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基于Radarsat-2全极化数据的张掖地区土壤水分的反演
作者姓名:王睿馨  宋小宁  马建威  孙川
作者单位:1.中国科学院大学资源与环境学院, 北京 101408;2.中国水利水电科学研究院, 北京 100038
基金项目:中国科学院西部行动计划项目子课题(KZCX2-XB3-15)资助
摘    要:土壤水分是水文、农业、生态等众多研究领域中的重要参数。基于已建立的简化土壤水分反演模型,结合全极化雷达数据的特征,消除简化模型中的粗糙度参数,以提高反演的精度。通过简化经验模型分析,结果显示VV/HH、VV/VH和HH/HV极化组合均能够较好地模拟裸土区土壤水分,其中VV/VH极化组合模拟结果最好。利用Radarsat-2数据反演黑河中游裸土区和植被区的土壤水分,并利用实测数据对结果进行验证。结果表明:VV/VH极化组合反演结果与实测结果吻合度较好,裸土区和植被区土壤水分反演RMSE分别为0.006和0.017 cm3·cm-3,说明VV/VH极化组合能够较好地反演裸土区域土壤水分。这为快速准确获取区域土壤水分反演奠定了基础。

关 键 词:土壤水分  Radarsat-2  经验模型  AIEM  
收稿时间:2017-04-07
修稿时间:2017-05-08

Retrieval of soil moisture in Zhangye Prefecture based on Radarsat-2 data
Authors:WANG Ruixin  SONG Xiaoning  MA Jianwei  SUN Chuan
Institution:1.College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China;2.China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Abstract:Soil moisture is an important parameter in hydrology, agriculture, ecology, and many other research fields. A simplified model for the retrieval of soil moisture is established in the paper. The simplified model improves inversion accuracy by incorparating the characteristics of qual polarization radar data and eliminates roughness in model. The results show that VV/HH, VV/VH, and HH/HV can be used in the inversion of soil moisture in the bare soil region and the VV/VH polarization combination performs the best. In this paper we use Radarsat-2 data to retrieve the soil moisture in bare soil and plant areas in the middle basin of Heihe River and use field survey data to verify the results. The results show that the VV/VH polarization combination inversion results are in good agreement with the field survey results and the RMSE values in the bare and plant areas are 0.006 and 0.017cm3·cm-3, respectively. The VV/VH polarization can be used to invert the soil water content in the bare soil region. This work has laid a foundation for quick and accurate acquirement of the regional soil moisture.
Keywords:soil moisture                                                                                                                        Radarsat-2                                                                                                                        experience model                                                                                                                        AIEM
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