Please wait a minute...
img

官方微信

遥感技术与应用  2019, Vol. 34 Issue (6): 1181-1189    DOI: 10.11873/j.issn.1004-0323.2019.6.1181
冰雪遥感专栏     
基于积雪调查数据的东北地区被动微波积雪遥感产品精度验证与分析
陈秀雪1,2(),李晓峰1,3(),王广蕊1,2,赵凯1,3,郑兴明1,3,姜涛1,3
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
Based on Snow Cover Survey Data of Accuracy Verification and Analysis of Passive Microwave Snow Cover Remote Sensing Products in Northeast China
Xiuxue Chen1,2(),Xiaofeng Li1,3(),Guangrui Wang1,2,Kai Zhao1,3,Xingming Zheng1,3,Tao Jiang1,3
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(3739 KB)   HTML
摘要:

积雪遥感数据产品可以提供积雪的时空分布信息,是积雪监测的重要数据源。对现有的不同遥感产品进行精度验证和对比分析,明确其适用范围,有利于积雪数据产品的进一步发展和应用。为验证积雪产品在东北地区的适用性,以中国积雪特性及分布调查项目为依托,精心设计野外实验,观测了东北地区25 km典型样方和积雪线路调查数据,验证了在阔叶林和农田两种下垫面下,FY-3B雪深产品、AMSR-2雪深产品、GlobSnow雪水当量产品在东北地区的反演精度。结果表明:GlobSnow雪水当量产品精度最高,不区分下垫面的情况下,最大偏差和均方根误差分别为10.87 cm和12.53 cm。考虑下垫面的影响,GlobSnow雪水当量产品和FY-3B雪深产品在两种下垫面下的雪深反演精度差别很小,偏差和均方根误差的差值小于2.11 cm和3.46 cm,AMSR-2积雪产品在两种下垫面下反演精度差别很大,两种下垫面下偏差和均方根误差的差值大于9.94 cm和7.19 cm。对于3种积雪产品,下垫面为农田的雪深反演精度均高于下垫面为阔叶林的反演精度。

关键词: 积雪产品雪深被动微波GlobSnowFY?3BAMSR?2    
Abstract:

Snow cover products can provide temporal and spatial information on snow cover distribution,which is an important data source for snow monitoring. The accuracy validation and contrastive analysis of the remote sensing products are helpful to the further development and application of snow cover data products. the survey project of snow cover characteristics and distribution in China are taken for validating the applicability of snow cover products in Northeast China,The inversion accuracy of FY-3B snow depth product, AMSR-2 snow depth product and GlobSnow snow equivalent product under two underlying surface of Broadleaf Forests and Croplands were verified by using the 25 km typical quadrats and snow path survey data in Northeast China. The results show that the precision of GlobSnow snow water equivalent product has the highest accuracy and the maximum deviation and root mean square error without distinguishing the underlying surface are respectively 10.87 cm and 12.53 cm.The inversion accuracy between GlobSnow snow equivalent product and FY-3B snow depth product under two underlying surfaces is very little and the difference of deviation and root mean square error between two products is respectively smaller than that of 2.11 cm and 3.46 cm.The inversion accuracy of AMSR-2 products under two kinds of underlying surfaces is significant different and the difference of deviation and root mean square error between the two underlying surfaces is greater than that of 9.94 cm and 7.19 cm. For the three snow products, The inversion accuracy of snow depth on the underlying surface of croplands is higher than that of broadleaf forests on the underlying surface.

Key words: Snow products    Snow depth    Passive microwave    GlobSnow    FY-3B    AMSR-2
收稿日期: 2018-12-15 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);天-空-地一体化的农业灾害信息检测研究(KFJ?STS?ZDTP?048?04?03);国家自然科学基金项目“东北农田区积雪演化过程及其微波辐射特性研究”(41871248);吉林省人才基金“卫星星座观测体系下农业遥感信息处理关键技术研究”(Y8D7011001)
通讯作者: 李晓峰     E-mail: chenxiuxue@iga.ac.cn;lixiaofeng@neigac.ac.cn
作者简介: 陈秀雪(1995-),女,山东济宁人,硕士研究生,主要从事积雪遥感研究。E?mail:chenxiuxue@iga.ac.cn
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  
陈秀雪
李晓峰
王广蕊
赵凯
郑兴明
姜涛

引用本文:

陈秀雪,李晓峰,王广蕊,赵凯,郑兴明,姜涛. 基于积雪调查数据的东北地区被动微波积雪遥感产品精度验证与分析[J]. 遥感技术与应用, 2019, 34(6): 1181-1189.

Xiuxue Chen,Xiaofeng Li,Guangrui Wang,Kai Zhao,Xingming Zheng,Tao Jiang. Based on Snow Cover Survey Data of Accuracy Verification and Analysis of Passive Microwave Snow Cover Remote Sensing Products in Northeast China. Remote Sensing Technology and Application, 2019, 34(6): 1181-1189.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2019.6.1181        http://www.rsta.ac.cn/CN/Y2019/V34/I6/1181

图1  样方和积雪路线调查采样点总体分布情况(积雪划分标准引用自文献[21]) 审图号:GS(2019)3266
图2  25 km×25 km样方采样点分布情况
图3  东北地区土地覆盖分类图审图号:GS(2019)3266
下垫面GlobSnowAMSR-2FY-3B
积累期阔叶林16916
农田111011
稳定期阔叶林5916
农田81211
消融期阔叶林13813
农田131413
表1  不同下垫面类型下采样点数量
图4  样方实测雪深和产品雪深对比
GlobSnowAMSR-2FY-3B
偏差/cm5.485.403.07
表2  积雪产品雪深值与实测雪深值平均偏差
图5  两种下垫面下3种产品雪深值和实测雪深分布
积累期稳定期消融期
GlobSnowAMSR-2FY-3BGlobSnowAMSR-2FY-3BGlobSnowAMSR-2FY-3B
偏差/cm7.5023.579.287.4047.7514.4210.8730.3616.31
均方根误差/cm8.9319.9112.498.4954.3517.6612.5336.6218.73
表3  不区分土地覆盖类型情况下3类积雪产品偏差和均方根误差比较
积累期稳定期消融期
GlobSnowAMSR-2FY-3BGlobSnowAMSR-2FY-3BGlobSnowAMSR-2FY-3B
偏差/cm6.7034.4611.858.2865.2820.6211.9736.1412.88
均方根误差/cm8.9636.8015.5910.1969.8723.1813.6439.3214.28
表4  下垫面为阔叶林时3种积雪产品偏差和均方根误差比较
积累期稳定期消融期
GlobSnowAMSR-2FY-3BGlobSnowAMSR-2FY-3BGlobSnowAMSR-2FY-3B
偏差/cm5.485.895.986.1716.338.4310.2126.2019.52
均方根误差/cm6.297.997.056.7319.0410.5112.4232.1322.76
表5  下垫面为农田时3种积雪产品偏差和均方根误差比较
GlobSnowAMSR-2FY-3B
阔叶林农田阔叶林农田阔叶林农田
偏差/cm8.907.6345.6517.3515.2911.94
均方根误差/cm11.099.4251.3323.3318.3715.77
表6  两种土地覆盖类型下3类积雪产品偏差和均方根误差比较
1 Grippa M, Mognard N, Toan T L, et al. Siberia Snow Depth Climatology derived from SSM/I Data Using A Combined Dynamic and Static Algorithm[J]. Remote Sensing of Environment, 2004, 93(1-2): 30-41.
2 Che Tao, Li Xin. Spatial Distribution and Temporal Variation of Snow Water Resources in China during 1993~2002[J]. Journal of Glaciology and Geocryology, 2005,27(1):64-67.
2 车涛, 李新. 1993~2002年中国积雪水资源时空分布与变化特征[J]. 冰川冻土, 2005,27(1):64-67.
3 Ke Changqing, Li Peiji, Wang Caiping. Variation Trends of Snow Cover over the Tibetan Plateau and Their Relations to Temperature and Precipitation[J]. Journal of Glaciology and Geocryology, 1997, 19(4):289-294.
3 柯长青, 李培基, 王采平. 青藏高原积雪变化趋势及其与气温和降水的关系[J]. 冰川冻土, 1997, 19(4):289-294.].
4 Hall D K, Riggs G A, Salomonson V V, et al. MODIS Snow-cover Products[J]. Remote Sensing of Environment, 2002,83:181-194.
5 Pulliainen J. Mapping of Snow Water Equivalent and Snow Depth in Boreal and Sub-arctic Zones by Assimilating Space-borne Microwave Radiometer Data and Ground-based Observations[J]. Remote Sensing of Environment, 2006, 101(2):257-269.
6 Takala M, Luojus K, Pulliainen J, et al. Estimating Northern Hemisphere Snow Water Equivalent for Climate Research Through Assimilation of Space- borne Radiometer Data and Ground-based Measure-ments[J]. Remote Sensing of Environment, 2011, 115(12):3517-3529.
7 Kelly R. The AMSR-E Snow Depth Algorithm: Description and Initial Results[J]. Journal of the Remote Sensing Society of Japan, 2009, 29(1):307-317.
8 Jiang Lingmei, Wang Pei, Zhang Lixin, et al. Improvement of Snow Depth Retrieval for FY3B-MWRI in China[J]. Science China(Earth Science), 2014, 44(3):531-547.
8 蒋玲梅, 王培, 张立新,等. FY3B-MWRI中国区域雪深反演算法改进[J], 中国科学:地球科学, 2014, 44(3):531–547.
9 Huang Xiaodong, Zhang Xuetong, Li Xia, et al. Accuracy Analysis for MODIS Snow Products of MOD10A1 and MOD10A2 in Northern Xinjiang Area[J]. Journal of Glaciology and Geocryology, 2007, 29(5): 721-729.
9 黄晓东, 张学通, 李霞,等. 北疆牧区MODIS积雪产品MOD10A1和MOD10A2的精度分析与评价[J]. 冰川冻土, 2007, 29(5):721-729.
10 Yang Xiaofeng, Zheng Zhaojun, Yang Zhongdong. Validation of AMSR-E Snow Depth Products in Inner Mongolia[J]. Remote Sensing Information, 2011, 39(6):61-68.
10 杨晓峰, 郑照军, 杨忠东. AMSR-E积雪产品在内蒙地区的精度验证[J]. 遥感信息,2011,39(6):61-68.
11 Hancock S, Baxter R, Evans J, et al. Evaluating Global Snow Water Equivalent Products for Testing Land Surface Models[J]. Remote Sensing of Environment,2013,128:107-117.
12 Snauffer A M, Hsieh W W, Cannon A J. Comparison of Gridded Snow Water Equivalent Products with in Situ Measurements in British Columbia, Canada[J]. Journal of Hydrology, 2016, 541:714-726.
13 Zhou Shengnan, Che Tao, Dai Liyun. Based on the Type of Ground Site Representative of Snow Remote Sensing Products Precision Evaluation[J]. Remote Sensing Technology and Application, 2017, 32(2):228-237.
13 周胜男, 车涛, 戴礼云,等. 基于地面站点类型代表性的积雪遥感产品精度评价[J]. 遥感技术与应用, 2017, 32(2):228-237.
14 Larue F, Royer A, De Sève D, et al. Validation of GlobSnow-2 Snow Water Equivalent over Eastern Canada[J]. Remote Sensing of Environment. 2017,194:264-277.
15 Li Peiji, Mi Desheng. Distribution of Snow Cover in China[J]. Journal of Glaciology and Geocryology, 1983, 5(4):9-18.
15 李培基, 米德生. 中国积雪的分布[J]. 冰川冻土, 1983, 5(4):9-18.
16 Pulliainen J T, Grandell J, Hallikainen M T. HUT Snow Emission Model and Its Applicability to Snow Water Equivalent Retrieval[J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(3):1378 -1390.
17 Takala M, Pulliainen J, Metsamaki S J, et al. Detection of Snowmelt Using Spaceborne Microwave Radiometer Data in Eurasia from 1979 to 2007[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(9):2996-3007.
18 Hu Tongxi, Zhao Tianjie, Shi Jiancheng, et al. Inter-calibration of AMSR-E and AMSR2 Brightness Temperature [J]. Remote Sensing Technology and Application, 2016,31(5):919-924.
18 胡同喜, 赵天杰, 施建成,等. AMSR-E与AMSR2被动微波亮温数据交叉定标[J]. 遥感技术与应用, 2016, 31(5):919-924.
19 Foster J L, Chang A, Hall D K. Comparison of Snow Mass Estimates from Prototype Passive Microwave Snow Algorithm, a Revised Algorithm and A Snow Depth Climatology[J]. Remote Sensing of Environment, 1997, 62(2):132-142.
20 Wang Jian, Che Tao, Li Zhen, et al. Investigation on Snow Characteristics and Their Distribution in China[J]. Advances in Earth Science, 2018, 33(1):12-26.
20 王建, 车涛, 李震,等. 中国积雪特性及分布调查[J]. 地球科学进展, 2018, 33(1):12-26.
21 Zhang Tingjun, Zhong Xinyue. Classification and egionalization of the Seasonal Snow Cover Across the Eurasian Continent[J]. Journal of Glaciology and Geocryology, 2014, 36(3):481-490.
21 张廷军, 钟歆玥. 欧亚大陆积雪分布及其类型划分[J]. 冰川冻土, 2014, 36(3): 481-490.
22 Friedl M A, Mciver D K, Hodges J C F, et al.Global Land Cover Mapping from MODIS: Algorithms and Early Results[J]. Remote Sensing of Environment, 2002, 83(1):287-302.
[1] 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.
[2] 李长春, 徐轩, 包安明, 刘雪峰, 杨文攀.  基于FY3B-MWRI数据新疆区域积雪深度反演[J]. 遥感技术与应用, 2018, 33(6): 1030-1036.
[3] 陈鹤, 车涛, 戴礼云. 基于FY-MWRI的中国西部被动微波积雪判识算法[J]. 遥感技术与应用, 2018, 33(6): 1037-1045.
[4] 刘畅,李震,张平,田帮森,周建民. 基于Google Earth Engine评估新疆西南部MODIS积雪产品[J]. 遥感技术与应用, 2018, 33(4): 584-592.
[5] 侯海艳,侯金,黄春林,王昀琛. 基于人工神经网络和AMSR2多频微波亮温的北疆地区雪深反演[J]. 遥感技术与应用, 2018, 33(2): 241-251.
[6] 张帅,师春香,梁晓,贾炳浩,吴捷. 风云三号积雪覆盖产品评估[J]. 遥感技术与应用, 2018, 33(1): 35-46.
[7] 胡文星,柴琳娜,赵少杰,赵天杰. 寒区复杂地表冻融状态判别式算法改进[J]. 遥感技术与应用, 2017, 32(3): 395-405.
[8] 王增艳,王建,车涛. 机载L波段微波辐射计数据反演表层土壤水分研究[J]. 遥感技术与应用, 2017, 32(2): 185-194.
[9] 王功雪,蒋玲梅,武胜利,刘晓敬,郝诗睿. FY-3B与FY-3C/MWRI交叉定标及雪深算法应用[J]. 遥感技术与应用, 2017, 32(1): 49-56.
[10] 臧琳,宋冬梅,单新建,崔建勇,邵红梅,沈晨,时洪涛,宋先月. 基于被动微波与时空联合算法的云下像元LST重建[J]. 遥感技术与应用, 2016, 31(4): 764-772.
[11] 邱玉宝,郭华东,石利娟,施建成. 基于AMSR-E的全球陆表被动微波发射率数据集[J]. 遥感技术与应用, 2016, 31(4): 809-819.
[12] 杜一男,李晓峰,赵凯,武黎黎,郑兴明,姜涛. NASA系列雪参数反演算法在单像元内的时间序列验证与分析[J]. 遥感技术与应用, 2016, 31(2): 332-341.
[13] 郑雷,张廷军,车涛,钟歆玥,王康. 利用实测资料评估被动微波遥感雪深算法[J]. 遥感技术与应用, 2015, 30(3): 413-423.
[14] 王琦,柴琳娜,赵少杰,张涛. 基于多角度微波辐射亮温数据反演冬小麦光学厚度[J]. 遥感技术与应用, 2015, 30(3): 424-430.
[15] 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.