Due to factors such as loss of coherence between repeated SAR acquisitions, the interferograms are often very noisy. The quadtree filter can remove such discrete noise in unwrapped interferograms and obtain clean deformation results. A mining area in Yunnan is investigated, discussing how the minimum division window and the gross error threshold affect the performance of quadtree filtering. The surface deformation time series of the mining area is obtained using Sentinel-1 satellite data from 2019 to 2021. The results show that the mining area has significant surface deformation during our monitoring period, focusing mainly on three regions with both uplift and subsidence. The deformation time series present wave-like variations in the time domain. The maximum incremental subsidence and uplift are up to 67.3 and 79.4 mm in 12 days, respectively. The difference is $\pm 10.9\mathrm{m}\mathrm{m}$ between InSAR and 41 repeated observations from 32 ground-based total stations.

GPS observations are influenced by external factors, resulting in various types of noise in their time series. It is necessary to use appropriate noise model to estimate the station’s velocity accuracy. Spectral analysis method and maximum likelihood estimation method are used to perform optimal noise model analysis on the time series of 259 GPS continuous stations in the China Continental Crustal Movement Observation Network. Three consecutive days of observation data are extracted from the annual continuous station data as simulated campaign stations. The main noise models of the continuous stations of China Crustal Movement Observation Network of China are white noise plus flicker noise resulted from spectral analysis method. The difference in velocity calculated using the optimal noise model and the FOGMEx model is not significant, and the speed uncertainty is 1.5 ± 0.7 times (E), 1.0 ± 0.5 times (N), and 1.8 ± 1.1 times (U) of the FOGMEx model, respectively. For the simulated campaign station, the velocity uncertainty obtained from the original continuous station covariance matrix is 0.8 ± 0.2 times (E), 1.0 ± 0.2 times (N), and 0.9 ± 0.2 times (U), respectively, compared with the velocity uncertainty estimated by the noise model established by reducing its degree of freedom and there is no significant difference in velocity. Therefore, for the majority of real campaign stations in China’s mainland, reducing the degrees of freedom (i.e., assuming the number of observed values at campaign stations equals the number of years of observation) is suggested to estimate their uncertainty.