基于粗糙度定标的IEM模型的土壤含水率反演  

Surface soil moisture estimation using IEM model with calibrated roughness

作　　者：黄对[1] 王文[1] 

机构地区：[1]河海大学水文水资源与水利工程科学国家重点实验室,南京210098

出　　处：《农业工程学报》2014年第19期182-190,共9页Transactions of the Chinese Society of Agricultural Engineering

基　　金：高等学校博士学科点专项科研基金课题(20130094110007);"十二五"国家科技支撑计划项目资助课题(2013BAB05B01);江苏省普通高校研究生科研创新计划资助项目(CXLX12_0251)

摘　　要：为研究基于粗糙度定标的模型进行土壤含水率反演的可行性,该文利用2幅不同时相的高级合成孔径雷达ASAR影像,以经验相关长度(lopt)代替相关长度l,实现对积分方程模型IEM(integral equation model)的粗糙度定标,以改进IEM模型对后向散射系数的模拟。在此基础上模拟了后向散射系数与土壤体积含水率(Mv)、lopt、均方根高度(hRMS)的关系,以组合粗糙度Zs(hRMS2/lopt)代替lopt与hRMS,建立土壤含水率反演的经验与半经验方法。对比2个不同时相的土壤含水率反演值与实测站点观测数据表明,经验方法下应用2004年8月18日、2004年8月24日2个时相的反演值与实测值的相关系数分别为0.785、0.837,半经验方法下则分别为0.900、0.863,表明半经验方法精度更好。该研究为利用两幅不同时相的ASAR影像获取两幅土壤含水率数据提供依据。The ENVISAT/ASAR image is an important remote sensing data source for estimating soil moisture, and the integral equation model （IEM） is the most widely used, physically based radar backscatter model for bare soil and sparsely vegetated landscapes. However, the soil moisture retrieval from ASAR images using the IEM is not fully operational at present, mainly due to the difficulties in the parameterization of soil surface roughness and the elimination of spatial and temporal variation of soil roughness. The IEM simulated backscattering coefficients are often in poor agreement with satellite radar measurements because of un-accurate description of the surface roughness, especially the correlation length l parameter. Baghdadi proposed to replace correlation length l with a fitted parameter lopt for the IEM, which can be expressed as the function of root mean square height hRMS and incidence angle. So far, there is still lack of application of this method in semi-arid areas. This paper applied this approach in the Walnut Gulch Experimental Watershed of southeast Arizona, and showed that the IEM performed better in simulating radar backscattering coefficient when lopt was used as the input. Based on the improvement in radar backscattering coefficient simulation, lopt and hRMS are replaced by the combined roughness Zs （hRMS2/lopt）, and the relationship between surface roughness Zs, soil moisture and the simulated backscatter coefficients is analyzed. The results showed that the simulated backscattering coefficient was logarithmically correlated with both Zs and soil moisture. Then, maps of Zs in two dates are estimated with a logistic regression equation using the difference between backscattering coefficients at incidence angles of IS6 and IS2. Using Zs estimates and IEM simulated backscattering coefficients, the empirical formula of soil moisture inversion under two incidence angles was established with the nonlinear least squares method for VV （vertical vertical） polarization mode. On analyzing the param

关 键 词：地表粗糙度 土壤 含水率 微波遥感 IEM模型 

分 类 号：TP722.6[自动化与计算机技术—检测技术与自动化装置]