两种被动微波遥感混合像元分解方法比较

Comparsion of Two Passive Microwave Unmixing Methods

被动微波遥感混合像元分解是成功应用被动微波辐射计数据的关键,研究被动微波遥感混合像元分解方法具有重要的应用意义。提出基于地物分类信息的被动微波混合像元分解方法,通过模拟具有不同组分亮温分布的观测地区,将其与具有代表性的Bellerby提出的被动微波混合像元分解方法进行对比研究,分析两种分解方法的适用范围及影响其求解精度的主要因素。Bellerby的方法适合于观测地区中陆地组分亮温不变的情况,当观测地区陆地组分亮温发生变化时,笔者提出的方法能得到更加准确的分解结果。研究结果表明:笔者提出的方法进行混合像元分解后,其陆地组分亮温误差的平均值约为0.1K,水体组分亮温误差的平均值约为0.2K;而采用Bellerby方法计算得到陆地组分亮温误差的平均值约为3.1K,水体组分亮温误差的平均值约为5.9K。

Passive microwave unmixing methods are very important for successful applications of passive microwave radiometer data. We proposed a passive microwave unmixing method based on land surface classification information. Through designing the simulation study areas with the component brightness temperature distributions based on different land surface types, we compared the proposed method with the method introduced by Bellerby. Furthermore, the two unmixing methods were evaluated and the main factors affecting the unmixing result were analyzed. Bellerby＇s method is more suitable for the observation area where the land component brightness temperature was stable. When obvious change of the land component brightness temperature occurs in the observation area, the proposed method can obtain more high precision unmixing results. The research results demonstrated that the accuracy of the component brightness temperature using the proposed method was better than that using the Bellerby＇s method. The precision of land component brightness temperature and water brightness temperature using the proposed method is approximately 0.1 K and 0.2 K respectively, while that using the Bellerby＇s method is approximately 3.1 K and 5.9 K respectively.