Beijing is one of the most water-stressed cities in the world. Due to over-exploitation of groundwater, the Beijing region has been suffering from land subsidence since 1935. In this study, the Small Baseline InSAR technique has been employed to process Envisat ASAR images acquired between 2003 and 2010 and TerraSAR-X stripmap images collected from 2010 to 2011 to investigate land subsidence in the Beijing region. The maximum subsidence is seen in the eastern part of Beijing with a rate greater than 100 mm/year. Comparisons between InSAR and GPS derived subsidence rates show an RMS difference of 2.94 mm/year with a mean of 2.41 ± 1.84 mm/year. In addition, a high correlation was observed between InSAR subsidence rate maps derived from two different datasets (i.e., Envisat and TerraSAR-X). These demonstrate once again that InSAR is a powerful tool for monitoring land subsidence. InSAR derived subsidence rate maps have allowed for a comprehensive spatio-temporal analysis to identify the main triggering factors of land subsidence. Some interesting relationships in terms of land subsidence were found with groundwater level, active faults, accumulated soft soil thickness and different aquifer types. Furthermore, a relationship with the distances to pumping wells was also recognized in this work.

Timely and effective access to agricultural land-change information is of great significance for the government when formulating agricultural policies. Due to the vast area of Shandong Province, the current research on agricultural land use in Shandong Province is very limited. The classification accuracy of the current classification methods also needs to be improved. In this paper, with the support of the Google Earth Engine (GEE) platform and based on Landsat 8 time series image data, a multiple machine learning algorithm was used to obtain the spatial variation distribution information of agricultural land in Shandong Province from 2016 to 2020. Firstly, a high-quality cloud-free synthetic Landsat 8 image dataset for Shandong Province from 2016 to 2020 was obtained using GEE. Secondly, the thematic index series was calculated to obtain the phenological characteristics of agricultural land, and the time periods with significant differences in terms of water, agricultural land, artificial surface, woodland and bare land were selected for classification. Feature information, such as texture features, spectral features and terrain features, was constructed, and the random forest method was used to select and optimize the features. Thirdly, the random forest, gradient boosting tree, decision tree and ensemble learning algorithms were used for classification, and the accuracy of the four classifiers was compared. The information on agricultural land changes was extracted and the causes were analyzed. The results show the following: (1) the multi-spatial index time series method is more accurate than the single thematic index time series when obtaining phenological characteristics; (2) the ensemble learning method is more accurate than the single classifier. The overall classification accuracy of the five agricultural land-extraction results in Shandong Province obtained by the ensemble learning method was above 0.9; (3) the annual decrease in agricultural land in Shandong Province from 2016 to 2020 was related to the increase in artificial land-surface area and urbanization rate.

Land subsidence prediction remains challenging. Conventional grid-based or sequence-only neural networks struggle to represent these spatial dependencies and often lack structured mechanisms for incorporating region-level deformation priors and local physical consistency. This study develops PEGNet, a Peridynamics-inspired and emergent-feature-conditioned spatio-temporal graph neural network for land subsidence prediction. PEGNet uses a per-node GRU for temporal encoding and an EF-conditioned GAT for spatial aggregation. At the node level, historical InSAR deformation and auxiliary hydroclimatic observations are augmented with a PD-derived state and an EF neighborhood context, allowing a per-node GRU to learn temporal evolution and produce a base estimate of future incremental deformation. At the edge level, a fixed spatial graph supports EF-conditioned graph attention, where edge representations combine spatial distance, PD-derived bond-strain information, and the EF same/cross-region gate to generate a spatial residual prediction. At the training and prediction level, the temporal and spatial branches are fused through a learned gate, cumulative deformation is reconstructed from the last observed value, and training combines a data-fitting loss with a PD-inspired local consistency term weighted by distance and EF relations. EF therefore conditions both the network and the coupled regularizer, although it introduces no separate loss term, and the PD component remains a local consistency mechanism rather than a complete Peridynamic solver. PEGNet is evaluated using 60 months of PS-InSAR observations in Tongzhou District, Beijing, four overlapping purged test windows covering nine unique target months and five random seeds. It achieves an incremental-deformation RMSE of 2.153 ± 0.007 mm and a reconstructed cumulative-deformation RMSE of 2.631 ± 0.072 mm. These results outperform the deterministic baselines and remain comparable to the unconstrained GRU-GAT model. Mechanism diagnostics indicate that EF mainly redistributes graph attention, whereas the PD components improve local consistency without producing a substantial global accuracy gain. Overall, the comparable aggregate performance and improved local consistency support the use of PEGNet for conditional subsidence monitoring and scenario analysis.

Land subsidence is a widespread geological hazard in urban areas, yet accurate prediction remains challenging due to the complex, non-linear spatio-temporal dynamics governing subsurface deformation. In this work, the PEGNet (Peridynamics-Emergent-GAT-GRU Network) method integrates Graph Attention Networks (GAT) with Gated Recurrent Units (GRU) under a three-level embedding strategy to predict land subsidence at monitoring points. Peridynamics gradient and emergent velocity features are fused with multi-source data at the feature level; multi-head graph attention captures spatial dependencies among monitoring points at the structure level; and a Peridynamics-based loss regularizer penalizes unrealistic spatial gradients while an attention entropy regularizer encourages focused attention patterns at the constraint level. Evaluated in Tongzhou District, Beijing over 60 months, the model achieves an RMSE of 5.16 mm, outperforming the GAT-GRU baseline by 25.1% and reducing RMSE by 51.7% relative to GCN-LSTM. Comprehensive ablation studies and baseline comparisons validate each component, showing that the dual-constraint framework effectively suppresses large-magnitude outliers. This work demonstrates that coupling macro-micro priors with spatio-temporal deep learning provides an effective paradigm for integrating physical and geoscience principles into data-driven subsidence prediction.