双极化SA R数据反演裸露地表土壤水分

摘要

In order to retrieval soil moisture accurately,a model w hich is non-linear equations is introduced to estimate the surface soil moisture by using dual-polarization advanced synthetic aperture radar data.In this paper,the improved particle swarm optimization(PSO)is used to solve the non-linear equations for obtaining soil moisture.Firstly,a database linked to SAR backscattering coefficients,surface roughness parameters,and soil moisture are built by AIEM(advanced integral equation model).Through regression analysis of the simulated database,a new roughness parameter and the relationship between soil moisture and backscattering coefficient is obtained.Then the empirical relationship between radar backscattering coefficient and combined roughness and soil moisture is established.By using ASAR C-band dual polarization radar data,the soil moisture can be retrieved based on the empirical relationship and improved particle swarm optimization algorithm.The model is validated by the measured data in Heihe River.It concludes that there is a good relationship between the estimated data and measured data.The correlation coefficient is as high as 0.778 6.Compared with the previous regional research result,the inversion accuracy of this paper has been improved,w hich is more suitable for bare surface soil moisture inversion.%为了较高精度地获取大范围地表土壤水分,提出一种基于双极化合成孔径雷达数据的裸露地表土壤水分反演模型即非线性方程组,通过改进的粒子群算法求解非线性方程组从而得到土壤水分.首先通过AIEM 模型数值模拟和回归分析,得到一种新的组合粗糙度,然后模拟分析得到土壤水分与雷达后向散射系数的关系,从而建立雷达后向散射系数与组合粗糙度、土壤水分的经验关系.利用ASAR C波段双极化雷达数据,基于经验关系和改进的粒子群算法即可实现土壤水分的反演.经过黑河流域实测土壤水分数据对模型进行验证,反演结果与实测数据具备良好的相关性(R2=0.7786).与以往同一区域研究成果比较,文中的方法反演精度有所提高,更适用于裸露地表土壤水分反演.