Channel selection for AIUS temperature inversion based on GF-5

Xifeng CAO; Xiaoying LI

doi:10.11834/jrs.20209049

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Channel selection for AIUS temperature inversion based on GF-5
Vol. 24, Issue 10, Pages: 1157-1167(2020)
DOI： 10.11834/jrs.20209049

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曹西凤，李小英.2020.高分五号AIUS气温反演通道的选择.遥感学报，24（10）： 1157-1167

Cao X F and Li X Y. 2020. Channel selection for AIUS temperature inversion based on GF-5. Journal of Remote Sensing（Chinese）， 24（10）：1157-1167［DOI：10.11834/jrs.20209049］
曹西凤，李小英.2020.高分五号AIUS气温反演通道的选择.遥感学报，24（10）： 1157-1167 DOI： 10.11834/jrs.20209049.

Cao X F and Li X Y. 2020. Channel selection for AIUS temperature inversion based on GF-5. Journal of Remote Sensing（Chinese）， 24（10）：1157-1167［DOI：10.11834/jrs.20209049］ DOI：

摘要

高分五号卫星搭载的大气环境红外甚高光谱分辨率探测仪AIUS（Atmospheric Infrared Ultraspectral Sounder）是中国研发的第一个红外波段具有甚高光谱分辨率的掩星探测仪，为大气分布状态的研究提供了强有力的数据支持。气温是表征大气热力状态的重要参数，其分布状态直接影响地—气系统长波辐射和太阳短波辐射的相互作用，进而对全球辐射能量的收支平衡产生影响。采用高光谱数据直接进行气温反演的数据量较大、存储不便，并且不同波谱信息之间存在相关性，因此需要进行通道选择。考虑到实际气温反演精度受干扰成分的影响较大，致使反演精度较低，本文基于信息熵的通道选择算法与目标成分及干扰成分的敏感性分析相结合开展试验完成AIUS掩星观测数据的通道选择，为后期AIUS的温度反演奠定基础。首先，基于RFM（Reference Forward Model）正向辐射传输模型进行目标成分及干扰成分的敏感性分析，探究通道选择的可行性及进行初步的通道选择；然后，基于信息熵的理论进行通道选取，并对结果进行分析讨论；最后，依据通道选取结果，结合最优化算法进行温度反演效果验证。研究表明，掩星观测对气温变化具有较高的敏感性，该通道选择方法在掩星观测模式下是可行的；随通道数目的增加，信息量大体呈对数型增加，在通道个数为1000时接近饱和；在保证运算效率条件下，选用AIUS的100个通道进行温度反演，可基本满足精度需求。

Abstract

Atmospheric ultraspectral sounder (AIUS) of GF-5 satellite was successfully launched in May 2018. AIUS is the first very high-resolution infrared-band occultation observation sensor developed in China that provides strong data support for investigating atmospheric distribution state. Air temperature is an important parameter that characterizes the thermodynamic state of the atmosphere. Its distribution state directly affects the interaction between long- and short-wave radiation of the earth-atmosphere system and subsequently influences global balance of radiation energy. Direct inversion of temperature using hyperspectral data is limited by its large size, inconvenient storage, and requirement of channel selection due to the correlation between different spectral information. The strong influence of interfering components on the accuracy of actual air temperature inversion results in low inversion accuracy. Channel selection algorithm based on information entropy is combined with sensitivity analysis of target and interfering components in this study to carry out experiments, complete the channel selection of AIUS occultation observation data, and provide a theoretical foundation for future AIUS temperature inversion investigations. First, the sensitivity analysis of target and interference components based on the RFM model is conducted to explore the feasibility of channel selection and perform preliminary channel selection. Second, channel selection is carried out on the basis of information entropy theory, and the results are analyzed and discussed. Finally, the temperature inversion effect is verified using optimization algorithm on the basis of the channel selection results. Occultation observation is highly sensitive to temperature changes, and channel selection is feasible in the occultation observation mode. The increase in number of channels logarithmically increases the amount of information and is close to saturation when the number of channels is equal to 1000. One hundred AIUS channels are selected for temperature inversion to improve operational efficiency and meet precision requirements.

关键词

遥感高分五号AIUS掩星探测气温敏感性通道信息熵

Keywords

remote sensingGF-5AIUSoccultation detectiontemperaturesensitivitychannelinformation entropy

references

Cameron J, Collard A and English S. 2005. Operational use of AIRS observations at the Met Office//Proceedings of the 4th International TOVS Study Conferences. Beijing: [s.n.]:25-31

Crevoisier C, Chedin A and Scott N A. 2003. AIRS channel selection for CO2 and other trace-gas retrievals. Quarterly Journal of the Royal Meteorological Society, 129(593): 2719-2740 [DOI: 10.1256/qj.02.180http://dx.doi.org/10.1256/qj.02.180]

Du H D, Huang S X and Shi H Q. 2008. Method and experiment of channel selection for high spectral resolution data. Acta Physica Sinica, 57(12): 7685-7692

杜华栋, 黄思训, 石汉青. 2008. 高光谱分辨率遥感资料通道最优选择方法及试验. 物理学报, 57(12): 7685-7692 [DOI: 10.3321/j.issn:1000-3290.2008.12.045http://dx.doi.org/10.3321/j.issn:1000-3290.2008.12.045]

Dudhia A. 2017. The reference forward model (RFM). Journal of Quantitative Spectroscopy and Radiative Transfer, 186: 243-253 [DOI: 10.1016/j.jqsrt.2016.06.018http://dx.doi.org/10.1016/j.jqsrt.2016.06.018]

Fourrié N, Thépaut J N. 2002. Validation of the NESDIS Near-Real-Time AIRS Channel Selection. Reading: European Centre for Medium Range Weather Forecasts

He Q R. 2017. Study on Retrieving the Atmospheric Temperature and Humidity Profiles from Measurements of Microwave Humidity and Temperature Sounder on FY-3C Satellite. Beijing: National Space Science Center, the Chinese Academy of Sciences: 1-5

贺秋瑞. 2017. FY-3C卫星微波湿温探测仪反演大气温湿廓线研究. 北京: 中国科学院国家空间科学中心: 1-5

Huang J. 2003. The Study on the Algorithm of Retrieving the Temperature and Moisture Profile from Satellite Infrared Measurements. Lanzhou: Lanzhou University: 1-4

黄静. 2003. 利用卫星红外辐射资料反演大气温湿廓线的算法研究. 兰州: 兰州大学: 1-4

Jiang D M. 2007. Approach to High Spectral Resolution Infrared Remote Sensing of Atmospheric Temperature and Humidity Profiles. Nanjing: Nanjing University of Information Science and Technology: 1-15

蒋德明. 2007. 高光谱分辨率红外遥感大气温湿度廓线反演方法研究. 南京: 南京信息工程大学: 1-15

Li X Y, Chen L F, Guo A Y, Su L, Jia S L, Tao J H and Zhang Y. 2015. Sub-millimeter wave limb sounding simulation of the plume flow of a high-flying vehicle. Journal of Remote Sensing, 19(1): 54-61

李小英, 陈良富, 国爱燕, 苏林, 贾松林, 陶金花, 张莹. 2015. 高空飞行器尾焰亚毫米波临边探测模拟与分析. 遥感学报, 19(1): 54-61 [DOI: 10.11834/jrs.20153314http://dx.doi.org/10.11834/jrs.20153314]

Li X Y, Chen L F, Su L, Zhang Y and Tao J H. 2013. Overview of sub-millimeter limb sounding. Journal of Remote Sensing, 17(6): 1325-1344

李小英, 陈良富, 苏林, 张莹, 陶金花. 2013. 亚毫米波临边探测发展现状. 遥感学报, 17(6): 1325-1344 [DOI: 10.11834/jrs.20132279http://dx.doi.org/10.11834/jrs.20132279]

Li X Y, Cheng T H, Xu J, Shi H L, Zhang X Y, Ge S L, Zou M M, Wang H M, Wang Y P, Zhu S Y and Miao J. 2018. Trace gas retrieval from AIUS: algorithm description and O3 retrieval assessment. Preprints [DOI: 10.20944/preprints201804.0257.v1http://dx.doi.org/10.20944/preprints201804.0257.v1]

Liu H, Dong C H, Zhang W J and Zhang P. 2008. Retrieval of clear-air atmospheric temperature profiles using AIRS observations. Acta Meteorologica Sinica, 66(4): 513-519

刘辉, 董超华, 张文键, 张鹏. 2008. AIRS晴空大气温度廓线反演试验. 气象学报, 66(4): 513-519 [DOI: 10.11676/qxxb2008.048http://dx.doi.org/10.11676/qxxb2008.048]

Rabier F, Fourrié N, Chafäi D and Prunet P. 2002. Channel selection methods for infrared atmospheric sounding interferometer radiances. Quarterly Journal of the Royal Meteorological Society, 128(581): 1011-1027 [DOI: 10.1256/0035900021643638http://dx.doi.org/10.1256/0035900021643638]

Rodgers C D. 1996. Information content and optimization of high-spectral-resolution measurements//Proceedings of SPIE 2830, Optical Spectroscopic Techniques and Instrumentation for Atmospheric and Space Research II. Denver, CO, United States: SPIE: 136-147 [DOI: 10.1117/12.256110http://dx.doi.org/10.1117/12.256110]

Wang H M, Li X Y, Chen L F, Wang Y P, Zhang Y, Zou M M, Yu C and Zhu S Y. 2017. Simulating sensitivity of O3 and HCl with THz limb sounding. Journal of Remote Sensing, 21(5): 653-664

王红梅, 李小英, 陈良富, 王雅鹏, 张莹, 邹铭敏, 余超, 朱松岩. 2017. 太赫兹临边探测O3与HCl敏感性分析. 遥感学报, 21(5): 653-664

Wang H M, Li X Y, Xu J, Zhang X Y, Ge S L, Chen L F, Wang Y P, Zhu S Y, Miao J and Si Y D. 2018. Assessment of retrieved N2O, NO2, and HF profiles from the atmospheric infrared ultraspectral sounder based on simulated spectra. Sensors, 18(7): 2209 [DOI: 10.3390/s18072209http://dx.doi.org/10.3390/s18072209]

Wang Y P. 2017. Research on Temperature/Pressure and Ozone Retrieval Algorithm Based on Atmospheric Infrared Ultraspectral Spectrometer. Beijing: Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences: 1-7

王雅鹏. 2017. 大气红外甚高分辨率掩星探测仪温压及臭氧廓线反演算法研究. 北京: 中国科学院大学(中国科学院遥感与数字地球研究所): 1-7

Xu P. 2005. Retrieval of Atmospheric Temperature and Humidity Profiles from NOAA/ATOVS and Data Assimilating Experiments in Meteorological Mesoscale Model. Qingdao: Ocean University of China: 1-4

徐萍. 2005. NOAA卫星ATOVS资料反演大气温、湿廓线及其在中尺度气象模式中的同化试验. 青岛: 中国海洋大学: 1-4

Zhang J W, Wang G, Zhang H, Huang J, Chen J and Wu L L. 2011. Experiment on hyper-spectral atmospheric infrared sounder channel selection based on the cumulative effect coefficient of principal component. Transactions of Atmospheric Science, 34(1): 36-42

张建伟, 王根, 张华, 黄静, 陈靖, 吴玲玲. 2011. 基于主成分累计影响系数法的高光谱大气红外探测器的通道选择试验. 大气科学学报, 34(1): 36-42 [DOI: 10.3969/j.issn.1674-7097.2011.01.005http://dx.doi.org/10.3969/j.issn.1674-7097.2011.01.005]

Zhang K. 2016. New Methods in Retrieving Atmospheric Temperature and Moisture Profiles from Satellite Observations. Beijing: Chinese Academy of Meteorological Sciences: 1-6

张堃. 2016. 卫星资料反演大气温湿度廓线的新方法研究. 北京: 中国气象科学研究院: 1-6

Zhang S P. 2009. Hyperspectral atmospheric sounding information channel seletion study. Scientia Meteorologica Sinica, 29(4): 475-481

张水平. 2009. AIRS资料反演大气温度廓线的通道选择研究. 气象科学, 29(4): 475-481 [DOI: 10.3969/j.issn.1009-0827.2009.04.007http://dx.doi.org/10.3969/j.issn.1009-0827.2009.04.007]

Zhou A M. 2017. Atmospheric Temperature and Humidity Profiles Retrieval from Hyperspectral Infrared Simulation Data Based on FY-4. Nanjing: Nanjing University of Information Science and Technology: 1-10

周爱明. 2017. 基于风云四号高光谱红外模拟资料反演大气温湿廓线试验研究. 南京: 南京信息工程大学: 1-10

Zhou Y C. 2010. Retrieval of Atmospheric Temperature and Humidity Profiles by Ground Based Multi-Channel Microwave Sounders. Beijing: Graduate School of Chinese Academy of Sciences (Space Science and Application Research Center): 1-4

周玉驰. 2010. 地基多通道微波辐射计反演大气温湿廓线的研究. 北京: 中国科学院研究生院(空间科学与应用研究中心): 1-4

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