TerraMosaic Daily Digest: Jan 25, 2026
Daily Summary
This digest synthesizes 80 selected papers and focuses on flood generation, routing, and hydroclimatic forcing, high-resolution remote-sensing monitoring workflows, landslide process mechanics and slope evolution. Top-ranked studies examine mass-movement initiation and runout dynamics, flood generation and hydroclimatic forcing, and earthquake-triggered slope response and liquefaction.
Across the full set, evidence converges on mechanism-constrained analysis with operational relevance, especially for risk, fragility, and resilience quantification and seismic source-to-ground response pathways. The strongest contributions pair interpretable process evidence with monitoring or forecasting workflows that support warning design and risk prioritization.
Key Trends
- Flood analyses are becoming event-specific and process-based: Papers emphasize precipitation structure, antecedent wetness, and catchment controls rather than static hazard descriptors.
- Monitoring workflows rely on integrated remote-sensing products: Multi-source satellite and airborne observations are used for deformation retrieval, change detection, and rapid post-event mapping.
- Landslide studies increasingly resolve process chains: Contributions connect triggering conditions, slope deformation, and mobility outcomes, improving the basis for warning thresholds and scenario testing.
- Risk studies move beyond hazard mapping to consequence pathways: Vulnerability, fragility, exposure, and recovery metrics are integrated to compare interventions under compound hazards.
- Seismic hazard research links source behavior to ground response: Recurring topics connect rupture or loading conditions with geotechnical performance and consequence assessment.
Selected Papers
This digest features 80 selected papers from 2,457 papers analyzed across multiple journals. Each paper has been evaluated for its relevance to landslide research and includes links to the original publications.
1. Large-strain finite element analyses of a retrogressive landslide triggered by pile driving in sensitive clays: the case of the 1978 Rigaud landslide in Québec
Core Problem: Understanding the triggering and propagation of retrogressive landslides in sensitive clays due to pile driving.
Key Innovation: Large-strain finite element modeling using a Eulerian-based approach to simulate landslide initiation and progression, highlighting the role of pile driving and soil properties.
2. Chain effects of landslide activity intensity decay on landslide sediment transfer and debris flow activity
Core Problem: Investigating the spatiotemporal relationship between landslide activity, sediment transfer, and debris flow activity in earthquake-affected areas.
Key Innovation: Monitoring landslide activity and sediment transfer to understand how the decay of landslide intensity affects debris flow activity, revealing a linear decay relationship.
3. A time-varying critical acceleration framework for an embedded cantilever retaining wall in saturated sand
Core Problem: Accurate prediction of seismic deformation for embedded cantilever retaining walls with saturated backfill, considering excess pore pressures.
Key Innovation: A time-varying critical acceleration framework that dynamically adjusts the critical acceleration based on excess pore pressure ratio and suction-induced strength regain.
4. Experimental study on local scour around a pile in a silty bed under combined waves and current
Core Problem: Understanding seabed erosion around monopile foundations in silty soils under wave and current conditions.
Key Innovation: Systematic experimental investigation revealing a lower critical KC number for scour initiation in silt compared to sand, and the amplification of scour depth due to seabed liquefaction.
5. Seismic centrifuge modelling of repowered offshore wind turbine considering monopile foundation strengthening and superstructure replacement
Core Problem: Assessing the seismic performance of repowered offshore wind turbines with strengthened foundations.
Key Innovation: Centrifuge shaking table tests demonstrating the impact of superstructure replacement on the dynamic characteristics and seismic responses of monopile-supported OWTs.
6. Reducing local scour around monopiles using arc-shaped sacrificial piles under unidirectional flow
Core Problem: Traditional cylindrical sacrificial piles may exacerbate local scour around monopile foundations due to complex vortex interactions.
Key Innovation: A novel arc-shaped sacrificial pile is introduced to divert flow laterally, reducing approach velocity and bed shear stress upstream of the monopile, thereby reducing scour.
7. Seismic performance of monopile-supported offshore wind turbines in operation under near-field and far-field ground motions considering soil-structure interaction
Core Problem: Understanding the seismic behavior of monopile-supported wind turbines under different ground motions and operational conditions, considering soil-structure interaction (SSI).
Key Innovation: Dedicated shaking table tests on a scaled MWT in a wind tunnel, considering SSI, various operational conditions, ground motion characteristics, and PGA levels, to clarify SSI effect on MWT's seismic responses.
8. Experimental study on displacement field of seabed sediments under the track load of deep-sea mining vehicle
Core Problem: Characterizing sediment mechanical properties and evaluating environmental impacts of deep-sea mining requires understanding the deformation of deep-sea sediments under the working load of tracked vehicles.
Key Innovation: Laboratory experiments using DIC techniques to investigate seabed sediment displacement during crawler vehicle traversal, considering ground pressure, water-covering condition, and track posture, revealing distinct displacement patterns and assessing the effects on disturbance depth.
9. Knowledge-Aware Progressive Fusion Network for Heterogeneous Remote Sensing Image Semantic Segmentation
Core Problem: Fusing optical and SAR data for semantic segmentation is limited by modal shift and data heterogeneity.
Key Innovation: A knowledge-aware progressive fusion network (KPFNet) with a two-stage learning strategy, progressive feature fusion, and a semantic knowledge-guided layer.
10. SAM2-MDESD: An SAM2-Assisted Multilevel Dual Encoder–Single Decoder Method for Optical Remote Sensing Image Change Detection
Core Problem: Existing deep learning-based change detection (CD) methods often suffer from problems such as high missed detection ratio and poor boundary segmentation accuracy when dealing with changed regions with considerable scale differences and irregular geometric shapes.
Key Innovation: A segment anything model 2 (SAM2) assisted multilevel dual encoder–single decoder (SAM2-MDESD) CD method.
11. A Multiscale Attention Transformer for Martian Dust Devil Detection in Remote Sensing Imagery
Core Problem: Detecting Martian dust devils is challenging due to limited data, scale variations, blurred boundaries, and complex surface textures.
Key Innovation: A transformer-based detection network (MDT) with a multiscale attention fusion module and a shape-aware localization loss function.
12. Spatial–Frequency Feature Coupling Network for Semantic Segmentation of Remote Sensing Images
Core Problem: Semantic segmentation of remote sensing images is limited by diverse scale variations and significant intraclass variability.
Key Innovation: A spatial–frequency feature coupling network (SFFCNet) with a dual-domain feature coupling module, cognitive state-space module, and global–local feature integration module.
13. Unsupervised Scene Segmentation and Optimization Methods for Earth’s Surface Anomalies Detection Using Remote Sensing Data
Core Problem: On-orbit Earth’s surface anomaly detection using remote sensing data requires precise scene segmentation, but current studies fail to account for boundary features.
Key Innovation: An unsupervised scene segmentation and optimization method that integrates adjacent edge features with spectral information.
14. SRSDNet: Super-Resolution Snow Depth Retrieval and Mapping Over the Qinghai-Tibet Plateau
Core Problem: Conventional passive microwave (PMW) snow depth (SD) retrieval faces limitations over the Qinghai-Tibetan Plateau due to coarse spatial resolution and representativity errors.
Key Innovation: A deep learning framework (SRSDNet) integrating FengYun-3 PMW data with auxiliary variables and Sentinel-1 SD products as labels.
15. Flood Extent Mapping From SAR Images by a Dynamic Threshold: A Case Study of 2022 Compound Flood in Northeastern Bangladesh
Core Problem: Rapid flood mapping using satellite images is challenging due to cloud cover and shadows, particularly in monsoon regions.
Key Innovation: A rapid thresholding algorithm for flood delineation using Sentinel-1 SAR images and monthly climatology of water extent.
16. Mapping and Revealing the River Ice Distribution and Changes in the Three Rivers Source Region From 1990 to 2023 Using Google Earth Engine
Core Problem: River ice data in the Three Rivers Source Region (TRSR) are scarce, limiting related research.
Key Innovation: A 30-m spatial resolution river ice extent dataset (1990–2023) for the TRSR developed using Google Earth Engine (GEE) and Landsat imagery.
17. Data-driven interface for shallow rainfall-induced landslide forecasting considering trigger randomness and spatiotemporal dependencies
Core Problem: Current landslide models struggle to deliver large-scale operational warnings, partly because they reduce rainfall to cumulative or hypothetical patterns and ignore its intrinsic randomness.
Key Innovation: A novel deep learning framework for landslide forecasting, rigorously benchmarked against logistic regression and validated with a deterministic slope stability model.
18. A novel approach of mapping snow disaster-prone areas based on areal disaster density optimization: a case study of South Korea
Core Problem: Identifying snow disaster-prone areas remains a challenge due to the complex interactions among climatic, topographic, and exposure factors.
Key Innovation: The Maximum Disaster Spatial Density (MDSD) method, a novel framework that directly integrates observed disaster records into an optimized spatial clustering system.
19. A simplified approach to analyzing the safety of earthquake-triggered landslides
Core Problem: Assessing the safety factor of slopes prone to earthquake-induced landslides, considering soil properties, ground motion, and groundwater.
Key Innovation: A simplified analytical formulation incorporating soil flexibility to evaluate slope stability during earthquakes, validated against numerical analyses, experiments, and actual landslides.
20. Quantifying groundwater level variability and annual slope failure probability using multi-year groundwater level observations
Core Problem: Assessing annual slope failure probability considering uncertainties in groundwater level and soil properties.
Key Innovation: A method combining multi-year groundwater data with slope stability analysis to quantify annual failure probability, highlighting the influence of groundwater variability on landslide likelihood.
21. A simplified approach to analyzing the safety of earthquake-triggered landslides
Core Problem: The sliding of slopes caused by earthquakes is a critical issue, resulting in significant losses and casualties worldwide.
Key Innovation: A simplified analytical formulation for evaluating the safety factor; the approach is essentially coupled, as it incorporates the flexibility of the sliding block.
22. Probabilistic assessment of dynamic urban evacuation-sheltering functionality under typhoons based on interdependent road-shelter network
Core Problem: Assessing the functionality of urban evacuation-sheltering systems (UESS) during typhoons, considering the dynamic degradation due to temporal variations in physical damage and traffic demand.
Key Innovation: A holistic functionality metric capturing temporal variations in evacuation timeliness and shelter availability, combined with a probabilistic assessment framework for UESS, revealing a three-stage evolution of UESS functionality during typhoons.
23. Identification and control of core causes of urban systemic risks under extreme precipitation
Core Problem: Identifying and managing the core causes of urban systemic risks under extreme precipitation events to improve governance and reduce disaster impact.
Key Innovation: A core cause analysis method using social network analysis to identify and categorize core causes, demonstrating its effectiveness in the 'July 20' heavy rainstorm disaster in Zhengzhou, China.
24. Measuring the resilience of urban healthcare service availability using metro-bus double-layer network against extreme disturbances
Core Problem: Assessing the resilience of urban healthcare service availability under disturbances in public transportation systems caused by extreme events.
Key Innovation: A multi-layer network framework integrating community population, hospital capacity, and public transit network characteristics to simulate disturbance scenarios and reveal cascading risk effects between metro and bus systems.
25. Rapid post-earthquake functionality prediction of subway systems based on graph neural networks and attentive transfer learning
Core Problem: Predicting the post-earthquake functionality of subway systems to aid in rapid response and recovery efforts.
Key Innovation: A graph neural network (FuncGNN) with a Hierarchical Gate-Query Attention (HGQA) mechanism for attentive transfer learning, enabling robust generalization across earthquake scenarios and significant enhancement of cross-domain transferability.
26. Daily 4D landslide movements monitoring via InSAR: A fusion framework integrating physics-based and data-driven models
Core Problem: Challenges in continuous landslide monitoring using InSAR, including incomplete monitoring dimensions, slow update efficiency, and insufficient temporal resolution.
Key Innovation: A fusion framework of physics-based and data-driven models, built on a Kalman Filter, to rapidly obtain daily 4D landslide movements based on InSAR and hydrometeorological factors. Editor's Choice — This paper stands out due to its innovative approach to addressing a critical challenge in landslide monitoring: achieving continuous, high-resolution temporal data. By fusing physics-based and data-driven models within a Kalman Filter framework, the authors demonstrate a method to rapidly obtain daily 4D landslide movements using InSAR data and hydrometeorological factors. This addresses the limitations of traditional InSAR, which can suffer from temporal decorrelation and infrequent data acquisition. The ability to monitor landslide movements in near real-time has significant implications for early warning systems and risk management, making this a highly impactful contribution.
27. Observing irrigation using SWOT SAR Ka-band data from daily calibration and validation acquisitions
Core Problem: Detecting irrigation events is crucial for agricultural water management, but current methods have limitations.
Key Innovation: Using SWOT's Ka-band SAR data to detect soil moisture variations due to irrigation, showing increased backscattering.
28. Remote sensing facilitates the exploration of algal bloom dynamics and its hydrometeorological drivers in tributary bays of the Three Gorges Reservoir
Core Problem: Algal blooms in reservoir bays threaten water quality, but their spatiotemporal patterns are difficult to identify.
Key Innovation: Using Sentinel-2 imagery and random forest models to characterize algal bloom dynamics and their drivers in the Three Gorges Reservoir.
29. PACE (Plankton, Aerosol, Cloud, ocean Ecosystem): Preliminary analysis of the consistency of remote sensing reflectance product over aquatic systems
Core Problem: Understanding the quality and potential applications of the spectral remote sensing reflectance (Rrs(λ)) product from NASA’s PACE mission.
Key Innovation: Preliminary validation of the provisional PACE-OCI Rrs product using AERONET-OC stations, indicating overall consistency.
30. Environmental controls on soil organic matter composition across a glacial-landscape gradient: A case study of debris-covered glaciers in Gongga Mountain, Southwest China
Core Problem: Understanding soil formation on debris-covered glaciers and the factors influencing organic matter composition in these extreme environments.
Key Innovation: Analysis of supraglacial debris organic matter (SDOM) using pyrolysis-gas chromatography–mass spectrometry to identify chemical compounds and their relationship to environmental factors like temperature, humidity, and debris texture.
31. Multi-annual evolution of coastal dunes: Transition from fixed to transgressive dunes state
Core Problem: Understanding the factors that cause coastal dunes to transition from stable, vegetation-fixed states to mobile, transgressive states.
Key Innovation: Analysis of high-resolution Digital Terrain Models (DTMs) and satellite-derived vegetation cover maps over a 12-year period, linked to wind data, to analyze coastal dune changes and the role of sediment budget.
32. Investigation on artificial boundary problems in three-dimensional nodal-based continuous-discontinuous deformation analysis method for the seismic dynamic analyses of geotechnical structures
Core Problem: Accurate simulation of seismic responses of geotechnical structures requires appropriate boundary conditions to avoid fictitious reflected waves.
Key Innovation: Enhanced 3D-NCDDAM with various boundary conditions (viscous, viscoelastic, free field, static-dynamic unified) for accurate seismic response analysis and landslide simulation.
33. Rockburst in circular openings under varying confining stress: Acoustic emission characteristics and precursors
Core Problem: Rockbursts in underground structures pose a major threat; understanding the role of confining stress is crucial for predicting their initiation and intensity.
Key Innovation: True triaxial rockburst experiments with AE monitoring reveal that increased confining stress leads to more abrupt failure, and variance/Hurst exponent can provide early warning signals.
34. Experimental study on the permeability characteristics of reconsolidated salt: Effects of gas and confining pressure
Core Problem: Understanding the gas permeability of reconsolidated salt is essential for its use as a sealing material in high-level radioactive waste repositories.
Key Innovation: Experiments show gas permeability decreases with increasing gas and confining pressures, with the Klinkenberg effect being dominant due to the microporous structure.
35. Centrifuge modelling of dewatering-excavation effects on overlying and adjacent large-diameter shield tunnels
Core Problem: Foundation pit dewatering and excavation in groundwater-rich strata severely threatens adjacent large-diameter shield tunnels.
Key Innovation: A novel testing device was developed to simulate sequential dewatering, excavation, and backfilling while dynamically controlling groundwater levels
36. Prediction of future landslide frequency and population impact in China based on machine learning model
Core Problem: Landslides are a common geological disaster, and due to their strong disaster-causing nature, they pose a serious threat to social and economic development.
Key Innovation: The model with the best fitting effect for landslides frequency is random forest (RF), the second is bootstrap aggregating (Bagging), and the worst effect is support vector machine (SVM).
37. Plant-water interactions shape coastal dune evolution in a changing climate
Core Problem: Coastal dunes provide flood protection, but their stability is threatened by climate change, particularly rainfall variations.
Key Innovation: Coupled an ecohydrological model with a dune development model to simulate plant-water interactions and their influence on dune development under various rainfall scenarios.
38. Sediment transport mechanisms in sediment-starved subaqueous deltas: insights from storm-induced gravity flows
Core Problem: Subaqueous deltas are threatened by erosion due to storms and reduced sediment supply, but the sediment transport mechanisms are not well understood.
Key Innovation: Direct observation of storm-driven sediment dynamics, including fluid mud layers, wave-induced seabed liquefaction, and gravity flows, in a sediment-starved delta.
39. SV wave scattering by a circular-arc depositional canyon in an unsaturated site
Core Problem: Seismic wave scattering in unsaturated soil near canyon topography is complex and not well understood.
Key Innovation: A semi-analytical solution using Fourier-Bessel series to model SV wave scattering by a circular-arc depositional canyon, considering geometry, incidence angle, frequency, and saturation.
40. Scattering of SV waves by the canyon topography: Numerical analysis and shaking table test verification
Core Problem: Canyon topography significantly affects seismic ground motions due to scattering and amplification effects.
Key Innovation: Numerical solution for SV wave scattering by a V-shaped canyon using the singular boundary method, validated with shaking table tests.
41. Rapid evaluation of post-earthquake track irregularity and running safety threshold of railway track-bridge systems
Core Problem: Need for rapid post-earthquake assessment of railway track-bridge systems to ensure operational safety.
Key Innovation: Analytical mapping model relating bridge seismic deformation to post-earthquake track irregularity, enabling rapid assessment of safety speed thresholds.
42. Improving rockfall monitoring with distributed acoustic sensing: The role of operational parameters
Core Problem: Optimizing distributed acoustic sensing (DAS) for effective rockfall monitoring requires understanding how operational parameters influence signal response.
Key Innovation: Comprehensive field experiment evaluating the impact of gauge lengths, installation methods, and fiber-optic cable packaging on DAS signals from simulated rockfall impacts, providing practical guidelines for system design and deployment.
43. Normal vector estimation method for rock mass point clouds with sharp feature preservation via local geometric adjustment
Core Problem: Accurate extraction of rock mass discontinuity parameters from point clouds is crucial for stability assessment, but conventional methods struggle to preserve sharp features.
Key Innovation: A normal estimation method based on local geometric adjustment that enhances feature extraction while maintaining sharp geometries, improving structural plane extraction accuracy.
44. Seismological Characterization of Northern Hikurangi Margin Slow Slip Regions Associated With Normal Faults, Seamounts, and Seeps
Core Problem: Understanding the deeper portion (10-30 km) of slow slip events (SSEs) at the northern Hikurangi margin, New Zealand, and its relationship to seismicity.
Key Innovation: Identifies normal faults, formed by slab bending and seamount uplift, as pathways for mantle-derived fluids that influence forearc dewatering and SSE genesis.
45. Inelastic Dilatancy as a Mechanism for Coseismic Fluid Depressurization of a Shallow Fault Zone
Core Problem: Investigating the role of inelastic dilation in causing pore pressure reduction during earthquakes in shallow fault zones.
Key Innovation: Demonstrates that inelastic dilation from coseismic fault damage leads to significant pore pressure reduction near the fault, influencing fluid migration and potentially detectable through hydrologic measurements.
46. Fluid‐Induced Earthquake Nucleation on Aging Rate‐and‐State Faults: Influence of Hydraulic Diffusivity and Injection Rate Under Different Nucleation Regimes
Core Problem: Understanding how hydraulic diffusivity and injection rate affect the nucleation length of injection-induced seismicity on rate-and-state faults.
Key Innovation: Shows that the effects of injection rate and hydraulic diffusivity on nucleation length depend on the nucleation regime (no-healing vs. constant-weakening), providing insights for field- and lab-scale injection-induced ruptures.
47. Signals without action: a value chain analysis of Luxembourg's 2021 flood disaster
Core Problem: Early flood indicators did not lead to better anticipatory action in Luxembourg's 2021 floods.
Key Innovation: Value chain approach identifies how thresholds, procedures, and institutional responsibilities limited the use of available forecast information.
48. Small and Medium‐Sized Inland Waterbodies: Water Volume Predictions and Flood Implications
Core Problem: Inaccurate water volume estimation in small/medium waterbodies hinders flood mitigation strategies.
Key Innovation: Machine learning model estimates monthly volume changes, projects future volumes, and analyzes flood mitigation potential, proposing new waterbody creation/deepening.
49. Multi‐Objective Urban Observational Strategies: A Risk‐Based Framework for Expanding Flood Sensor Networks
Core Problem: Developing an observation strategy which maximizes insight into both the natural system and the human system is a challenging multi‐objective optimization problem.
Key Innovation: A new approach for assessing the spatial extent through which a flood sensor's observations can be assumed to be relevant, and estimate the population served within each sensor's area of information using downscaled socio‐demographic data.
50. More Water, More of the Time: Spatial Changes in Flooding Over 83 Years in the Upper Mississippi River Floodplain and Relationships With Streamgage‐Derived Proxies
Core Problem: Changes in discharge over 83 years have manifested across the UMR floodplain in ways that may have consequences for ecological patterns and processes.
Key Innovation: A geospatial model to simulate daily inundation depths, demonstrating that changes in discharge have manifested across the UMR floodplain in ways that may have consequences for ecological patterns and processes.
51. The Relative Importance of Floodplain Storage and Flow Path Dispersion on Flood Attenuation in Mountain Streams
Core Problem: Intensifying flooding throughout the western United States threatens human infrastructure, human life, and ecological integrity.
Key Innovation: Quantified flood attenuation provided by beads by utilizing two‐dimensional hydrodynamic models simulating flash floods in three river beads located in the Colorado Rocky Mountains, United States.
52. Evaluating the Functional Realism of Deep Learning Rainfall‐Runoff Models Using Catchment Hydrology Principles
Core Problem: Deep learning models achieve high accuracy in rainfall-runoff modeling, but their functional reasoning may not be physically reliable.
Key Innovation: A hydrology-specific Explainable AI framework extracts Impulse Response Functions to evaluate the functional relationships that LSTM uses to reflect the influence of precipitation, temperature, and potential evapotranspiration on simulated streamflow.
53. A Novel Hybrid Predictive Model Based on Mixture Density Networks With Weighted Conformal Inference Strategy for Runoff Interval Prediction Across Australia
Core Problem: Accurate runoff forecasting helps mitigate flooding and drought risks and ensure water security under changing conditions.
Key Innovation: Innovatively incorporated the Weighted Conformal Inference (WCI) strategy, which accounts for distributional shifts in runoff sequences, and integrated it with MDN to develop the WCI‐MDN model for runoff interval prediction.
54. Damage assessment
Core Problem: Political turnover can rapidly reshape governance of U.S. science agencies, but the durability of those institutional changes is uncertain.
Key Innovation: Provides a forward-looking policy assessment that distinguishes likely persistent changes from reversible ones in federal science-agency oversight.
55. Temperature forecasting and derivatives pricing in the Yangtze River economic belt of China
Core Problem: Climate variability impacts agriculture and energy sectors in the Yangtze River Economic Belt.
Key Innovation: Compares SARIMA and Ornstein-Uhlenbeck models for temperature forecasting and designs temperature-indexed weather derivatives.
56. Seasonal variability of particulate matter pollutants in Abuja, Nigeria
Core Problem: High particulate matter levels in Abuja, Nigeria pose public health risks.
Key Innovation: Analyzes spatiotemporal trends of PM2.5 and PM10 using low-cost sensors and identifies major contributing factors.
57. Estimating the variability of climate temperature extremes using statistical distributions over a diverse climatic region
Core Problem: Extreme temperature estimation for climate adaptation strategies.
Key Innovation: Applies statistical probability distributions to estimate extreme temperature values for different return periods.
58. Hydrological drought attribution analysis of six rivers in China by the coupled model of machine learning and hydrological model
Core Problem: Complex evolution of hydrological drought in river basins due to climate change and human activities.
Key Innovation: Coupled machine learning-hydrological model to simulate runoff changes and quantify contributions of climate and human factors to hydrological drought.
59. Exploring climate–ecological driving mechanisms of vegetation change in central asia: a hierarchical assessment framework based on trend tests, multiple regression, and structural equation modeling
Core Problem: Insufficient understanding of climate-ecological mechanisms driving vegetation dynamics in Central Asia.
Key Innovation: Developed an integrated analytical framework combining trend tests, multiple regression, and structural equation modeling to assess LAI dynamics.
60. Ecological vulnerability assessment and control factor analysis based on vegetation productivity in Yinshanbeilu of Inner Mongolia
Core Problem: Ecosystem vulnerability in a changing climate.
Key Innovation: Couples long-term NDVI and NPP indicators with the Geographic Detector to assess ecological vulnerability.
61. Geospatial assessment of soil salinity–nutrient stratification and soil health responses to groundwater table dynamics in the Bohai Rim region, China
Core Problem: Soil salinization threatens ecosystem sustainability and agricultural productivity.
Key Innovation: Establishes a groundwater–soil–ecosystem coupling framework integrating groundwater depth monitoring, soil profiling, and remote sensing indicators.
62. Research on GAN based sample expansion method for dam earthquake damage assessment
Core Problem: Lack of stability and accuracy in dam damage assessment models due to scarcity of dam-damage samples.
Key Innovation: Proposes a Generative Adversarial Network (GAN)-based seismic data enhancement method to expand dam-damage samples and improve model generalization ability.
63. Nonuniform variations of drought driven by spatially heterogeneous climate changes
Core Problem: Limited understanding of regional drought heterogeneity, drivers, and future persistence under global warming.
Key Innovation: Analyzes global drought patterns, drivers, and persistence using the Temperature Vegetation Dryness Index (TVDI) and various statistical methods.
64. Quantifying the spatiotemporal evolution of NDVI and Its response to driving factors based on the geographical detector model in the Northwest China
Core Problem: Understanding vegetation spatiotemporal dynamics for ecosystem management.
Key Innovation: Quantifies NDVI changes using multitemporal remote sensing data and geographical detectors.
65. Scenario-driven data fusion for compound coastal flood risk and exposure assessment using night-time lights in Jeddah
Core Problem: Coastal cities are increasingly vulnerable to compound flooding, but integrated and scenario-based assessments are limited.
Key Innovation: Develops a rainfall-adjusted, scenario-driven bathtub flood model for Jeddah, Saudi Arabia, to project future flood extent and urban exposure using night-time light data.
66. Quantifying the cooling effect of green spaces on urban heat island effect
Core Problem: Urban heat island (UHI) effect intensified by urbanization.
Key Innovation: Combines spatial autocorrelation and buffer zone analysis to determine the cooling distance of UGS and explores the impact of spatial distribution pattern of UGS on the UHI effect.
67. Formulating Z-number for identifying suitable areas for groundwater recharge and enhancing reliability in decision-making
Core Problem: Groundwater decline due to overexploitation.
Key Innovation: Delineates the artificial recharge potential index (ARPI) that captures reliability using fuzzy rules.
68. Assessing driving factors and future trends of ecological vulnerability in the upper reaches of the yangtze river
Core Problem: Ecosystem faces dual threats from climate change and human activities.
Key Innovation: Assessed the characteristics of EV in the URYR and analyzed the individual and interactive effects of various factors on EV.
69. Assessment of drought vulnerability zones by integrating TRFNAHP and GIS approaches
Core Problem: Drought is a critical global challenge that severely impacts farmers and communities.
Key Innovation: Determine Drought Vulnerability Zones (DVZ) by integrating Trapezoidal Fuzzy Numbers Analytical Hierarchy Process (TRFNAHP) method and Geographic Information System (GIS) approaches.
70. Spatial and temporal changes of social flood vulnerability in municipalities of Slovakia
Core Problem: Social flood vulnerability analysis is an inevitable task for effective flood risk assessment and management.
Key Innovation: Assess the spatial and temporal changes in social flood vulnerability between 2001, 2011, and 2021 using geographic information systems (GIS) and spatial‒statistical methods.
71. Spatial multi-criteria decision analysis for groundwater recharge in a water-stressed region: a case study of the Guadalupe Valley Basin, Baja California, Mexico
Core Problem: Groundwater depletion from intensive agriculture, over-extraction, and declining precipitation.
Key Innovation: Identified suitable sites for Managed Aquifer Recharge (MAR) through an integrated geospatial and hydrogeological analysis.
72. Regional differences in the impact of climate extremes on future global rice yield variability
Core Problem: Understanding how different types of extremes affect future yield variability across regions remains limited.
Key Innovation: Used extreme gradient boosting to predict global gridded rice yields under three Shared Socioeconomic Pathways (SSPs).
73. Automated oil spill detection using deep learning and SAR satellite data: a case study of the Lower Congo Basin
Core Problem: Limitations of existing segmentation models in natural oil spill detection.
Key Innovation: Proposes a novel semantic segmentation model, NOS-Net, based on the U-Net architecture, integrating ResNet and a lightweight Residual Convolutional Block Attention Module (RCBA).
74. Multimodal fusions for defect detection of photovoltaic panels by mask R-CNN and hawkfish optimization algorithm
Core Problem: Accurate detection of photovoltaic (PV) module defects remains challenging.
Key Innovation: Proposes a multimodal PV defect segmentation framework based on a modified Mask R-CNN architecture that fuses RGB, IR, and EL modalities at the feature level.
75. Characteristics of extreme precipitation and precipitation estimation under different return periods in the Pamir Plateau
Core Problem: Research on its extreme precipitation is therefore vital for water resource assessment, hydrological modeling, and disaster risk planning.
Key Innovation: Using daily observational data and the Peak Over Threshold method, we constructed extreme precipitation series across the region.
76. Coupling Time-Series Sentinel-2 Imagery with Multi-Scale Landscape Metrics to Decipher Seasonal Waterbird Diversity Patterns
Core Problem: Most existing studies rely on static land-cover representations or single spatial scales, limiting our ability to characterize how waterbirds respond to seasonally shifting habitats across scales.
Key Innovation: Combined high-frequency waterbird surveys from 2019–2021 with multi-temporal, season-matched Sentinel-2 imagery and the Dynamic World dataset.
77. Enhanced Deep Convolutional Neural Network-Based Multiscale Object Detection Framework for Efficient Water Resource Monitoring Using Remote Sensing Imagery
Core Problem: Present operational systems are small and provide only a subset of the information needed.
Key Innovation: A Deep Neural Network-Based Object Detection for Water Resource Monitoring and Earth Observation (DNNOD-WRMEO) model is introduced.
78. Short-Term Degradation of Aquatic Vegetation Induced by Demolition of Enclosure Aquaculture Revealed by Remote Sensing
Core Problem: Its ecological benefits for key biotic components, particularly AV communities, remain unclear.
Key Innovation: This study utilized Landsat and Sentinel-1 satellite imagery to analyze the dynamic evolution of enclosure aquaculture (EA) and AV in 25 lakes (>10 km2) within the MLRYR basin from 1989 to 2023.
79. Multi-Source Remote Sensing Data-Driven Susceptibility Mapping of Retrogressive Thaw Slumps in the Yangtze River Source Region
Core Problem: Quantified assessment of the spatial occurrence probability of Retrogressive Thaw Slumps (RTSs) in the Yangtze River Source Region (YRSR) is lacking.
Key Innovation: A susceptibility assessment framework is established to model the spatial distribution and risk levels of RTSs, integrating time-series spectral features with static topographic variables and using ensemble learning algorithms with SHAP analysis.
80. Simultaneous Hyperspectral and Radar Satellite Measurements of Soil Moisture for Hydrogeological Risk Monitoring
Core Problem: Emerging landslides and severe floods highlight the urgent need to analyse and support predictive models and early warning systems.
Key Innovation: Determining the volumetric water content (VWC) using both radar and hyperspectral data to monitor landslide risk.