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遥感技术与应用  2020, Vol. 35 Issue (3): 606-614    DOI: 10.11873/j.issn.1004-0323.2020.3.0606
模型与反演     
一种基于雪粒径演化过程的积雪亮温模拟新方法
武黎黎1,2(),陈月庆1,2,3,朱明4,李晓峰3,赵凯3
1.信阳师范学院 地理科学学院,河南 信阳 464000
2.信阳师范学院 河南省水土环境污染协同防治重点实验室,河南 信阳 464000
3.中国科学院东北地理与农业生态研究所,吉林 长春 130102
4.河南大学 环境与规划学院,河南 开封 475004
A New Method of Simulating Bright Temperature of Snow Cover based on Snow Grain Size Evolution Process
Lili Wu1,2(),Yueqing Chen1,2,3,Ming Zhu4,Xiaofeng Li3,Kai Zhao3
1.School of Geographic Sciences, Xinyang Normal University, Xinyang 464000, China
2.Henan Key Laboratory for Synergistic Prevention of Water and Soil Environmental Pollution, Xinyang Normal University, Xinyang 464000, China
3.Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
4.School of Environment and Planning, Henan University, Kaifeng, 475004, China
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摘要:

本研究采用HUT模型、DMRT模型和MEMLS模型模拟积雪雪粒子与不同波段(18.7 GHz和36.5 GHz)微波相互作用(吸收和消光),并用于辐射传输模型。而雪粒径的获取一直是一个难点,本研究由Jordan91雪粒径演化模型演化得到雪粒径,并将其作为辐射传输模型的输入参数,基于像元内实测数据进行混合像元18.7和36.5 GHz水平极化亮温模拟。结果表明:采用HUT模型、DMRT模型和MEMLS模型的消光系数在18.7 GHz时模拟亮温的偏差分别为-3.6、-1.8和-0.7 K,在36.5 GHz时分别为4.0、10.4和14.4 K。对于18.7 GHz水平极化和36.5 GHz水平极化,基于有效雪粒径的亮温模拟与基于雪粒径演化过程的亮温模拟精度呈现出很好的线性关系。因此,基于雪粒径演化过程的方法是一种合适的获取辐射传输模型中雪粒径参数的方法。

关键词: 积雪粒子散射特性亮温模拟微波成像仪(MWRI)    
Abstract:

This research used HUT model, DMRT model and MEMLS model to simulate interactions(absorption and extinction) between snow grainsfor different wave bands (18.7 GHz and 36.5 GHz) of microwave which were used for radiative transfer model. Obtaining the snow grain size is always a difficulty. So this research used Jordan91 snow grain size evolution model to evolve snow grain size which was regarded as input parameter of radiative transfer model, and used measured data to simulate spaceborne brightness temperature for 18.7 GHz horizontal polarization and 36.5 GHz horizontal polarization in a mixed pixel. The results showed that the bias of simulation brightness temperature using extinction coefficient of HUT model, DMRT model and MEMLS model for 18.7 GHz horizontal polarization were -3.6 K、-1.8 K and -0.7 K respectively, and for 36.5 GHz horizontal polarization were 4.0 K、10.4 K and 14.4 K respectively. For 18.7 GHz horizontal polarization and 36.5 GHz horizontal polarization, the bright temperature simulation based on effective snow grain size shows a good linear relationship with the brightness temperature simulation basedon snow grain size evolution process. Therefore, the method based on the snow grain size evolution process is a suitable method for obtaining the snow grain size parameter in the radiative transfer model.

Key words: Snow grain    Scattering characteristics    Brightness temperature simulation    Microwave Radiation Imager (MWRI)
收稿日期: 2019-05-24 出版日期: 2020-07-10
ZTFLH:  TP79  
基金资助: 国家自然科学基金项目(41701395)
作者简介: 武黎黎(1988-),女,山东菏泽人,副教授,博士,主要从事雪深被动微波遥感反演方法研究。E?mail: wu.lili0330@163.com
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引用本文:

武黎黎,陈月庆,朱明,李晓峰,赵凯. 一种基于雪粒径演化过程的积雪亮温模拟新方法[J]. 遥感技术与应用, 2020, 35(3): 606-614.

Lili Wu,Yueqing Chen,Ming Zhu,Xiaofeng Li,Kai Zhao. A New Method of Simulating Bright Temperature of Snow Cover based on Snow Grain Size Evolution Process. Remote Sensing Technology and Application, 2020, 35(3): 606-614.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2020.3.0606        http://www.rsta.ac.cn/CN/Y2020/V35/I3/606

图1  10 km×10 km像元内的土地利用类型图
频率/GHz10.6518.723.836.589
极化V,HV,HV,HV,HV,H
主波束效率≥90%
地面分辨率≤km×km51×8530×5027×4518×309×15
带宽/MHz1802004009002×2 300
扫描方式圆锥扫描
幅宽/Km1 400
扫描周期/s1.7±0.1
天线视角/°45±0.1
表1  FY-3B微波成像仪(MWRI)的性能参数
图2  基于Jordan91模型模拟的雪粒径和实测雪粒径对比图
图3  基于有效雪粒径的模拟亮温与微波成像仪(MWRI)亮温的对比图(a) 18.7 GHz水平极化 (b) 36.5 GHz水平极化
消光系数HUT模型DMRT模型MEMLS模型
频率和极化18.7H36.5H18.7H36.5H18.7H36.5H
RMSE(K)7.76.25.66.54.913.1
Bias(K)-6.5-4.2-3.84.6-2.711.7
ME(K)6.95.04.75.54.012.0
表2  采用不同消光系数模拟亮温的均方根误差、偏差和平均误差
图4  基于雪粒径演化过程的模拟亮温与微波成像仪(MWRI)亮温的对比图(a) 18.7 GHz水平极化 (b) 36.5 GHz水平极化
消光系数HUT模型DMRT模型MEMLS模型
频率和极化18.7H36.5H18.7H36.5H18.7H36.5H
RMSE(K)5.55.74.411.44.115.6
Bias(K)-3.64.0-1.810.4-0.714.4
ME(K)4.64.93.510.53.014.5
表3  采用不同消光系数模拟亮温的均方根误差、偏差和平均误差
图5  基于有效雪粒径的亮温模拟与基于雪粒径演化过程的亮温模拟精度的相关关系
1 Xiao Lin,Che Tao,Dai Liyun. Evaluation on the Spatial Characteristics of Multiple Snow Depth Datasets over China[J].Remote Sensing Technology and Application,2019,34(6):1133-1145.
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 Xiao X, Zhang T, Zhong X,et al. Support Vector Regression Snow-depth Retrieval Algorithm Using Passive Microwave Remote Sensing Data[J]. Remote Sensing of Environment,2018,210:48-64.doi:10.1016/j.rse.2018.03.008.
doi: 10.1016/j.rse.2018.03.008
3 Zhang Zheng, Xiao Pengfeng, Zhang Xueliang, et al. Analysis of the Characteristics of Snow Albedo during the Snowmelt Period of the Qinghai-Tibet Plateau[J].Remote Sensing Technology and Application,2019,34(6):1146-1154.
3 张正, 肖鹏峰, 张学良, 等. 青藏高原融雪期积雪反照率特性分析[J]. 遥感技术与应用,2019,34(6):1146-1154.
4 Liang J Y, Liu X P, Huang K M. Improved Snow Depth Retrieval by Integrating Microwave Brightness Yemperature and Visible/Infrared Reflectance[J]. Remote Sensing of Environment,2015,156:500-509.
5 Cohen J. Snow Cover and Climate[J]. Weather, 1994, 49: 150-156.
6 Gong G, Cohen J, Entekhabi D, et al Y.Hemispheric-scale Climate Response to Northern Eurasia Land Surface Characteristics and Snow Anomalies[J]. Global and Planetary Change,2007, 56: 359–370.
7 Kontu A, Lemmetyinen J, Vehviläinen J,et al. Coupling SNOWPACK-Modeled Grain Size Parameters with the HUT Snow Emission Model[J]. Remote Sensing of Environment, 2017, 194:33-47.
8 Che Tao, Li Xin, Gao Feng. Estimation of Snow Water Equivalent in the Tibetan Plateau Using Passive Microwave Remote Sensing Data (SSM/I) [J]. Journal of Glaciology and Geocryology, 2004,26(3):363-368.
8 车涛, 李新, 高峰.青藏高原积雪深度和雪水当量的被动微波遥感反演[J].冰川冻土,2004,26(3):363-368.
9 Kunzi K F, Patil S, Rott H. Snow Cover Parameters Retrieved from NIMBUS-7 Scanning Multichannel Microwave Radiometer (SMMR) Data[J]. IEEE Transactions on Geoscience and Remote Sensing, 1982, 20(4): 452 - 467.
10 Chang A T C, Foster J L, Hall D K, et al. Snow Water Equivalent Estimation by Microwave Radiometry[J]. Cold Regions Science and Technology,1982, 5(3): 259-267.
11 Ulaby F T, Moore R K, Fung A K. Microwave Remote Sensing: Active and Passive. Volume II: Radar Remote Sensing and Surface Scattering and Emission Theory [M]. Norwood, MA: Artech House, 1982: 457 - 1064.
12 Cao Meisheng, Li Xin, Chen Xianzhang, et al. Remote Sensing on Cryosphere[M].Beijing:Science Press,2006.曹梅盛,李新,陈贤章,等.冰冻圈遥感[M].北京:科学出版社, 2006.
13 Chen Xiuxue, Li Xiaofeng, Wang Guangrui, et al. Based on Snow Cover Survey Data of Accuracy Verification and Analysis of Passive Microwave Snow Cover Remote Sensing Products in Northeast China[J]. Remote Sensing Technology and Application,2019,34(6):1181-1189.
13 陈秀雪, 李晓峰, 王广蕊, 等.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.
14 Liu Baokang, Feng Shuqing, Du Yu'e, et al. Research Progress and Prospect of Remote Sensing with Passive Microwave from Snow[J].Pratacultural Science, 2009,26(11):37-43.
14 刘宝康,冯蜀青,杜玉娥,等.积雪被动微波遥感研究进展与前景展望[J].草业科学,2009,26(11):37-43.
15 Wiesmann A, Mätzler C. Microwave Emission Model of Layered Snowpacks[J]. Remote Sensing of Environment, 1999, 70(3): 307 - 316.
16 Mätzler C, Wiesmann A. Extension of the Microwave Emission Model of Layered Snowpacks to Coarse-grained Snow[J] .Remote Sensing of Environment, 1999, 70(3): 317 - 325.
17 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.
18 Tsang L, Chen C T, Chang A T C, et al. Dense Media Radiative Transfer Theory based on Quasicrystalline Approximation with Applications to Passive Microwave Remote Sensing of Snow[J] .Radio Science, 2000, 35 (3) : 731 - 749.
19 Jin Y Q. Radiative Transfer of Snowpack/Vegetation Canopy at the SSM/I Channels and Satellite Data Analysis[J]. Remote Sensing of Environment, 1997, 61(1): 55 - 63.
20 Hallikainen M T, Ulaby F, Deventer T V E. Extinction Behavior of Dry Snow in the 18 to 90 GHz Range[J]. IEEE Transactions on Geoscience and Remote Sensing, 1987, 25(6):737–745.
21 JordanR.A One-dimensional Temperature Model for a Snow Cover:Technical Documentation for SNTHERM.89[R].Hanover, NH, SpecialReport,1991: 91-16.
22 Christian M. Relation Between Grain-size and Correlation Length of Snow[J]. Journal of Glaciology, 2002, 48(162):461-466.
23 Kontu A, Pulliainen J. Simulation of Spaceborne Microwave Radiometer Measurements of Snow Cover Using In Situ Data and Brightness Temperature Modeling[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(3):1031–1044.
24 Wegmüller U, Mätzler C.Rough Bare Soil Reflectivity Model [J]. IEEE Transactions on Geoscience and Remote Sensing, 1999,37(3): 1391–1395.
25 Dai L, Che T, Wang J, et al. Snow Depth and Snow Water Equivalent Estimation from AMSR-E Data based on a Priori Snow Characteristics in Xinjiang, China[J]. Remote Sensing of Environment, 2012, 127(1): 14-29.
26 Huang C L, Margulis S A, Durand MT, et al. Assessment of Snow Grain-size Model and Stratigraphy Representation Impactson Snow Radiance Assimilation: Forward Modeling Evaluation[J]. IEEE Transactions on Geoscience and Remote Sensing,2012, 50(11): 4551-4564.
27 Meng Xianyu. Forest Measurement(Third Edition)[M]. Beijing: Chinese Forestry Press, 2006.
27 孟宪宇. 测树学(第三版)[M].北京:中国林业出版社,2006.
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