI,taking four similar sensors including COMS/GOCI from North Korea,GEOS-16/ABI form USA,and Himawari-8/9/AHI from Japan for an example for the experiment.The solar spectrum widely used including ASTM-E490,ASTM-G173-03,Kurucz,WRC,Wehrli85 and Thuilliersolar spectrum and every different sensor's spectral response functions were chosen to participate in the calculation of BAESI and then compare the estimated value with the standard values respectively.Results show that the Mean Absolute Error(MAE)and Root Mean Square Error(RMSE)of Kuruczsolar spectrum are both the smallest among all the spectrums,which is an ideal choice in the optional spectrums.Besides,compared with the standard values of the different sensors,Kurucz's estimated value's maxed errors are-2.39%(GOCI-band3),1.49%(ABI-band6),4.28%(AHI-08-band6)and 4.42%(AHI-09-band6)respectively,which are all less than 4.5%.Hence,Kurucz is chosen to calculate the BAESI values of every band of FY-4/AGRI,and the uncertainty of the results is analyzed.The results have solved the problem between the apparent reflectance and the radiance.Besides,the method is beneficial to calculate BAESI of other geostationary satellites.
作者
白杰
王国杰
牛铮
邬明权
BAI Jie;WANG Guojie;NIU Zheng;WU Mingquan(The State Key Laboratory of Remote Sensing Sciences,Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100101,China;University of Chinese Academy of Sciences,Beijing 100049,China;School of Remote Sensing&Geomatics Engineering,Nanjing University of Information Science&Technology,Nanjing 210044,China;School of Geographic Sciences,Nanjing University of Information Science&Technology,Nanjing 210044,China)
出处
《气象科学》
北大核心
2020年第4期520-526,共7页
Journal of the Meteorological Sciences
基金
中国科学院战略性先导科技专项(A类)(XDA19030304)
江苏省大学生创新创业训练计划(201710300031Z)。