基于Radarsat2与Landsat8协同反演植被覆盖地表土壤水分的一种新方法

主动微波遥感与被动光学遥感在反演地表土壤水分方面分别具有各自的优缺点,为了将这两者的优势结合弥补缺点,提出了一种基于Radarsat 2与Landsat 8数据协同反演植被覆盖地表土壤水分的半经验耦合模型.该模型基于水云模型,将光学遥感反演得到的植被冠层含水量作为水云模型的关键输入参数,并同时考虑植被冠层与土壤以及其之间的部分对雷达后向散射系数的影响,以此来去除雷达回波中的植被部分.最后选用内蒙古呼伦贝尔市额尔古纳市大兴安岭西侧研究区的Radarsat 2与Landsat 8遥感数据,利用新的耦合模型反演得到植被覆盖区土壤水分含量,并利用地面测量数据对模型进行验证.结果表明:利用Landsat 8数据反演植被含水量算法精度较高(R2=0.89),论文提出的耦合模型反演植被覆盖地表土壤水分精度比之前算法也有了较大的提高,其中HH极化效果最好,R2由0.27提高至0.65.这表明该耦合模型具有较好的反演精度,可以应用于植被覆盖区土壤水分含量的反演.

A new method for soil moisture inversion in vegetation-covered area based on Radarsat 2 and Landsat 8

Active microwave remote sensing and passive optical remote sensing have their own advantages and disadvantages in inversion of soil moisture. In order to combine the advantages of both of them to make up for shortcomings, a semi-empirical model based on Radarsat 2 data and Landsat 8 data has been presented for vegetation-covered soil moisture inversion. The model is based on water-cloud model with the vegetation water content estimated by the optical remote sensing as the key input parameter. Thus the influence of vegetation on the backscattering coefficient would be reduced. Combination of Radarsat 2 and Landsat 8 data were used to estimate the vegetation-covered soil moisture with the new coupling model in the studied area located in Eerguna City of Inner Mongolia, west of Greater Khingan. Then it was verified with the ground survey data. The research showed that the precision is high in the retrieval of vegetation water content (R2=0.89) using Landsat 8 data. The inversion accuracy of coupling model is higher than former algorithms. The R2 of HH polarization is raised from 0.27 to 0.65. These results showed that the proposed coupling model has a better inversion accuracy, and can be used in the inversion of vegetation-covered soil moisture.