| 利用SSM/I数据判识我国及周边地区雪盖 |
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| 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. |
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| 作者姓名： | 李晓静 刘玉洁 朱小祥 郑照军 陈爱军 |
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| 作者单位： | 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 |
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| 基金项目： | 国家高技术研究发展计划(863计划);国家重点基础研究发展计划(973计划) |
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| 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。
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| 关 键 词： | 雪盖 SSM/I 积雪判识 |
| 收稿时间： | 2006-01-06 |
| 修稿时间： | 2006-08-16 |
Snow Cover Identification with SSM/I Data in China |
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| Li Xiaojing Liu Yujie Zhu Xiaoxiang Zheng Zhaojun Chen Aijun.Snow Cover Identification with SSM/I Data in China[J].Quarterly Journal of Applied Meteorology,2007,18(1):12-20. |
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| Authors: | Li Xiaojing Liu Yujie Zhu Xiaoxiang Zheng Zhaojun Chen Aijun |
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| Institution: | 1.Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, China Meteorological Administration, Beijing 1000812.Nanjing University of Information Science & Technology, Nanjing 210044 |
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| 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 (TB22V-TB85V) and (TB19V-TB37V), 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, (TB22V-TB85V)-(TB19V-TB37V), 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. |
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| Keywords: | SSM/I |
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