Comparative analysis of microwave brightness temperature data in Northeast China using AMSR-E and MWRI products
详细信息    查看全文
  • 作者:Lingjia Gu (1) (2)
    Kai Zhao (1)
    Shuwen Zhang (1)
    Shuang Zhang (2)
  • 关键词:AMSR ; E ; MWRI ; FY ; 3A satellite ; brightness temperature ; spatial resolution ; spatial position matching ; Northeast China
  • 刊名:Chinese Geographical Science
  • 出版年:2011
  • 出版时间:February 2011
  • 年:2011
  • 卷:21
  • 期:1
  • 页码:84-93
  • 全文大小:772KB
  • 参考文献:1. Bellerby T, Taberner M, Wilmshurst A / et al., 1998. Retrieval of land and sea brightness temperatures from mixed coastal pixels in passive microwave data. / IEEE Transactions on Geoscience and Remote Sensing, 36(6): 1844-851. doi: 10.1109/36.7293-55 CrossRef
    2. Carsey Frank D, 1992. Microwave remote sensing of sea ice. Washington, DC: American Geophysical Union (Geophysical Monograph), 473-76.
    3. Che Tao, Li Xin, 2004. The Development and prospect of estimating snow water equivalent using passive microwave remote sensing data. / Advance in Earth Science, 19(2): 204-10. (in Chinese)
    4. Christopher M U Neale, Marshall J Mcfarland, Chang Kai, 1990. Land-surface-type classification using microwave brightness temperatures from the special sensor microwave/imager. / IEEE Transaction on Geoscience and Remote Sensing, 28(5): 829-38. doi: 10.1109/36.58970 CrossRef
    5. Derksen C, Walker A, Ledrew E / et al., 2003. Combining SMMR and SSM/I data for time series analysis of central North American snow water equivalent. / Journal of Hydrometeorology, 4(2): 304-16. doi: 10.1175/1525-7541(2003)4<304:CS-AIDF>2.0.CO;2 CrossRef
    6. Dong Chaohua, Yang Jun, Zhang Wenjian / et al., 2009. An overview of a new Chinese weather satellite FY-3A. / Bulletin of the American Meteorological Society, 90(10): 1531-544. doi: 10.1175/2009BAMS2798.1 CrossRef
    7. Gavrila D M, Giebel J, 2001. Virtual sample generation for template-based shape matching. In: / Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1676-681. doi: 10.1109/CVPR.2001.990540
    8. Giuseppe Papari, Nicolai Petkov, 2009. Reduced inverse distance weighting interpolation for painterly rendering. / Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns, 5702: 509-16. doi: 10.1007/978-3-642-03767-2_62
    9. Kawanishi T, Sezai T, Ito Y / et al., 2003. The advanced microwave scanning radiometer for the Earth Observing System (AMSR-E), NASDA’s contribution to the EOS for global energy and water cycle studies. / IEEE Transactions on Geoscience and Remote Sensing, 41(2): 184-94. doi: 10.1109/TGRS.200-2.808331 CrossRef
    10. Liu Dianwei, Wang Zongming, Song Kaishan / et al., 2009. Land use/cover changes and environmental consequences in Songnen Plain, Northeast China. / Chinese Geographical Science, 19(4): 299-05. doi: 10.1007/s11769-009-0299-2 CrossRef
    11. Liu Zenglin, Tang Bohui, Li Zhaoliang, 2009. Calculation of Land Surface Temperature Based on AMSR-E Data. / Science & Technology Review, 27(4): 24-7. (in Chinese)
    12. Lu Qifeng, Bell Bill, Bauer Peter / et al., 2009. Monitoring and assimilation of Fy-3A data. / ECMWF Newsletter, (122): 18-0.
    13. Mao Kebiao, Shi Jiancheng, Li Zhaoliang / et al., 2007. A physics-based statistical algorithm for retrieving land surface temperature from AMSR-E passive microwave data. / Science in China ( / Series D), 50(7): 1115-120. doi: 10.1007/s11430-00-7-2053-x CrossRef
    14. Mcfarland M J, Miller R L, Neal C M U, 1990. Land surface temperature derived from the SSM/I passive microwave brightness temperature. / IEEE Transactions on Geoscience and Remote Sensing, 28(5): 839-45. doi: 10.1109/36.58971 CrossRef
    15. Meier Walter N, Dai Mingrui, 2006. High-resolution sea-ice motions from AMSR-E imagery. / Annals of Glaciology, 44(1): 352-56. doi: 10.3189/172756406781811286 CrossRef
    16. Maa? Nina, Kaleschke Lars, 2010. Improving passive microwave sea ice concentration algorithms for coastal areas: applications to the Baltic Sea. / Tellus Series A Dynamic Meteorology and Oceanography, 62(4): 393-10. doi: 10.1111/j.1600-0870.20-10.00452.x
    17. Njoku E G, Chan T, Crosson W / et al., 2004. Evaluation of the AMSR-E data calibration over land. / Rivista Italiana di Telerilevamento —Italian Journal of Remote Sensing, 30(31): 19-7.
    18. Njoku E G, Li L, 1999. Retrieval of land surface parameters using passive microwave measurements at 6-18 GHz. / IEEE Transactions on Geoscience and Remote Sensing, 37(1): 79-3. doi: 10.1109/36.739125 CrossRef
    19. Paloscia Simonetta, Macelloni Giovanni, Pampaloni Paolo / et al., 2006. Global scale monitoring of soil and vegetation by using AMSR-E multi-temporal data. In: / International Geoscience and Remote Sensing Symposium ( / IGARSS), 1609-612. doi: 10.1109/IGARSS.2006.415
    20. Raul Zurita-Milla, Jan G P W Clevers, Michael E Schaepman / et al., 2008. Unmixing-based Landsat TM and MERIS FR data Fusion. / IEEE Transactions on Geoscience and Remote Sensing, 5(3): 453-57. doi: 10.1109/LGRS.2008.919685 CrossRef
    21. Song Kaishan, Zhang Yuanzhi, Jin Cui / et al., 2008. Seasonal snow monitoring in Northeast China using space-borne sensors: preliminary results. / Annals of GIS, 14(2): 113-19. doi: 10.10-80/10824000809480646 CrossRef
    22. Sun Zhiwen, Shi Jiancheng, Yang Hu / et al., 2007. A study on snow depth estimating and snow water equivalent algorithm for FY-3 MWRI. / Remote Sensing Technology and Application, 22(2): 264-67. (in Chinese)
    23. Ulaby F T, Moore R K, Fung A K, 1986. / Microwave Emote Sensing: Active and Passive,Volume II: Radar Remote Sensing and Surface Scattering and Emission Theory. MA: Artech House Publishers, 200-10.
    24. Yang Hu, Shi Jiancheng, 2005. On the estimation of land surface parameters by using FY-3A microwave radiometer imager (MWRI). / Remote Sensing Technology and Application, 20(1): 194-00. (in Chinese)
    25. Zhang Peng, Yang Jun, Dong Chaohua / et al., 2009. General introduction on payloads, ground segment and data application of Fengyun 3A. / Frontiers of Earth Science in China, 3(3): 367-73. doi: 10.1007/s11707-009-0036-2 CrossRef
    26. Zhang Shuwen, Zhang Yangzhen, Li Ying / et al., 2006. / Land Use/Cover Spatial Character Analyzing Based on Northeast China. Beijing: Science Press, 25-7. (in Chinese)
    27. Zheng Xingming, Zhao Kai, 2010. A method for surface roughness parameter estimation in passive microwave remote sensing. / Chinese Geographical Science, 20(4): 345-52. doi: 10.1007/s11769-010-0407-3 CrossRef
  • 作者单位:Lingjia Gu (1) (2)
    Kai Zhao (1)
    Shuwen Zhang (1)
    Shuang Zhang (2)

    1. Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130012, China
    2. College of Electronic Science & Engineering, Jilin University, Changchun, 130012, China
  • ISSN:1993-064X
文摘
With such significant advantages as all-day observation, penetrability and all-weather coverage, passive microwave remote sensing technique has been widely applied in the research of global environmental change. As the satellite-based passive microwave remote sensor, the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) loaded on NASA’s (National Aeronautics and Space Administration of USA) Aqua satellite has been popularly used in the field of microwave observation. The Microwave Radiation Imager (MWRI) loaded on the Chinese FengYun-3A (FY-3A) satellite is an AMSR-E-like conical scanning microwave sensor, but there are few reports about MWRI data. This paper firstly proposed an optimal spatial position matching algorithm from rough to exact for the position matching between AMSR-E and MWRI data, then taking Northeast China as an example, comparatively analyzed the microwave brightness temperature data derived from AMSR-E and MWRI. The results show that when the antenna footprints of the two sensors are filled with either full water, or full land, or mixed land and water with approximate proportion, the errors of brightness temperature between AMSR-E and MWRI are usually in the range from ?0 K to +10 K. In general, the residual values of brightness temperature between the two microwave sensors with the same spatial resolution are in the range of ±3 K. Because the spatial resolution of AMSR-E is three times as high as that of MWRI, the results indicate that the quality of MWRI data is better. The research can provide useful information for the MWRI data application and microwave unmixing method in the future.

© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号

地址:北京市海淀区学院路29号 邮编:100083

电话:办公室:(+86 10)66554848;文献借阅、咨询服务、科技查新:66554700