Tropospheric delay is a major limitation in interferometric synthetic aperture radar (InSAR) for measuring landslide deformation. It exhibits complex spatial-temporal variability, stochastic behavior, and is particularly difficult to model in the mountainous areas of southwest China, where terrain is highly heterogeneous. Accurate estimation of tropospheric delay is therefore critical for improving the precision of InSAR-based landslide monitoring. In this article, we propose a tropospheric delay correction method that explicitly accounts for its periodic characteristics and evaluate its applicability in landslide monitoring across the mountainous regions of southwest China. By analyzing the temporal characteristics of tropospheric delay, we examine its periodic properties and construct a model that combines periodic and stochastic properties to estimate the delay. The method is applied to Sentinel-1 time-series InSAR data in southwestern mountainous study areas, with performance assessed at the levels of interferograms, linear deformation rates, and time-series. Results show that, in both study areas, more than 98% of single-master interferograms exhibit a reduced standard deviation (std), with average reductions exceeding 40% and maximum improvements surpassing 80% in interferometric phase std. These findings demonstrates that the proposed temporal tropospheric delay correction method effectively accounts for both time-related and stochastic components of tropospheric delay, substantially mitigating its impact spatially and temporally. Consequently, this approach significantly enhances the accuracy and reliability of InSAR-based landslide monitoring.

Conventional multitemporal InSAR (MT-InSAR) requires temporal coherence persistence throughout the observation period, limiting its effectiveness in dynamic environments where urbanization causes frequent surface changes. We propose a lifecycle-aware multitemporal InSAR (LA-MTInSAR) framework that leverages optical imagery to synergistically monitor evolving infrastructure and its associated discontinuous coherent scatterers. The method integrates three components: 1) initializing scatterer lifecycles via detection of surface transition epochs using the pruned exact linear time (PELT) algorithm on Sentinel-2 time series; 2) refining the onset of radar signal stability through cascaded search to eliminate the time lag between optical changes and radar coherence establishment; and 3) reconstructing deformation fields via dynamic network construction and least squares QR (LSQR) sparse inversion. Applied to Hong Kong International Airport's (HKIAs) third runway reclamation, LA-MTInSAR corrects an average 42-day physical time lag and increases monitoring point density by 1.7 & times; overall and 28 & times; (416-11 669 points) in the reclamation zone, capturing subsidence rates of-80 mm/year. Validation against continuous global navigation satellite system (GNSS) observations at six sites yields RMSE values of 4.27-6.43 mm. By explicitly accounting for scatterer lifecycle heterogeneity, LA-MTInSAR extends time-series InSAR monitoring capability from static environments to actively evolving construction zones.

Forest growing stock volume (GSV) reflects both timber volume and carbon sequestration capacity, serving as a crucial parameter for assessing forest ecosystem functionality. Traditional GSV monitoring methods are costly, time-consuming, and incapable of rapid, large-scale surveillance. Microwave remote sensing technology offers advantages such as strong penetration capability and the ability to acquire data under all weather conditions and at any time, providing a feasible approach for large-scale forest monitoring. The interferometric water cloud model (IWCM), as a semi-empirical model for estimating forest GSV, has demonstrated good application results, but suffers from issues such as excessive parameters, low solution efficiency, and strong dependence on measured data. Therefore, we categorize IWCM parameters into backscatter parameters and coherence parameters. The backscattering parameters are solved by a feature space method constructed by backscattering coefficients and fractional vegetation cover based on the water cloud model, and the coherence parameters are determined by the least squares method. This approach reduces reliance on measured data, maintains the physical significance of parameters, and enhances solution efficiency. The final estimated results have a correlation coefficient of 0.49 with the National Forest Inventory data, a root mean square error (RMSE) of 114.88 m(3)/ha, and a relative RMSE of 79.00%. Finally, we conducted parameter convergence analysis and comparative experiments with backscattering and coherence fitting results to further verify the effectiveness of this method in estimating GSV.

In this research paper, we introduce an improved multiple-component decomposition technique based on the refined volume scattering models (MCSMRV) for polarimetric interferometric synthetic aperture radar (PolInSAR) system. The primary objective of this methodology is to address the issue of overestimation in volume scattering (OVS) and to clarify the mixed ambiguities associated with scattering mechanisms. Our approach incorporates an innovative inversion technique for rotation angles in urban areas, alongside the newly proposed volume scattering models. Furthermore, a refined Wishart mixture model (RWMM) is proposed for distinguishing building regions from non-building regions, which can effectively support the rational selection of volume scattering models. Additionally, the polarimetric interferometric similarity parameter (PISP) is employed to modify the volume scattering models for buildings with diverse orientation angles. To validate the effectiveness of MCSMRV, we utilize ESAR PolInSAR data and the PolInSAR data collected by the Aerospace Information Research Institute. Various mathematical methods are applied to assess the performance of MCSMRV. The experimental results clearly demonstrate that MCSMRV represents a robust method for characterizing the scattering mechanisms across different terrain types.

Tangshan City in eastern Hebei Province is experiencing rapid economic growth and expanding urbanization, leading to substantial groundwater extraction and significant land subsidence. However, the spatiotemporal characteristics of ground deformation and its relationship to groundwater level variations remain insufficiently understood, posing challenges to sustainable development and environmental stability. Here, we employ small baseline subset interferometric synthetic aperture radar (InSAR) analysis using RADARSAT-2 and Sentinel-1 data to retrieve southern Tangshan's decadal (2012-2021) spatial-temporal deformation. Principal component analysis indicates that the region is dominated by long-term and seasonal patterns, spatially consistent with groundwater depression cones. Longterm deformation has decelerated, while seasonal amplitudes, particularly annual and semiannual, have increased. Seasonal ground deformation corresponds with deep groundwater level fluctuations, declining from spring to summer and recovering from autumn to winter. We also identified evident semi-annual amplitude in the Tanghai-Laoting and Laoting-East deep groundwater depression cones and Fengnan-Tanghai shallow groundwater depression cone, indicating rapid responses to rainfall in highly permeable aquifers. We further constrained the groundwater volume loss in southern Tangshan to 1.28 x 108 m3 from 2012 to 2021, equivalent to 11.4 % of the total groundwater storage in Tangshan City. These findings demonstrate the value of geodetic observations in understanding deformation and groundwater dynamics in the North China Plain, informing sustainable water management. (c) 2025 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

The coherency matrix serves as a valuable tool for explaining the intricate details of various terrain targets. However, a significant challenge arises when analyzing ground targets with similar scattering characteristics in polarimetric synthetic aperture radar (PolSAR) target decomposition. Specifically, the overestimation of volume scattering (OVS) introduces ambiguity in characterizing the scattering mechanism and uncertainty in deciphering the scattering mechanism of large oriented built-up areas. To address these challenges, based on the generalized five-component decomposition (G5U), we propose a hierarchical extension of the G5U method, termed ExG5U, which incorporates orientation and phase angles into the matrix rotation process. The resulting transformed coherency matrices are then subjected to a five-component decomposition framework, enhanced with four refined volume scattering models. Additionally, we have reformulated the branch conditions to facilitate more precise interpretations of scattering mechanisms. To validate the efficacy of the proposed method, we have conducted comprehensive evaluations using diverse PolSAR datasets from Gaofen-3, Radarsat-2, and ESAR, covering varying data acquisition timelines, sites, and frequency bands. The findings indicate that the ExG5U method proficiently captures the scattering characteristics of ambiguous regions and shows promising potential in mitigating OVS, ultimately facilitating a more accurate portrayal of scattering mechanisms of various terrain types.

Early detection of active deformation areas (ADAs) is valuable for potential landslides monitoring and waring. This paper proposes a novel two-stage ensemble learning framework that synergizes multi-source geospatial data for robust ADAs detection. In the first stage, three convolutional neural network base classifiers independently analyze deformation and topographic features to obtain preliminary ADAs classification. In the second stage, a classifier ensemble based on majority voting synthesizes these outputs, leveraging complementary model strengths to reduce uncertainties and enhance decision reliability. Experimental results demonstrate that the proposed ensemble method achieves superior precision in ADAs detection compared to individual convolutional neural network model. This work advances landslide risk assessment by providing a scalable solution for large-scale monitoring and offering critical insights for risk management activities.

Landslides are one of the most destructive natural disasters in the world, threatening human life and safety. With excellent performance as a foundation model for image segmentation, the segment anything model (SAM) has provided a novel paradigm for semantic segmentation research. However, the lack of remote sensing images in the SAM training data limits its ability to recognize landslides. In addition, despite the transfer learning approach can transfer SAM feature extraction capability to the landslide segmentation task, but it will consume a lot of computational resources and training time. In order to solve these challenges, this study proposes a TransLandSeg model that transfers the segmentation capability of SAM while learning landslide features at a low training cost. To limit model training parameters, the adaptive transfer learning (ATL) module is purposely designed, the image encoder is frozen during model training, only the ATL module and mask decoder are trained, and the knowledge learned from the ATL module is input into the original network. Moreover, to select the best ATL module, we also designed 9 kinds of ATL modules and analyzed the accuracy of the TransLandSeg model with different ATL modules. We selected the Bijie landslide dataset and the Landslide4Sense dataset for model training and testing. The experiment results show that the TransLandSeg model increases the mean intersection over union by 1.48% -13.01% compared to other state-of-the-art semantic segmentation models. In addition, TransLandSeg requires only 1.3% of SAM parameters to enable SAM's powerful capabilities to transfer to landslide segmentation.

As a natural disaster, landslide often brings tremendous losses to human lives, so it urgently demands reliable detection of landslide risks. When detecting relic landslides that present important information for landslide risk warning, problems such as visual blur and small-sized dataset cause great challenges when using remote sensing images. To extract accurate semantic features, a hyper-pixel-wise contrastive learning augmented segmentation network (HPCL-Net) is proposed, which augments the local salient feature extraction from boundaries of landslides through HPCL and fuses heterogeneous information in the semantic space from high-resolution remote sensing images and digital elevation model data. For full utilization of precious samples, a global hyper-pixel-wise sample pair queues-based contrastive learning method is developed, which includes the construction of global queues that store hyper-pixel-wise samples and the updating scheme of a momentum encoder, reliably enhancing the extraction ability of semantic features. The proposed HPCL-Net is evaluated on the Loess Plateau relic landslide dataset and experimental results verify that the proposed HPCL-Net greatly outperforms existing models, where the mIoU is increased from 0.620 to 0.651, the Landslide IoU is improved from 0.334 to 0.394 and the F1score is enhanced from 0.501 to 0.565.

In recent years, torrential rainfall has triggered numerous shallow landslides in southeastern China, especially in Hong Kong. Therefore, giving a spatial-temporal hazard prediction for rainfall-induced shallow landslides in Hong Kong on a fine-grained scale is imperative. Nowadays, most empirical hazard prediction methods are qualitative and consider only rainfall features. The generated prediction results lack sufficient spatial details, and the areas of high and very high hazard account for a large proportion. By improving the qualitative empirical hazard prediction method based on frequency statistics, this article proposes a quantitative natural terrain landslide hazard prediction model for Hong Kong based on antecedent rainfall intensity, maximum 24-h rolling rainfall intensity, and landslide susceptibility, which simultaneously considers landslide triggering and predisposing factors. The spatial accuracy and fineness of the prediction results generated by the proposed method are enhanced compared to those predicted by the qualitative method based on rainfall features, demonstrating its efficacy and benefits.

this article, an improved three-component decomposition method for pi /4 compact polarimetric synthetic aperture radar Interferometry (PolInSAR) is proposed to interpret the scattering mechanisms of various terrain types. In the proposed method, refined volume scattering models proposed in fully polarimetric synthetic aperture radar (SAR) is applied to the compact SAR system through certain transformation relations, which can be used to interpret the scattering mechanisms of various land covers. Meanwhile, compact polarimetric interferometric similarity parameter is proposed. This parameter is used to modify the refined volume scattering models, which can overcome the overestimation of volume scattering (OVS) due to the large angle between the building and the radar line-of-sight direction. In addition, extractors for building and nonbuilding regions are proposed, which can be used for adaptive selection of the volume scattering models. The performance of the proposed method is demonstrated and evaluated using C-band and L-band airborne PolInSAR data acquired at different study sites, and the fully PolInSAR data are converted to pi /4 compact PolInSAR data. The experimental results show that the proposed method can be used to reasonably interpret the scattering mechanism of various terrain types (especially the built-up areas with large orientation angles) and overcome the OVS problem, which is an important supplement to the target decomposition method of pi /4 compact PolInSAR.

Relic landslide, formed over a long period, possess the potential for reactivation, making them a hazardous geological phenomenon. While reliable relic landslide detection benefits the effective monitoring and prevention of landslide disaster, semantic segmentation using high-resolution remote sensing images for relic landslides faces many challenges, including the object visual blur problem, due to the changes of appearance caused by prolonged natural evolution and human activities, and the small-sized dataset problem, due to difficulty in recognizing and labelling the samples. To address these challenges, a semantic segmentation model, termed mask-recovering and interactive-feature-enhancing (MRIFE), is proposed for more efficient feature extraction and separation. Specifically, a contrastive learning and mask reconstruction method with locally significant feature enhancement is proposed to improve the ability to distinguish between the target and background and represent landslide semantic features. Meanwhile, a dual-branch interactive feature enhancement architecture is used to enrich the extracted features and address the issue of visual ambiguity. Self-distillation learning is introduced to leverage the feature diversity both within and between samples for contrastive learning, improving sample utilization, accelerating model convergence, and effectively addressing the problem of the small-sized dataset. The proposed MRIFE is evaluated on a real relic landslide dataset, and experimental results show that it greatly improves the performance of relic landslide detection. For the semantic segmentation task, compared to the baseline, the precision increases from 0.4226 to 0.5347, the mean intersection over union (IoU) increases from 0.6405 to 0.6680, the landslide IoU increases from 0.3381 to 0.3934, and the F1-score increases from 0.5054 to 0.5646.

The Baige landslide, which experienced two major collapses on October 10 and November 3, 2018, resulted in the formation of a landslide dam on the Jinsha River, causing significant socio‐economic damage. Despite these catastrophic events, ongoing deformation has been observed, indicating persistent landslide activity and a continued risk of future failures. In this study, we integrated multi‐source remote sensing imagery to investigate the post‐failure kinematics of the Baige landslide from 2019 to 2023. Small baseline subset interferometric synthetic aperture radar (SBAS‐InSAR) was employed to derive slow moving displacement rates of Baige landslide from the Sentinel‐1 and ALOS‐2 PALSAR‐2 datasets. Two‐dimensional (2D) displacement by integration of InSAR measurements revealed maximum vertical and eastward displacement rates of −357.1 mm/yr and 382.1 mm/yr, respectively. Pixel offset tracking (POT) analysis of Sentinel‐2 and ALOS‐2 PALSAR‐2 datasets further facilitated the derivation of three‐dimensional (3D) displacement rates, with maximum vertical and horizontal displacements of −7.2 m/yr and 5.4 m/yr in the upper sections, respectively. The significant variations in displacement rates are related to the fractured surfaces within the landslide. A one‐dimensional pore pressure diffusion model estimated the hydraulic diffusivity of the landslide as approximately 4.95 × 10 −5 m 2 /s, with an unstable mass thickness of ~ 65 m near the head scarp. Seasonal accelerations correlated with rainfall highlight the role of hydrological factors in landslide dynamics. This study demonstrates the value of integrating multi‐source remote sensing data to monitor landslides, providing critical insights for hazard assessment and mitigation in the Jinsha River Basin and similar high‐risk regions.

The North China Plain(NCP)is the political and cultural center of China,including the metropolises of Beijing and Tianjin,the pro-vince of Hebei,and parts of Henan and Shandong provinces.Adopted from previous hydrological studies,the NCP is bounded by the Taihang Mountains in the west and the Yellow River in the south,with an area of approximately 140,000 km2,hosting a population of 350 million[1](Fig.1a).The annual precipitation in the NCP is limited(i.e.,500-600 mm/a)and unevenly dis-tributed in time due to the continental monsoon climate[3].Groundwater thus accounts for approximately 70％of the total water demand in this region[4].Since the 1970s,the long-term over-extraction of groundwater has led to a substantial decline in groundwater levels at a rate ranging from 0.5 to 2.0 m/a,making the NCP one of the largest and most rapidly expanding subsidence areas in the world[1].

Currently, Multi-Temporal InSAR (MT-InSAR) is extensively employed to predict the trend of ground deformation. The deformation data acquired through MT-InSAR has been utilized as a single parameter in the model for predicting land deformation. Nevertheless, these models still necessitate enhancement in terms of their ability to generalize and accurately predict outcomes. In this paper, a combined Long Short Term Memory (C-LSTM) is proposed to combine groundwater level, rainfall, and surface deformation information from MT-InSAR. We assess the predictive accuracy of single-factor and multi-factor models. The results show that after feature combination optimization, the R 2 of the C-LSTM subsidence prediction model with multi-feature training improves the prediction results by 9.7%, 0.48%, and 21.82%, respectively, over the prediction results of the single-feature-factor model. By improving the C-LSTM’s feature factors, this method improves the accuracy of the forecast of ground subsidence change areas.

Lakes are an important component of global water resources. In order to achieve accurate lake extractions on a large scale, this study takes the Tibetan Plateau as the study area and proposes an Automated Lake Extraction Workflow (ALEW) based on the Google Earth Engine (GEE) and deep learning in response to the problems of a low lake identification accuracy and low efficiency in complex situations. It involves pre-processing massive images and creating a database of examples of lake extraction on the Tibetan Plateau. A lightweight convolutional neural network named LiteConvNet is constructed that makes it possible to obtain spatial–spectral features for accurate extractions while using less computational resources. We execute model training and online predictions using the Google Cloud platform, which leads to the rapid extraction of lakes over the whole Tibetan Plateau. We assess LiteConvNet, along with thresholding, traditional machine learning, and various open-source classification products, through both visual interpretation and quantitative analysis. The results demonstrate that the LiteConvNet model may greatly enhance the precision of lake extraction in intricate settings, achieving an overall accuracy of 97.44%. The method presented in this paper demonstrates promising capabilities in extracting lake information on a large scale, offering practical benefits for the remote sensing monitoring and management of water resources in cloudy and climate-differentiated regions.

In this letter, an improved three-component decomposition method for compact PolInSAR under pi/4 mode is proposed. In the proposed algorithm, the volume scattering model is refined by the polarimetric interferometric similarity parameter and the volume scattering can be reasonably reduced. Airborne L-band ESAR PolInSAR data are used to simulate the compact PolInSAR data and evaluate the performance of the method. The experimental results demonstrate that the proposed method can be used to characterize the scattering mechanisms of various terrain types and is a complementary approach to the decomposition method for compact PolInSAR.

While significant progress has been achieved in utilizing remote sensing technologies for landslide investigation in China, there remains a notable gap in consolidating information on applicable conditions, application stages, and workflows across various remote sensing methodologies. This paper proposes a comprehensive framework for active landslide detection, incorporating multiple stages and data sources, successfully implemented in a vast region of southwestern China. Furthermore, detailed discussions are provided on the effects of the geometric distortion, land cover type, and various InSAR methods on the accuracy of active landslide identification results. Additionally, the paper delves into the advantages of integrated remote sensing technology in active landslide investigation, encompassing the assessment of current landslide activity status, precise delineation of boundaries, identification of different deformation stages, and determination of damage patterns. Through comprehensive analysis of multisource data, it enhances understanding of the active landslide process, ultimately contributing to the mitigation of casualties and property damage.

Old landslides in the Loess Plateau, Northwest China usually occurred over a relatively long period, and their sizes are usually smaller compared to old landslides in the alpine valley areas of Sichuan, Yunnan, and Southeast Tibet. These landslide areas may have been changed either partially or greatly, and they are usually covered with vegetation and similar to their surrounding environment. Therefore, it is a great challenge to detect them using high-resolution remote sensing images with only orthophoto view. This paper proposes the optimal-view and multi-view strategic hybrid deep learning (OMV-HDL) method for old loess landslide detection. First, the optimal-view dataset in the Yan’an area (YA-OP) was established to solve the problem of insufficient optical features in orthophoto images. Second, in order to make the process of interpretation more labor-saving, the optimal-view and multi-view (OMV) strategy was proposed. Third, hybrid deep learning with weighted boxes fusion (HDL-WBF) was proposed to detect old loess landslides effectively. The experimental results with the constructed optimal-view dataset and multi-view data show that the proposed method has excellent performance among the compared methods—the F1 score and AP (mean) of the proposed method were improved by about 30% compared with the single detection model using traditional orthophoto-view data—and that it has good detection performance on multi-view data with the recall of 81.4%.

Tropospheric delay significantly hinders the accurate acquisition of high-precision surface deformation by Time series Interferometric Synthetic Aperture Radar (InSAR). The main challenge for current InSAR tropospheric delay estimation lies in effectively utilizing the time-dependent characteristics of the tropospheric delay for accurate atmospheric delay estimation. This paper develops a model to estimate the time-dependent and stochastic components of the delay based on periodic and random characteristics. The experiment demonstrates the effectiveness of the proposed method regardless of whether terrain-dependent delays dominate or random delays dominate at Danba-Xiaojin. The Std of the corrected decreases in 89/90 of the interferograms. The average and maximum improvement of Std is more than 40% and 80% respectively. From the time series, the proposed method can effectively suppress the periodic signals in both non-deformation and deformation regions and can obtain smoother time series. Overall, the proposed method outperforms the other four models for InSAR tropospheric delay correction.