Surface deformation data can be used to provide early warnings of geohazards and are useful in a variety of research fields. The Small BAseline Subset InSAR (SBAS-InSAR) boosts the data sampling rate and improves the accuracy of deformation extraction by restricting the temporal and spatial baselines. However, various factors, such as the types of ground objects and seasons, affect the coherence between SAR images. Traditional SBAS-InSAR employs a fixed temporal baseline, which does not guarantee good coherence and might lead to decorrelation. In this paper, we propose that instead of using a fixed temporal baseline, we directly use the average coherence between SAR images as the baseline constraint index to perform an optimized selection of SBAS-InSAR interferometric pairs, ensuring good coherence of the interferometric pairs and improving the quality of the interferometric fringes. The proposed approach was used to extract surface deformation in two test experiment areas: Houston and Sydney. Compared with the conventional SBAS-InSAR and GPS data, the standard deviation of error of Houston and Sydney dropped from 0.813 to 0.589 and 0.291 to 0.246, respectively; the root mean square error (RMSE) decreased from 1.082 to 1.041 and 0.485 to 0.334, respectively, indicating that the proposed method has better surface deformation extraction accuracy. After demonstrating the accuracy of the proposed method, it was applied to Pingxiang area, a mining city in China, to effectively extract and analyze the surface deformation induced by mining activities, which proves the universality of this method in different scenarios.

The forward prediction of mining subsidence in coal mining areas is key to evaluating mining risk and improving mine management plans. At present, the conditions that ensure successful application of the time function method to predict future subsidence in coal mining areas remain poorly known, and a prediction method suitable for large-scale subsidence prediction during coal mining has not been established. Based on the characteristics of logistic model and simulation experiments, we determined its predictable conditions for coal mining subsidence prediction. We propose a small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) coal mining subsidence prediction method with a predictable dynamic range based on the predictable condition obtained. The method uses time-series subsidence data obtained by SBAS-InSAR as the fitting data. The logistic model parameters are obtained pixel-by-pixel via the Levenberg–Marquardt (LM) algorithm. The predictable range is subsequently determined based on the predictable condition. Finally, future subsidence in the predictable range is predicted. The methodology was tested in two coal mining areas in Inner Mongolia—first one is a single working face mine and another one is a parallel double working face mine. The predicted results agree well with the InSAR monitoring results. The average root-mean-square error (RMSE) of the predicted results was 0.0119 m. In addition, we used the Knothe model to conduct comparative experiments without considering predictable conditions. The results reflect the advantages of our proposed method and the necessity of predictable conditions. The new prediction method is beneficial for risk assessment and coal mining planning.

After mastering the key technologies of manufacturing spaceborne Synthetic Aperture Radar (SAR), China’s SAR satellites have been successfully launched into space. As the only civil microwave satellite listed in the “National High-resolution Earth Observation System Major Project,” the Gaofen-3 (GF-3) 01 satellite is the first C-band multi-polarization SAR satellite with a resolution of 1 m. GF-3 series satellites stand out among civil SAR satellites worldwide because of their high resolution, wide swath, high geometric radiation quality, multiple imaging modes, and long operation time. Taking GF-3 series satellites as an example, this study introduces the development of China’s civil SAR satellites, as well as their processing strategies and applications. The success of the GF-3 series satellites shows that China’s SAR remote sensing technology has stepped into a new era of high-quality and high-precision Earth observations.

The atmospheric propagation delay of radar signals is a systematic error that occurs in the atmospheric environment, and is a key issue in the high-precision geometric calibration of spaceborne SAR. A multimode hybrid geometric calibration method for spaceborne SAR that considers the atmospheric propagation delay is proposed in this paper. Error sources that affect the accuracy of the geometric calibration were systematically analyzed. Based on correction of the atmospheric propagation delay, a geometric calibration model for spaceborne SAR was established. The high precision geometric calibration scheme for spaceborne SAR was explored by considering the pulse-width and bandwidth of the signal. A series of experiments were carried out based on high-resolution Yaogan 13 (YG-13) SAR satellite data and ground control data. The experimental results demonstrated that the proposed method is effective. The plane positioning accuracy of YG-13 in stripmap mode without control points is better than 3 m, and the accuracy of the sliding spotlight mode is better than 1.5 m.