双极化雷达反演裸露地表土壤水分

Soil moisture inversion by radar with dual-polarization

为了快速高效地获取大面积地表土壤水分,本文提出一种适用于双极化SAR(synthetic aperture rader)的裸露地表土壤水分反演经验模型。首先通过AIEM(advanced integral equation model)模型数值模拟和回归分析,提出一种新的粗糙度参数,将2个传统的粗糙度参数简化为1个参数;然后模拟地表土壤水分与雷达后向散射系数的关系,从而建立裸露地表的经验散射模型,模型的未知参数仅为粗糙度参数和土壤体积含水量,通过双极化的雷达数据即可实现土壤水分的反演。通过2008年甘肃张掖黑河流域实测数据对模型进行了初步验证,发现在入射角大于25°时,模型反演值与实测值有着良好的相关关系(相关系数为0.745)。该模型仅需双极化的雷达数据就能实现土壤水分的反演,无需测量地面粗糙度,尤其适用于大面积干旱区域的地表土壤水分的获取。

Soil moisture plays a key role in the interactions among the hydrosphere, biosphere, and atmosphere. Traditionally, soil moisture information is measured by ground-based soil moisture monitoring networks, which is accurate but time-consuming and laborious. In this study, a new empirical model is developed for estimating the soil moisture of bare surfaces by dual-polarization ASAR. The steps are as follows： first, a database linked to SAR backscattering coefficients, surface roughness parameters, and soil moisture is built by AIEM （advanced integral equation model）. Through mathematical analysis of a simulated database, the influence of roughness and soil moisture are taken into account, respectively. For roughness impact, a new roughness parameter R s =S 3 /L 2 is defined by combining the traditional roughness parameter S with L. Then, the unknown parameters in the empirical model are only roughness parameter R s and volumetric soil moisture m v . The soil moisture can be retrieved from dual-polarization SAR observations. Concerning the influence of soil moisture, the Fresnel reflection coefficient Г 0 is brought in to take place of m v because a better relationship can be built between the Fresnel reflection coefficientГ 0 and the backscattering coefficient σ 0 . In this case, Fresnel reflection coefficient Г 0 can be directly retrieved from the empirical model, not soil moisture m v . The soil dielectric constant ε can be determined by Fresnel reflection coefficient Г 0 and the Dobson Model, in which soil moisture can be linked with dielectric constant ε. To estimate the accuracy of the empirical model, the results of the empirical model were compared with in-situ data in the same location collected at Heihe river basin, Zhangye, in 2008. It concluded that, when θ＆gt;25°, S＆lt;1.5 cm, L∈（4,18） cm, there was a good relationship between the estimated data and in-situ data. The correlation coefficient R 2 could be as high as 0.745, meanwhile the RMSE （root mean square error） was 0.478. Because this method requires only dual-polarization SAR data for retrieving soil moisture, and does not need any ground roughness observations, it is suitable for soil moisture retrieval in large regions. However, this new model needs to be validated by more in-situ experiments and combined with vegetation models in order to to meet regions covered by vegetation.