利用SSM/I数据判识我国及周边地区雪盖

引用本文

李晓静, 刘玉洁, 朱小祥, 郑照军, 陈爱军. 利用SSM/I数据判识我国及周边地区雪盖[J]. 应用气象学报, 2007, 18(1): 12-20. 复制到剪切板

Li Xiaojing, Liu Yujie, Zhu Xiaoxiang, Zheng Zhaojun, Chen Aijun. Snow Cover Identification with SSM/I Data in China[J]. Journal of Applied Meteorological Science, 2007, 18(1): 12-20 复制到剪切板

利用SSM/I数据判识我国及周边地区雪盖

李晓静¹, 刘玉洁¹, 朱小祥¹, 郑照军¹, 陈爱军²

1. 中国气象局中国遥感卫星辐射测量和定标重点开放实验室国家卫星气象中心, 北京 100081;
2. 南京信息工程大学, 南京 210044

2006-01-06 收到, 2006-08-16 收到修改稿.

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) 和973计划课题“空间微波遥感地海表和大气数据验证” (2001CB309402) 共同资助

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。

关键词: 雪盖 SSM/I 积雪判识

Snow Cover Identification with SSM/I Data in China

Li Xiaojing¹, Liu Yujie¹, Zhu Xiaoxiang¹, Zheng Zhaojun¹, Chen Aijun²

1. Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, China Meteorological Administration, Beijing 100081;
2. Nanjing University of Information Science & Technology, Nanjing 210044

Abstract: Snow piling and melting dynamically couple with the hydrological and climatological. It's very important to obtain the accurate snow parameters in time for the study of hydrology and climatology. It is also useful in mitigating or preventing from snow disaster. Microwave remote sensing has the advantage to penetrate the cloud, which is superior to the optical sensor data. With the microwave data from the satellite, it could better identify the snow, even those covered by cloud, or with the higher solar zenith angle. Not only snow cover, but also snow depth and snow water equivalent (SWE) could be obtained. SSM/I is a 7-channel 4-frequency radiometer whose frequencies are 19.35, 22.235, 37.0 and 85.5 GHz respectively. All frequencies are received in dual polarization (V and H) except 22 GHz (V). So, it is very suitable for snow parameters' retrieval. Since 1987, SSM/I data nearly 20 years are collected. Meanwhile, several global snow identifying methods are developed with these data. But it is found that the snow cover is overestimated, when compared with the operational visible-infrared products of the China National Satellite Meteorological Center (NSMC), especially over the Qinghai-Xizang Plateau. Similar conclusion is drawn by Basist. Therefore, it is necessary to develop an advanced method to improve the accuracy of snow cover identification. Using SSM/I data, an improved method is proposed to identify the snow cover in China and its adjacent areas (17°—57°N, 65°—145°E). Snow, rain, cold desert and frozen ground are the scattering matters, and have the similar signatures in the microwave band. That means either the maximum one of (T_B22V-T_B85V) and (T_B19V-T_B37V), or both of them are not less than 5 K. With the snow measurements from the ground observation stations in China and the operational snow cover products of the NSMC, the cause of overestimating snow cover of the old global methods is analyzed, and the conclusion is drawn that parts of the scattering matters, especially the frozen ground, are incorrectly identified as snow, which is the main cause of snow cover overestimated in Qinghai-Xizang Plateau and Mongolia with SSM/I data. Based on the foreign global snow identifying methods and the statistic analysis around six years' measurements from the observation stations in Inner Mongolia about snow, rain and frozen soil matching with SSM/I data, an advanced method depending on five identifying parameters is put forward: a new parameter, (T_B22V-T_B85V)-(T_B19V-T_B37V), is added, which can effectively reduce the influence of the frozen ground and the other scattering matters in snow identification. Finally, the results obtained using the improved method are validated with snow measurements from ground observation stations and operational snow cover products of NSMC in two aspects: one is the temporal variation, the other is the spatial distribution of snow cover. It is found that the improved method is more accurate in snow identifying than the existing global methods.

Key words: snow cover SSM/I snow identification

[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


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 7. 积雪判识效率E_H的时间序列图 Fig 7. Temporal sequence map of snow cover identifying efficiency E_H


图 8. 积雪判识效率E_H空间分布图 Fig 8. Spacial distribution map of snow cover identifying efficiency E_H