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遥感技术与应用  2017, Vol. 32 Issue (4): 606-614    DOI: 10.11873/j.issn.1004-0323.2017.4.0606
数据同化专栏     
联合同化MODIS地表温度与机载L波段微波亮度温度估计土壤水分
曹永攀1,2,3,黄春林1,2,陈玮婧4,张莹1,2,3
(1.中国科学院西北生态环境资源研究院 黑河遥感试验研究站,甘肃 兰州730000; 2.中国科学院西北生态环境资源研究院 甘肃省遥感重点实验室,甘肃 兰州730000;3.中国科学院大学,北京100049;4.武汉大学 资源与环境科学学院,湖北 武汉430079)
Improving Soil Moisture Estimation by Joint Assimilationof MODIS Land Surface Temperature and Airborne L band Microwave Brightness Temperature
Cao Yongpan1,2,3,Huang Chunlin1,2,Chen Weijing4,Zhang Ying1,2,3
(1.Heihe Remote Sensing Experimental Research Station,Northwest Institute of EcoEnvironmentand Resources,CAS,Lanzhou 730000,China;2.Key Laboratory of Remote Sensing of Gansu Province,Northwest Institute of EcoEnvironment and Resources,CAS,Lanzhou 730000,China;
3.University of Chinese Academy of Sciences,Beijing 100049 4.School of Resource andEnvironmental Science,Wuhan University,Wuhan 430079,China)
 全文: PDF(5727 KB)  
摘要:
构建了基于通用陆面模型(CoLM,Common Land Model)、微波辐射传输模型LMEB(L band Microwave Emission of the Biosphere)和集合平滑算法(EnKS,Ensemble Kalman Smoother)的土壤水分数据同化框架,用于联合同化MODIS地表温度和机载L波段被动微波亮温数据。以2012年HiWATER试验期间中游大满超级站为实验站点,分析了3种LAI数据产品对土壤温度模拟结果的影响,进而分析了联合同化地表温度和微波亮度温度对土壤水分估计结果的影响。研究结果表明:3种LAI数据对土壤温度模拟结果的影响显著,MODIS LAI产品在该研究区显著低估,导致土壤温度模拟结果高估4~6 K;同化亮度温度、同化地表温度以及联合同化两者均可以改进土壤水分的估计精度,联合同化地表温度和亮度温度对于土壤水分的改进最为显著,土壤水分同化结果的RMSE减少31%~53%。
关键词: 数据同化土壤水分土壤温度EnKSCommon Land Model    
Abstract: In this work,a novel soil moisture data assimilation scheme was developed,which was based land surface model (CoLM,Common Land Model),microwave radioactive transfer model (L MEB,L band Microwave Emission of the Biosphere),and data assimilation algorithm (EnKS,Ensemble Kalman Smoother).This scheme is used to improve the estimation of soil moisture profile by jointly assimilatingMODIS land surface temperature and airborne Lband passive microwave brightness temperature.The groundbased data observed at DAMAN superstation,which is located at Yingke oasisdesert area in the middle stream of the Heihe River Basin,are used to conduct this experiment and validate assimilation results.Three LAI products are used to analyze the influence of LAI on soil temperature.Three assimilation experiments are also designed in this work,including assimilation of MODIS LST,assimilation of microwave brightness temperature,and assimilation of MODIS LST and microwave brightness temperature.The results show that the uncertainties in LAI influence significantly soil temperature simulations in different soil layers.MODIS LAI product is seriously underestimated in this study area,which results soil temperature overestimation about 4~6 K.Three assimilation schemes can improve soil moisture estimations to different extend.Joint assimilation of MODIS LST and microwave brightness temperature achieved the best performance,which can reduce the RMSE of soil moisture to 31%~53%.
Key words: Data assimilation    Soil moisture    Soil temperature    Ensemble Kalman smoother    Common Land Model
收稿日期: 2016-10-09 出版日期: 2017-09-13
:  TP 79  
基金资助: 国家自然科学基金项目(41271358、91325106)资助。

 
作者简介: 曹永攀(1982-),男,甘肃兰州人,博士研究生,主要从事陆面数据同化研究。Email:cyp@lzb.ac.cn。
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曹永攀,黄春林,陈玮婧,张莹. 联合同化MODIS地表温度与机载L波段微波亮度温度估计土壤水分[J]. 遥感技术与应用, 2017, 32(4): 606-614.

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http://www.rsta.ac.cn/CN/Y2017/V32/I4/606

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