地球科学进展 ›› 2009, Vol. 24 ›› Issue (7): 769 -775. doi: 10.11867/j.issn.1001-8166.2009.07.0769

遥感反演与估算 上一篇    下一篇

利用主被动微波数据联合反演土壤水分
赵天杰 1,2,张立新 1,2,蒋玲梅 1,2,陈权 3,张志玉 1,2,张勇攀 1,2   
  1. 1.北京师范大学地理学与遥感科学学院,北京  100875;  2.北京师范大学/中国科学院遥感应用研究所遥感科学国家重点实验室,北京  100875;
    3.中国科学院对地观测与数字地球科学中心,北京  100190
  • 收稿日期:2009-01-08 修回日期:2009-07-07 出版日期:2009-06-10
  • 通讯作者: 赵天杰 E-mail:zhaotianjie@gmail.com
  • 基金资助:

     国家重点基础研究发展计划项目“陆表生态环境要素主被动遥感协同反演理论与方法”(编号:2007CB714400)和“被动遥感反射、辐射机理与参数反演”(编号:2007CB714403);中国科学院西部行动计划(二期)项目“黑河流域遥感—地面观测同步试验与综合模拟平台建设”(编号:KZCX2-XB2-09)资助.

Joint Inversion of Soil Moisture Using Active and Passive Microwave Data

Zhao Tianjie 1,2,Zhang Lixin 1,2,Jiang Lingmei 1,2,Chen Quan 3,Zhang Zhiyu 1,2,Zhang Yongpan 1,2   

  1. 1.School of Geography and Remote Sensing Science, Beijing Normal University, Beijing  100875, China;
    2.State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing  100875, China;
    3.Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, Beijing  100190, China
  • Received:2009-01-08 Revised:2009-07-07 Online:2009-06-10 Published:2009-07-10

在黑河中游干旱区水文试验的基础上,以临泽站为研究区域,探讨主被动微波数据联合反演土壤水分的方法。针对ALOS/PALSAR数据,使用AIEM理论模型计算地表的同极化后向散射系数,Oh半经验模型描述交叉极化散射特征,通过对大量后向散射模拟数据的分析,建立裸露地表粗糙度计算模型;利用模拟数据分析地表辐射亮温随土壤水分和粗糙度的变化规律,在此基础上构建NN模型结合粗糙度计算结果和辐射计飞行数据反演研究区域的土壤水分。地面同步测量数据的验证结果表明,该方法充分发挥了主被动微波数据各自的优势,同时避免了主被动协同过程中的尺度问题,为流域尺度的土壤水分监测提供了一种新的有效途径。

In this paper, on the basis of the Heihe hydrological drought experiments, the active and passive microwave data joint inversion method of soil moisture has been explored as Linze Station for the study area. For ALOS/PALSAR data, co-polarization backscattering coefficient was calculated using the theoretical model AIEM, and Oh semi-empirical model was used to describe the characteristics of cross-polarization scattering. By a large number of back-scattering simulation data analysis, a calculation model of surface roughness was established. With the use of simulation data, changes of surface radiation brightness temperature with soil moisture and roughness were analyzed. Based on this, a neural network model was built to combine roughness calculation results and the flight data, and soil moisture of the study area was estimated with the trained model. Verified results with synchronous measurement data showed that the method can give full play to the active and passive microwave data on their respective strengths, while avoiding the main problem of scaling issues with passive and active data. And it provides a new effective way for basin-scale monitoring of soil moisture.

中图分类号: 

[1] Lee K H, Anagnostou E N. A combined passive/active microwave remote sensing approach for surface variable retrieval using Tropical Rainfall Measuring Mission observations[J].Remote Sensing of Environment, 2004,92:112-125.
[2] Narayan U, Lakshmi V, Jackson T J. High resolution change estimation of soil moisture using L-band radiometer and radar observations made during the SMEX02 experiments[J].IEEE Transactions on Geoscience and Remote Sensing,2006,44:1 545-1 554.
[3] Li Xin,Ma Mingguo, Wang Jian, et al.Simultaneous remote sensing and ground-based experiment in the Heihe River Basin: Scientific objectives and experiment design[J].Advances in Earth Science,2008,23(9):897-914.[李新,马明国,王建,等.黑河流域遥感—地面观测同步试验: 科学目标与试验方案[J].地球科学进展,2008,23(9):897-914.]
[4] Ulaby F T, Batlivala P, Dobson M. Microwave backscatter dependence on surface roughness, soil moisture and soil texture: Part I-bare soil[J].IEEE Transactions on Instrumentation and Measurement,1978,16:286-295.
[5] Fung A K, Li Zongqian, Chen K S. Backscattering from a randomly rough dielectric surface[J].IEEE Transactions on Geoscience and Remote Sensing,1992,30(2):195-200.
[6] Chen K S, Wu T D, Tsang L,et al. Emission of rough surfaces calculated by the integral equation method with comparison to three-dimensional moment method simulations[J].IEEE Transactions on Geoscience and Remote Sensing,2003,41(1):90-101.
[7] Shi J, Chen K S, Li Q, et al. A parameterized surface reflectivity model and estimation of bare surface soil moisture with L-band Radiometer[J].IEEE Transactions on Geoscience and Remote Sensing,2002,40(12):2 674-2 686.
[8] Oh Y, Sarabandi K, Ulaby F T. Semi-empirical model of the ensemble averaged differential Mueller matrix for microwave backscattering from bare soil surfaces[J].IEEE Transactions on Geoscience and Remote Sensing, 2002,40(6):1 348-1 355.
[9] Dobson M C, Ulaby F T, Hallikainen M T,et al.Microwave dielectric behavior of wet soil Part II: Dielectric mixing models[J].IEEE Transaction on Geoscience and Remote Sensing,1985,23:35-46.
[10] Jia Yonghong. Multi-source Remote Sensing Image Data Fusion Technology[M].Beijing: Survey and Mapping Press, 2005.[贾永红.多源遥感影像数据融合技术[M].北京: 测绘出版社,2005.]
[11] Xu Dong, Wu Zheng. The Analysis and Design Based on MATLAB 6.x-Neural Network[M].Xi′an: Xi′an University of Electronic Science and Technology Publishing House, 2002.[许东,吴铮.基于 MATLAB 6.x的系统分析与设计——神经网络[M].西安:西安电子科技大学出版社,2002.]

[1] 赵文智, 周宏, 刘鹄. 干旱区包气带土壤水分运移及其对地下水补给研究进展[J]. 地球科学进展, 2017, 32(9): 908-918.
[2] 邵明安, 贾小旭, 王云强, 朱元骏. 黄土高原土壤干层研究进展与展望[J]. 地球科学进展, 2016, 31(1): 14-22.
[3] 高江波, 吴绍洪, 戴尔阜, 侯文娟. 西南喀斯特地区地表水热过程研究进展与展望[J]. 地球科学进展, 2015, 30(6): 647-653.
[4] 李大治, 晋锐, 车涛, 高莹, 耶楠, 王树果. 联合机载PLMR微波辐射计和MODIS产品反演黑河中游张掖绿洲土壤水分研究 *[J]. 地球科学进展, 2014, 29(2): 295-305.
[5] 朱忠礼,林柳莺,徐同仁. 海河流域不同下垫面土壤水分动态模拟研究[J]. 地球科学进展, 2012, 27(7): 778-787.
[6] 张添,黄春林,沈焕锋. 土壤水分对土壤参数的敏感性及其参数优化方法研究[J]. 地球科学进展, 2012, 27(6): 678-685.
[7] 陈书林,刘元波,温作民. 卫星遥感反演土壤水分研究综述[J]. 地球科学进展, 2012, 27(11): 1192-1203.
[8] 刘元波,傅巧妮,宋平,赵晓松,豆翠翠. 卫星遥感反演降水研究综述[J]. 地球科学进展, 2011, 26(11): 1162-1172.
[9] 冉有华,李新,王维真,晋 锐. 黑河流域临泽盐碱化草地网格尺度多层土壤水分时空稳定性分析[J]. 地球科学进展, 2009, 24(7): 817-824.
[10] 周剑,根绪,李新,杨永民,潘小多. 数据同化算法在青藏高原高寒生态系统能量—水分平衡分析中的应用[J]. 地球科学进展, 2008, 23(9): 965-973.
[11] 李新荣,何明珠,贾荣亮. 黑河中下游荒漠区植物多样性分布对土壤水分变化的响应[J]. 地球科学进展, 2008, 23(7): 685-691.
[12] 宋孝玉,李亚娟,蒋俊,马玉霞. 非饱和土壤水分运动参数空间变异性研究进展与展望[J]. 地球科学进展, 2008, 23(6): 613-618.
[13] 李宁,顾卫,杜子璇,史培军,任学慧,KevinLevy. 内蒙古中西部地区不同土壤类型下土壤水分的研究[J]. 地球科学进展, 2006, 21(2): 151-156.
[14] 孙秉强;张强;董安祥;陈少勇. 甘肃黄土高原土壤水分气候特征[J]. 地球科学进展, 2005, 20(9): 1041-1046.
[15] 李彰俊;李宁;顾卫;吴学宏. 内蒙古中西部地区土壤水分对沙尘暴的贡献[J]. 地球科学进展, 2005, 20(1): 24-028.
阅读次数
全文


摘要