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遥感技术与应用  2015, Vol. 30 Issue (3): 565-572    DOI: 10.11873/j.issn.1004-0323.2015.3.0565
遥感应用     
被动微波雪深反演算法在东北地区的时空分析与验证
武黎黎1,2,李晓峰1,3,赵凯1,3,郑兴明1,3,丁艳玲1,2,李洋洋1,2,任建华1,2
(1.中国科学院东北地理与农业生态研究所,吉林 长春 130102;
2.中国科学院大学,北京 100049;
3.中国科学院长春净月潭遥感试验站,吉林 长春 130102)
The Space-time Analysis and Validation of Snow Depth Inversion Algorithm of Passive Microwave in Northeast China
Wu Lili1,2,Li Xiaofeng1,3,Zhao Kai1,3,Zheng Xingming1,3,Ding Yanling1,2,Li Yangyang1,2,Ren Jianhua1,2
(1.Northeast Institute of Geography and Agroecology,Chinese Academy of Sciences,Changchun 130102,China;
2.University of Chinese Academy of Sciences,Beijing 100049,China;
3.Changchun Jingyuetan Remote Sensing Test Site of Chinese Academy of Sciences,Changchun 130102,China)
 全文: PDF(4825 KB)  
摘要:

Chang算法及改进算法是被动微波遥感雪深反演算法中较简单的经验算法。为了评价改进的Chang算法在东北地区的适用性,对改进的Chang算法进行分析与验证。从空间上,选取了84个野外数据采样点和48个气象站点对改进的Chang算法进行分析与验证。结果表明:森林下垫面改进的Chang算法会低估雪深3.6 cm,而农田下垫面改进的Chang算法会高估雪深1.5 cm。从时间序列上,选取五营、呼中、庆安和巴彦4个气象站点2012年11月15日~2013年2月28日的时间序列雪深数据,对改进的Chang算法进行分析与验证。结果表明:森林下垫面改进的Chang算法会低估雪深,五营站点低估雪深13.7 cm,呼中站点低估雪深8.3 cm,农田下垫面改进的Chang算法会高估雪深,庆安站点高估雪深3.4 cm,巴彦站点高估雪深0.8 cm。无论从空间上还是时间序列上,验证结果都表明,农田下垫面时改进的Chang算法的精度比森林下垫面时要高。此外,站点雪深不变而改进的Chang算法反演的雪深却在增大,这可能是由于期间雪粒径不断增大的缘故。

关键词: 雪深遥感被动微波微波成像仪东北地区    
Abstract:

Chang algorithm and improved Chang algorithm are the simple empirical algorithms of snow depth inversion algorithms of passive microwave remote sensing.In order to evaluate the applicability of the improved Chang algorithm in Northeast China,this paper analyzed and validated improved Chang algorithm.In spatial analysis,this study selected 84 field sampling points and 48 meteorological stations to analyze and validate the improved Chang algorithm.The results showed that when the underlying surface is forest improved Chang algorithm underestimated the snow depth of 3.6 cm,however when the underlying surface is farmland improved Chang algorithm overestimated the snow depth of 1.5cm.In the time series analysis,this study selected snow depth data of four meteorological stations from 15 November 2012 to 28 February 2013 to analyze and validate the improved Chang algorithm,and four meteorological stations are Wuying,Huzhong,Qingan and Bayan respectively.The results showed that when the underlying surface was forest improved Chang algorithm underestimated the snow depth.It underestimated the snow depth of 13.7 cm for Wuying and 8.3 cm for Huzhong.However when the underlying surface was farmland improved Chang algorithm overestimated the snow depth.It overestimated the snow depth of 3.4 cm for Qingan and 0.8 cm for Bayan.The results also showed that when the underlying surface is farmland the accuracy of the improved Chang algorithm is better than that when the underlying surface is forest in spatial analysis and in the time series analysis.Moreover,the snow depth of improved Chang algorithm inversion was increasing and the depth of meteorological stations was constant.The possible cause was that snow grain size was increasing.

Key words: Snow depth    Remote sensing    Passive microwave    Microwave radiation imager    Northeast China
收稿日期: 2013-11-25 出版日期: 2015-08-14
:  TP 79  
基金资助:

国家863计划项目“遥感产品真实性检验关键技术及其试验验证”(2012AA12A305-5-2),
国家自然科学基金项目“东北地区季节性积雪层中雪粒径的谱分布特征与微波(辐射、散射)特性研究”(41001201),
国家自然科学基金项目“东北地区森林下雪深被动微波遥感反演的关键影响参数观测与研究”(41471289),
吉林省科技发展计划项目“我国东北地区积雪与土壤湿度多源遥感数据产品的开发与应用”(20140101158JC),
国家自然科学基金项目“被动微波遥感土壤水分反演精度与空间异质特征的相关性研究”(41301369)。

通讯作者: 赵凯(1962-),男,吉林长春人,研究员,博士生导师,主要从事微波遥感机理研究以及仪器研发。Email:zhaokai@neigae.ac.cn。    
作者简介: 武黎黎(1988-),女,山东菏泽人,博士研究生,主要从事积雪遥感研究。Email:wu.lili0330@163.com。
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引用本文:

武黎黎,李晓峰,赵凯,郑兴明,丁艳玲,李洋洋,任建华. 被动微波雪深反演算法在东北地区的时空分析与验证[J]. 遥感技术与应用, 2015, 30(3): 565-572.

Wu Lili,Li Xiaofeng,Zhao Kai,Zheng Xingming,Ding Yanling,Li Yangyang,Ren Jianhua. The Space-time Analysis and Validation of Snow Depth Inversion Algorithm of Passive Microwave in Northeast China. Remote Sensing Technology and Application, 2015, 30(3): 565-572.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2015.3.0565        http://www.rsta.ac.cn/CN/Y2015/V30/I3/565

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