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.

In this research, 66 Sentinel-1 images spanning from January 2018 to June 2023 were analyzed using Persistent Scatterer Interferometric (PSI) technology to map ground deformation in Fuzhou City. The accuracy of the results was demonstrated by cross-validation. The study reveals that the overall deformation rate in Fuzhou City ranges from −44~18 mm/a, with five distinct regions experiencing significant ground subsidence. Among these, four regions are located near subway lines, and the maximum subsidence occurs at the intersection of Xiazhang Road and Fubei Road in Changle District. The occurrence of subsidence in Fuzhou is the consequence of a complex interplay between natural and anthropogenic factors. In terms of natural factors, the subsidence is primarily distributed in areas with Quaternary sediments. The anthropogenic factors primarily include construction activities such as subway construction, building construction and demolition, as well as changes in ground loading due to warehouse operations such as cargo loading and unloading. The study results can provide a reference basis for urban construction and prevention management of subsidence disasters in Fuzhou.

Traditional offset tracking primarily relies on normalized cross correlation tracking method based on the regular matching window. However, in the analysis of optical remote sensing images, pixels representing disturbance factors such as cloud layers, water bodies, shadows are often present within the regular window. When applied to landslide monitoring, these disturbance factors may lead to errors in the offset estimation. In order to address this issue, an adaptive offset tracking algorithm is presented. Prior to the offset estimation, a pre-processing step is carried out to identify the locations of these disturbance factors in the study area and generate the corresponding masks. During offset estimation process, the disturbance factors of cross-correlation window can be found from its masks, then pixels representing disturbance factors within the cross-correlation window are excluded, thereby improving the accuracy and reliability of offset estimation experimental validation on the Baige landslide , which has demonstrated that this method can significantly enhance the accuracy and reliability of offset tracking.

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.