东北典型林区雪深反演算法的验证与分析

doi:10.3724/SP.J.1047.2014.00320

地球信息科学学报 ›› 2014, Vol. 16 ›› Issue (2): 320-327.doi: 10.3724/SP.J.1047.2014.00320

东北典型林区雪深反演算法的验证与分析

武黎黎^1,2, 李晓峰^1,3, 赵凯^1,3, 郑兴明^1,3, 戴礼云^2,4

1. 中国科学院东北地理与农业生态研究所, 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, 长春 130102;
4. 中国科学院寒区旱区环境与工程研究所, 兰州 730000

收稿日期:2013-04-12 修回日期:2013-05-16 出版日期:2014-03-10 发布日期:2014-03-10
通讯作者: 赵凯（1962- ），男，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。E-mail：zhaokai@neigae.ac.cn E-mail:zhaokai@neigae.ac.cn
作者简介:武黎黎（1988- ），女，山东菏泽人，硕士生，研究方向为积雪遥感。E-mail：wu.lili0330@163.com
基金资助:
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）；国家高技术研究发展计划（“863计划”）“遥感产品真实性检验关键技术及其试验验证”（2012AA12A305-5-2）。

Validation and Analysis of Snow Depth Inversion Algorithm in Northeast Typical Forest Based on the FY3B-MWRI Data

WU Lili^1,2, LI Xiaofeng^1,3, ZHAO Kai^1,3, ZHENG Xingming^1,3, DAI Liyun^2,4

1. Northeast Institute of Geography and Agroecology, CAS, 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;
4. Cold and Arid Regions Environmental and Engineering Research Institute, CAS, Lanzhou 730000, China

Received:2013-04-12 Revised:2013-05-16 Online:2014-03-10 Published:2014-03-10
Contact: 10.3724/SP.J.1047.2014.00320 E-mail:zhaokai@neigae.ac.cn

摘要/Abstract

摘要：

积雪对自然环境和人类活动都有极其重要的影响。积雪参数（雪面积、雪深和雪水当量）反演对水文模型和气候变化研究有着实际的意义。然而，目前森林区的雪深遥感反演精度一直有待于进一步提高。东北地区是我国最大的天然林区和重要的季节性积雪区之一，本文利用FY3B卫星微波成像仪（MWRI）L1级亮温数据和L2级雪水当量数据，以及东北典型林区野外实测雪深数据，对Chang算法、NASA 96算法和FY3B雪深业务化反演算法进行了验证与分析。结果表明：在东北典型林区的雪深反演中，Chang算法和NASA 96算法反演的雪深波动都比较大，当森林覆盖度f≤0.6时，NASA 96算法表现比较好，均方根误差值在3种算法中较小，但当f >0.6时，NASA 96算法失真严重。当考虑纯森林像元（f=1）时，Chang算法低估了雪深47%。当f≤0.3时，FY3B业务化算法始终优于Chang算法。整体上，FY3B业务化算法相对稳定，具有较高的精度。

关键词: 积雪, FY3B-MWRI, 微波遥感, 雪深反演

Abstract:

Snow cover is one of the active components of the cryosphere. Snow cover has a very important impact on the natural environment and human activities. Snow parameters (snow area, snow depth and snow water equivalent) inversion has practical significance to hydrological models and climate change research. However, the accuracy of snow depth inversion of remote sensing in the forest area should be further improved at present. Northeast is one of China's largest natural forest areas and important seasonal snow areas. This paper used L1 level brightness temperature data and L2 level snow water equivalent data of Microwave Radiation Imager (MWRI) mounted on FY3B satellite, and used field snow depth data in Northeast typical forest regions. Chang algorithm, NASA 96 algorithm and FY3B operational inversion algorithm were validated and analyzed. The results showed that, in Northeast typical forest regions, the retrieved snow depth of Chang algorithm and NASA 96 algorithm had large fluctuations. The performance of NASA 96 algorithm was better than Chang algorithm and FY3B operational inversion algorithm when fractional forest cover (f) was 0.6 or less, because the root mean square error value of NASA 96 algorithm was smaller than the other two algorithms. However, NASA96 algorithm had serious distortion when f was bigger than 0.6. Considering the pure forest pixel (f=1), Chang algorithm underestimated the snow depth of 47%. When f was 0.3 or less, FY3B operational inversion algorithm is better than Chang algorithm. On the whole, FY3B operational algorithm was relatively stable, and FY3B operational algorithm had higher accuracy compared with Chang algorithm and NASA 96 algorithm.

Key words: microwave remote sensing, FY3B-MWRI, snow depth inversion, snow cover

武黎黎, 李晓峰, 赵凯, 郑兴明, 戴礼云. 东北典型林区雪深反演算法的验证与分析[J]. 地球信息科学学报, 2014, 16(2): 320-327.DOI:10.3724/SP.J.1047.2014.00320

WU Lili, LI Xiaofeng, ZHAO Kai, ZHENG Xingming, DAI Liyun. Validation and Analysis of Snow Depth Inversion Algorithm in Northeast Typical Forest Based on the FY3B-MWRI Data[J]. , 2014, 16(2): 320-327.DOI:10.3724/SP.J.1047.2014.00320

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东北典型林区雪深反演算法的验证与分析

Validation and Analysis of Snow Depth Inversion Algorithm in Northeast Typical Forest Based on the FY3B-MWRI Data

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