月球表层微波辐射模型及月壤厚度反演方法研究
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摘要
获取月壤厚度信息是我国探月工程的重要目标之一,其探测方法包括直接和间接两种方法,嫦娥一号搭载的微波探测仪是基于月球微波辐射间接地探测月壤厚度信息,从而估算全月的氦-3资源的分布。
本文首先介绍目前已有的月表微波辐射传输模型,对模型及其参数的选择进行分析,并将Apollo15地区的实测亮温与利用模型计算的模拟亮温进行比较,分析了月表参数(温度、月球阴影及粗糙表面)对亮温的影响。
为了从嫦娥一号实测亮温反演月壤厚度,本文分别使用约束最优化搜索方法和神经网络方法进行厚度反演研究,并结合Apollo地区进行反演验证,将反演厚度值与实测值进行对比。
分析结果表明月表温度是亮温影响的主要因素,在低纬地区,月球阴影存在的情况下,对高频的亮温影响很大;粗糙表面对亮温的影响会直接导致厚度反演的误差变大;基于约束最优化搜索方法和神经网络的厚度反演能够反演出合理的厚度,但与Apollo实测厚度有一定差距。
One of the important aims of the lunar exploration is to detect the lunar regolith depth, including direct way and indirect ways. The microwave radiometer carried on the CE-1 is a indirect way to detect the surface temperature, dielectric constant and the heat flow information of the lunar surface. The final purpose is to retrieve the lunar regolith depth and then evaluate the full moon He-3 distribution.
This paper introduces the existing lunar surface microwave transfer models, and compares the models from the layer and parameter selection. The brightness temperatures simulated from the models are compared with the brightness temperature measured by CE-1 at the Apollo 15 area. Then the lunar surface parameters' influence on the brightness temperature is analyzed, such as the temperature, lunar shadow effect and the rough surface.
In order to retrieve the lunar regolith depths from the measured brightness temperature, the constraint optimization search method and neural network method are studied. Considering the parameters at the Apollo area are sufficient, the Apollo area is chosen to evaluate the lunar regolith depth retrieval method.
The results show that the lunar surface temperature is the main factors affecting the brightness temperature. At the low latitude area, when the lunar surface shadow area is obvious, the shadow effect to the high frequency brightness temperature should be considered. The brightness temperature influenced by the rough surface can directly lead to the error of the retrieval lunar regolith depth become greater. Though the lunar regolith depths retrieved form constraint optimization search method and the neural network at the Apollo area are in the rational limits, compared with the measured regolith thickness, the differences are obvious.
本文首先介绍目前已有的月表微波辐射传输模型,对模型及其参数的选择进行分析,并将Apollo15地区的实测亮温与利用模型计算的模拟亮温进行比较,分析了月表参数(温度、月球阴影及粗糙表面)对亮温的影响。
为了从嫦娥一号实测亮温反演月壤厚度,本文分别使用约束最优化搜索方法和神经网络方法进行厚度反演研究,并结合Apollo地区进行反演验证,将反演厚度值与实测值进行对比。
分析结果表明月表温度是亮温影响的主要因素,在低纬地区,月球阴影存在的情况下,对高频的亮温影响很大;粗糙表面对亮温的影响会直接导致厚度反演的误差变大;基于约束最优化搜索方法和神经网络的厚度反演能够反演出合理的厚度,但与Apollo实测厚度有一定差距。
One of the important aims of the lunar exploration is to detect the lunar regolith depth, including direct way and indirect ways. The microwave radiometer carried on the CE-1 is a indirect way to detect the surface temperature, dielectric constant and the heat flow information of the lunar surface. The final purpose is to retrieve the lunar regolith depth and then evaluate the full moon He-3 distribution.
This paper introduces the existing lunar surface microwave transfer models, and compares the models from the layer and parameter selection. The brightness temperatures simulated from the models are compared with the brightness temperature measured by CE-1 at the Apollo 15 area. Then the lunar surface parameters' influence on the brightness temperature is analyzed, such as the temperature, lunar shadow effect and the rough surface.
In order to retrieve the lunar regolith depths from the measured brightness temperature, the constraint optimization search method and neural network method are studied. Considering the parameters at the Apollo area are sufficient, the Apollo area is chosen to evaluate the lunar regolith depth retrieval method.
The results show that the lunar surface temperature is the main factors affecting the brightness temperature. At the low latitude area, when the lunar surface shadow area is obvious, the shadow effect to the high frequency brightness temperature should be considered. The brightness temperature influenced by the rough surface can directly lead to the error of the retrieval lunar regolith depth become greater. Though the lunar regolith depths retrieved form constraint optimization search method and the neural network at the Apollo area are in the rational limits, compared with the measured regolith thickness, the differences are obvious.
引文
[1]欧阳自远.天体化学.北京:科学出版社, 1988.
[2]欧阳自远.月球科学概论.北京:中国宇航出版社, 2005.
[3]李雄耀,王世杰,陈丰等.月壤厚度的研究方法与进展.矿物学报. 2007, 27(1): 64-68.
[4] Nakamura Y, Dorman J, Duennebier F, et al. Shallow lunar structure determined from the passive seismic experiment. The Moon. 1975, 13(1-3): 3-15.
[5] Cooper M R, Kovach R L, Watkins J S. Lunar near surface structure. Rev Geophys Space Phys. 1974, 12: 291-308.
[6] Duenneber F K, Watkins J S, Kovach R L. Results from the lunar surface profiling experiment. Abstracts of the lunar and Planetary Science Conference. 1974, 5: 183.
[7] D, Strangway. Geophysics and Lunar Resources, Lunar Bases and Space Activities of the 21st . Century. Houston: TX: Lunar and Planetary Institute, 1985.
[8] Oberbeck V R, Quaide W L. Genetic implication of lunar regolith thickness variations. Icarus, 1968, 9: 446-465.
[9] Quaide W L, Oberbeck V R. Thickness determination of the lunar surface layer from lunar impact craters. J Geophys Res. 1968, 73: 5247-5270.
[10] Rennilson J J, Dragg J L, Mbrris E C, et al. Lunar surface topography. Surveyor I mission report, part II: Scientific data and results, NASA JPL Technical Report. 1966, 32-1023: 7-44.
[11] Willcox B B, Robinson M S, Thomas P C, et al. Constraints on the depth and variability of the lunar regolith. Meteoritics & Planetary Science. 2005, 40: 695-710.
[12] Shkuratov Y G, Bondarenko N V. Regolith layer thickness mapping of the moon by radar and optical data. Icarus. 2001, 149: 329-338.
[13]法文哲,金亚秋.月球表面多通道辐射亮度温度的模拟与月壤厚度的反演.自然科学进展. 2006, 16(1): 86-94.
[14]郑永春.模拟月壤研制与月壤的微波辐射特性研究[博士学位论文].中国科学院地球化学研究所, 2005.
[15]王振占,李芸,张晓辉等.“嫦娥一号”卫星微波探测仪数据处理模型和月表微波亮温反演方法.中国科学D辑:地球科学. 2009, 39(8): 1029-1044.
[16]法文哲,金亚秋.“嫦娥”1号对月球表面微波辐射观测分析及其月壤厚度反演.中国科学:信息科学. 2010, 40: 115-127.
[17]孟治国.月壤参数的辐射传输模拟与查找表反演技术研究[博士学位论文].吉林大学, 2008.
[18]李青侠,郭伟,张祖荫,神经网络用于反演微波亮度温度,微波学报,2006,22卷增刊:162-165.
[19] Mejia C, Thiria S, Badran F, et al. Determination of the geophysical model function of ERS1 scatterometer by the use of neural networks. Journal of Geophysical Research. 1998, 103(C6): 12853-12868.
[20] Mejia C, Badran F, Bentamy A, et al. Determination of the geophysical model function of NSCAT and its corresponding variance by the use of neural networks. Journal of Geophysical Research. 1999, 104: 11539-11556.
[21] Tran N, Thiria S, Crepon M, et al. Validation of the QS-CAT NRCS on the advanced neural network NSCAT GMF and estimation of neural network QSCAT GMF. Proceedings of the 2000 International Geoscience and Remote Sensing Symposium. 2000.
[22]解学通,方裕,陈克海等. SeaWinds散射计海面风场神经网络建模研究.地理与地理信息科学. 2007, 23(2): 12-17.
[23]陈克海,解学通,黄舟等.人工神经网络在星载散射计海面风场反演建模中的应用.北京大学学报:自然科学. 2007, 43(4): 460-467.
[24]宋新改.神经网络反演散射计风场算法的研究[硕士学位论文].中国海洋大学. 2005.
[25]毛克彪,唐华俊,陈仲新等.一个用神经网络优化的针对ASTER数据反演地表温度和发射率的多波段算法.国土资源遥感. 2007, 3: 18-22.
[26]毛克彪,唐华俊,李丽英等.一个MODIS数据同时反演地表温度和发射率的神经网络算法.遥感信息(理论研究). 2007, 9-15.
[27]张雪惠,官莉,王振会等.利用人工神经网络方法反演大气温度廓线.气象. 2009, 35(11): 137-142.
[28]施英妮.基于人工神经网络技术的高光谱遥感浅海水深反演研究[硕士学位论文].中国海洋大学, 2005.
[29]丁静.基于神经网络的二类水体大气修正与水色要素反演[博士学位论文].中国海洋大学, 2004.
[30] Zhenzhan Wang, Yun Li, Jingshan Jiang, et al. Microwave transfer models and brightness temperature simulations of MWS for remote sensing lunar surface on CE-1 satellite. ICMMT Proceedings. Nanjing, 2008.
[31] Zhang D H, Zhang X H, Wang Z Z, et al. Mechanism of Lunar Soil Depth Sounding and Ground Validation Experiment for the CE-1 Lunar Microwave Sounder. Sci China Ser D-Earth Sci. 2009, 39(8): 1097-1104.
[32]蓝爱兰.月球表层媒质的被动遥感机理及厚度反演研究[硕士学位论文].中国科学院研究生院, 2004.
[33]李青侠,李雄耀,陈萍等.粗糙表面对月表微波辐射亮度温度影响的初步分析.绕月探测工程探测数据应用研究进展论文集. 2009, 247-252.
[34] Hu G.P., Li Q. X., Zheng Y. C., et al. Brightness temperature of the global moon: Comparison between theoretical simulation and observation by Chang'E-1 lunar orbiter. 2010 International Conference on Microwave and Millimeter Wave Technology (ICMMT 2010), 2010 , 1735-1738.
[35] A. R. Vasavada, D. A. Paige, S. E. Wood. Near-surface temperatures on Mercury and the Moon and the stability of polar ice desposits. Icarus. 1999, 141: 179-193.
[36] G. H. Heiken, D. T. Vaniman, B. M. French. Lunar sourcebook: a user’s guide to the moon. Cambridge University Press. 1991.
[37] Olhoeft G R. Strangway D W. Dielectric properties of the first 100 meters of the Moon. Earth Planet Sci. Lett. 1975, 24: 394-404.
[38] S. J. Keihm, M. G. Langsth. Surface brightness temperature at the Apollo 17 heat flow site: Thermal conductivity of the upper 15cm of regolith. Proc.4th Lunar Science Conference. 1973.
[39]李芸,王振占,姜景山.月表温度剖面对于“嫦娥一号”卫星微波探测仪探测亮温影响的模拟研究.中国科学D辑:地球科学. 2009, 39(8): 1045-1058.
[40] David L, Mitchell, Imke de Pater. Microwave Imaging of Mercury’s Thermal Emission at Wavelengths from 0.3 to 20.5cm. ICARUS. 1994, 110: 2-32.
[41] S.J.Keihm, M.G. Langseth. Surface brightness temperatures at the Apollo 17 heat flow site: thermal conductivity of the upper 15cm of regolith. Proceedings of the Fourth science conference. 1980, 3: 2503-2513.
[42] Ulaby F T, Moore R K, Fung A K. Microwave Remote Sensing, Active and Passive, Vol.I. Addison-Wesley Publishing Company, 1981.
[43]宋铮,张建华.天线与电波传播.西安:西安电子科技大学出版社. 2003.
[44] B. J. Choudhury, T. J. Schmugge, A. Chang, and R.W. Newton,“Effect of surface roughness on the microwave emission from soil,”J. Geophys. Res., 1979, 84(C9). 5699–5706.
[45] J. Shi, L. Jiang, L. Zhang, K. Chen, J. Wigneron, and A. Chanzy, "A Parameterized Multifrequency-Polarization Surface Emission Model," IEEE Trans. Geosci. Remote Sensing, 2005, 43(12): 2831-2841..
[46] T. Mo and T. J. Schmugge,“A parameterization of the effect of surface roughness on microwave emission,”IEEE Trans. Geosci. Remote Sensing, 1987, 25: 47–54.
[47]冯天谨.神经网络技术.青岛:青岛海洋大学. 1994.
[48]王振占,李芸,姜景山等.用“嫦娥一号”卫星微波探测仪亮温反演月壤厚度和3He资源量评估的方法及初步结果分析.中国科学D辑:地球科学. 2009, 39(8): 1069-1084.
[2]欧阳自远.月球科学概论.北京:中国宇航出版社, 2005.
[3]李雄耀,王世杰,陈丰等.月壤厚度的研究方法与进展.矿物学报. 2007, 27(1): 64-68.
[4] Nakamura Y, Dorman J, Duennebier F, et al. Shallow lunar structure determined from the passive seismic experiment. The Moon. 1975, 13(1-3): 3-15.
[5] Cooper M R, Kovach R L, Watkins J S. Lunar near surface structure. Rev Geophys Space Phys. 1974, 12: 291-308.
[6] Duenneber F K, Watkins J S, Kovach R L. Results from the lunar surface profiling experiment. Abstracts of the lunar and Planetary Science Conference. 1974, 5: 183.
[7] D, Strangway. Geophysics and Lunar Resources, Lunar Bases and Space Activities of the 21st . Century. Houston: TX: Lunar and Planetary Institute, 1985.
[8] Oberbeck V R, Quaide W L. Genetic implication of lunar regolith thickness variations. Icarus, 1968, 9: 446-465.
[9] Quaide W L, Oberbeck V R. Thickness determination of the lunar surface layer from lunar impact craters. J Geophys Res. 1968, 73: 5247-5270.
[10] Rennilson J J, Dragg J L, Mbrris E C, et al. Lunar surface topography. Surveyor I mission report, part II: Scientific data and results, NASA JPL Technical Report. 1966, 32-1023: 7-44.
[11] Willcox B B, Robinson M S, Thomas P C, et al. Constraints on the depth and variability of the lunar regolith. Meteoritics & Planetary Science. 2005, 40: 695-710.
[12] Shkuratov Y G, Bondarenko N V. Regolith layer thickness mapping of the moon by radar and optical data. Icarus. 2001, 149: 329-338.
[13]法文哲,金亚秋.月球表面多通道辐射亮度温度的模拟与月壤厚度的反演.自然科学进展. 2006, 16(1): 86-94.
[14]郑永春.模拟月壤研制与月壤的微波辐射特性研究[博士学位论文].中国科学院地球化学研究所, 2005.
[15]王振占,李芸,张晓辉等.“嫦娥一号”卫星微波探测仪数据处理模型和月表微波亮温反演方法.中国科学D辑:地球科学. 2009, 39(8): 1029-1044.
[16]法文哲,金亚秋.“嫦娥”1号对月球表面微波辐射观测分析及其月壤厚度反演.中国科学:信息科学. 2010, 40: 115-127.
[17]孟治国.月壤参数的辐射传输模拟与查找表反演技术研究[博士学位论文].吉林大学, 2008.
[18]李青侠,郭伟,张祖荫,神经网络用于反演微波亮度温度,微波学报,2006,22卷增刊:162-165.
[19] Mejia C, Thiria S, Badran F, et al. Determination of the geophysical model function of ERS1 scatterometer by the use of neural networks. Journal of Geophysical Research. 1998, 103(C6): 12853-12868.
[20] Mejia C, Badran F, Bentamy A, et al. Determination of the geophysical model function of NSCAT and its corresponding variance by the use of neural networks. Journal of Geophysical Research. 1999, 104: 11539-11556.
[21] Tran N, Thiria S, Crepon M, et al. Validation of the QS-CAT NRCS on the advanced neural network NSCAT GMF and estimation of neural network QSCAT GMF. Proceedings of the 2000 International Geoscience and Remote Sensing Symposium. 2000.
[22]解学通,方裕,陈克海等. SeaWinds散射计海面风场神经网络建模研究.地理与地理信息科学. 2007, 23(2): 12-17.
[23]陈克海,解学通,黄舟等.人工神经网络在星载散射计海面风场反演建模中的应用.北京大学学报:自然科学. 2007, 43(4): 460-467.
[24]宋新改.神经网络反演散射计风场算法的研究[硕士学位论文].中国海洋大学. 2005.
[25]毛克彪,唐华俊,陈仲新等.一个用神经网络优化的针对ASTER数据反演地表温度和发射率的多波段算法.国土资源遥感. 2007, 3: 18-22.
[26]毛克彪,唐华俊,李丽英等.一个MODIS数据同时反演地表温度和发射率的神经网络算法.遥感信息(理论研究). 2007, 9-15.
[27]张雪惠,官莉,王振会等.利用人工神经网络方法反演大气温度廓线.气象. 2009, 35(11): 137-142.
[28]施英妮.基于人工神经网络技术的高光谱遥感浅海水深反演研究[硕士学位论文].中国海洋大学, 2005.
[29]丁静.基于神经网络的二类水体大气修正与水色要素反演[博士学位论文].中国海洋大学, 2004.
[30] Zhenzhan Wang, Yun Li, Jingshan Jiang, et al. Microwave transfer models and brightness temperature simulations of MWS for remote sensing lunar surface on CE-1 satellite. ICMMT Proceedings. Nanjing, 2008.
[31] Zhang D H, Zhang X H, Wang Z Z, et al. Mechanism of Lunar Soil Depth Sounding and Ground Validation Experiment for the CE-1 Lunar Microwave Sounder. Sci China Ser D-Earth Sci. 2009, 39(8): 1097-1104.
[32]蓝爱兰.月球表层媒质的被动遥感机理及厚度反演研究[硕士学位论文].中国科学院研究生院, 2004.
[33]李青侠,李雄耀,陈萍等.粗糙表面对月表微波辐射亮度温度影响的初步分析.绕月探测工程探测数据应用研究进展论文集. 2009, 247-252.
[34] Hu G.P., Li Q. X., Zheng Y. C., et al. Brightness temperature of the global moon: Comparison between theoretical simulation and observation by Chang'E-1 lunar orbiter. 2010 International Conference on Microwave and Millimeter Wave Technology (ICMMT 2010), 2010 , 1735-1738.
[35] A. R. Vasavada, D. A. Paige, S. E. Wood. Near-surface temperatures on Mercury and the Moon and the stability of polar ice desposits. Icarus. 1999, 141: 179-193.
[36] G. H. Heiken, D. T. Vaniman, B. M. French. Lunar sourcebook: a user’s guide to the moon. Cambridge University Press. 1991.
[37] Olhoeft G R. Strangway D W. Dielectric properties of the first 100 meters of the Moon. Earth Planet Sci. Lett. 1975, 24: 394-404.
[38] S. J. Keihm, M. G. Langsth. Surface brightness temperature at the Apollo 17 heat flow site: Thermal conductivity of the upper 15cm of regolith. Proc.4th Lunar Science Conference. 1973.
[39]李芸,王振占,姜景山.月表温度剖面对于“嫦娥一号”卫星微波探测仪探测亮温影响的模拟研究.中国科学D辑:地球科学. 2009, 39(8): 1045-1058.
[40] David L, Mitchell, Imke de Pater. Microwave Imaging of Mercury’s Thermal Emission at Wavelengths from 0.3 to 20.5cm. ICARUS. 1994, 110: 2-32.
[41] S.J.Keihm, M.G. Langseth. Surface brightness temperatures at the Apollo 17 heat flow site: thermal conductivity of the upper 15cm of regolith. Proceedings of the Fourth science conference. 1980, 3: 2503-2513.
[42] Ulaby F T, Moore R K, Fung A K. Microwave Remote Sensing, Active and Passive, Vol.I. Addison-Wesley Publishing Company, 1981.
[43]宋铮,张建华.天线与电波传播.西安:西安电子科技大学出版社. 2003.
[44] B. J. Choudhury, T. J. Schmugge, A. Chang, and R.W. Newton,“Effect of surface roughness on the microwave emission from soil,”J. Geophys. Res., 1979, 84(C9). 5699–5706.
[45] J. Shi, L. Jiang, L. Zhang, K. Chen, J. Wigneron, and A. Chanzy, "A Parameterized Multifrequency-Polarization Surface Emission Model," IEEE Trans. Geosci. Remote Sensing, 2005, 43(12): 2831-2841..
[46] T. Mo and T. J. Schmugge,“A parameterization of the effect of surface roughness on microwave emission,”IEEE Trans. Geosci. Remote Sensing, 1987, 25: 47–54.
[47]冯天谨.神经网络技术.青岛:青岛海洋大学. 1994.
[48]王振占,李芸,姜景山等.用“嫦娥一号”卫星微波探测仪亮温反演月壤厚度和3He资源量评估的方法及初步结果分析.中国科学D辑:地球科学. 2009, 39(8): 1069-1084.