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遥感-测站相结合的动态雪深反演方法初探
A Dynamic Approach to Retrieving Snow Depth Based on Integration of Remote Sensing and Observed Data
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摘要: 该文结合2000年专用传感器微波成像仪（SSM/I）的亮温数据和我国观测站雪深资料，提出了一种遥感测站相结合的动态雪深反演方法，试图用统计关系的时空动态化方案克服理论上亮温与不同类型积雪之间物理关系的复杂性，从而提高测站稀疏区和雪盖边缘区的雪深反演精度。其最大特点在于反演系数并不固定，而随时间和空间变化, 较好地改善了单一系数反演方法中积雪物理性质的区域性差异和时间（季节）性差异带来的反演误差。初步分析表明：这种遥感测站相结合的反演方法所得的积雪空间分布连续性好，在雪盖边缘区和站点稀疏区也能得到较合理的雪深数据；与静态遥感反演法和可见光雪盖面积相比，这种方法克服了它们在华北和华中低估雪盖面积的缺点，积雪面积分布更接近真实场，对西部积雪分布的反演也有一定改善。Abstract: Both the observed data and remote sensing data have respective different advantages and disadvantages. Based on integration of observed and remote sensing data, a temporal spatial dynamic approach to retrieve snow depth is explored by skillfully combining observation station data in China and brightness temperature (Tb) from the Special Sensor Microwave Imager (SSM/I). The aim is to utilize the dynamic scheme of the statistical relation to overcome the complexity of the physical relation between Tb and snow depth, accordingly, to improve the retrieval precision in marginal regions of snow cover and the regions where there are few observation stations. The dynamic scheme is implemented by the following steps: For the first time, according to the linear relationship between observed snow depth and Tb difference at each station, the retrieval coefficients of all stations at this time can be achieved, which guarantees the coefficients' spatial difference. Second, after reasonable influencing radius decided, by using of Cressman interpolation algorithm, the retrieval coefficients at all grid points at this time can be obtained, which guarantees the coefficients' spatial continuity. Third, unreasonable stations and grids are eliminated through quality control. Last, for the next time, the previous steps are repeated, and so on, which guarantees temporal dynamics. Its biggest characteristic is that the retrieval coefficients are not fixed, but variable with time and space, which overcomes the errors from regional and temporal (seasonal) differences of the physical features. By comparing it with another retrieval approach, the primary analysis indicates that the error of the snow data through the dynamic approach to retrieving snow depth based on integrated observed and remote sensing data is generally smaller, and the accuracy percentage is higher. Compared to observed data, it has a continuous snow depth distribution that is more reasonable than that of observed field, and in the regions where there are few stations, more appropriate snow depth data could still be obtained. Moreover, compared with the results from direct remote sensing retrieval approach and visible snow cover, the distribution of snow cover obtained by the approach is closer to real field, while the results from static remote sensing retrieval approach and visible snow cover usually underestimate snow cover extent in North China and Central China, and the retrieval result in the western China is also improved using the dynamic approach.
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Key words:
- snow depth;
- retrieving;
- passive microwave remote sensing;
- SSM/I
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图 1 中国境内各格点到相邻最近观测站点的距离分布 (阴影) 以及2000年1月15日有雪 (白点) 和无雪 (黑点) 站点分布
Fig. 1 The distribution of distances between each grid point and its nearest station (shaded area), stations with snow (white dot) and without snow (black dot) in China on 15 Jan 2000
图 2 遥感-测站相结合的时空动态雪深反演方法技术路线
Fig. 2 Technical route of the temporal-spatial dynamic approach to retrieving snow depth based on integration of remote sensing and observed data
图 3 2000年1月21日我国境内动态反演系数 (a)、动态反演雪深 (b)、观测雪深 (c) 和Chang92方法反演雪深 (d) 及2000年1月17-23日NSIDC周雪盖分布 (e)
Fig. 3 Dynamic retrieval coefficients (a), dynamic retrieval snow depth (b), observed snow depth (c) and retrieval snow depth by Chang 92 approach (d) on 21 Jan 2000 with NSIDC snow cover distribution during 17-23 Jan 2000(e) in China
图 4 2000年1月27日我国境内动态反演系数 (a)、动态反演雪深 (b)、观测雪深 (c) 和Chang92方法反演雪深 (d) 及2000年1月24-30日NSIDC周雪盖分布 (e)
Fig. 4 Dynamic retrieval coefticients (a), dynamic retrieval snow depth (b), observed snow depth (c) and retrieval snow depth by Chang92 approach (d) on 21 Jan 2000 with NSIDC distribution during 24-30 Jan 2000(e) in China
图 5 应用不同方法得到的2000年1月21日站点反演雪深与观测雪深散点图
Fig. 5 Scatter plots of observed and retrieval snow depth in each station by different approaches on 21 Jan 2000
图 6 不同方法的误差空间分布以及总体误差百分率分布
Fig. 6 The spatial distribution of error and the distribution of global error percentage
图 7 2000年1月9日-12月31日有雪站点的平均雪深 (a)、标准差 (b) 和误差 (c) 的逐日演变
Fig. 7 The daily evolutions of averaged snow depth (a), its standard deviation (b) and error (c) of the stations with snow from 9 Jan to 31 Dec in 2000
图 8 2000年1月9日-12月31日动态反演方法和Chang92方法反演准确率 (曲线，误差阈值:5cm) 与有雪站数 (柱状图) 逐日演变
Fig. 8 The daily evolutions of retrieval accuracy rates (curve, error threshold: 5 cm) of the dynamic and Chang92 approaches, and the number (histogram) of stations with snow from 9 Jan to 31 Dec tn 2000
表 1 2000年1月15日不同站点观测雪深与SSM/I亮温差的关系
Table 1 The relationship between observed snow depth and SSM/I brightness temperature difference at 5 stations on 15 Jan 2000
表 2 北京站2000年1月11-28日观测雪深与亮温差关系
Table 2 The relationship between observed snow depth and brightness temperature difference in Beijing during 11-28 Jan 2000
表 3 动态反演方法与Chang92方法准确率比较 (单位:%)
Table 3 The comparison of accuracy rates between the dynamic retrieval approach and Changes92 approach (unit:%)
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