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	基于粗糙度定标的IEM模型的土壤含水率反演
其他题名	Surface soil moisture estimation using IEM model with calibrated roughness
	黄对; 王文
来源期刊	农业工程学报
ISSN	1002-6819
出版年	2014
卷号	30 期号:19 页码:182-190
中文摘要	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(l_(opt))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(M_v)、l_(opt)、均方根高度(h_(RMS))的关系,以组合粗糙度Z_s(h_(RMS)~2/l_(opt))代替l_(opt)与h_(RMS),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。
英文摘要	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 l_(opt) for the IEM, which can be expressed as the function of root mean square height h_(RMS) 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 l_(opt) was used as the input. Based on the improvement in radar backscattering coefficient simulation, l_(opt) and h_(RMS) are replaced by the combined roughness Z_s (h_(RMS)~2/ l_(opt)), and the relationship between surface roughness Z_s, soil moisture and the simulated backscatter coefficients is analyzed. The results showed that the simulated backscattering coefficient was logarithmically correlated with both Z_s and soil moisture. Then, maps of Z_s in two dates are estimated with a logistic regression equation using the difference between backscattering coefficients at incidence angles of IS6 and IS2. Using Z_s 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 parametric formula of simulated IEM data, a semi-empirical method was further applied based on Taylor series expansion. Therefore, two surface roughness and two soil moisture maps are obtained using ASAR images in two dates, i.e., August 18 and August 24, 2004. Comparison between the surface roughness maps in two dates shows that the surface roughness has similar spatial distribution characteristics, but the surface roughness on August 18 was less than that on August 24. Dynamic changes of the surface roughness in two dates are consistent with the occurrence of rainfall events. Comparison between the estimated soil moisture with observations of 19 stations in the Walnut Gulch watershed shows that the correlation coefficients were 0.785 and 0.837 between the observed and the empirically estimated soil moisture, and 0.900 and 0.863 between the observed and the semi-empirically estimated soil moisture, for August 18 and August 24 respectively. It means that both the empirical method and the semi-empirical method are effective, but the semi-empirical method performs better. The method quantifies the impact of surface roughness on IEM model simulations and the influence of roughness change on surface roughness estimation, which is effective for retrieving soil moisture at the watershed scale.
中文关键词	地表粗糙度 ; 土壤 ; 含水率 ; 微波遥感 ; IEM模型
英文关键词	surface roughness soils moisture microwave remote sensing IEM model
语种	中文
国家	中国
收录类别	CSCD
WOS类目	REMOTE SENSING
WOS研究方向	Remote Sensing
CSCD记录号	CSCD:5270951
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/231490
作者单位	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, 江苏 210098, 中国
推荐引用方式 GB/T 7714	黄对,王文. 基于粗糙度定标的IEM模型的土壤含水率反演[J],2014,30(19):182-190.
APA	黄对,&王文.(2014).基于粗糙度定标的IEM模型的土壤含水率反演.农业工程学报,30(19),182-190.
MLA	黄对,et al."基于粗糙度定标的IEM模型的土壤含水率反演".农业工程学报 30.19(2014):182-190.