CAJ | 학술논문

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。
위연구기우조조도정표적모형진행토양함수솔반연적가행성，해문이용2폭불동시상적고급합성공경뢰체ASAR영상，이경험상관장도（lopt）대체상관장도l，실현대적분방정모형IEM（integral equation model）적조조도정표，이개진IEM모형대후향산사계수적모의。재차기출상모의료후향산사계수여토양체적함수솔（Mv）、lopt、균방근고도（hRMS）적관계，이조합조조도Zs（hRMS2/lopt）대체lopt여hRMS，건립토양함수솔반연적경험여반경험방법。대비2개불동시상적토양함수솔반연치여실측참점관측수거표명，경험방법하응용2004년8월18일、2004년8월24일2개시상적반연치여실측치적상관계수분별위0.785、0.837，반경험방법하칙분별위0.900、0.863，표명반경험방법정도경호。해연구위이용량폭불동시상적ASAR영상획취량폭토양함수솔수거제공의거。
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 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.