提出了一个针对重复轨道SIR-C数据的极化干涉SAR植被覆盖地表土壤水分反演方法, 同时, 探讨和分析了极化干涉SAR最大似然反演分解模型在植被覆盖区土壤水分反演中的潜力和有效性。利用极化干涉SAR最大似然反演分解模型重建消除了体散射分量的植被覆盖下地表极化相干矩阵; 计算同极化和交叉极化地表后向散射系数, 并利用Oh模型和二面角(Dihedral)散射模型估算植被覆盖下地表的土壤水分; 将反演的植被覆盖下地表土壤水分结果与野外实测土壤水分结果和该区域1951—2006年的气候数据进行比较和分析, 初步结果表明: 基于极化干涉SAR最大似然反演分解模型的植被覆盖区土壤水分反演方法得到的结果具有足够高的估计精度。

From repeat pass SIR-C L band polarimetric SAR interferometric data and fully maximum likelihood inversion decomposition model of PolInSAR, a method for sub-canopy soil moisture estimation using repeat pass SIR-C PolInSAR data is proposed. At the same time, the potential and validity of fully maximum likelihood inversion decomposition model of PolInSAR for sub-canopy soil moisture inversion is investigated. Firstly, from the random oriented volume over ground two layer coherent scattering model and the statistical characteristics of Pol-InSAR coherency matrix, the fully maximum likelihood inversion decomposition model is used to reconstruct or recover the surface polarimetric coherency matrix with volume scattering components significantly removed; then, from recovered surface polarimetric coherency matrix, co-HH, VV and cross-HV polarization backscattering coefficient are obtained, and the sub-canopy soil moisture are inverted from Oh and Dihedral scattering model. At last, Compared the inversion result with the field measurement and the climate data of hetan region from 1951 to 2006, the preliminary result indicates that the proposed method based on fully maximum likelihood inversion decomposition model has enough high inversion accuracy, if the new spaceborne or airborne polarimetric SAR interferometric data with synchronously spaceborne or airborne-ground measurement will be acquired, the validity and accuracy of proposed inversion method will be further investigated and validated.