微扰法结合最小二乘支持向量机反演土壤湿度  

Inversion of bare soil moisture by the least squares support vector machine approach combined with SPM

作　　者：张清河[1] 徐飞[1] 邹启源 

机构地区：[1]三峡大学理学院,湖北宜昌443002

出　　处：《电波科学学报》2015年第2期300-306,共7页Chinese Journal of Radio Science

基　　金：国家自然科学基金项目(61179025);湖北省教育厅重点项目(D20111201)

摘　　要：将最小二乘支持向量回归技术应用到土壤湿度反演研究.利用微扰法数值模拟不同雷达参数下裸露土壤微波后向散射特性.经过数据敏感性分析,选取雷达频率为L波段(1.4GHz),双入射角(40°、50°),并设计多种反演方案,分别以单极化、双极化及同极化后向散射系数比值作为微波信号样本信息,经过适当的训练,利用最小二乘支持向量回归技术对土壤含水量进行了反演研究.结果表明:当采用多入射角、同极化后向散射系数比值作为微波信号样本信息时,反演结果具有较高的精度.同时,经过与人工神经网络结果比较,证明了该方法的有效性及抗噪声能力,为土壤湿度的实时反演研究提供了一种新方法.The least squares support vector machine(LS-SVM)techniques are applied to the inversion of soil moisture.The backscattering properties of bare soil under different radar parameters are numerically simulated by using small perturbation method(SPM).After data sensitivity analysis,with the L-band radar frequency(1.4GHz)and dual angle of incidence(40°/50°)selected,designed a variety of inversion scheme,herein the single polarization,dual polarization and co-polarization ratio of the backscattering coefficient are selected as the microwave signal sample information.Through appropriate training,the least squares support vector regression techniques are adopted to estimate soil moisture under different inversion schemes.The inversion results demonstrate high accuracy when multiple incident angles and the ratio of co-polarization backscattering coefficients are used as the microwave signal sample information.Comparison with the results of the artificial neural network(ANN)proved the validity and the anti-noise ability of the presented method,thus providing a new approach for the real-time retrieval of soil moisture.

关 键 词：微扰法 土壤湿度反演 最小二乘支持向量机 

分 类 号：S152.7[农业科学—土壤学] TP18[农业科学—农业基础科学]