## 3 Methodology
### SBAS-InSAR Method  
Our approach for SAR data processing consists of the following steps: first, we generate a stack of Gaofen-3 and Sentinel-1 SAR images co-registered and resampled with respect to prior images using GAMMA software. After the fine registration and spectral offset filtering, we generated 28 Gaofen-3 interferograms based on the strategy of SBAS algorithms for taking full advantage of available SAR images. The temporal and perpendicular baseline thresholds of SBAS interferograms were 200 days and 2000 m, respectively. Then the flat-earth phase and topographic phase were removed in single-look interferograms. The combined orbit information and interferogram fringe frequency were applied to estimate the baseline [17; 39]. The 2-D quadratic polynomial method was applied to model the residual phase of the baseline. Finally, the artifacts of residual orbit and atmospheric were removed from the wrapped interferograms based on the above method [40].  
To maintain a high-quality correlation of interferograms, we applied a coherence threshold of 0.4 to select the interferograms for time series processing. We dropped some interferograms based on the mean coherence, the difference in Doppler centroid frequency, and some other artifacts from the refined wrapped interferograms. Only 9 of the 28 interferograms satisfied the criteria for SBAS-InSAR processing. Subsequently, we performed a time-series analysis using the SBAS-InSAR module in the software of StaMPS-InSAR (Standard Method for Persistent Scatters) [41; 42].  
The SBAS-InSAR technique employs all possible interferograms with small spatial and temporal baselines to improve the temporal sampling rate while maintaining high coherence. The SBAS algorithm in StaMPS was selected for time series processing, as this methodology could detect land subsidence by InSAR without prior information on land subsidence [43]. It could also identify the SDFP (slowly decorrelating filtered phase) pixels that surround by pixels that completely decorrelate on single-look images at the highest resolution [44]. The SDFP pixels candidates are initially selected by the amplitude difference dispersion index, as shown in Equation (6) [45; 46],  
\[D_{\Delta A}\equiv\frac{\sigma_{\Delta A}}{\mu_{A}} \tag{7}\]  
Equation (7) is used to select the SDFP's candidates' pixels represented by \(D_{\Delta A}\), where \(D_{\Delta A}\) is the threshold value, \(\sigma_{\Delta A}\) is the standard deviation of amplitude difference between the primer image and the second image, \(\mu_{A}\) is the mean of a series of amplitudes values. The spectral filtering is then applied to multi-prime small baseline interferograms for SDFP pixel candidate selection. The decorrelation noise \(\gamma_{x}\) is estimated by subtracting the spatially correlated term and spatially uncorrelated term from the interferograms, calculating the variance of the residuals for the SDFP candidate pixels. The SDFP pixels are identified based on \(\gamma_{x}\) from amidst the pixels candidates.  
\[\gamma_{x}=\frac{1}{N}\bigg{|}\sum_{i=1}^{N}exp\Big{\{}\sqrt{-1}\Big{(}\psi_ {x,i}-\widetilde{\psi}_{x,i}-\Delta\hat{\phi}_{\theta,x,i}^{u}\Big{)}\Big{\}} \bigg{|} \tag{8}\]  
where \(N\) is the number of interferograms, \(\psi_{x,i}\) is the wrapped phase of pixel \(x\) in the \(i\)th interferogram, \(\widetilde{\psi}_{x,i}\) and \(\Delta\hat{\phi}_{\theta,x,i}^{u}\) are the estimated spatially correlated term and spatially correlated term.  
uncorrelated look angle error term [47,48]. Once the SDFP pixels are selected, the 3-D phase unwrapping method and deformation to phase solution are applied to the SDFP pixels [46,49,50].