Intensive and large-scale underground coal mining has caused geological disasters such as local ground subsidence, cracks and collapse in the Datong coalfield, China, inducing serious threats to local residents. Interferometric synthetic aperture radar (InSAR) has the capability of surface deformation detection with high precision in vast mountainous areas. DInSAR, stacking-InSAR and SBAS-InSAR are commonly used InSAR-related deformation analysis methods. They can provide effective support for mine ecological security monitoring and prevent disasters. We use the three methods to conduct the deformation observation experiments in the Datong coalfield. Sentinel-1A data from November 2020 to October 2021 are used. As a result, a total of 256 deformations in the Datong coalfield were successfully detected by the three methods, of which 218 are mining deformations, accounting for 85% of the total deformations. By comparing the results of the three methods, we found that DInSAR, stacking-InSAR, and SBAS-InSAR detected 130, 256, and 226 deformations in the Datong coalfield, respectively, while the deformations caused by coal mining were 128, 218, and 190. DInSAR results with long spatiotemporal baselines are seriously incoherent. SBAS-InSAR results of displacement rate are more precise than stacking-InSAR, and the mean standard deviation is 1.0 mm/a. However, for areas with lush vegetation or low coherence, SBAS-InSAR has poor performance. The detection deformation area results of DInSAR and SBAS-InSAR are subsets of stacking-InSAR. The displacement rates obtained by stacking-InSAR and SBAS-InSAR are consistent; the mean difference in the displacement rate between the two methods is 2.7 mm/a, and the standard deviation is 5.1 mm/a. The mining deformation locations and their shapes in the study area can be identified with high efficiency and power by stacking-InSAR. Therefore, with a comprehensive understanding of the advantages and limitations of the three methods, stacking-InSAR can be an effective and fast method to identify the level, location and range of mining deformation in lush mountainous areas.

After a coal mine is closed, the coal rock mass could undergo weathering deterioration and strength reduction due to factors such as stress and groundwater, which in turn changes the stress and bearing capacity of the fractured rock mass in the abandoned goaf, leading to secondary or multiple surface deformations in the goaf. Currently, the spatiotemporal evolution pattern of the surface deformation of closed mines remains unclear, and there is no integrated monitoring and prediction model for closed mines. Therefore, this study proposed to construct an integrated monitoring and prediction model for closed mines using small baseline subset (SBAS) interferometric synthetic aperture radar (InSAR) and a deep learning-based long short-term memory (LSTM) neural network algorithm to achieve evolution pattern and dynamic prediction of spatiotemporal surface deformation of closed mines. Taking a closed mine in the western part of Xuzhou, China, as an example, based on Sentinel-1A SAR data between 21 December 2015, and 11 January 2021, SBAS InSAR technology was used to obtain the spatiotemporal evolution pattern of the surface during the 5 years after mine closure. The results showed that the ground surface subsided in the early stage of mine closure and then uplifted. In 5 years, the maximum subsidence rate in the study area is −43 mm/a, and the cumulative maximum subsidence is 310 mm; the maximum uplift rate is 29 mm/a, and the cumulative maximum uplift is 135 mm. Moreover, the maximum tilt and curvature are 3.5 mm/m and 0.19 mm/m², respectively, which are beyond the safety thresholds of buildings; thus, continuous monitoring is necessary. Based on the evolution pattern of surface deformation, the surface deformation prediction model was proposed by integrating SBAS InSAR and an LSTM neural network. The experiment results showed that the LSTM neural network can accurately predict the deformation trend, with a maximum root mean square error (RMSE) of 5.1 mm. Finally, the relationship between the residual surface deformation and time after mine closure was analyzed, and the mechanisms of surface subsidence and uplift were discussed, which provide a theoretical reference for better understanding the surface deformation process of closed mines and the prevention of surface deformation.

The potential use of TerraSAR-X and Radarsat-2 data for soil moisture retrieval over bare agricultural areas was investigated using both empirical and semi-empirical approaches. For the empirical approach, the Support Vector Regression (SVR) model was used with two cases: (1) using only one C-band or X-band image; and (2) using a pair of C-band and X-band images jointly. For the semi-empirical approach, the modified Dubois model based on C-band and X-band SAR data was developed to estimate soil moisture content. The experiments were implemented over two bare agricultural areas, and in-situ measurements were carried out to assess the methods. The results showed that the TerraSAR-X and Radarsat-2 are suitable remote sensing tools for the estimation of surface soil moisture, with an accuracy of about 3 vol % (root mean square error, RMSE) over bare agricultural areas. Compared with the results obtained by Radarsat-2 data, TerraSAR-X data gives a slight improvement in estimating soil moisture. The accuracy of the soil moisture estimation was improved further when the two bands SAR data were used (RMSE of about 2.2 vol %) instead of only one. Moreover, the modified Dubois model showed comparable accuracy to the empirical model independent of the surface roughness.

Phase unwrapping (PU) represents a key step in the reconstruction of digital elevation models (DEMs) and the monitoring of surface deformation from interferometric synthetic aperture radar (InSAR) data. Compared with single-baseline (SB) PU, multi-baseline (MB) PU can resolve the phase discontinuities caused by noise and phase layover induced by terrain undulations. However, the MB PU performance is limited primarily by its poor robustness to measurement bias and noise. To address this problem, we propose a refined 2-D MB PU method based on the two-stage programming approach (TSPA). The proposed method uses the unscented Kalman filter (UKF) to improve the performance of the second stage of the original TSPA method. Specifically, the proposed method maintains the first stage of the TSPA to estimate the range and azimuth gradients between neighbouring pixels. Then, median filtering is slightly used to reduce the effects of the noise gradients on the estimated phase gradients. Finally, the UKF model is used to unwrap the interferometric phases using an efficient quality-guided strategy based on heap-sort. This paper is the first to integrate the UKF into the TSPA framework. The proposed method is validated using bistatic and monostatic MB InSAR datasets, and the experimental results show that the proposed method is effective for MB PU problems.

Coal mining leads to surface subsidence, landslides, soil erosion and other problems that seriously threaten the life and property safety of residents in mining areas, and it is urgent to obtain mining subsidence information using high-frequency, high-precision and large-scale monitoring methods. Therefore, this paper mainly studies the deformation monitoring of the Datong mining area using Lutan-1 monostatic and bistatic SAR data. Firstly, the latest Lutan-1 bistatic data are used to reconstruct the DSM, and the interferometric calibration method is used to improve the accuracy of the DSM. Then, the surface deformation monitoring of the mining area is implemented by using DInSAR, SBAS-InSAR and Stacking-InSAR with the reconstructed DSM data and Lutan-1 monostatic SAR data. Finally, the deformation monitoring results are compared with the surface deformation results based on the TanDEM data, and both the results are evaluated using the filed leveling data. Taking 20 images covering the Datong mining area as the data sources, the surface deformation results obtained using different InSAR methods in the mining area were quantitatively evaluated and analyzed. The results indicated that: (1) the DSM obtained using the Lutan-1 bistatic SAR data was assessed and demonstrated with the ICESat laser altimetry data an error of 2.8 m, which meets the Chinese 1:50,000 scale DEM cartographic accuracy standard, and the difference analysis with the TanDEM data shows that the terrain changes are mainly distributed in mountainous areas; (2) Due to the improvement in resolution, the registration accuracy of the SAR images and LT-DSM is higher than that of the TanDEM data in the range direction and azimuth direction; (3) Via evaluation with the filed leveling data, it is found that the surface deformation measurement results based on LT-DSM are less affected by terrain, and the accuracy of LT-DSM-SBAS and LT-DSM-DInSAR is improved by 11.5% and 16.3%, respectively, compared with TanDEM-SBAS and TanDEM-DInSAR, which demonstrates the effectiveness of the Lutan-1 bistatic and monostatic data for mine deformation monitoring.