农业工程学报Issue(10)：109-115,7.DOI:10.3969/j.issn.1002-6819.2013.10.015
双极化雷达反演裸露地表土壤水分
Soil moisture inversion by radar with dual-polarization
摘要
Abstract
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 Rs=S3/L2 is defined by combining the traditional roughness parameter S with L. Then, the unknown parameters in the empirical model are only roughness parameter Rs and volumetric soil moisture mv. The soil moisture can be retrieved from dual-polarization SAR observations. Concerning the influence of soil moisture, the Fresnel reflection coefficientГ0 is brought in to take place of mv because a better relationship can be built between the Fresnel reflection coefficientГ0and the backscattering coefficient σ0. In this case, Fresnel reflection coefficient Г0 can be directly retrieved from the empirical model, not soil moisture mv. The soil dielectric constantεcan be determined by Fresnel reflection coefficientГ0 and the Dobson Model, in which soil moisture can be linked with dielectric constantε. 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>25°, S<1.5 cm, L∈(4,18) cm, there was a good relationship between the estimated data and in-situ data. The correlation coefficient R2 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.关键词
土壤水分/遥感/雷达/双极化/反演/粗糙度参数Key words
soil moisture/remote sensing/radar/dual-polarization/inversion/roughness parameter分类
天文与地球科学引用本文复制引用
陈晶,贾毅,余凡..双极化雷达反演裸露地表土壤水分[J].农业工程学报,2013,(10):109-115,7.基金项目
国家重点基础研究发展计划（973项目）“陆表生态环境要素主被动遥感协同反演理论与方法”（2007CB714407）；自然科学基金青年基金（项目编号41101321）；国家支撑计划项目（2011BAH12B03）、（2012BAH34B02）、（2012BAJ15B04）共同资助。 （）
