FY-3B微波成像仪数据质量评价与参数反演
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摘要
微波遥感是利用电磁波和地物微波频段特性对地面信息进行获取的一种技术手段,微波遥感的主要传感器之一微波辐射计已成功为大气及海洋等各个领域提供了大量观测数据。我国的星载微波辐射计发展较慢,目前运行的只有FY-3A/B系列卫星携带的微波成像仪。FY-3B卫星于2010年11月5日发射,为了充分认识FY-3B微波成像仪数据的性能,掌握国产微波辐射计的应用前景,本文采用星星对比方法以AMSR-E微波辐射计数据作为对比数据,开展了针对FY-3B的数据处理,亮温质量评价,海表参数反演等研究工作。
本文具体研究内容及结论如下:
1、完成了FY-3B微波成像仪亮温数据处理及其全球亮温分布图的生成,通过与AMSR-E对比,对FY-3B微波成像仪各通道响应地表辐射特性的能力进行了分析。结果发现FY-3B全球亮温分布图显示地面信息准确,海陆分界明显,且FY-3B和AMSR-E在不同频率及不同极化方式上的亮温响应特性一致。
2、建立了渤海和黄海部分海域的FY-3B与AMSR-E亮温匹配数据集,对FY-3B亮温数据进行了定性定量的评价,结果表明FY-3B与AMSR-E各通道亮温均有线性关系,尤其在海洋上,28.7GHz、23.8GHz和36.5GHz三个波段线性相关系数在0.85以上。
3、将NCEP数据作为实测数据,完成了FY-3B微波成像仪反演海面温度和风速的研究,并与AMSR-E反演结果对比,发现采用相关性强的多通道组合反演精度较高。在研究区域FY-3B和AMSR-E相同的十通道组合以及全通道组合分析中,FY-3B的亮温反演海表温度和海面风速能力都要优于AMSR-E,其中FY-3B十通道亮温反演海表温度的相关性为0.86,均方根误差为0.75℃,反演海面风速的相关性为0.79,均方根误差为1.16m/s。AMSR-E十二通道亮温反演海表温度和海面风速的结果好于十通道反演结果,相关性分别为0.84和0.78,均方根误差为0.81℃和1.18m/s。
Microwave remote sensing is a technology that makes use of the feature of electromagnetic wave and microwave to get information of the Earth. Microwave radiometer-- one of the main sensors of microwave remote sensing has provided successfully a number of observations for the fields of atmosphere and ocean and so on. China's technology in satellite microwave radiometer developed lately. At present ,there are few microwave imagers operational that are be carried on FY-3A / B series of satellites.FY-3B satellite was launched on November 5, 2010. In order to fully assess the performance of FY-3B Microwave Imager’s data, and analyze its related applications in the future, the paper has used the method of comparison between satellites,and referred to AMSR-E microwave radiometer data as the standard data. FY-3B data has been processed, then its brightness temperature assessed, finally ocean parameters been retrieved and so on
The specific contents and conclusions of this study are as follows:
1、The FY-3B Radiation Microwave Imager brightness temperature data were processed and global distribution maps for the brightness temperature data were also generated, Compared with the AMSR-E data, the surface radiation response were analyzed for eachl channel of FY-3B Microwave Radiation Imager. The results showed that land information gathered by FY-3B surface brightness temperature global distribution map is accurate, the boundarie between sea and land is clear, and the brightness temperature response characteristics at different frequencies and different type of polarization are much the same between FY-3B and AMSR-E.
2、Matched data set between part of the Bohai Sea and Yellow Sea were established for FY-3B and AMSR-E brightness temperature data, and the FY-3B brightness temperature data were evaluated qualitatively and quantitatively, the results show that the FY-3B and the AMSR-E brightness temperature of each channel are linear, especially in the ocean. linearly dependent coefficient for 28.7GHZ、23.8GHZ and 36.5GHZ band is above 0.85.
本文具体研究内容及结论如下:
1、完成了FY-3B微波成像仪亮温数据处理及其全球亮温分布图的生成,通过与AMSR-E对比,对FY-3B微波成像仪各通道响应地表辐射特性的能力进行了分析。结果发现FY-3B全球亮温分布图显示地面信息准确,海陆分界明显,且FY-3B和AMSR-E在不同频率及不同极化方式上的亮温响应特性一致。
2、建立了渤海和黄海部分海域的FY-3B与AMSR-E亮温匹配数据集,对FY-3B亮温数据进行了定性定量的评价,结果表明FY-3B与AMSR-E各通道亮温均有线性关系,尤其在海洋上,28.7GHz、23.8GHz和36.5GHz三个波段线性相关系数在0.85以上。
3、将NCEP数据作为实测数据,完成了FY-3B微波成像仪反演海面温度和风速的研究,并与AMSR-E反演结果对比,发现采用相关性强的多通道组合反演精度较高。在研究区域FY-3B和AMSR-E相同的十通道组合以及全通道组合分析中,FY-3B的亮温反演海表温度和海面风速能力都要优于AMSR-E,其中FY-3B十通道亮温反演海表温度的相关性为0.86,均方根误差为0.75℃,反演海面风速的相关性为0.79,均方根误差为1.16m/s。AMSR-E十二通道亮温反演海表温度和海面风速的结果好于十通道反演结果,相关性分别为0.84和0.78,均方根误差为0.81℃和1.18m/s。
Microwave remote sensing is a technology that makes use of the feature of electromagnetic wave and microwave to get information of the Earth. Microwave radiometer-- one of the main sensors of microwave remote sensing has provided successfully a number of observations for the fields of atmosphere and ocean and so on. China's technology in satellite microwave radiometer developed lately. At present ,there are few microwave imagers operational that are be carried on FY-3A / B series of satellites.FY-3B satellite was launched on November 5, 2010. In order to fully assess the performance of FY-3B Microwave Imager’s data, and analyze its related applications in the future, the paper has used the method of comparison between satellites,and referred to AMSR-E microwave radiometer data as the standard data. FY-3B data has been processed, then its brightness temperature assessed, finally ocean parameters been retrieved and so on
The specific contents and conclusions of this study are as follows:
1、The FY-3B Radiation Microwave Imager brightness temperature data were processed and global distribution maps for the brightness temperature data were also generated, Compared with the AMSR-E data, the surface radiation response were analyzed for eachl channel of FY-3B Microwave Radiation Imager. The results showed that land information gathered by FY-3B surface brightness temperature global distribution map is accurate, the boundarie between sea and land is clear, and the brightness temperature response characteristics at different frequencies and different type of polarization are much the same between FY-3B and AMSR-E.
2、Matched data set between part of the Bohai Sea and Yellow Sea were established for FY-3B and AMSR-E brightness temperature data, and the FY-3B brightness temperature data were evaluated qualitatively and quantitatively, the results show that the FY-3B and the AMSR-E brightness temperature of each channel are linear, especially in the ocean. linearly dependent coefficient for 28.7GHZ、23.8GHZ and 36.5GHZ band is above 0.85.
引文
[1]徐建平,国内外气象卫星发展.国际太空,2001年1月号.
[2]钟若飞郭华东等,SZ-4飞船多模态传感器辐射模态数据处理与质量评价研究.国土资源遥感,2004,4(62):19:22
[3]钟若飞神舟四号飞船微波辐射计数据处理与地表参数反演研究. [博士学位论文],北京:中国科学院研究生院,2005.
[4]伍玉梅等,利用AMSR-E资料反演实时海面气象参数的个例.高技术通信,2007,17(6),633-637.
[5]王振占,李芸,利用星载微波辐射计AMSR-E数据反演海洋地球物理参数.遥感学报,2009,13(3),335-370.
[6]殷小斌,刘玉光等,红外和微波辐射计参演海表面温度的比较.海洋通报,2007,26 No.5: 3-10
[7]王振占等,全极化微波辐射计遥感海面风场的关键技术和科学问题.中国工程科学,2008,10(6),76-85.
[8] Choudhury, B. J. Estimating soil wetness using satellite data. International Journal of Remote Sensing, 9, 1251-1257,1988
[9] Wang, J. R. Choudhury, B. J. Remote sensing of soil moisture content over bare field at 1.4GHz frequency. Journal of Geophysical Research, 86, 5277-5282, 1981
[10] Mo. T and T. J. Schmugge, A parameterization of the effect of surface roughness on microwave emission, IEEE Transactions on Geoscience and remote Sensing, 25, 47-54,1987
[11] Saatchi, S. S., E. G.. Njoku, Sunergism of active and passive microwave data for estimation bare soil surface moisture. In passive Microwave Remote Sensing of Land-Atmosphere Interactions, ESA/NASA international workshop,205-224,1995
[12] Wegmuller, rough bare soil reflectivety model, IEEE Transactions on Geoscience and Remote Sensing, 37(3): 1391-1395, May, 1999.
[13] Wigneronm J. P, A simple parameterization of the L-band microwave emission from rough agricultural soil, IEEE Transations on Geoscience and Remote Sensing, 39(8): 1697-1707, August,2001
[14] T. D. Wu, K. S. Chen, A transition model for the reflection coefficient in surface scattering, IEEE Transactions on Geoscience and Remote Sensing, 39(9), 2040-2050. 2001
[15] Tzong Dar Wu, K. S. Chen, A transition model for the reflection coefficient in surface scattering, IEEE Transactions on Geoscience and Remote Sensing, 39(9), 2040-2050. 2001
[16] M. Zribi, J. Paille. Modelisation of roughness and microwave scattering of bare soil surfaces based on fractal Brownian geometery, IGARSS’98,1213-1215,1998
[17] Ferrazzoli, P., Wigneron, J. P., A Multifrequency emission of wheat: modeling and applications. IEEE Transactions on Geoscience and Remote Sensing, 38,2598-2607,1992
[18] Dobson, M. C., Ulaby, Microwave dielectric behavior of wet soil: PartП. Dielectric mixing models, IEEE Transactions on Geoscience and Remote Sensing, 23,35-46,1985
[19] Tsong, L., Kong, J. A. Thermal microwave emission from a three-layer random medium with three dimensional variations. IEEE Transactions on Geoscience and Remote Sensing,, 18,212-216,1980
[20] Tsong, L., and Newton, R. W., Microwave emission from soils with rough surfaces. J. Geophys. Res., 87: 9017-9024,1982
[21] Li, L., Vivekanandan, J., Chan, C. H., M icrowave radiometric technique to retrieve vapor, liquid and ice: part 1. Development pf a neural network-based inversion method. IEEE Transactions on Geoscience and Remote Sensing, 35(2),224-236,1997
[22] K. S. Chen, T. D. Wu, The Emission of Rough Surfaces Calculated by the Integral Equation Method with a Comparison to a Three-Dimensional Method Method Simulations, IEEE Transactions on Geoscience and Remote Sensing, 41(1), 1-12, 2003
[23] Koike T. Study on Spatial and Temporal Variability of Surface Soil Wetness on Tibetan Plateau by Using the satellite-based Microwave Radiometer. Annual J Hydraulic Eng, 41: 915-919, 1997
[24] Richard A. M. de Jeu, retrieval of land surface prarameters using pasing microwave remote sensing, Ph.D. Thesis VRUE University Amsterdam, 2003
[25] McFarland, Land Sruface Temperature Derived From the SSM/I Passive Microwave Brightness Temperature. IEEE Transactions on Geoscience and Remote Sensing, 28(5),1990
[26] Pulliainen, J., Hallikainen, M. Development of gephysical retrieval algorithms for the MIMR. IEEE Transactions on Geoscience and Remote Sensing, 31(1) 268-277,1993
[27] Pulliainen, Retrieval of Surface Temperature in Boreal Forest Zone from SSM/I data. IEEE Transactions on Geoscience and Remote Sensing, vol35,No5,1997
[28] Njoku, Eni G. AMSR Land Surface Parameters: Algorithm Theoretical Basis Document Version 2.0, November 10, 1997
[29] Njoku, E. G., Li, L. Retrieval of land surface parameters using passive microwave measurements at 6-18GHz. IEEE Transactions on Geoscience and Remote Sensing, 37(1) 79-93, 1999
[30] Wentz F J. Algorithm Theoretical Basis Document, AMSR Ocean Algorithm. 2000. RSS Tech. proposal: 121599A-1
[31] Wentz F J. A well-calibrated ocean algorithm for SSM/I. J. geophys.Res.,1997, 102: 8703-8718
[32] Liu G R, Liu C C, Kuo T H. A contrast and comparison of near-sea surface air temperature/humidity from GMS and SSM/I data with an improved algorithm. IEEE Trans Geosci Remote Sensing, 2001,39(10): 2148-2157
[33] Jones C, Peterson P, Gautier C. A new method for deriving surface specific humidity and air temperature: an artificial neural network approach. J App Meteorol, 1999,38: 1229-1245
[34]孟晋宝,张青,我国气象卫星的现状及未来.新疆气象,2003,26(3),28-30.
[35]姜景山.微波遥感信息科技发展若干问题的讨论.遥感技术与应用,2005,20(1):1-5
[36]徐希儒,遥感物理。北京:北京大学出版社,2005
[37]刘勇卫,贺雪鸿,《遥感精解》,测绘出版社,1993.
[38] Han Y. and Ed R.Westwater, Analysis and improvement of tipping calibration for ground-based microwave radiometers, iEEE Trans.Geosci. Remote Sensing, vol.38,no.3, 1260-1276
[39]王振占,海面风场全极化微波辐射测量——原理、系统设计与模拟研究. [博士学位论文],北京:中国科学院空间科学与应用研究中心, 2005.
[40]李粉玲,高山高原地区地表温度遥感反演研究. [硕士学位论文],兰州:兰州大学,2006
[41] Fawwaz Ulaby, Richard K. Moore, Adriank. Fung. Microwave Remote Sensing, 1986,vol 3.
[42]张培吕,王振会。大气微波遥感基础[M],北京:气象出版社, 1995,346-384.
[43] Janssen,M.A., Atmospheric remote sensing by microwave radiometry, Wiley Series in Remote Sesming, 1993 chapter1.
[44] Goodrum G., K. B. Kidwell, W. Winston, CALIBRATION OF NOAA KLM INSTRUMENTS, NOAA KLM USER’S GUIDE 2001 chapter 7.
[45] Fawwaz Ulaby, Richard K. Moore, drian k. Fung. Microwave Remote Sensing, 1981,vol 1.
[46]王波,地基微波辐射计大气环境遥感研究. [硕士学位论文],青岛:中国海洋大学,2007.
[47] Kalnay E, Kanamitsu M, Kistler R,et al. The NCEP/NCAR 40years reanalysis project. Bull Amer. Meteor. Soc. 1996,77(3):437-471
[48] International GEWEx Project Office(IGPO). GEWEX/WCRP News, 1999,9(9) :12.
[49] kananistsu M. Description of the NMC global data assimilation and forecast system. Wea. Forecasting, 1989, 4 :334-342.
[50] kananistsu M et al, Recent changes implemented into the global forecast system at NMC. Wea Forecasting, 1991, 6 :425-435.
[51]徐影等,美国NCEP/NCAR近50年全球再分析资料在我国气候变化研究中可信度的初步分析.应用气象学报,2001, 12(3):337-347.
[52]杨虎施建成等,FY_3微波成像仪仪器性能及业务化产品介绍. 2007年中国气象学会年会.
[2]钟若飞郭华东等,SZ-4飞船多模态传感器辐射模态数据处理与质量评价研究.国土资源遥感,2004,4(62):19:22
[3]钟若飞神舟四号飞船微波辐射计数据处理与地表参数反演研究. [博士学位论文],北京:中国科学院研究生院,2005.
[4]伍玉梅等,利用AMSR-E资料反演实时海面气象参数的个例.高技术通信,2007,17(6),633-637.
[5]王振占,李芸,利用星载微波辐射计AMSR-E数据反演海洋地球物理参数.遥感学报,2009,13(3),335-370.
[6]殷小斌,刘玉光等,红外和微波辐射计参演海表面温度的比较.海洋通报,2007,26 No.5: 3-10
[7]王振占等,全极化微波辐射计遥感海面风场的关键技术和科学问题.中国工程科学,2008,10(6),76-85.
[8] Choudhury, B. J. Estimating soil wetness using satellite data. International Journal of Remote Sensing, 9, 1251-1257,1988
[9] Wang, J. R. Choudhury, B. J. Remote sensing of soil moisture content over bare field at 1.4GHz frequency. Journal of Geophysical Research, 86, 5277-5282, 1981
[10] Mo. T and T. J. Schmugge, A parameterization of the effect of surface roughness on microwave emission, IEEE Transactions on Geoscience and remote Sensing, 25, 47-54,1987
[11] Saatchi, S. S., E. G.. Njoku, Sunergism of active and passive microwave data for estimation bare soil surface moisture. In passive Microwave Remote Sensing of Land-Atmosphere Interactions, ESA/NASA international workshop,205-224,1995
[12] Wegmuller, rough bare soil reflectivety model, IEEE Transactions on Geoscience and Remote Sensing, 37(3): 1391-1395, May, 1999.
[13] Wigneronm J. P, A simple parameterization of the L-band microwave emission from rough agricultural soil, IEEE Transations on Geoscience and Remote Sensing, 39(8): 1697-1707, August,2001
[14] T. D. Wu, K. S. Chen, A transition model for the reflection coefficient in surface scattering, IEEE Transactions on Geoscience and Remote Sensing, 39(9), 2040-2050. 2001
[15] Tzong Dar Wu, K. S. Chen, A transition model for the reflection coefficient in surface scattering, IEEE Transactions on Geoscience and Remote Sensing, 39(9), 2040-2050. 2001
[16] M. Zribi, J. Paille. Modelisation of roughness and microwave scattering of bare soil surfaces based on fractal Brownian geometery, IGARSS’98,1213-1215,1998
[17] Ferrazzoli, P., Wigneron, J. P., A Multifrequency emission of wheat: modeling and applications. IEEE Transactions on Geoscience and Remote Sensing, 38,2598-2607,1992
[18] Dobson, M. C., Ulaby, Microwave dielectric behavior of wet soil: PartП. Dielectric mixing models, IEEE Transactions on Geoscience and Remote Sensing, 23,35-46,1985
[19] Tsong, L., Kong, J. A. Thermal microwave emission from a three-layer random medium with three dimensional variations. IEEE Transactions on Geoscience and Remote Sensing,, 18,212-216,1980
[20] Tsong, L., and Newton, R. W., Microwave emission from soils with rough surfaces. J. Geophys. Res., 87: 9017-9024,1982
[21] Li, L., Vivekanandan, J., Chan, C. H., M icrowave radiometric technique to retrieve vapor, liquid and ice: part 1. Development pf a neural network-based inversion method. IEEE Transactions on Geoscience and Remote Sensing, 35(2),224-236,1997
[22] K. S. Chen, T. D. Wu, The Emission of Rough Surfaces Calculated by the Integral Equation Method with a Comparison to a Three-Dimensional Method Method Simulations, IEEE Transactions on Geoscience and Remote Sensing, 41(1), 1-12, 2003
[23] Koike T. Study on Spatial and Temporal Variability of Surface Soil Wetness on Tibetan Plateau by Using the satellite-based Microwave Radiometer. Annual J Hydraulic Eng, 41: 915-919, 1997
[24] Richard A. M. de Jeu, retrieval of land surface prarameters using pasing microwave remote sensing, Ph.D. Thesis VRUE University Amsterdam, 2003
[25] McFarland, Land Sruface Temperature Derived From the SSM/I Passive Microwave Brightness Temperature. IEEE Transactions on Geoscience and Remote Sensing, 28(5),1990
[26] Pulliainen, J., Hallikainen, M. Development of gephysical retrieval algorithms for the MIMR. IEEE Transactions on Geoscience and Remote Sensing, 31(1) 268-277,1993
[27] Pulliainen, Retrieval of Surface Temperature in Boreal Forest Zone from SSM/I data. IEEE Transactions on Geoscience and Remote Sensing, vol35,No5,1997
[28] Njoku, Eni G. AMSR Land Surface Parameters: Algorithm Theoretical Basis Document Version 2.0, November 10, 1997
[29] Njoku, E. G., Li, L. Retrieval of land surface parameters using passive microwave measurements at 6-18GHz. IEEE Transactions on Geoscience and Remote Sensing, 37(1) 79-93, 1999
[30] Wentz F J. Algorithm Theoretical Basis Document, AMSR Ocean Algorithm. 2000. RSS Tech. proposal: 121599A-1
[31] Wentz F J. A well-calibrated ocean algorithm for SSM/I. J. geophys.Res.,1997, 102: 8703-8718
[32] Liu G R, Liu C C, Kuo T H. A contrast and comparison of near-sea surface air temperature/humidity from GMS and SSM/I data with an improved algorithm. IEEE Trans Geosci Remote Sensing, 2001,39(10): 2148-2157
[33] Jones C, Peterson P, Gautier C. A new method for deriving surface specific humidity and air temperature: an artificial neural network approach. J App Meteorol, 1999,38: 1229-1245
[34]孟晋宝,张青,我国气象卫星的现状及未来.新疆气象,2003,26(3),28-30.
[35]姜景山.微波遥感信息科技发展若干问题的讨论.遥感技术与应用,2005,20(1):1-5
[36]徐希儒,遥感物理。北京:北京大学出版社,2005
[37]刘勇卫,贺雪鸿,《遥感精解》,测绘出版社,1993.
[38] Han Y. and Ed R.Westwater, Analysis and improvement of tipping calibration for ground-based microwave radiometers, iEEE Trans.Geosci. Remote Sensing, vol.38,no.3, 1260-1276
[39]王振占,海面风场全极化微波辐射测量——原理、系统设计与模拟研究. [博士学位论文],北京:中国科学院空间科学与应用研究中心, 2005.
[40]李粉玲,高山高原地区地表温度遥感反演研究. [硕士学位论文],兰州:兰州大学,2006
[41] Fawwaz Ulaby, Richard K. Moore, Adriank. Fung. Microwave Remote Sensing, 1986,vol 3.
[42]张培吕,王振会。大气微波遥感基础[M],北京:气象出版社, 1995,346-384.
[43] Janssen,M.A., Atmospheric remote sensing by microwave radiometry, Wiley Series in Remote Sesming, 1993 chapter1.
[44] Goodrum G., K. B. Kidwell, W. Winston, CALIBRATION OF NOAA KLM INSTRUMENTS, NOAA KLM USER’S GUIDE 2001 chapter 7.
[45] Fawwaz Ulaby, Richard K. Moore, drian k. Fung. Microwave Remote Sensing, 1981,vol 1.
[46]王波,地基微波辐射计大气环境遥感研究. [硕士学位论文],青岛:中国海洋大学,2007.
[47] Kalnay E, Kanamitsu M, Kistler R,et al. The NCEP/NCAR 40years reanalysis project. Bull Amer. Meteor. Soc. 1996,77(3):437-471
[48] International GEWEx Project Office(IGPO). GEWEX/WCRP News, 1999,9(9) :12.
[49] kananistsu M. Description of the NMC global data assimilation and forecast system. Wea. Forecasting, 1989, 4 :334-342.
[50] kananistsu M et al, Recent changes implemented into the global forecast system at NMC. Wea Forecasting, 1991, 6 :425-435.
[51]徐影等,美国NCEP/NCAR近50年全球再分析资料在我国气候变化研究中可信度的初步分析.应用气象学报,2001, 12(3):337-347.
[52]杨虎施建成等,FY_3微波成像仪仪器性能及业务化产品介绍. 2007年中国气象学会年会.
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