Line of Sight (LOS) deformation based on Differential Interferometric Synthetic Aperture Radar (DInSAR) techniques cannot be used in traditional probability integration method (PIM) parameter inversion. To improve the accuracy of parameter inversion, a model based on 3D deformation was proposed. The model simulates 3D deformation using PIM directly. The inverse of the Sum of the Squared Errors (SSE) of the PIM results and the measured deformation results was used as a fitting function within the GA. Reliable PIM parameters can be obtained based on this GA model. To identify the surface movement law of the Jinfeng coal mine, 6 Global Navigation Satellite System (GNSS) monitor points were established over the 011207 and 011809 working panels. Due to the limited number of points and the large distance between the points, it is not sufficient to obtain reliable PIM parameters using GNSS only. As a complement, 83 Sentinel-1A images were analyzed with small baseline subset (SBAS) DInSAR, and the LOS direction deformation was obtained. The reliable PIM parameters were calculated with the 3D inversion model based on the combination of LOS direction deformation and GNSS-monitored deformation. Then, those parameters were used to predict the coal mine deformation of panels 011207 and 011809, which demonstrated that the prediction results coincide with the measured results. The model can be used to study the laws of mining subsidence combined with DInSAR and GNSS, which can reduce the requirements of the number of GNSS points and the impact of radar decoherence. This provides a new technical approach for studying the law of surface movement in mining subsidence research.

The ecological stability of mining areas in Northwest China has been threatened by desertification for a long time. Remote sensing information combined with machine learning algorithms can effectively monitor and evaluate desertification. However, due to the fact that the geological environment of a mining area is easily affected by factors such as resource exploitation, it is challenging to accurately grasp the development process of desertification in a mining area. In order to better play the role of remote sensing technology and machine learning algorithms in the monitoring of desertification in mining areas, based on Landsat images, we used a variety of machine learning algorithms and feature combinations to monitor desertification in Ningdong coal base. The performance of each monitoring model was evaluated by various performance indexes. Then, the optimal monitoring model was selected to extract the long-time desertification information of the base, and the spatial-temporal characteristics of desertification were discussed in many aspects. Finally, the factors driving desertification change were quantitatively studied. The results showed that random forest with the best feature combination had better recognition performance than other monitoring models. Its accuracy was 87.2%, kappa was 0.825, Macro-F1 was 0.851, and AUC was 0.961. In 2003–2017, desertification land in Ningdong increased first and then slowly improved. In 2021, the desertification situation deteriorated. The driving force analysis showed that human economic activities such as coal mining have become the dominant factor in controlling the change of desert in Ningdong coal base, and the change of rainfall plays an auxiliary role. The study comprehensively analyzed the spatial-temporal characteristics and driving factors of desertification in Ningdong coal base. It can provide a scientific basis for combating desertification and for the construction of green mines.

The FengYun-3E Global Navigation Satellite System (GNSS) occultation sounder II (FY-3E GNOS II) was launched on 5 July 2021. For the first time, based on the new GNOS II sensor, this mission realizes radio occultation (RO) and reflectometry observations using the navigation signals from the third-generation BeiDou System (BDS-3), and it is hence important to assess and analyze the BDS-3 remote sensing performances relative to other systems. Here, we assessed FY-3E GNOS II RO atmospheric retrievals by inter-comparing with corresponding data from the NCEP FNL global atmospheric analysis and FY-3D GNOS mission. The GNOS RO data quality and consistency of the different FY-3 meteorological satellites, i.e., FY-3D and FY-3E, as well as different GNSS systems (GPS, BDS-2, BDS-3) were analyzed. We find that the FY-3E GNOS II RO data exhibit better quality than FY-3D GNOS, particularly in the number, penetration height toward surface, and global coverage by BDS RO profiles, due to the integration of BDS-2 and BDS-3. Additionally, comparing with co-located NCEP FNL analysis profiles, the mean difference (and standard deviation) of the FY-3E GNOS II RO atmospheric refractivity profile retrievals is found to be smaller than 0.2% (and 1%), in the upper troposphere and lower stratosphere, from 5 to 30 km, and remains consistent at this accuracy and precision level with the FY-3D GNOS RO data. These features provide clear evidence for a high utility of the new GNOS II RO data for weather and climate research and applications.

Open-pit mining activities inevitably affect the surrounding ecological environment. Therefore, it is crucial to clarify the disturbance characteristics of open-pit mining activities on the surrounding vegetation and scientifically implement ecological restoration projects. This study investigates the impact of open-pit coal mining in arid and semi-arid regions on surrounding vegetation from a vegetation phenology perspective. Initially, we construct a high-frequency time series of vegetation indices by Harmonized Landsat 8 and Sentinel-2 surface reflectance dataset (HLS). These time series are then fitted using the Double Logistic and Asymmetric Gaussian methods. Subsequently, we quantify three pivotal phenological phases: Start of Season (SOS), End of Season (EOS), and Length of Season (LOS) from the fitted time series. Finally, utilizing mine boundaries as spatial units, we create a buffer zone of 100 m increments to statistically analyze changes in phenological phases. The results reveal an exponential variation in vegetation phenological metrics with increasing distance from the mining areas of Heidaigou-Haerwusu (HDG-HEWS), Mengxiang (MX), and Xingda (XD) in northwest China. Then, we propose a method to identify the disturbance range. HDG-HEWS, MX, and XD mining areas exhibit disturbance ranges of 1485.39 m, 1571.47 m, and 671.92 m for SOS, and 816.72 m, 824.73 m, and 468.92 m for EOS, respectively. Mineral dust is one of the primary factors for the difference in the disturbance range. The HDG-HEWS mining area exhibits the most significant disruption to vegetation phenological metrics, resulting in a delay of 6.4 ± 3.4 days in SOS, an advancement of 4.3 ± 3.9 days in the EOS, and a shortening of 6.7 ± 3.5 days in the LOS. Furthermore, the overlapping disturbance zones of the two mining areas exacerbate the impact on phenological metrics, with disturbance intensities for SOS, EOS, and LOS being 1.38, 1.20, and 1.33 times those caused by a single mining area. These research results are expected to provide a reference for the formulation of dust suppression measures and ecological restoration plans for open-pit mining areas.

The subway alleviates the traffic pressure in the city but also brings the potential risk of land subsidence. The land subsidence caused by the subway is a global problem that seriously affects the safety of subway operations and surrounding buildings. Therefore, it is very important to carry out long-term deformation monitoring on the subway system. StaMPS-PS is a time-series Interferometric Synthetic Aperture Radar (InSAR) technique that serves as an effective means for monitoring urban ground subsidence. However, the accuracy of external (Digital Elevation Models) DEM will affect the accuracy of StaMPS-PS monitoring, and previous studies have mostly used SRTM-1 arc DEM (30 m) as the external DEM. In this study, to obtain a more precise measurement of surface deformation caused by the excavation of the Hohhot subway, a total of 85 scenes of Sentinel-1A data from July 2015 to October 2021, as well as two different resolution digital elevation models (DEMs) (ALOS PALSAR DEM and SRTM-1 arc DEM), were used to calculate and analyze the subsidence along the subway line in Hohhot city. The StaMPS-PS monitoring results showed the ALOS PALSAR DEM, as an external DEM, had higher accuracy, and there was regional subsidence in both the construction processes of Line 1 and Line 2 of the Hohhot subway, with a maximum subsidence rate of −21.1 mm/year. The dynamic changes in subway subsidence were fitted using the Peck formula and the long short-term memory (LSTM) model. The Peck formula results showed the width and maximum subsidence of the settlement troughs gradually expanded during the construction of the subway. The predicted values of the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) of the LSTM model were less than 4 mm and 10%, respectively, consistent with the measured results. Furthermore, we discussed the factors that affect settlement along the subway line and the impact of two external DEMs on StaMPS-PS. The study results provide a scientific method for DEM selection and subsidence analysis calculations in the StaMPS-PS monitoring of urban subway subsidence.