Sarscape For Envi 5 149
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We used the InSAR-derived 3D displacements from different datum (Cohn et al. 2012; Samsonov et al. 2008) to obtain the best range and azimuthal resolutions with a least-squares adjustment to the matched-pair GNSS results (GPS, GLONASS, and BeiDou). The least-squares adjustment method is as follows: Given an interferogram of a pair of data that have known displacement between them, we firstly detrend the interferogram with GNSS. Secondly, we estimate the large-scale displacement component by regressing the GNSS data to the InSAR data. Finally, we apply the InSAR-derived 3D displacement to the regressed GNSS data to calculate the inverse displacement (backward displacement) of the InSAR data. The inverse displacement is then subjected to a further regression to the GNSS data. The final displacement vector is obtained after subtracting the two displacements from the InSAR-derived vector. The least-squares adjustment method used in this study can be found in (Yang and Peng 2016). We have applied the least-squares adjustment method to the SAR interferograms in three different environmental conditions, namely, in the Fildes region (low vegetation and medium roughness), at a place with medium vegetation, and at a place with high vegetation (Yang and Peng 2016). In order to obtain the best solution, we performed the least-squares adjustment on the InSAR data using the range and azimuth data with different resolutions and weight factors as well as different GNSS data. Usually, the weight was defined to be equal to the variance of the observations in the linear combination of GNSS and InSAR data. The weights for the InSAR data and GNSS data were 5% and 95% for the vegetation-covered area, respectively; and the weight of the InSAR data was 10% for the bare soil area.
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