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利用SSM/I数据判识我国及周边地区雪盖

李晓静 刘玉洁 朱小祥 郑照军 陈爱军

李晓静, 刘玉洁, 朱小祥, 等. 利用SSM/I数据判识我国及周边地区雪盖. 应用气象学报, 2007, 18(1): 12-20.
引用本文: 李晓静, 刘玉洁, 朱小祥, 等. 利用SSM/I数据判识我国及周边地区雪盖. 应用气象学报, 2007, 18(1): 12-20.
Li Xiaojing, Liu Yujie, Zhu Xiaoxiang, et al. Snow cover identification with SSM/I data in China. J Appl Meteor Sci, 2007, 18(1): 12-20.
Citation: Li Xiaojing, Liu Yujie, Zhu Xiaoxiang, et al. Snow cover identification with SSM/I data in China. J Appl Meteor Sci, 2007, 18(1): 12-20.

利用SSM/I数据判识我国及周边地区雪盖

资助项目: 

863计划课题“卫星遥感雪盖范围、雪深和雪水当量试验研究” 2002AA135250

973计划课题“空间微波遥感地海表和大气数据验证” 2001CB309402

Snow Cover Identification with SSM/I Data in China

  • 摘要: 积雪参数是气候学和水文学研究中所需的重要物理量, 确保积雪参数测定的准确性与及时性对于气候学研究、水文应用以及防灾减灾都非常重要。利用微波数据可获取有云存在时的积雪覆盖图, 遥感雪深和雪水当量信息。采用微波数据判识雪盖并得到积雪状态 (干、湿) 信息, 不仅可以弥补利用光学遥感数据判识雪盖的不足之处, 而且也是利用微波数据反演雪深和雪水当量参数必需的先期工作。该文介绍利用SSM/I的多频双极化微波数据开展我国及周边地区积雪判识方法研究的结果。分析国外全球判识方法的雪盖判识结果指出, 国外算法易在青藏高原等地区将冻土误判为积雪, 造成雪盖面积的偏高估计。研究给出了在我国及周边地区 (17°~57°N, 65°~145°E) 利用SSM/I数据判识积雪的改进方法, 在完成积雪判识的同时还给出了雪深和积雪状态的定性信息, 与已有全球雪盖判识方法相比有较大改进, 大大减小了青藏高原等地区冻土对积雪判识的影响。
  • 图  1  地面站点分布图

    Fig. 1  Distribution of ground observation stations

    图  2  文献[12]中的全球积雪判识方法

    Fig. 2  The global snow identifying method in reference[12]

    图  3  利用国外两种不同方法判识2002年12月22-26日雪盖图

    (a) 方法A,(b) 方法B

    Fig. 3  The snow cover maps during Dec 22-26, 2002 identified by two foreign methods

    (a) Method A, (b) Method B

    图  4  国家卫星气象中心AVHRR积雪检测图

    (a) 2002年12月下旬,(b) 2003年3月下旬

    Fig. 4  The snow cover maps with AVHRR data made by oprational method in NSMC

    (a) Dec 21-31, 2002, (b) Mar 21-31, 2003

    图  5  利用SSM/I数据建立的我国及其周边地区积雪判识方法 (方法C)

    Fig. 5  The improved snow cover identifying method with SSM/I data in China and its adjacent areas (Method C)

    图  6  内蒙古东北部草原地区1997年10月—2003年3月间积雪 (a)、降雨 (b)、冻土 (c) 样本以及塔克拉玛干沙漠区样本 (d) 的判识因子3散点图

    Fig. 6  The scattering maps of identifying parameter calculated with the samples of snow (a), rain (b), frozen soil (c) collected in grassland of north-east Inner Mongolia Auto nomous Region, and the samples collected in Takelamagan Desert (d)

    图  7  积雪判识效率EH的时间序列图

    Fig. 7  Temporal sequence map of snow cover identifying efficiency EH

    图  8  积雪判识效率EH空间分布图

    Fig. 8  Spacial distribution map of snow cover identifying efficiency EH

    图  9  采用方法C判识的雪盖图

    (a)2002年12月22-26日, (b)2003年3月21-27日 (0: 背景;1: 厚干雪;2: 厚湿雪;3: 浅干雪;4: 浅湿雪或森林覆盖下的浅雪;5: 厚的很湿雪)

    Fig. 9  The snow cover maps identified by Method C

    (0: background; 1: thick dry snow; 2: thick wet snow; 3: shallow dry snow; 4: shallow wet snow or shallow snow under forest; 5: thick very wet sonw)

    表  1  SSM/I参数信息表

    Table  1  Instrument information of SSM/I

    表  2  式 (3) 中的参数说明

    Table  2  The statement of parameters in formula (3)

  • [1] Saraf A K, Foster J L, Singh P, et al. Passive microwave data for snow-depth and snow-extent estimations in the Himalayan mountains. Int J Remote Sensing, 1999, 20 (1): 83-95. doi:  10.1080/014311699213613
    [2] Wang James R, Will Manning. Near concurrent MIR, SSM/T-2, and SSM/I observations over snow-covered surfaces. Remote sensing of Environment, 2003, 84: 457-470. doi:  10.1016/S0034-4257(02)00134-7
    [3] Foster J L, Chang A T C, Hall D K. Comparison of snow massestimates from a prototype passive microwave algorithm and a snow depth climatology. Remote Sensing of Environment, 1997, 62: 132-142. doi:  10.1016/S0034-4257(97)00085-0
    [4] Chang A T C, Foster J L, Hall D K, et al. Snow parameters derived from microwave measurements during the BOREAS winter field campaign. J Geophys Res, 1997, 102 (D24): 29663-29671. doi:  10.1029/96JD03327
    [5] Adrian K Fung. Microwave Sattering and Emission Models and Their Applications. London: Artech House, 1994: 339-448.
    [6] Fawwaz T Ulaaby, Richardk K Moore, Adrian K Fung. Microwave Remote Sensing, Active and Passive, Vol Ⅲ from Theory to Applications. Norwood: Artech House, 1986: 1522-1642.
    [7] Chris Derksen, Anne E Walker. Identification of systematic bias in the cross-platform (SMMR and SSM/I) ease-grid brightness temperature time series. IEEE Transaction on Geoscience and Remote Sensing, 2003, 41 (4): 910-915. doi:  10.1109/TGRS.2003.812003
    [8] Peter Bauer, Norman C Grody. The potential of combining SSM/I and SSM/T2 measurements to improve the identification of snowcover and precipitation. IEEE Transaction on Geoscience and Remote Sensing, 1995, 33 (2): 252-261. doi:  10.1109/36.377925
    [9] Christopher M U Neale, Marshall J Mcfarland, Chang Kai. Land-surface-type classification using microwave brightness temperatures from the special sensor microwave/imager. IEEE Transaction on Geoscience and Remote Sensing, 1990, 28 (5): 829-837. doi:  10.1109/36.58970
    [10] Norman C Grody. Classification of snow cover and precipitation using the special sensor microwave imager. J Geophys Res, 1991, 96 (D4): 7423-7435. doi:  10.1029/91JD00045
    [11] Norman C Grody, Alan N Basist. Global identification of snowcover using SSM/I measurements. IEEE Transaction on Geoscience and Remote Sensing, 1996, 34(1): 237-249. doi:  10.1109/36.481908
    [12] Ralph R Ferraro, Weng Fuzhong, Norman C Grody, et al. An eight-year (1987-1994) time series of rainfall, cloud, water vapor, snow cover, and sea ice derived from SSM/I measu rement. Bull Amer Meteor Soc, 1996, 77 (5): 891-905. doi:  10.1175/1520-0477(1996)077<0891:AEYTSO>2.0.CO;2
    [13] Purushottam Raj Singh, Thian Yew Gan. Retrieval of snow water equivalent using passing microwave brightness temperature data. Remote Sensing of Environment, 2000, 74: 275-286. doi:  10.1016/S0034-4257(00)00121-8
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出版历程
  • 收稿日期:  2006-01-06
  • 修回日期:  2006-08-16
  • 刊出日期:  2007-02-28

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