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遥感技术与应用  2019, Vol. 34 Issue (6): 1133-1145    DOI: 10.11873/j.issn.1004-0323.2019.6.1133
冰雪遥感专栏     
多源雪深数据在中国的空间特征评估
肖林1,2,3(),车涛1,3(),戴礼云1,3
1.中国科学院西北生态环境资源研究院,甘肃 兰州 730000
2.中国科学院大学,北京 100049
3.中国科学院黑河遥感实验研究站,甘肃 兰州 730000
Evaluation on the Spatial Characteristics of Multiple Snow Depth Datasets over China
Lin Xiao1,2,3(),Tao Che1,3(),Liyun Dai1,3
1.Northwest Institute of Eco-environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
2.University of Chinese Academy of Sciences, Beijing 100049, China
3.Heihe Remote Sensing Experimental Research Station, Chinese Academy of Sciences, Lanzhou 730000, China
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摘要:

积雪的年际和年内变化强烈地影响着区域及全球的水量平衡,同时,积雪反照率反馈也显著地影响着气候变化。目前长时间序列的格网雪深数据主要来自被动微波遥感及再分析资料,但不同数据之间存在着明显差异。基于多源雪深数据的评估,特别是空间特性的评估还很缺乏。因此,本研究选取了AMSR-E、WESTDC、GlobSnow、RA-Interim及MERRA2这5种雪深数据,以站点观测数据为参考真值,对它们进行了中国地区的空间误差对比及基于误差排序的相对表现分析。评估结果初步显示:①WESTDC在我国西北及东北积雪区表现较好,适合用于我国北方的雪深研究;②MERRA2在西北和东北积雪区也有较好的表现,但由于其分辨率较粗,缺乏细节的空间信息,因此认为比较适用于大区域的统计分析;③AMSR-E在我国中部和东南地区表现最好,因此认为适合我国中部及东南部的雪深研究。

关键词: 遥感数据再分析资料雪深数据评估中国    
Abstract:

Snow cover is of great significance to both global water balance and the climate system, due to its significant variability, as well as snow albedo feedback. Current grid snow depth datasets of long time series are mainly derived from passive microwave remote sensing and reanalysis, but apparent inconsistencies exist among them. Nevertheless, the assessment on multi-source snow depth datasets is still inadequate, especially assessment on their spatial characteristics. Therefore, 5 snow depth datasets, including AMSR-E, WESTDC, GlobSnow, ERA-Interim and MERRA2, were evaluated against ground observations on their spatial uncertainties and relative performances. Preliminary results were: (1)Due to the fine performances at northwest and northeast part of China, WESTDC is quite suitable in snow depth study over northern China; (2)MERRA2 shows general good performances in northwest and northeast part of China, while it lacks of detailed information due to the coarse resolution, it is recommended to conduct statistical analysis over large regions. (3)AMSR-E performs best in middle to southeast part of China, which makes it a good choice in snow depth analysis over mid-southeast part of China.

Key words: Remote sensing datasets    Reanalysis    Snow depth datasets    Evaluation    China
收稿日期: 2019-03-12 出版日期: 2020-03-23
ZTFLH:  TP79  
基金资助: 科技部国家科技基础资源调查专项“中国积雪特性及分布调查”(2017FY100500);国家自然科学基金项目(41771389)
通讯作者: 车涛     E-mail: xiaolin@lzb.ac.cn;chetao@lzb.ac.cn
作者简介: 肖 林(1989-),女,四川汶川人,博士研究生,主要从事积雪遥感研究。E?mail:xiaolin@lzb.ac.cn
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引用本文:

肖林,车涛,戴礼云. 多源雪深数据在中国的空间特征评估[J]. 遥感技术与应用, 2019, 34(6): 1133-1145.

Lin Xiao,Tao Che,Liyun Dai. Evaluation on the Spatial Characteristics of Multiple Snow Depth Datasets over China. Remote Sensing Technology and Application, 2019, 34(6): 1133-1145.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2019.6.1133        http://www.rsta.ac.cn/CN/Y2019/V34/I6/1133

图1  遥感数据、再分析资料及站点数据在2003~2011年的平均雪深分布
图2  遥感数据和再分析资料在2003~2011年的平均偏差
图3  遥感数据和再分析资料在2003~2011年的平均均方根误差
BIAS平均排名AMSR-EWESTDCGlobSnowERA-InterimMERRA2
青藏高原积雪区1.322.73NaN3.932.41
西北积雪区2.061.963.853.813.10
东北积雪区2.281.883.743.743.23
其他地区1.422.16NaN2.833.71
全国范围1.612.16NaN3.203.43
表1  各数据在不同积雪区的BIAS排名
RMSE平均排名AMSR-ENHSDGlobSnowERA-InterimMERRA2
青藏高原积雪区1.602.92NaN3.892.09
西北积雪区3.062.293.663.802.03
东北积雪区3.002.213.613.922.12
其他地区1.992.44NaN2.882.99
全国范围2.212.45NaN3.252.66
表2  各数据在不同积雪区的RMSE排名
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