Coastal aquaculture monitoring is vital for sustainable offshore aquaculture management. However, the dense distribution and various sizes of aquacultures make it challenging to accurately extract the boundaries of aquaculture ponds. In this study, we develop a novel combined framework that integrates UNet++ with a marker-controlled watershed segmentation strategy to facilitate aquaculture boundary extraction from fully polarimetric GaoFen-3 SAR imagery. First, four polarimetric decomposition algorithms were applied to extract 13 polarimetric scattering features. Together with the nine other polarisation and texture features, a total of 22 polarimetric features were then extracted, among which four were optimised according to the separability index. Subsequently, to reduce the “adhesion” phenomenon and separate adjacent and even adhering ponds into individual aquaculture units, two UNet++ subnetworks were utilised to construct the marker and foreground functions, the results of which were then used in the marker-controlled watershed algorithm to obtain refined aquaculture results. A multiclass segmentation strategy that divides the intermediate markers into three categories (aquaculture, background and dikes) was applied to the marker function. In addition, a boundary patch refinement postprocessing strategy was applied to the two subnetworks to extract and repair the complex/error-prone boundaries of the aquaculture ponds, followed by a morphological operation that was conducted for label augmentation. An experimental investigation performed to extract individual aquacultures in the Yancheng Coastal Wetlands indicated that the crucial features for aquacultures are Shannon entropy (SE), the intensity component of SE (SE_I) and the corresponding mean texture features (Mean_SE and Mean_SE_I). When the optimal features were introduced, our proposed method performed better than standard UNet++ in aquaculture extraction, achieving improvements of 1.8%, 3.2%, 21.7% and 12.1% in F1, IoU, MR and insF1, respectively. The experimental results indicate that the proposed method can handle the adhesion of both adjacent objects and unclear boundaries effectively and capture clear and refined aquaculture boundaries.

How to improve the phase signal-to-noise ratio (SNR) of distributed scatterers (DSs) is a key topic in DS interferometry (DSI). Although some state-of-the-art phase linking (PL) estimators have been proposed, their performance is still limited by the accuracy of the estimated sample coherence matrix (SCM). The key challenges arise from the biased estimation of the near-zero coherence matrix (the magnitude matrix of SCM) under conditions of small sample sizes and heterogeneous samples. To overcome this limitation, we present a sparse regularization-based PL estimator that considers the potential sparsity structure of the inverse covariance matrix. In this new estimator, we first introduced the graphical lasso (GLasso) algorithm into the small samples estimation problem of SCM, which suppresses the biased estimation of the sparse inverse covariance matrix by introducing ${L}_{{1}}$ -norm regularization, significantly reducing the impact of weakly coherent interferograms in fast decorrelation scenarios. Furthermore, we also attempt to generalize this scheme to long-term coherence cases through the utilization of ${L}_{{2}}$ -norm regularization. Both synthetic data tests and real Sentinel-1 data covering Changi Airport, Singapore, demonstrate the validity of the proposed approach.

Accurate time series forecasting is fundamental to geohazard early warning, yet remains a major challenge. Conventional in situ geotechnical monitoring remains costly and spatially constrained, whereas deep learning applied to remote sensing data has become increasingly prevalent but often suffers from opacity of model decision-making. To address this issue, we propose a Transformer-based forecasting framework, namely PatchTST-Fusion, adapted for multivariate Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) time series. The framework integrates model interpretability analysis through TimeSHAP, providing temporal and feature-level attributions across the input sequence. Landslide deformation time series are first derived from Copernicus Sentinel-1 SAR data. Variational Mode Decomposition is then applied to decompose the non-linear signals into trend, seasonal, and noise components. The denoised displacement series are modeled and forecast using the proposed PatchTST-Fusion, which incorporates rainfall and reservoir water level fluctuations as feature-level drivers. Application to the Daping landslide cluster in the Three Gorges Reservoir Area in China demonstrates that our method captures both the long-term and transient non-linear coupling between deformation and its triggers, surpassing state-of-the-art models including CNN-BiGRU-Attention, Informer and original PatchTST with 7–55% improvements in MAE and 10–52% improvements in RMSE. Beyond predictive gains, feature attribution of environmental triggers via TimeSHAP reveals that rainfall and reservoir regulation exert temporally distinct influences on slope kinematics, with high relative importance concentrated in specific periods and characteristic lagged responses. This interpretable framework provides both enhanced forecasting accuracy and process-based insights, offering a broadly applicable tool for landslide early warning in reservoir regions.

The uneven ground settlement caused by the reinforcement and compaction of filling materials across the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) has continuously threatened infrastructure and the lives and properties of the local people. We utilize a dataset comprising 71 Sentinel-1A images from 2017 to 2023. Applying the small baseline subset interferometric synthetic aperture radar technique, we investigate and analyze the surface deformation of the southeast GBA and its underlying causes and mechanism. The results indicate that the deformation is mainly concentrated over the Hong Kong International Airport and the waterfront areas of southeast GBA, with maximum deformation rates reaching 20 mm/year. Dynamic changes in tidal water levels cause periodic minor vertical deformations in reclaimed areas, with a correlation coefficient greater than 0.61 between them. Within reclaimed areas of the southeast GBA, except for the natural consolidation of soft soils, changes in tidal water levels also play a significant role in ground subsidence, a finding that has been rarely studied or mentioned in previous research, which should be taken into local government’s consideration and measurements. Generally, the study area can be divided into three risk levels, i.e., low-risk, medium-risk, and high-risk zones amounting to 1.87%, 0.11%, and 0.01%, respectively.

Shanghai Pilot Free Trade Zone (SPFTZ) is the first free trade zone in China, consisting of eight independent zones and located in Pudong New District. With the rapid development in the SPFTZ, frequent human activities may induce severe land subsidence. Based on time-series interferometric synthetic aperture radar (InSAR) technique and multitemporal C-band images, the temporal and spatial evolution of subsidence in Pudong New District is obtained from May 2016 to October 2020. The achieved results show that severe subsidence occurred in the east of the study area, where the maximum subsidence rate was −40 mm / year. Most of the land in the SPFTZ remained stable, but small-scale subsidence with a maximum subsidence rate of −18.1 mm / year occurred in two zones. InSAR-derived results are in good agreement with the manual measurements, with a root mean square error of 1.4 mm. Time-series analysis reveals that land subsidence in the SPFTZ mainly originates from urban constructions, including building construction and vehicle load. Meanwhile, the most severe subsidence occurred in the east of Pudong International Airport, which may be attributed to the airport renovation project. In a reclaimed area, land subsidence can be classified into different construction periods, which gradually weakens from north to south.