In order to effectively use the temporal information of multi-temporal InSAR and ensure the homogeneity of pixels in the window, a homogenous pixels-weighted interferometric phase filtering method is proposed. The method firstly uses the goodness-of-fit test to identify the statistically homogenous pixels (SHP) in the multi-temporal SAR images, and then the weighted interferometric phase filtering is performed just within those SHP, which ensures that the central pixels is not affected by the surrounding heterogeneous pixels. The experimental results based on real SAR interferograms show that compared with classic Goldstein filter and Lee filter, the proposed filter has advantages in visual effect, phase residues reduction, and phase derivative standard deviation, and achieves the original intention of the design of this filter.

It is difficult to monitor land surface soil moisture in high temporal and spatial resolution within a wide range for lack of ground observation data when the satellite is passing over. To solve this problem, a new integrated approach termed as “soil moisture retrieval with combined active and passive microwave remote sensing observation” was proposed. AMSR-E soil moisture product is compensated as “high temporal resolution observation control data” and soil moisture benchmark is retrieved together with ASAR alternating polarization mode data. Then both of them are integrated to build up a co-inversion model for soil moisture retrieval. This approach applies to areas where the land surface roughness is small and vegetation index (NDVI) is low. The approach is evaluated in Weibei Upland of Shaanxi Province. According to the regression analysis based on AIEM (advanced integrated equation model), the correlation coefficient between compensated AMSR-E soil moisture and downscaled ASAR backscattering coefficient was approximately 0.81. Verification analysis with the in-situ data of Fengxiang County in the study area shows that the soil moisture retrieved with combined active and passive microwave remote sensing observation displays a correlation coefficient of 0.92, and the root mean square errors (RMSE) of the soil volumetric moisture is 0.025. It indicates that the approach is credible and the soil moisture retrieval results could be used in simulating regional crop growth under water-limited environments.