激光与光电子学进展, 2020, 57 (3): 030101, 网络出版: 2020-02-17   

基于卫星偏振遥感的细粒子气溶胶光学厚度反演 下载: 972次

Retrieval of Fine Mode Aerosol Optical Depth Based on Satellite Polarization Remote Sensing
作者单位
1 中国海洋大学信息科学与工程学院, 山东 青岛 266100
2 国家海洋局第一研究所, 山东 青岛 266061
摘要
利用PARASOL卫星搭载的多角度偏振地球反射率探测仪-3(POLDER-3)的偏振数据,反演了香河地区细粒子气溶胶光学厚度。反演数据与POLDER、MODIS业务化产品及AERONET数据进行对比分析,POLDER的细粒子气溶胶光学厚度反演效果显著优于MODIS产品,相关系数由0.67升至0.93,平均误差由0.32降至0.15。将POLDER偏振数据与神经元网络方法相结合,相关系数升至0.94,平均误差降为0.11。将该神经网络(NN)训练模型应用于杭州和香港地区进行验证,在杭州地区反演精度相似,在香港地区适用性较差。研究表明,利用POLDER偏振数据结合神经网络方法来提取细粒子气溶胶信息是可行的。
Abstract
Fine mode aerosol optical depth in Xianghe was calculated, using data from the onboard multi-angle polarization sensor POLDER-3 in the PARASOL satellite. The retrieval results were compared with the operational products of POLDER, MODIS, and AERONET data. The results show that the accuracy of POLDER using polarization remote sensing is significantly better than that of unpolarized MODIS. The correlation coefficient is increased from 0.67 to 0.93, and the average error is reduced from 0.32 to 0.15. As combined with the neural network (NN) method, the method gave correlation coefficient of 0.94 and standard deviation of only 0.11. Then, NN was applied to Hangzhou and Hong Kong, respectively. The verification results show that it has similar accuracy in Hangzhou but poor applicability in Hong Kong. The research shows it is feasible to use the NN to extract fine mode aerosol information from polarized signals.

高迦南, 李丽萍, 崔廷伟, 陈晨. 基于卫星偏振遥感的细粒子气溶胶光学厚度反演[J]. 激光与光电子学进展, 2020, 57(3): 030101. Jianan Gao, Liping Li, Tingwei Cui, Chen Chen. Retrieval of Fine Mode Aerosol Optical Depth Based on Satellite Polarization Remote Sensing[J]. Laser & Optoelectronics Progress, 2020, 57(3): 030101.

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