TerraMosaic Daily Digest: July 16, 2026
Daily Summary
Slope-failure research now resolves how hydrologic state, material softening and seismic rupture govern the transition from instability to motion. Fully coupled particle modelling of the Gjerdrum quick-clay landslide reproduces retrogression through the interaction of excess pore pressure, effective stress and strain softening. A hybrid hydro-mechanical and machine-learning framework converts simulated failure probabilities into critical rainfall envelopes, with five-day accumulation providing the most stable threshold. Physical experiments further show that reverse-fault rupture amplifies hanging-wall hillslope accelerations by 1.2-2.6 times, promoting tensile cracking and rapid mass ejection.
The dominant controls change after failure begins. Seventeen years of observations across 73 Wenchuan catchments document a shift from rainfall-triggered, landslide-supplied debris flows to activity governed by channel storage and geomorphic connectivity; projected recovery extends to about 2048 in the sediment-rich central sector. Sequential reconstruction of the 2017 Nuugaatsiaq cascade links landslide descent, tsunami propagation and detectable coastal ground displacement, while identifying source-volume uncertainty as the principal control on wave amplitude. At Cancia, continuum modelling shows how obstacles and basin geometry focus hydraulic loading on a debris-flow barrier, clarifying why mitigation performance cannot be assessed independently of flow-structure interaction.
Hazard metrics are increasingly tested against observations that matter for consequences. Liquefaction indices derived from 143 CPT soundings rise with damage across 2,159 buildings, but their moderate discrimination demonstrates the need for local calibration. Ambient-noise measurements, arrays and boreholes resolve an 800-900 m-deep sedimentary basin for seismic microzonation, and interpretable flood modelling identifies river-network connectivity as a control comparable to land use. A review of 67 multi-hazard vulnerability studies finds that verification and sensitivity analysis commonly substitute for validation, whereas fewer than 15% evaluate decision or behavioural performance. Operational reliability therefore depends on independent observations and explicit tests of uncertainty and consequence-level performance.
Key Trends
Five linked shifts connect failure physics, cascade dynamics, geomorphic memory and consequence-level validation.
- Failure thresholds become state dependent: Pore-pressure evolution, strain softening, rainfall history and rupture geometry replace universal thresholds with process-conditioned estimates of failure probability.
- Cascades are modelled as connected transfers: Landslide descent, tsunami generation, sediment routing and barrier loading are treated as sequential exchanges of mass and momentum, with uncertainty propagated between stages.
- Geomorphic memory controls recovery: Long records reveal that coseismic sediment supply, channel storage and tectonic uplift can reorganize hazard rates for years to decades after an event.
- Structural interfaces govern amplification: Fault hanging walls, fracture zones, basin edges, river-network bottlenecks and engineered obstacles localize acceleration, deformation or hydraulic forcing.
- Validation moves from model fit to observed consequence: Building damage, field displacement, independent geology and decision performance expose where physically plausible models remain weakly discriminative or poorly constrained.
Selected Papers
The leading studies connect initiation physics to the subsequent cascade and its measurable consequences. They resolve pore-pressure-driven quick-clay retrogression, rainfall-conditioned failure probability, fault-controlled acceleration, decadal sediment recovery, landslide-tsunami coupling and the limits of commonly used vulnerability metrics.
1. Modelling retrogressive failure of quick clay using fully coupled flow-deformation SPH framework: the Gjerdrum (Norway) landslide case
Core Problem: Retrogressive quick-clay failures couple strain softening to evolving excess pore pressure, a mechanism that undrained total-stress models do not resolve reliably.
Key Innovation: A fully coupled flow-deformation SPH model, parameterized with CPTU stratigraphy and critical-state clay and sand behaviour, reproduces the Gjerdrum sequence and its shear-band pore-pressure evolution.
2. Coupled simulation of landslide, tsunami, and ground displacement for the 2017 Nuugaatsiaq event in Greenland
Core Problem: The 2017 Nuugaatsiaq event lacked coastal tide-gauge observations, leaving the links among landslide motion, tsunami propagation and recorded ground displacement incompletely constrained.
Key Innovation: Sequential landslide, tsunami and elastic-ground simulations reproduce the timing in local seismic records and show that uncertainty in the approximately 44 million cubic metre source volume dominates wave-amplitude estimates.
3. Spatiotemporal trajectories and heterogeneity of post-seismic debris flow activity after the wenchuan earthquake: deciphering decadal-scale evolution and driving mechanisms
Core Problem: Post-earthquake debris-flow hazard evolves as hillslope sediment is depleted and material is redistributed through channels, so a single recovery trajectory cannot represent the Wenchuan epicentral region.
Key Innovation: A 17-year, 73-catchment record resolves a transition from rainfall-triggered, landslide-supplied activity to channel-storage and pathway control, with subregional recovery projected from about 2030 to 2048.
4. A hybrid physics-machine learning framework for probabilistic landslide prediction based on critical rainfall envelopes
Core Problem: Empirical rainfall thresholds omit the coupled hydro-mechanical processes that determine slope failure and can become unreliable under nonlinear storm histories.
Key Innovation: Coupled seepage-stability simulations are converted to spatial failure probabilities and a critical rainfall envelope, while XGBoost provides a fast surrogate and identifies five-day accumulation as the most stable threshold window.
5. Reverse fault ruptures amplify coseismic landsliding on hanging wall hillslopes
Core Problem: The asymmetric hillslope response produced when reverse faults rupture through topography has remained poorly constrained despite its importance for coseismic landsliding.
Key Innovation: A fault-dislocation shaking-table experiment with particle-image velocimetry measures 1.2-2.6-fold hanging-wall acceleration amplification and links it to tensile cracking and projectile-like mass ejection.
6. Spatial distribution and its impact factors of loess-red bed landslides in the Longzhong Basin, China
Core Problem: The spatial organization of loess-red-bed landslides in the tectonically active Longzhong Basin and the relative influence of terrain, geology and precipitation required regional quantification.
Key Innovation: GIS clustering and nine conditioning factors delineate seven concentration zones and constrain characteristic elevation, slope, relief, rainfall and proximity ranges for slides and flows.
7. Scale-Sensitive and Confounding-Audited SBAS-InSAR Evidence Representation for Landslide Susceptibility Mapping
Core Problem: InSAR deformation layers can be confounded by observation availability, decorrelation and terrain geometry, making apparent susceptibility gains difficult to attribute to actual slope motion.
Key Innovation: The study separates deformation intensity from observation reliability across multiple neighbourhood scales; spatial-block AUC changes only from 0.948 for static factors to 0.950, showing that InSAR is diagnostic evidence rather than an automatically beneficial predictor.
8. Tectonic and structural control of landslide hazards in the Outer Carpathian Mountains, Ukraine
Core Problem: Regional susceptibility assessments in the Outer Carpathians have not adequately represented landslides localized by tectonic fracture zones of different orientations.
Key Innovation: Integrated regional and local analysis links landslide mechanisms and infrastructure damage to the width and orientation of topographically expressed fracture zones, motivating an explicitly tectonic classification.
9. Correlation of building damages with LPI and LSN in Hinode area during the 2011 Great East Japan Earthquake
Core Problem: CPT-based liquefaction indices are widely used, but their dependence on the triggering procedure and their correspondence to observed structural damage remain uncertain.
Key Innovation: Three triggering procedures are evaluated using 143 CPT soundings and damage to 2,159 buildings; LPI and LSN increase with damage, yet AUC values of 0.50-0.71 show only moderate discrimination and a need for local calibration.
10. Continuum Modelling of Flow-Structure Interaction during the 2009 Cancia Debris Flow (Italian Dolomites)
Core Problem: The fatal breach of the Cancia debris-flow storage barrier cannot be understood from runout alone because basin obstacles and local hydraulic loading affected the mitigation system.
Key Innovation: Continuum simulations use erosion as a constrained proxy for breach location and show that an abandoned building focuses flow toward the gabion barrier, while explicitly retaining uncertainty in rheological calibration.
11. A systematic review of model evaluation practices in multi-hazard vulnerability modeling
Core Problem: Inconsistent evaluation practices make multi-hazard vulnerability models difficult to compare and weaken confidence in their use for decisions.
Key Innovation: A structured review of 67 studies distinguishes verification from validation and finds decision-oriented or behaviour-inclusive validation in fewer than 15% of papers, supporting combined scenario, real-event and expert-informed tests.
12. Fifty Years of Shore Platform Erosion Monitoring at Kaikōura Peninsula, South Island, Aotearoa-New Zealand
Core Problem: Statistically significant seasonal differences in erosion with higher rates in summer than winter observed prior to the earthquake remained pronounced after 2016, but the subaerial processes responsible have likely changed.
Key Innovation: Since 1973, a network of micro-erosion metre (MEM) stations has been used to measure downwearing across mudstone and limestone platforms to explore processes responsible for and the rate of development of the shore platforms.
13. Atmospheric and Oceanic Influences on Seasonal Atlantic Tropical Cyclone Landfall Probability
Core Problem: However, the percentage of TCs that make landfall has varied widely year‐to‐year since 1980 (13%–75%) and is weakly correlated with the number of landfalling TCs.
Key Innovation: In this study, we investigated the relationship between the percentage of TCs that made landfall over the Americas and Caribbean Islands and ENSO and AMM using observations from 1980 to 2025.
14. Investigation of the rip current risks on the headland beaches with bathymetric anomalies
Core Problem: Rip currents are widespread natural hazards on headland beaches.
Key Innovation: Employing a high-order Boussinesq model, this work constructs the rip risk index (RRI) integrating Lagrangian rip flow intensity, hazardous coverage and 30-min drifter exit rate, aiming to clarify the rip risk formation mechanisms on the headland beach with bathymetric anomalies.
15. Overview of ParallelClim-WestUS-Daily - high-resolution observed and counterfactual daily climate data for the western United States, 1951-2025
Core Problem: Extreme climate events and trends commonly elicit curiosity as to the role of anthropogenic climate change.
Key Innovation: Here we introduce ParallelClim-WestUS-Daily, a new dataset of observed daily, high-resolution (~4 km) gridded climate data for the western US from 1951-2025 as well as a parallel, counterfactual realization of the observed sequence of climate but excluding anthropogenic changes in background mean climate or daily variances.
16. SNOWstorm (v1.0) - a deep-learning based model for near-surface winds and drifting snow in mountain environments
Core Problem: Wind-driven redistribution of snow and the resulting heterogeneous snow accumulation poses a major uncertainty in mountain hydrology and distributed glacier mass balance models as it is often neglected.
Key Innovation: To bridge this gap, we introduce SNOWstorm - the snow drift sublimation and transport model.
17. Detection of compound and seesaw hydrometeorological extremes in New Zealand: A copula-based approach
Core Problem: These results highlight the advances that a copula approach can provide in terms of better understanding the magnitude-frequency characteristics of compound and seesaw events, as well as their drivers - critically important for managing the impacts of these events, especially in the context of climate change.
Key Innovation: Standardised indices are constructed for soil moisture, temperature and precipitation using ERA5-Land for 1950-2021.
18. Four deep-sea technologies that could help detect deadly earthquakes and tsunamis
Core Problem: Direct observations remain exceptionally sparse across the deep ocean, limiting the detection and physical interpretation of offshore earthquakes and their tsunamis.
Key Innovation: This Nature News Explainer compares four emerging observation routes, including drilling platforms, sea-floor instruments and repurposed telecommunications cables, and relates their distinct measurements to faster detection and improved source constraints.
19. Tailored forecasting from short time series via meta-learning
Core Problem: Machine learning models can effectively forecast dynamical systems from time-series data, but they typically require large amounts of past data, making forecasting particularly challenging for systems with limited history.
Key Innovation: To overcome this, we introduce meta-learning for tailored forecasting using related time series (METAFORS), which generalizes knowledge across systems to enable forecasting in data-limited scenarios.
20. Geographical reaction profiles of flood occurrence: insights from statistical, machine-learning, and ensemble models
Core Problem: Flooding remains one of the most destructive natural hazards that threaten human lives, infrastructure, and sustainable land management.
Key Innovation: Despite advances in machine learning, limited research has systematically integrated multiple metaheuristic optimization algorithms with artificial neural networks (ANNs) to improve prediction accuracy and robustness, especially in less studied regions such as Golestan Province, Iran.
21. SAG-DeepLabV3+: An Enhanced Deep Learning Model for High-Precision Detection of Mining-Induced Ground Fissures from UAV Imagery
Core Problem: To address the challenges of low detection accuracy and weak generalization in identifying mining-induced ground fissures from UAV imagery, caused by their slender and discontinuous morphology, complex background clutter, and multi-scale surface features, this paper proposes an enhanced deep semantic segmentation model, SAG-DeepLabV3+ (with Spatial Vision Transformer, Attention mechanisms, and Adaptive Gated Fusion).
Key Innovation: Specifically, to enhance global context modeling and fine boundary delineation, we introduce a Spatial Vision Transformer (SVT) branch within the Atrous Spatial Pyramid Pooling (ASPP) module.
22. Contribution Disparity and Key Factor Screening of Lightning Identification and Nowcasting with Multi-Source Data
Core Problem: Correlated radar and satellite channels can increase lightning-nowcasting cost without adding independent predictive information.
Key Innovation: A PFI, SHAP, collinearity and wrapper-search workflow reduces 28 inputs to three channels for identification and four for 0-1 h nowcasting, with independent-test CSI improving from 0.415 to 0.433 and from 0.274 to 0.284, respectively.
23. Impact of soft-storey irregularity on the seismic safety of reinforced concrete buildings
Core Problem: The results showed that fragility curves generated based on inter-storey drifts yielded more sensitive results than those generated based on damage at the critical storey.
Key Innovation: The study evaluates the effect of soft-storey irregularity on the seismic fragility of reinforced concrete buildings by comparing fragility curves based on inter-storey drift and element damage.
24. Region-specific ground-motion model for fourier amplitude spectra and its interfrequency correlation of epsilon for the Sichuan-Yunnan region, China
Core Problem: Fourier amplitude spectrum ground-motion models (FAS-GMMs) provide an alternative framework for characterizing ground motions in the frequency domain; however, regional FAS-GMMs for China remain limited despite the high seismicity of the region.
Key Innovation: Based on the proposed ground-motion model, a region-specific interfrequency correlation model is developed, resulting in improved agreement with empirical correlations, particularly at frequencies above 1 Hz.
25. Mechanical and seismic performance analysis of the Tang Dynasty palace-style timber frame: multi-model comparative study and critical component identification
Core Problem: When stress is applied solely on the single-room model, it exhibits limited structural independence and diminished hysteretic behavior, albeit with a slightly enhanced energy dissipation capacity.
Key Innovation: It is distinguished by its unique Puzuo layer construction, clear load-transfer path, and profound cultural essence.
26. Effect of saddle-cable slippage on seismic performance of a multitower suspension bridge
Core Problem: The study investigated the effects of saddle-cable slippage on the seismic performance of a three-tower suspension bridge.
Key Innovation: The parametric analysis shows that reducing the stiffness of the middle tower increases the displacement at the tower top and the saddle-cable slippage.
27. Study on the fragility assessment of existing reinforced concrete buildings considering ageing processes
Core Problem: A common method for estimating building damage is the use of fragility functions.
Key Innovation: A moderate corrosion attack can result in a 64% reduction in the overall shear base and an increase in fragility of up to 33% compared to a new building from 2023.
28. Ambient vibration analysis contribution to the subsurface geological model definition for the Sulmona intermontane basin: (Central Italy)
Core Problem: Defining the deep geometry of sedimentary basins characterized by strong lateral heterogeneity and limited subsurface constraints remains a key challenge in engineering geology and seismic microzonation.
Key Innovation: The study presents an integrated methodological framework based on ambient vibration techniques to constrain the subsurface geological model of the Sulmona intermontane basin (Central Italy).
29. Pre-earthquake seismic resilience enhancement strategy of substation systems based on Bayesian Network and multi-objective optimization
Core Problem: To improve the efficiency of substation resilience assessment and formulate optimal pre-earthquake performance improvement strategies at the system level, this study proposes a novel framework for enhancing substation resilience.
Key Innovation: First, a novel MCBN resilience assessment method integrating the equipment repair process Markov chain and the system Bayesian Network is developed.
30. Rockburst prediction based on empirical criteria-guided machine learning methods
Core Problem: Underground engineering projects are frequently threatened by rockbursts.
Key Innovation: In this study, an empirical criteria-guided (ECG) module is proposed to address the shortcomings of traditional ML for rockburst prediction.
31. Decoding flood hazard responses to reticular river network changes using interpretable machine learning
Core Problem: The structure of river networks critically governs flood propagation and hazard distribution, especially in reticular networks of low-lying delta plains where natural and engineered channels interweave.
Key Innovation: Among topology‑based rehabilitation strategies, targeted dredging (capacity expansion) reduced flood hazards most effectively (mean improvement 7.95%), outperforming new channel construction (4.37%) and sluice‑gate optimization (1.74%).
32. MTMD optimization for seismic vibration mitigation in high-rise structures, considering soil-structure interaction effect via a novel parallel GWO-DE algorithm
Core Problem: Tuned Mass Dampers (TMDs) designed under fixed-base assumptions neglect critical Soil-Structure Interaction (SSI) effects.
Key Innovation: These findings indicate that the parallel GWO-DE algorithm successfully handles SSI-integrated MTMD optimization across multiple soil types.
33. A damage-informed three-stage framework for the seismic rehabilitation of earthquake-damaged buildings
Core Problem: Many buildings damaged during earthquakes remain in service because immediate demolition is often impractical.
Key Innovation: Results show that the proposed strategy reduces peak inter-story drifts by up to 45% and prevents extensive damage under subsequent seismic events.
34. Shaking table tests and fragility analysis of an ancient timber structure subjected to mainshock-aftershock sequences
Core Problem: To investigate the seismic performance and damage mechanisms of ancient timber structures under mainshock-aftershock (MS-AS) ground motion sequences, a simplified 1/5-scale model was designed, fabricated, and tested on a shaking table.
Key Innovation: Under low-to-moderate mainshock intensity, a strong aftershock may amplify the acceleration, displacement, and inter-story drift responses, whereas under higher mainshock intensity, the aftershock effect is reduced because of the development of a high-damping and high-energy-dissipation structural state.
35. Impact of changing hydroclimate on bedload yield
Core Problem: Advances in hydroclimate change predictions in rivers include regional estimates of changes in annual flow volumes and storm peakedness.
Key Innovation: The model (MFACC, Magnitude -Frequency Analysis for Climate Change) provides a rapid screening tool for estimating ‘first order ’fluvial geomorphology risks attributable to changes in hydroclimate that could be subsequently combined with estimates of sediment supply changes and mediation by channel morphology adjustment.
36. Paired U-Series and (U-Th)/He Analyses of Pleistocene Hematite Fault Surfaces Reveal Interplay Between Groundwater U-Series Disequilibrium and Open-System Behavior
Core Problem: Young, fluid-rich fault surfaces are difficult to date because groundwater U-series disequilibrium and post-crystallization isotope loss can bias conventional (U-Th)/He ages.
Key Innovation: Paired U-series and (U-Th)/He measurements from 28 hematite aliquots on southern San Andreas fault surfaces constrain approximately 700-400 ka mineralization and quantify alpha-recoil loss as a source of open-system behaviour.
37. Capturing Runoff-Driven Seasonal Velocity Variations of Fast-Flowing Outlet Glaciers in Coupled Ice-Hydrology Simulations
Core Problem: The seasonal evolution of the subglacial drainage system is a critical control on velocity variations of Greenland marine‐terminating glaciers.
Key Innovation: Together, these modifications promote channelization beneath fast‐flowing glaciers, which has previously been elusive.
38. Precipitation Over the Contiguous United States Is Coming From Farther Away Than in the Past
Core Problem: Atmospheric moisture transport is an essential component of the Earth's water cycle, carrying water vapor thousands of miles and controlling which regions receive precipitation and which do not.
Key Innovation: Here, we employ atmospheric moisture tracking, applied to 35 years of reanalysis data to investigate decadal trends in atmospheric moisture transport for moisture precipitating in various watershed regions within the contiguous United States of America (CONUS).
39. Discrepancy in Satellite Altimetry Products Hinders Robust Retrieval of GIA Signals From Bedrock GNSS Data in Greenland
Core Problem: This elastic correction depends on ice‐surface elevation changes derived from satellite altimetry and firn corrections, which differ substantially across available products, yet these uncertainties are rarely accounted for in GNSS‐based GIA inferences.
Key Innovation: Here we quantify the sensitivity of elastic VLM at 53 Greenland stations to six contemporary ice‐loading products derived from altimetry and firn‐height corrections provided by three processing centers.
40. Certified Domain Consistency for Multi-Domain Retrieval: Label-Free Per-Domain Contamination Control with Conformal Risk Guarantees
Core Problem: Retrieval over corpora that mix several domains often returns relevant but wrong-domain evidence that ranking metrics miss and that conformal risk control bounds only marginally, under-covering the worst domains.
Key Innovation: The study introduces C3R, a drop-in control layer that, from an inferred domain posterior and no query-time label, certifies a per-domain contamination budget where feasible and otherwise abstains rather than silently violating; on the hardest domains it guarantees a reduction, not a tight bound.
41. Beyond scalar losses: calibrating segmentation models via gradient vector field surgery
Core Problem: However, models trained using region-based loss functions are notoriously miscalibrated and typically yield over-confident predictions.
Key Innovation: Region-based loss functions, such as the Dice loss, have established themselves as the de facto standard for highly classand region-imbalanced segmentation tasks.
42. Interleaved Noise Injection Improves Clean, Corrupted, and OOD Performance
Core Problem: Noise injection is a well-known technique in stochastic optimization.
Key Innovation: But the interleaved nature of our proposed schedule produces superior results even for the optimization objective: mixing phases of noisy data permits the optimizer to escape local minima and increase exploration without the risk of catastrophically forgetting the important features from the clean data.
43. Reinforcing Egocentric Spatial Perception in Multimodal Large Language Models via Ego Scene Augmentation
Core Problem: However, existing MLLMs struggle to perform effective spatial reasoning in complex egocentric scenes due to their limited spatial perception capabilities.
Key Innovation: Our proposed ESA framework presents significant performance improvement on the EgoTextVQA benchmark.
44. Multimodality as Supervision: Self-Supervised Specialization to the Test Environment via Multimodality
Core Problem: This enables an alternative scenario where the need for external Internet-scale datasets for pre-training models is reduced.
Key Innovation: This presents an opportunity to apply cross-modal learning to the multimodal data sensed by these devices to learn representations.
45. Evaluating Epistemic Uncertainty: Beyond OOD Detection and Active Learning
Core Problem: Current evaluation of epistemic uncertainty relies on tasks such as out-ofdistribution detection and active learning.
Key Innovation: The authors instead propose to evaluate the achievable risk, regret, coverage surface of the decomposition as a diagnostic for joint disentanglement and utility.
46. RoGS: Adaptive Meshgrid Gaussian for Large-Scale Road Surface Mapping
Core Problem: Recent mesh-based road surface reconstruction methods have shown promising results, but they still suffer from limited reconstruction quality and high optimization cost, especially in large-scale driving scenarios.
Key Innovation: To further improve representation efficiency and structural fidelity, we introduce a road-structure-aware adaptive meshgrid strategy, which allocates denser Gaussian surfels to geometrically or semantically complex regions, such as lane markings, road boundaries, and height discontinuities, while maintaining a compact representation in flat road areas.
47. An Introduction to Sparse Identification of Nonlinear Dynamics for Engineering Applications
Core Problem: Many engineering problems involve phenomena whose governing equations are poorly characterized or only partially known.
Key Innovation: This tutorial introduces the SINDy method and progressively builds toward its main extensions, from noise-robust weak-form and ensembling-based variants to constrained and parametrizable formulations.
48. RTS Smoother-Guided Learning of Physics-Based Neural Differential Models
Core Problem: Ordinary differential equations (ODEs) are widely used to model dynamical systems in physics, biology, neuroscience, and physiology, but in many applications some equations of the dynamics are unknown and only a subset of the state variables are measured.
Key Innovation: Across these settings, the proposed method learns missing ODE components from incomplete measurements while exploiting and retaining interpretable mechanistic structure and improving latent-state reconstruction and long-horizon prediction.
49. Subgrid-Scale Parameterization in Burgers' Equation Using Structure-Preserving Neural Networks and Entropy Variables
Core Problem: The authors present a machine learning approach for developing subgrid-scale (SGS) parametrizations in coarse simulations of partial differential equations.
Key Innovation: Furthermore, we show that our approach is robust and applicable to parameters outside the training regime.
50. A Weak Penalty Neural ODE for Learning Chaotic Dynamics from Noisy Time Series
Core Problem: A significant challenge arises from noise-corrupted measurements, which severely degrade the performance of data-driven models.
Key Innovation: In chaotic dynamical systems, where small initial errors amplify exponentially, it is particularly difficult to develop a model from noisy data that achieves short-term accuracy while preserving long-term invariant properties.
51. From physical surfaces to human-centric heat stress: LST and UTCI heat mapping reveals nonlinear effects of urban morphology
Core Problem: However, most planning-oriented studies rely on land surface temperature (LST), and whether LST adequately represents human heat exposure and how it differs from physiologically relevant heat stress remains insufficiently examined.
Key Innovation: Here, using Landsat-retrieved 30-m LST and GPU-accelerated 1-m universal thermal climate index (UTCI) in Singapore, this study establishes a comprehensive "Modeling-Comparing-Assessing" framework to systematically evaluate the spatial and mechanistic differences between these two metrics.
52. Skewness-Robust Causal Discovery in Location-Scale Noise Models
Core Problem: Crucially, we need to be able to distinguish cause $X$ from effect $Y$ in bivariate models, that is, distinguish the two graphs $X \to Y$ and $Y \to X$.
Key Innovation: To approach this limitation, we propose SkewD, a likelihood-based algorithm for bivariate causal discovery under LSNMs with skewed noise distributions.
53. Prediction formulae for wave pressures in permeable breakwaters protecting land reclamations
Core Problem: Reliable prediction of internal wave pressures is critical for designing rubble mound breakwaters that protect land reclamations, primarily to prevent the loss of fine fill material through the breakwater core, a hydraulic process known as suffusion.
Key Innovation: These formulae provide a significant improvement for engineers designing filters and assessing stability at the breakwater/fill interface.
54. An unstructured wave model on a mountainous coast: Wave propagation, coastal sheltering and wave growth in narrow fetch
Core Problem: While the limited availability of measurements near the coast increases the importance of numerical models, it simultaneously makes their in-depth validation challenging.
Key Innovation: The authors show that the current operational coastal wave models have limitations, and present a new unstructured implementation of WAVEWATCH III for about 450 km of the Norwegian coast with a resolution up to about 250 m.
55. PeatCover: Towards a peat mapping at 90 m using satellite remote sensing and a priori inventories
Core Problem: Peatlands cover just 3-4 % of Earth's land surface, yet store an estimated 600-700 Pg carbon (PgC), approximately one-third of Earth's soil carbon, making them critical regulators of the global carbon cycle.
Key Innovation: These limitations hinder accurate assessments of peatland-climate feedbacks, carbon budgets, policy development and restoration efforts.
56. TundraFlux: a database of ecosystem respiration with biotic and abiotic metadata from Arctic and alpine tundra warming experiments
Core Problem: Empirical in-situ measurements of ecosystem carbon dioxide respiration ( R eco ) in high-latitude ecosystems remain limited, yet they are essential for understanding how tundra carbon cycling responds to climate warming across different environmental contexts and for reducing uncertainties in upscaled carbon budgets and carbon-climate feedbacks.
Key Innovation: Here, we present the TundraFlux Database, which to date is the most comprehensive synthesis of tundra R eco responses to experimental warming.
57. The next generation sea-ice model neXtSIM, version 2
Core Problem: Sea ice is a key component of the climate system, and sea-ice models are required to realistically simulate climate, ocean, or atmosphere in general circulation models at high latitudes.
Key Innovation: In this paper, we present the latest version of the next-generation sea-ice model, neXtSIM, which has been developed with a particular focus on ice dynamics and motion.
58. JCM v1.1: a differentiable, intermediate-complexity atmospheric model
Core Problem: However, the training and validation of hybrid methods in traditional models remain difficult due to the absence of gradients and the complexity of legacy code.
Key Innovation: In this paper we present version 1.1 of the JAX Circulation Model (JCM).
59. Capturing the extremes: a quasi-comonotonicity-based algorithm for disaggregating daily to hourly rainfall
Core Problem: The method combines a Fréchet-Hoeffding upper bound copula to constrain sub-daily maxima with a K-nearest neighbours approach and an iterative adjustment algorithm to ensure consistency with daily totals and multiple sub-daily constraints.
Key Innovation: Disaggregating daily precipitation data into hourly time scales is crucial for hydrological modelling, urban drainage design, and extreme rainfall risk assessment.
60. Climate and landscape jointly control European streamflow behaviour
Core Problem: Other HRTs remain difficult to distinguish, as these catchments represent more transitional conditions with increasingly overlapping characteristics between HRTs.
Key Innovation: In this study, we use an unprecedented sample of more than 7000 catchments in Europe from the EStreams dataset to identify and map functionally similar catchments, together with their spatially variable climate and landscape controls.
61. High-resolution imaging of a sediment core reconstructs past monsoon seasonality
Core Problem: Assessing the response of the South Asian monsoon to rapid climate change requires understanding its seasonal driving mechanisms under changing climate conditions.
Key Innovation: Now, advanced geochemical imaging of an Arabian Sea sediment core reveals distinct seasonal responses to global and high-latitude climate forcing in the South Asian monsoon variability during the last deglaciation.
62. Spatiotemporal Dynamics of Glacier Changes in Tibet from 1990 to 2025
Core Problem: Glaciers serve as sensitive indicators of climate change, yet the automated extraction of glacier boundaries over large areas and long time periods remains challenging.
Key Innovation: To resolve this issue, we proposed a remote sensing indicator system based on the Google Earth Engine (GEE) platform (spectral, textural, topographic features and band differences) and applied a random forest (RF) algorithm to establish an automatic classification method.
63. Nonlinear Responses and Spatial Heterogeneity of Net Ecosystem Productivity to Extreme Weather Events in Central Asia
Core Problem: The increasing frequency and intensity of extreme weather are profoundly affecting the ecological carbon cycle in arid regions, yet there remains a lack of quantitative understanding regarding the nonlinear response of net ecosystem productivity (NEP) in Central Asia to changes in extreme weather conditions.
Key Innovation: The study focused on the five Central Asian countries and China’s Xinjiang region, where NEP was estimated using MODIS Net Primary Productivity and daily meteorological data, and 12 extreme climate indices (ECIs) were constructed.
64. Spatial Variability of Undrained Shear Strength in Champlain Sea Clay in the Ottawa Region
Core Problem: Sensitivity is a critical factor in assessing the risk of slope failure in sensitive clays.
Key Innovation: The remolded S u obtained from VST is also used to evaluate soil sensitivity, and S t that represents the loss of S u upon remolding.
65. Nonlinear permeability evolution in clay-rich fault zones under cyclic hydraulic gradients: Insights from in-situ experiments
Core Problem: Compressed faults are critical geological structures in dam engineering, where their permeability evolution directly affects stability under hydraulic conditions.
Key Innovation: The study investigates the permeability behavior of compressed fault zones subjected to cyclic hydraulic gradients.
66. Mega-pockmarks and fluid-escape systems in the Northern Tyrrhenian Sea: Morphometry, classification, and tectono-oceanographic controls
Core Problem: The authors document, for the first time, the occurrence of twelve mega-pockmarks in the Etruschi Seamount and in the surrounding sectors of the northern Tyrrhenian Basin (central Mediterranean Sea).
Key Innovation: They stand out from 230 mapped depressions, in having the dimensional criteria representative of mega-pockmarks: major axes exceeding 1000 m, internal depths greater than 100 m, and volumes in the order of ~108 m3.
67. A Sparse Bayesian Extreme Learning Machine with Input Uncertainty for Probabilistic Prediction of Lateral Excavation Displacements
Core Problem: Automated inclinometers enable near-continuous monitoring of lateral displacements in deep excavations, but cost constraints often result in sparse sensor deployment, introducing significant uncertainty in reconstructing complete deformation profiles.
Key Innovation: Systematic comparisons with six baseline models confirm that incorporating input uncertainty consistently improves both accuracy and uncertainty quantification.
68. Probability of detection for airborne sensing of methane plumes using long wavelength infrared hyperspectral imaging
Core Problem: Hyperspectral imaging over the long wavelength infrared (LWIR) or “thermal” spectrum is a promising means to both detect and quantify methane (CH4) emissions, particularly those arising from upstream oil and gas activities.
Key Innovation: The study presents the outcomes of three measurement campaigns conducted under conditions relevant to the Canadian oil and gas industry, including probability of detection (POD) curves that may be used to identify the likelihood of identifying an emission of a given rate under prescribed environmental conditions.
69. Unmasking shoreline responses and land-use intensity on oceanic islands through a 26-year analysis from Gran Canaria
Core Problem: Critically, this evidence establishes an empirical foundation for developing differentiated coastal management strategies for beaches.
Key Innovation: The study provides an island-scale case study demonstrating how beach color and sediment composition have influenced urban and tourism development while affecting shoreline stability.
70. Ice-channel detection using deep networks in visible and infrared from shipborne camera
Core Problem: In Arctic navigation, the autonomous detection of ice-channels is critical for operational safety.
Key Innovation: The study establishes an performance baseline for shipborne ice-channel detection using early visible-thermal infrared fusion.
71. A coupled moisture-solute-heat transfer model for saline frozen soil considering non-equilibrium phase change kinetics
Core Problem: Saline frozen soils are extensively encountered in cold-region engineering and Artificial Ground Freezing (AGF) applications, posing substantial challenges to the stability and safety of geotechnical infrastructure.
Key Innovation: This assumption neglects the kinetic resistance during the phase transition, rendering the models incapable of describing the hysteresis characteristics of unfrozen water under sharp temperature gradients, which subsequently leads to biased predictions of freezing curtain development in engineering designs.
72. Effects of soil fabric and particle strength on cyclic resistance ratio and shear moduli of the volcanic soils
Core Problem: These findings suggest that particle crushability and deformation characteristics may play a critical role in the cyclic undrained behavior of volcanic soils.
Key Innovation: Furthermore, the authors investigated the relationship between the change ratios of G 0 and CRR due to the variations in the soil fabric effect.
73. Modeling the Influence of Macro-Roughness on Gravel-Bed River Morphodynamics
Core Problem: Reach-scale theory has not resolved how logs, boulders and other large roughness elements alter gravel-bed channel geometry after natural or managed changes in obstruction density.
Key Innovation: A process-based model and Washington State case study identify widening, coupled widening-aggradation and overbank-flow-driven channel filling as three nonlinear response regimes to increasing macro-roughness.
74. Position: Explainability Research Must Prioritize Foundations over Ad-hoc Methods
Core Problem: This gap reflects foundational shortcomings: research has not yet established methodologies for integrating explanations into end-to-end, human-in-the-loop systems.
Key Innovation: The authors conclude by outlining a practical checklist designed to shift XAI toward a more human-centered, action-oriented paradigm.
75. Local Additive Feature Attribution: A Mathematical Taxonomy and Reporting Checklist
Core Problem: Feature-attribution methods are central to explainable artificial intelligence.
Key Innovation: This survey proposes a common framework for local additive feature attribution.
76. Towards a Unified Multidimensional Explainability Metric: Evaluating Trustworthiness in AI Models
Core Problem: In this paper, we present a comprehensive framework for assessing the explainability of various XAI methods, such as LIME and SHAP, across multiple datasets and machine learning models, with the ultimate goal of creating a unified multidimensional explainability score.
Key Innovation: The authors leverage benchmarking experiments to systematically evaluate these aspects and use the insights gained to construct an offline knowledge base.
77. G$^2$SR: Geometric Methods for Fast and Memory-Efficient Gaussian-based Surface Reconstruction
Core Problem: Few-view surface reconstruction recovers the visible surfaces of a scene from a few posed RGB images, providing the 3D models that robots need to explore and interact online.
Key Innovation: 3D Gaussian Splatting (3DGS) offers a high-fidelity scene representation, but building it from a few views is ill-posed, as many distinct surfaces reproduce the same images, making traditional photometric methods prone to "floater" artifacts.
78. Immediate 3D Gaussian Splat Reconstruction of Unordered Input with Global Consistency
Core Problem: 3D Gaussian Splatting (3DGS) has become the method of choice for reconstructing and real-time rendering of captured scenes.
Key Innovation: The authors first introduce a method for fast matching in out-of-order sequences, by repurposing visual place recognition models and a covisibility graph, and provide an efficient way to find highly connected keyframes, improving quality even for ordered sequences.
79. Hough-SIFT: Robust Image Registration for Linear Structures via Hough Space
Core Problem: Scale-Invariant Feature Transform (SIFT), a widely used local keypoint detector and descriptor, typically provides accurate registration; however, it often fails in scenes with strong linear structures (e.g., shutters), where local features become ambiguous.
Key Innovation: The authors propose Hough-SIFT, a robust registration method that performs SIFT descriptor matching in Hough space.
80. ESAR: Event-Based Synthetic Aperture Reconstruction
Core Problem: This decomposition exposes a synthetic-aperture structure: under near-nadir motion, successive projections are approximately shifted views of a common scene, while the composite operator $AP$ remains ill-conditioned because it combines spatial averaging with temporal differencing.
Key Innovation: Instead of reconstructing a latent pixel-time volume $v \in \mathbb{R}^{N_pN_t}$, we impose the geometric relation $v=P\theta$, where $P$ maps the fixed scene into motion-dependent latent views.
81. When Pretty Isn't Useful: Investigating Why Modern Text-to-Image Models Fail as Reliable Training Data Generators
Core Problem: Overall, our findings challenge a growing assumption in vision research, namely that progress in generative realism implies progress in data realism.
Key Innovation: The authors generate large-scale synthetic datasets using state-of-the-art T2I models released between 2022 and 2025, train standard classifiers solely on this synthetic data, and evaluate them on real test data.
82. Escaping Model Collapse via Synthetic Data Verification: Near-term Improvements and Long-term Convergence
Core Problem: However, recent studies raise key concerns that iteratively retraining a generative model on its self-generated synthetic data may keep deteriorating model performance, a phenomenon often coined model collapse.
Key Innovation: In this paper, we investigate ways to modify the synthetic retraining process to avoid model collapse, and even possibly help reverse the trend from collapse to improvement.
83. Hydration, temperature, and loading paths effect on mechanical and stress relaxation behavior of montmorillonite clay: an MD study
Core Problem: The mechanical behavior of clay under varying conditions is pivotal for evaluating the stability of deep geological formations, yet its underlying nanoscale mechanisms remain elusive.
Key Innovation: The study employs molecular dynamics simulations to investigate the elastic properties, dynamic behavior, and mechanical responses of hydrated montmorillonite across a range of hydration levels (0%-40%), temperature (300-370 K), and loading paths (compression, tension, shear).
84. Sampling disturbance effects on deformation and strength properties of Coode Island Silt
Core Problem: Creep tests are particularly important due to limited research on sample disturbance effects on creep deformation, as CIS is prone to significant creep.
Key Innovation: The study investigates sampling disturbance effects on the deformation and strength properties of Coode Island Silt (CIS), a problematic soft soil in Melbourne, Australia.
85. Oscillatory rheological properties and particle aggregation of marine clay with different pore-water salinities
Core Problem: The mechanism of how salinity affects the mechanical behavior of marine clays remains an unresolved issue.
Key Innovation: The study integrated oscillatory rheology with aggregation dynamics theory to elucidate the physical mechanisms governing marine clay behavior under varying pore-water salinities.
86. Microscopic mechanisms of particle segregation during hindered settling of polydisperse sand-mud mixtures using CFD-DEM
Core Problem: However, the underlying microscopic mechanisms remain insufficiently understood.
Key Innovation: The study employes coupled computational fluid dynamics and discrete element method (CFD-DEM) approach to investigate microscopic segregation mechanisms during the hindered settling of polydisperse sand-mud mixtures, with emphasis on the effects of sand content and mud concentration.
87. Salinity effect on the drying-induced mineralogical and microstructural evolution of lime-treated silty soil
Core Problem: In this study, the mineralogical and microstructural variations of lime-treated low-plasticity silty soil were investigated with emphasis on the combined effects of salinity and drying.
Key Innovation: Physicochemical analyses revealed that the carbonation rate of lime-treated soils decreased with increasing salinity.
88. Synchrotron small angle X-ray scattering of nanoscale porosity evolution in kaolinite and montmorillonite
Core Problem: A comprehensive understanding of nanoscale microstructural evolution (2-100 nm) is essential for interpreting the hydro-mechanical behavior of clayey soils, yet its quantitative characterization remains challenging.
Key Innovation: The study employs synchrotron small-angle X-ray scattering (SAXS) to investigate the wetting-induced microstructural evolution of two end-member clays viz Texas montmorillonite and Georgia kaolinite under high-suction conditions.
89. Numerical investigation of local scour mitigation using solidified soil for a jacket-large-diameter cylinder combined structure
Core Problem: However, the influence mechanism of large-diameter cabin cylinders on local scour around jacket foundations remains unclear, leading to a lack of scientific guidance for scour protection design.
Key Innovation: In this study, a computational fluid dynamics (CFD) method is employed to systematically investigate the local scour evolution of a jacket-large-diameter cylinder combined structure under combined wave-current conditions.
90. Detection of Submerged Heritage: Application of an Unmanned Surface Vehicle Equipped With a High-Resolution Multibeam Echosounder
Core Problem: The recent availability of unmanned surface vehicles (USVs) equipped with precise positioning systems and multibeam echosounders enables high-resolution bathymetric surveys, yet their application in submerged heritage research remains limited.
Key Innovation: The study evaluates the feasibility and accuracy of using these tools in the Belesar reservoir (northwest of the Iberian Peninsula), under which lies the ancient village of Portomarín.
91. TSGE-Net: A Topology-Spectrum Guided Enhancement Network for SAR Target Classification
Core Problem: However, spatial-domain models struggle to preserve the geometric configuration of sparse and discontinuous SAR scattering centers.
Key Innovation: Frequency-domain analysis can reveal scattering variations that are less explicit in spatial features, but existing spatial-frequency aggregation often introduces frequency-domain responses without explicitly assessing their relevance to target-related scattering patterns, which may bring structure-irrelevant cues into the final representation.
92. A Signal Processing Method for Azimuth Multichannel SAR With Partial Channels Missing
Core Problem: However, this increases the likelihood of at least one channel failure.
Key Innovation: The azimuth multichannel synthetic aperture radar (SAR) system enhances spatial sampling by increasing channels, resolving the conflict between high resolution and wide mapping swath.
93. GAN-Based PAN-to-RGB Image Translation for True-Color 3-D Texture Generation Using GaoFen-7 Stereo Imagery
Core Problem: However, due to resource constraints, typically only one view is equipped with both high-resolution panchromatic (PAN) and multispectral sensors, whereas the other view carries only a PAN sensor, as exemplified by the GaoFen-7 satellite.
Key Innovation: The authors conducted experimental validation on Gaofen-7 satellite data, and the results demonstrated that the proposed algorithm effectively mitigates the issues of unsaturated, inaccurate, and distorted colors, achieving significant improvements in key metrics, such as Fréchet inception distance and $\boldsymbol {\Delta {CF}}$.
94. ChinaTCC30: An annual 30 m tree canopy cover dataset for China from the 1970s to 2024
Core Problem: However, accurate long-term national-scale TCC mapping remains challenging because strong spatial heterogeneity requires large and representative reference samples that are difficult to obtain consistently across space and time.
Key Innovation: Tree canopy cover (TCC) is a key biophysical indicator of forest structure and function, and long-term fine-resolution TCC data are essential for quantifying carbon cycling, monitoring forest succession, and supporting forest management under ongoing climate and land-use change.
95. BOWTIE: ship-based measurements of atmosphere and ocean within the moist tropical Atlantic
Core Problem: First, it examines the range and uncertainties of selected quantities measured by multiple instruments, including column-integrated water vapor, rain detection, surface ocean currents, and sea surface temperature.
Key Innovation: Second, it illustrates the diversity of sampled weather regimes through two representative cases: calm doldrum conditions and a gusty, precipitating convective state.
96. The Integrated Multi-proxy Paleoclimate Database for the Northeastern Tibetan Plateau
Core Problem: However, differences in temporal resolution, chronological depth, and proxy sensitivity make direct comparisons among these archives difficult.
Key Innovation: The authors present a paleoclimate database for the NETP that integrates these records within a unified framework.
97. Machine learning significantly improves the simulation of hourly-to-yearly scale cloud nuclei concentration and radiative forcing in polluted atmosphere
Core Problem: The accurate prediction of cloud condensation nuclei (CCN) number concentration ( N CCN ) on a large spatiotemporal scale is challenging but critical to evaluate the aerosol cloud interaction effect.
Key Innovation: Combining multi-source dataset and the N CCN simulated by the Weather Research and Forecasting coupled with Chemistry (WRF-Chem) model, we have developed a Random Forest Regression method (RFRM) model which achieves well prediction of hourly-to-yearly scale N CCN at typical supersaturations in polluted North China Plain (NCP).
98. CMIP7 data request: ocean and sea ice priorities and opportunities
Core Problem: Recent decades have seen rapid advances in Earth System Models (ESMs), but limitations remain in simulating and comparing key oceanic and cryospheric processes across models.
Key Innovation: To address these opportunities, we request new high-frequency and depth-integrated variables, support improved diagnostics of ocean heat uptake, sea ice processes, and model-observation comparison, and build on lessons from CMIP6.
99. A systematic evaluation of 15 actual evapotranspiration formulations within conceptual hydrological models
Core Problem: However, even this equation showed limitations in reproducing observed AET, suggesting persistent issues across commonly used formulations.
Key Innovation: To investigate AET representation within conceptual hydrological models, we systematically tested 15 evapotranspiration (ET) equations that convert potential evapotranspiration (PET) and soil moisture to AET.
100. A deep learning model for predicting daily PM2.5 concentration in response to emission reduction
Core Problem: Air pollution remains a leading global health threat, with fine particulate matters (PM 2.5 ) causing millions of premature deaths annually.
Key Innovation: Here, we present CleanAir, a deep learning model that simulates daily PM 2.5 concentration and its chemical composition in response to emission reductions at a 36-kilometer horizontal resolution.
101. Station-Level Gap Filling of TROPOMI NO2 via Physics-Informed Shadow Manifold Reconstruction
Core Problem: Cloud and quality screening removes approximately 65% of daily TROPOMI tropospheric NO2 pixels, creating structured data gaps that coincide with meteorological conditions driving pollution extremes.
Key Innovation: Here we present a physically informed framework that treats urban NO2 as a forced nonlinear dynamical system and reconstructs missing satellite observations through geometric navigation on a shadow manifold rather than statistical interpolation.
102. Layer Assignment for Long-Term LiDAR Map Maintenance Using Geometric and Semantic Evidence
Core Problem: Most map-cleaning pipelines, however, output a single retained map, forcing transient observed-dynamic artifacts and movable but currently stationary scene content into the same keep/remove decision.
Key Innovation: Collapsing the output to a single retained map also improves the balance between static and PD retention relative to single-layer cleaning baselines.
103. Modified Freeman−Durden Decomposition and Deorientation Non-Negative Eigenvalue Decomposition for Multi-Look Polarimetric SAR Data
Core Problem: However, with advancements in model-based incoherent polarimetric decomposition techniques, their original algorithms have certain aspects that can be modified to enhance their decomposition performance.
Key Innovation: Therefore, two improved incoherent polarimetric decomposition algorithms, Modified Freeman−Durden Decomposition (MFDD) and Deorientation Non-Negative Eigenvalue Decomposition (DNNED), are proposed in this study.
104. RailDLA-Net: An Intensity-Aware Deep Local Aggregation Framework for Railway Point Cloud Semantic Segmentation
Core Problem: However, railway scenes contain elongated structures, locally similar objects, and severe class imbalance, while the discriminative value of LiDAR intensity is still insufficiently exploited.
Key Innovation: The proposed method achieved 96.89% OA, 91.92% mAcc, and 88.44% mIoU on Rail3D, and 92.36% OA, 91.84% mAcc, and 83.63% mIoU on WHU-Railway3D.
105. Snow Cover Classification Using High-Resolution Reconstructed FY-3E WindRAD Data
Core Problem: However, the relatively low spatial resolution of traditional scatterometer data limits their application in the fine-scale monitoring of snow cover distribution.
Key Innovation: To improve the spatial representation of snow cover and mitigate mixed-pixel effects in complex spring snowmelt scenarios, this study proposes an adaptive bilateral filtering scatterometer image reconstruction (SIR-ABF) algorithm based on the rotating fan-beam scanning characteristics of the FengYun-3E Wind Radar (FY-3E WindRAD).
106. Hyperspectral Imaging of Seagrass and Macroalgae Using Uncrewed Aerial and Surface Vehicles for Coastal Habitat Mapping
Core Problem: Seagrass and macroalgae are critical components of shallow coastal habitats undergoing fragmentation due to environmental and anthropogenic stressors.
Key Innovation: Mapping and monitoring their extent are essential for understanding and managing ecosystem changes.
107. Remote Sensing of Coastal Saline-Alkali Land in China: Progress in Identification, Ecological Restoration, and Sustainable Management
Core Problem: Finally, we discuss the major challenges that remain, including the limited transferability of salinity inversion models, the insufficient adaptability of remote sensing approaches under complex environmental conditions, and the weak integration of remote sensing technologies with field-scale management practices.
Key Innovation: First, we examine the development of remote sensing data sources, soil salinity inversion algorithms, and information extraction methods for saline-alkali land identification, with particular emphasis on the contributions of multi-source data fusion, machine learning, and deep learning techniques to improving mapping accuracy and reliability.
108. A Multi-Source Remote Sensing-Based AGB Synergistic Inversion Approach Integrating Terrain-Corrected Canopy Height and Forest-Type Heterogeneity
Core Problem: ICESat-2/ATLAS photon-counting LiDAR faces several challenges in regional-scale forest aboveground biomass (AGB) estimation.
Key Innovation: The framework combines structural, spectral, textural, topographic, and climatic information derived from multiple remote sensing datasets to improve biomass estimation accuracy and model robustness across different forest types.
109. Influence of biochar with varied pyrolysis temperatures on soil desiccation cracking under dry-wet cycles
Core Problem: Climate change can lead to soil moisture loss and shrinkage, forming crack networks that reduce soil strength and impermeability, hence posing serious risks to engineering safety.
Key Innovation: The study aims to investigate the influence of biochar on controlling soil cracking through laboratory tests, considering different soil dry densities, biochar pyrolysis temperatures & contents, and dry-wet cycles.
110. Grain size distributions of natural soils: are they sums of lognormal or Weibull functions?
Core Problem: A debate remains open: are the GSD data better described by the sum of lognormal (LN) functions or Weibull (WEI) functions?
Key Innovation: A statistically sound comparison is made using the free and recently developed modal decomposition method (MDM).
111. Evolution of strength and microstructure of dredged mud slurry treated by conventional cement solidification method and physicochemical combined method during wetting-drying cycles
Core Problem: However, the difference between the effects of wetting-drying (WD) cycles on PCCM-treated MS (PCCM-MS) and CCSM-treated MS (CCSM-MS) is not well understood.
Key Innovation: The physicochemical combined method (PCCM), as an advanced approach, can treat dredged mud slurry (MS) more efficiently and repurpose it as high-performance embankment fill compared to the conventional cement solidification method (CCSM).
112. Anisotropic shear stiffness of biotreated carbonate sand with fines
Core Problem: Small-strain behavior of soils, for example, shear modulus, is a key parameter in marine geological engineering, as it governs the deformation response of offshore foundations.
Key Innovation: A modified modulus prediction model based on the unified equivalent intergranular void ratio, incorporating the contributions of fines and CaCO 3 precipitation, is proposed.
113. Extra-Long DSM Columns for Post-Collapse Stabilization of a Tunnel Constructed in Weak Ground
Core Problem: The Karaman-Ulukışla Railway Project in southern Türkiye includes the Karaman T-2 (Çakmak) Tunnel, which traverses weak clay units with medium to stiff consistency, reaching thicknesses of up to 60 m.
Key Innovation: The study investigates the failure mechanisms and assesses the feasibility, applicability, and performance of an unconventional deep soil mixing (DSM) ground improvement method using 60 m-long columns-an approach rarely implemented in practice.
114. Transient geomorphic processes revealed by luminescence sensitivity of Yellow River terrace sediments
Core Problem: However, direct sediment-based evidence for transient fluvial dynamics remains limited.
Key Innovation: By comparing the deconvolution-based fast-component proportion (%BOSLF(dec)) in quartz from terrace sediments above and below these knickpoints, we find a recurring tendency for downstream ancient terrace sediments to yield lower OSL sensitivity than upstream counterparts in several age-matched comparisons, whereas modern riverbed sediments show the opposite downstream increase.
115. Tectonic-climatic forcing on landscape evolution of the middle Yellow River since the late Miocene: Insights from weathering geochemistry
Core Problem: The long-term evolution of continental rivers reflects the interplay between tectonics and climate, yet disentangling their respective roles remains challenging.
Key Innovation: Here, we present a high-resolution clay mineralogical and bulk geochemical record from fine-grained fluvial sediments (late Miocene to Quaternary) in the Jinshaan Gorge, middle Yellow River.
116. Terracettes in the hyperarid Atacama Desert - fog as a key driver of stepped hillslope evolution?
Core Problem: Terracettes are step-like microtopographic features consisting of repetitious benches and risers and are documented from hillslopes in a range of climates.
Key Innovation: Nevertheless, this paper investigates terracette-covered slopes located close to the Rio Loa canyon in the Coastal Cordillera of the central Atacama Desert in N Chile.
117. Morphology and sedimentary dynamics of the Jaguaribe submarine canyon, Brazilian equatorial margin
Core Problem: In deep-marine environments, where direct observation is limited, high-resolution bathymetric surveys and acoustic backscatter data allow the identification of geomorphological features and the inference of sedimentary flow types and morphodynamic domains operating within submarine canyons.
Key Innovation: Detailed seafloor mapping is fundamental for understanding the morphology and organization of submarine systems.
118. Synergistic analysis and vertical transport attribution of carbon monoxide surface concentrations and satellite column loadings over China
Core Problem: However, most high-resolution satellite platforms primarily provide total column concentrations, posing challenges for accurately characterizing the vertical structure of CO.
Key Innovation: The study integrates TROPOMI column CO measurements, MOPITT CO vertical profile data, and ground-based surface CO observations to enhance the understanding of CO vertical transport and spatial variability across China.
119. A unified kernel-driven modeling framework adapted to continuous, discrete and row-planted canopies in the thermal infrared domain
Core Problem: However, the radiation observed by a sensor is generally angle-dependent due to the complex architecture of the canopy, which must be normalized with respect to a reference direction (e.g., the nadir direction).
Key Innovation: However, all existing kernel-driven models have been developed for discrete and continuous canopies, without considering the specific architecture of row structure.
120. CAMS radiation service v4.6 for solar energy: Evaluation of Himawari based surface solar irradiance products
Core Problem: However, broken cloud fields are observed to introduce notable overestimation.
Key Innovation: As a first step towards the global evolution of the service for solar energy applications, the Himawari measurements by Japan Meteorological Agency (JMA) are integrated since version 4.6.
121. A PROSPECT-guided multi-task learning hybrid framework for simultaneous retrieval of leaf chlorophyll and carotenoids
Core Problem: However, concurrent retrieval from leaf reflectance remains challenging because Cxc absorption is weaker and strongly overlaps with Chl in the visible range.
Key Innovation: Across datasets, the proposed MTL model consistently outperforms STL, PLSR and PHY for Chl and improves Cxc retrieval in most cases.
122. A two-stage machine learning framework for Chl-a reconstruction and short-term prediction in eutrophic lakes using GOCI observations
Core Problem: Harmful algal blooms (HABs) in eutrophic lakes threaten aquatic ecosystems and public health, but short-term chlorophyll-a (Chl-a) prediction remains challenging because satellite observations are often discontinuous and bloom dynamics are regulated by interacting environmental and ecological factors.
Key Innovation: The first-stage model (XGBoost_Interp) reconstructed Chl-a on dates without valid GOCI/GOCI-II retrievals and generated a continuous daily Chl-a series for 2011-2024, achieving good performance (R2 = 0.83, RMSE = 5.72 μg/L, MAPE = 9.16%).
123. Global retrieval of stokes vector of water-leaving radiance via Unet-based framework
Core Problem: However, most existing statistically optimized search-based atmospheric correction (AC) models neglect the multi-angle Stokes vector contribution of L w in retrieving polarimetric aerosol properties.
Key Innovation: The results showed that the global mean absolute percentage errors (MAPEs) for the L w Stokes parameters (I, Q, and U), remote sensing reflectance (R rs), aerosol optical thicknesses (AOTs), and degree of polarization (DOP) at 490 nm were 2.24 %, 5.86 %, 6.38 %, 2.2 %, 15.94 %, and 6.06 %, respectively.
124. PG-GVB: A prior constrained backscatter framework for P-band PolInSAR forest height inversion
Core Problem: However, at P-band, deviations of canopy backscattering from the exponential extinction assumption in RVoG lead to biased height estimates.
Key Innovation: A parameter-guided GVB (PG-GVB) variant is developed within a three-stage framework, achieving improved performance while explicitly analyzing how different parameters influence robustness.
125. Phenology-driven hierarchical framework for grass species classification in coastal wetlands based on Sentinel-1/2 imagery: A case study of the Jiangsu Coast, Eastern China
Core Problem: Accurate mapping of grass species distribution is essential for the control of invasive plants, biodiversity conservation, and vegetation carbon accounting, yet the task remains challenging due to tidal inundation, frequent cloud cover, and the high spectral similarity among coastal grass species.
Key Innovation: Subsequently, two phenology-based vegetation features derived from monthly NDVI were constructed to improve the discrimination of spectrally similar species in the supratidal zone and were incorporated into a random forest classifier.
126. Hybrid FAPAR and FVC retrieval from Sentinel-3 SYNERGY with Gaussian processes: Development, validation, and cloud-readiness
Core Problem: The study addresses the need for robust methods to retrieve essential vegetation traits (EVTs) from the Sentinel-3 SYNERGY 300 m reflectance product, named SY_2_SYN.
Key Innovation: To routinely retrieve the fraction of absorbed photosynthetically active radiation (FAPAR) and fraction of vegetation cover (FVC) from SY_2_SYN imagery at continental scales, we developed and evaluated Gaussian Process Regression (GPR) models trained using Soil Canopy Observation of Photosynthesis and Evapotranspiration (SCOPE) simulations.
127. Geographic similarity-based prediction of spatial distribution patterns of soil forests in the Yuanmou Basin
Core Problem: The spatial distribution of soil forests is critically important for studies of landscape evolution and the sustainable management of land resources.
Key Innovation: Therefore, this study proposes an Environmental Similarity Model (ESM) based on the theory of geographical similarity, which is suitable for small-scale sampling.
128. Synergetic inversion of leaf chlorophyll content and leaf area index from Sentinel-2 data using artificial neural networks trained with a radiative transfer model
Core Problem: Leaf area index (LAI) and leaf chlorophyll content (LCC) are two key vegetation traits affecting vegetation growth and function.
Key Innovation: Our results suggest that this synergetic retrieval principle and methodology may be utilized to support the development and improvement of LAI and LCC products for rice paddies, while further cross-site validation is required to assess broader generalizability.
129. Testing geochemical and weathering tracers for fingerprinting the contributions from karst landform sediment sources
Core Problem: Karst landscapes exhibit pronounced surface heterogeneity and complex subsurface drainage networks, which complicate the quantification of soil erosion and sediment yield.
Key Innovation: The results indicate that integrating weathering indices with conventional geochemical tracers enhances the robustness of sediment source fingerprinting in karst environments.
130. Late-Holocene sediment dynamics and contaminants in the Fecht River, Vosges Mountains, France: From medieval mining to modern floodplains
Core Problem: However, the onset of anthropogenic land use inflicted perturbations to sedimentary systems, including the enrichment of heavy metals and metallurgic components resulting from mining.
Key Innovation: Investigated here are floodplain sedimentation dynamics under increasing anthropogenic pressure for the Fecht River, France, providing the first late Holocene chronological constrained record of Vosgesian floodplain deposition.
131. Lithological and climatic controls on denudation rates: Insights from standard and local limestone tablets in the Slovak karst
Core Problem: Karst denudation represents a key process linking soil, hydrology and carbonate bedrock weathering, yet its seasonal variability and lithochemical modulation remain insufficiently constrained by field experiments.
Key Innovation: The study quantifies seasonal and depth-dependent limestone denudation in the Slovak Karst using a three-year tablet exposure experiment conducted at two plateau sites with similar geomorphological settings but contrasting climatic and lithochemical characteristics.
132. Peat deposits as records of quaternary tectonics and landscape dynamics in the semi-volcanic highlands of Madagascar
Core Problem: Peat in the highlands of Madagascar is widespread in semi-enclosed tectonic basins that occur close to the continental drainage divide.
Key Innovation: Here, the lithostratigraphy of multiple cores and sections displaying successive generations of radiocarbon-dated peat was studied in Ifanja Basin and Antsirabe Basin as a means of characterising and quantifying Quaternary landscape dynamics.
133. Glomalin-related soil protein in colluvial soils as a proxy for reconstructing millenary changes in landscape and soil health
Core Problem: However, the potential of GRSP as a palaeoecological proxy remains unexplored.
Key Innovation: The authors aimed to 1) identify whether GRSP is preserved, and 2) investigate whether GRSP could be used as a palaeoecological proxy after comparing its record with palynological and pedoanthracological records that account for landscape change.
134. Sr-Nd isotopic constraints on source-to-sink processes of fine-grained fluvial sediments in the South China
Core Problem: However, discrepancies in provenance signals across grain-size fractions introduce significant uncertainty.
Key Innovation: Here, we present a comprehensive SrNd isotopic study of the clay and fine-silt fractions within fine-grained river sediments from South China.
135. Parameterizing and modeling of three-dimensional gravel desert surfaces
Core Problem: Knowing the surface morphology of gravel is critical for understanding the landforms and surface processes of gravel deserts.
Key Innovation: To address this limitation, this study presents an innovative 3D characterization and reconstruction approach for desert gravels.
136. Machine learning for spatiotemporal variability of the provenance in the Tengger Desert, Northwest China
Core Problem: Nevertheless, long-term sediment sources for Asian deserts and their controlling mechanisms remain poorly constrained, hindering a comprehensive understanding of desert evolution and its driving forces.
Key Innovation: Our results show that, both over the past ∼3.0 Ma and at present, sediment supply to the Tengger Basin has been dominated by the Northeastern Tibetan Plateau (NETP) and the Alxa Block (AB), with only minor contributions from the Central Asian Orogenic Belt (CAOB).
137. 10Be glacial chronology and driving factors in the southeastern Tibetan plateau during the past two glacial cycles
Core Problem: The authors present 34 new 10Be surface exposure ages derived from glacial boulders in the Maiqu Basin to constrain the timing and duration of glaciation since Marine Isotope Stage (MIS) 6.
Key Innovation: Furthermore, periodic cooling during the Holocene not only triggered millennial-scale changes in glaciers extent but also contributed to three distinct glacial fluctuations over the past millennium.
138. Aeolian sediment provenance and supply mechanisms of the world's highest-elevation desert, Kumkol Desert: Insights from geochemical and geomorphological evidence
Core Problem: The sediment provenance and supply mechanisms of aeolian sediments in high-elevation deserts, a critically understudied component of the global sedimentary system, are still not well understood.
Key Innovation: Focusing on the Kumkol Desert (average elevation >4000 m) in the northern Tibetan Plateau, the world's highest-elevation desert, this study integrates geochemical and geomorphological evidence to decipher its sediment origins and transport pathways.
139. Late Holocene environmental dynamics and delta-lobe switching in the Razelm-Sinoe lagoon system, southern Danube delta (Romania)
Core Problem: The Razelm-Sinoe lagoon system in the southern Danube Delta is a key archive for understanding how coastal environments respond to changing river, marine, and climate conditions.
Key Innovation: The authors identify five main stages of environmental change, from an open, river-dominated embayment to a progressively enclosed lagoon with occasional marine water intrusions, eventually leading to the development of widespread wetlands.
140. Freeze-thaw durability and hydraulic transport behavior of lightweight cellular concrete subbase in cold regions
Core Problem: However, limited field-validated evidence exists regarding its hydraulic transport behavior and freeze-thaw durability under realistic exposure conditions.
Key Innovation: Recent discourses point to ultra-low-density lightweight cellular concrete (LCC) as a potential subbase alternative for pavements constructed in cold regions.
141. Prediction model and factor analysis of ice adhesion strength at concrete pavement surface
Core Problem: Ice accumulation on pavement surfaces reduces traffic efficiency and compromises driving safety in cold regions.
Key Innovation: A fundamental understanding of the ice adhesion strength at the pavement is crucial for developing more efficient and sustainable anti−/de-icing strategies.
142. Laboratory investigation of the correlation between frazil ice particle and floc properties
Core Problem: The ratio remained approximately constant at different steady air temperatures but decreased by 12% to 17% at lower turbulence dissipation rates, and by 6% to 12% when the air temperature was varied.
Key Innovation: A series of cold room laboratory experiments were performed in a frazil ice tank to investigate the time series correlation between frazil particle and floc number concentrations and mean sizes during supercooling events.
143. Mechanisms of stiffness enhancement in shield tunnels with internal structures under longitudinal differential deformation
Core Problem: Large-section shield tunnels are commonly equipped with longitudinal internal structures, yet their mechanical role in mitigating longitudinal differential settlement under localized surcharge remains insufficiently understood and is often neglected in design.
Key Innovation: The study presents an integrated investigation combining physical model tests, three-dimensional finite element (FE) simulations using a concrete damaged plasticity (CDP) model, and an optimized analytical framework to systematically quantify the influence of three representative internal structure configurations on tunnel deformation, redistribution of internal forces and bending moments, and stiffness degradation induced by damage.
144. Pillar stability in horizontal salt cavern energy storage: Model development and field application
Core Problem: Salt cavern storage facilities are critical infrastructure for national energy strategic reserves, and pillar stability is a core factor in ensuring the safe and efficient operation of cavern clusters.
Key Innovation: Drawing on coal mine pillar design theory, a quantitative evaluation model for the safety factor of horizontal salt cavern pillars was established, incorporating parameters such as pillar aspect ratio, height-to-width ratio, and cavern internal pressure into a unified safety factor framework.
145. Hydrological drivers of silicate weathering in snow-dominated alpine headwater catchments with variable forest cover (Tatra Mountains, Western Carpathians)
Core Problem: The results indicate a kinetically limited weathering regime in which reaction kinetics and MTT exert stronger controls on weathering fluxes than discharge or catchment area.
Key Innovation: The study quantifies silicate weathering rates (SWR) and carbon dioxide capture rates (CCR) in mid-latitude mountains with seasonal snow cover, with particular focus on snowmelt-driven weathering and vegetation effects.
146. Setting the standard? Comparison of the implementation of decision-support modeling standards in Australia and the Netherlands
Core Problem: Choices during model development can substantially affect its outcomes, raising concerns about accountability when these outcomes inform decision-making.
Key Innovation: Yet, standards may seem at odds with expert judgment and the specificity of modeling question, raising questions about who initiates the standards, and how they are established and enforced in practice.
147. ZY1-02D satellite spectral inversion of organic matter in highland mountainous red soils based on electric eel optimisation feature extraction and hybrid liquid neural network fusion
Core Problem: On ZY1-02D data, it achieved R2 = 0.5708, RMSE = 12.7520, RPD = 1.5264, and RPIQ = 2.5212, due to limitations in satellite spectral resolution, atmospheric interference, and surface heterogeneity, this level of accuracy is considered moderate, and the model’s ability to generalize to satellite data still needs to be further improved.
Key Innovation: The study investigates the highland red soils of Dongchuan District, Kunming City, Yunnan Province, China.
148. Moisture transport patterns over the Northeastern Tibetan Plateau revealed by tree-ring δ18O records
Core Problem: Understanding moisture transport in monsoon-westerly transition zones is critical for interpreting hydroclimatic change in arid and semi-arid regions.
Key Innovation: Here, we developed six tree-ring cellulose oxygen isotope (δ18Otree) chronologies spanning ∼100 years from Qinghai spruce along an east-west transect and analyzed their climatic signals using multivariate statistics and reanalysis data.
149. Large-strain consolidation and evaporation behaviors of sediments in annular geotube cofferdams: Experimental and simulation studies
Core Problem: However, the large-strain consolidation and evaporation behaviors of sediments under the multi-dimensional drainage boundaries provided by such a structure are not fully understood.
Key Innovation: The annular geotube cofferdam, which is constructed by stacked sand-filled annular geotubes, is a novel structure for the storage and rapid dewatering of sediments.
150. A general FEM framework for sedimentation and consolidation of diluted clay suspensions
Core Problem: In engineering practice, these two stages are often treated separately, which makes it difficult to describe the full evolution of very soft deposits within a single consistent model.
Key Innovation: Soft fine-grained sediments placed by hydraulic dredging usually pass through two main stages: an initial sedimentation stage, where particles settle in suspension, and a later self-weight consolidation stage, where interparticle contacts develop and pore water is progressively expelled.
151. Pore-scale investigation of post-breakthrough continued displacement in porous media: energy-based interpretation and practical implications
Core Problem: While most studies have focused on the pre-breakthrough stage, continued displacement after breakthrough can still substantially influence productivity and residual trapping, yet its physical basis remains insufficiently understood.
Key Innovation: Here, we investigate post-breakthrough continued displacement in a three-dimensional digital rock using a phase-field model from an energy perspective.
152. Low-order Biot-Cosserat FEM and hydro-dynamic analysis for elastoplastic porous media
Core Problem: A low-order Biot-Cosserat continuum FEM and the corresponding u-w formula are derived to simulate the hydro-dynamic behavior of saturated porous media exhibiting strain localization.
Key Innovation: Numerical implementation is achieved through secondary development of the finite element software ABAQUS.
153. Special development of 2D high-order boundary conditions for soil-structure interaction problems
Core Problem: The developed boundary condition is easily implemented at the element face level within the finite element framework, eliminating the need for specifically tailored boundary elements or absorbing layers, and thus facilitates accurate wave propagation modeling in unbounded media using a significantly reduced computational domain.
Key Innovation: In this study, a novel 2D high-order absorbing boundary condition is developed for FEM-based direct analysis of soil-structure interaction (SSI) systems.
154. Stress-dependent electrolysis desaturation of calcareous sand: pore-scale gas evolution and microstructural response
Core Problem: For calcareous sand foundations, however, the influence of vertical stress on desaturation effectiveness remains poorly understood, particularly at the pore scale where gas generation, retention, and migration govern the treatment response.
Key Innovation: In this study, X-ray computed tomography (CT) was employed to investigate the effect of vertical stress on electrolysis desaturation of calcareous sand.
155. Prediction model of excess pore water pressure of subgrade silty filler under cyclic loading with intermittence: An energy-based method
Core Problem: Although soil deformation under such loading has been examined, the excess pore water pressure (EPWP) response remains unclear.
Key Innovation: Load intermittency reduces the rate of cumulative dissipated energy development, indicating more constrained particle movement and rearrangement, which slows EPWP accumulation.
156. Numerical Simulation on Meso-characteristics of Frost Heave in High-Speed Railway Subgrade Fillers Based on Discrete Element Method
Core Problem: To explore the meso-characteristic changes of high-speed railway (HSR) subgrade fillers during frost heave, a thermal-hydro coupled scheme incorporating temperature field evolution and water-ice phase change expansion is proposed within the discrete element method (DEM) framework.
Key Innovation: An improved model of cross-graded soil-rock mixture is established using the particle flow program to simulate frost heave behavior in a closed system under overlying load condition.
157. Experimental investigation of inherent anisotropy effects on the simple shear behavior of silica sand considering full-range depositional plane attitudes
Core Problem: The mechanical behavior of sand exhibits direction-dependent characteristics due to the inherent anisotropy, but current studies have not fully considered the angle between the stress and the depositional plane (i.e., depositional plane attitude).
Key Innovation: In this study, the depositional plane attitude was comprehensively characterized using azimuth ω and elevation γ, and a novel sample preparation equipment was designed to achieve this.
158. Dynamic characteristics of anisotropic consolidated soft clay under intermittent cyclic loading
Core Problem: For Kc ≤ 1.5, soil deformation during the loading stage remains limited and decreases as Kc increases.
Key Innovation: To investigate this, intermittent cyclic triaxial tests were conducted on marine soft soil from Zhoushan, China, after anisotropic consolidation.
159. Enhanced X-ray computed tomography image segmentation for crack characterization and damage progression in granite
Core Problem: Accurately describing the initiation and early propagation patterns of thermal damage in granite under high-temperature impact conditions can provide fundamental insights into its crack nucleation mechanism, which in turn has significant theoretical and practical implications for predicting the long-term stability of engineering rock masses.
Key Innovation: The study proposed an improved data augmentation strategy to achieve precise segmentation and quantitative statistical analysis of the meso-structural features of damaged granite.
160. Hydro-mechanical analysis of granite fracture under different water contents
Core Problem: However, the influence of water distribution characteristics on the mechanical behavior of rocks remains unclear.
Key Innovation: Furthermore, the effects of water-stress loading sequences and water infiltration direction on the mechanical properties of granite containing prefabricated holes were investigated.
161. Evolution and prediction of dynamic shear behavior in cement-stabilized organic-rich soft soil under seawater dry-wet cycles
Core Problem: Even after ground improvement, these soils remain susceptible to the coupled effects of seawater dry-wet cycling and dynamic loading, which lead to progressive strength deterioration.
Key Innovation: The results indicate that high organic content significantly inhibits strength development at early curing stages but enhances it at later stages.
162. Microstructure and damage of granite residual soil improved by CLS-enhanced MICP during dry-wet cycles
Core Problem: Although microbially induced calcium carbonate precipitation (MICP) can improve its mechanical properties, its durability remains limited due to pore interconnection and the progressive deterioration of the cementation network.
Key Innovation: To address these limitations, calcium lignosulfonate (CLS) was introduced to establish a synergistic CLS-MICP treatment system.
163. A Cosserat-based binary medium constitutive model for rock materials
Core Problem: This extension employs a consistent tangent matrix formulation, eliminating the need for computationally expensive matrix inversions.
Key Innovation: This research presents a multiscale Cosserat-based binary medium model tailored for rock materials, grounded in the framework of binary media and micromechanics.
164. Modeling of dissolution/precipitation-induced permeability in granite fracture under seawater conditions
Core Problem: The objective of this study was to develop a model that is capable of performing fast calculations and incorporating the effects of secondary mineral precipitation on other geochemical reactions.
Key Innovation: The proposed model was validated by comparing the results of flow-through experiments using deionized water (E-4) and seawater (E-22 and E-23).
165. Effect of moisture-induced disintegration on the thermal conductivity of aggregated clays
Core Problem: However, both the disintegration process and the double-porosity structure are rarely considered in existing thermal conductivity models.
Key Innovation: A reference model is first developed to describe the thermal conductivity of dry aggregated soils, incorporating a structural parameter to account for the effect of inter-aggregate pores.
166. Directional hydration effects on hydro-mechanical behavior of bentonite pellet-block composites
Core Problem: The UPLB sample, however, experienced slower structural evolution, characterized by pellet breakage and subsequent crack sealing.
Key Innovation: To clarify this, two composite configurations, upper-block-lower-pellet (UBLP) and upper-pellet-lower-block (UPLB), were prepared to investigate swelling pressure and structural evolutions during constant-volume hydration.
167. Kinematic analysis of pile in anisotropic poroelastic soil subjected to vertical incident S-waves
Core Problem: Anisotropic poroelastic media are comprehensively present in nature, exhibiting mechanical properties distinctly different from purely elastic media, primarily attributed to the coupled fluid-solid interactions within their porous microstructure.
Key Innovation: A novel model is developed for analyzing the kinematic characteristics of an end-bearing pile interacting with the surrounding anisotropic poroelastic soil under vertically incident shear waves.
168. DialogueVPR: Towards Conversational Visual Place Recognition
Core Problem: Two task-aligned metrics guide learning: a Discriminative Difficulty Index (DDI) for curriculum sampling and a Positional Retrieval Gain (PRG) reward that directly measures retrieval improvement induced by a question.
Key Innovation: The authors propose a paradigm shift to reasoning retrieval and introduce Dialogue Place Recognition (DlgPR), which casts localization as an interactive, dialogue-driven reasoning process.
169. Generalized Neural Distributional Regression
Core Problem: This facilitates rigorous uncertainty quantification, generating observation-specific confidence bands and tolerance intervals via the multivariate Delta method.
Key Innovation: The authors introduce the Generalized Neural Distributional Regression (GNDR) framework, which seamlessly embeds deep neural networks into the parameter space of classical probability distributions.
170. Measuring Spatial Clustering via Metropolis-Hastings Diffusion Distance
Core Problem: The authors propose a novel measure of the discrepancy between two probability distributions $f$ and $g$ on a graph - which we call the diffusion distance - that measures the rate of convergence of $f$ to $g$ under a graph-constrained Markov chain with stationary distribution $g$.
Key Innovation: As a default choice for this Markov chain, we use the Metropolis-Hastings transition matrix targeting $g$ with proposals given by a random walk on the graph.
171. GSVisLoc: Generalizable Visual Localization for Gaussian Splatting Scene Representations
Core Problem: The authors introduce GSVisLoc, a visual localization method designed for 3D Gaussian Splatting (3DGS) scene representations.
Key Innovation: The authors evaluate GSVisLoc on both indoor and outdoor scenes, demonstrating competitive localization performance on standard benchmarks while outperforming existing 3DGS-based baselines.
172. VAN-AD: Visual Masked Autoencoder with Normalizing Flow For Time Series Anomaly Detection
Core Problem: Existing methods require training one specific model for each dataset, which exhibits limited generalization capability across different target datasets, hindering anomaly detection performance in various scenarios with scarce training data.
Key Innovation: However, existing approaches either repurpose large language models (LLMs) or construct largescale time series datasets to develop general anomaly detection foundation models, and still face challenges caused by severe cross-modal gaps or in-domain heterogeneity.
173. Distributionally Robust Optimization via Iterative Algorithms in Continuous Probability Spaces
Core Problem: The authors study distributionally robust optimization (DRO) for robust inference when the worst-case distribution is continuous, leading to significant computational challenges due to the infinite-dimensional nature of the optimization problem.
Key Innovation: Unlike traditional discrete DRO approaches, which often suffer from scalability issues, limited generalization, and costly worst-case inference, our framework exploits Brenier's theorem to characterize the least favorable distribution as the pushforward of a transport map from a continuous reference measure.
174. Neural Architectures for Amortized Bayesian Inference: Statistical Foundations and Empirical Assessments
Core Problem: Despite the growing popularity of amortized inference, its statistical interpretation and position within Bayesian inference remain poorly explored.
Key Innovation: In this paper, we present a statistical perspective on several major neural architectures, including feedforward networks, Deep Sets, and Transformers, and examine how they naturally support amortized Bayesian inference.
175. Integrated geospatial framework for floating offshore wind siting and maintenance routing
Core Problem: The scientific contribution is not the introduction of isolated screening constraints, but the reproducible integration of three analytical components that are usually treated separately: GIS-based feasibility filtering, non-compensatory lexicographic siting prioritization, and current-aware asymmetric maintenance routing.
Key Innovation: The study develops a geospatial decision-support framework for floating offshore wind planning that explicitly links strategic siting with maintenance-access assessment.
176. Robust SAR ship detection for maritime surveillance in complex coastal environments with a noise-aware lightweight detector
Core Problem: Robust ship detection in coastal synthetic aperture radar (SAR) imagery remains challenging due to multiplicative speckle noise and intense land-sea clutter.
Key Innovation: Experiments on SSDD and HRSID show LPG-DETR achieves mAP@0.5 values of 97.8% and 89.9%, respectively, with only 12.74 M parameters.
177. A data-efficient transfer learning framework with multi-fidelity simulation for predicting wind-induced responses of offshore wind turbines
Core Problem: Accurately predicting wind-induced responses is critical for the design and operation of offshore wind turbines (OWTs), which is often restricted by the scarcity of field data and the computational cost of high-quality simulations.
Key Innovation: The results demonstrate that the proposed framework can achieve high-precision predictions of tower-top displacement responses with a minimum mean absolute percentage error of 10.46% under unseen wind speed condition, without the need for massive high-quality data.
178. A data-driven approach for mooring line tension prediction of floating offshore wind turbines based on sparse sensor data
Core Problem: However, only limited sensors are available to monitor the overall condition of a FOWT.
Key Innovation: To address this issue, this study proposes an integrated data-driven framework for predicting global and segment-level mooring-line tensions in floating offshore wind turbines.
179. Ship-PFTracker: A robust ship tracking framework in short-time SAR imagery with particle filtering
Core Problem: Ship detection and tracking in SAR imagery are critical for maritime surveillance, vessel traffic management, and coastal security.
Key Innovation: In the tracking stage, particle filtering is integrated with an adaptive IoU-confidence fusion strategy and an edge fallback mechanism to improve target association and reduce tracking loss for small ships.
180. Sea Clutter Suppression Method Based on Pyramid Multiscale Residual Attention Generative Adversarial Network
Core Problem: Existing methods still show limited clutter suppression performance, and their generalization ability requires further improvement.
Key Innovation: To address this issue, a sea clutter suppression method based on the pyramid multiscale residual attention generative adversarial network (PMS-RA GAN) is proposed.
181. Weakly Supervised Fine-Grained Aircraft Detection in Remote Sensing Based on Prior-Knowledge Prototype Learning
Core Problem: The weakly supervised aircraft fine-grained detection task remains challenging due to the lack of fine-grained category labels.
Key Innovation: Experiments on the FAIR1M-Aircraft dataset demonstrate that the proposed method achieves 30.5% mAP with only 50 fine-grained annotated images, outperforming the baseline by 10.1 mAP points.
182. MambaHSINet: A Dual-Branch Bidirectional State Space Network for Hyperspectral Tree Species Classification
Core Problem: However, accurate tree species classification remains challenging due to subtle interspecific spectral differences, similar spatial structures among related species, redundant spectral bands, and the limited ability of existing methods to model long-range spatial–spectral dependencies efficiently.
Key Innovation: Experimental results on self-collected and public hyperspectral datasets demonstrate that the proposed method achieves excellent classification accuracy, inference efficiency, and generalization performance.
183. DMSF-Net: A Dual-Encoder Multi-Source Feature Fusion Network for Fine-Grained Urban Green Space Segmentation
Core Problem: However, for fine-grained classification tasks in high-resolution remote sensing imagery, existing methods suffer from significant challenges due to the high spectral similarity between low-growing and dense vegetation, as well as the complexity of spatial structures.
Key Innovation: The proposed method constructs a parallel encoding structure for RGB and auxiliary features (NDVI and LBP), introduces an Adaptive Feature Fusion Module (AFFM) during the encoding phase to achieve dynamic weighted fusion of cross-source features, and designs a Boundary-Aware Up-Sampling Module (BAM) during the decoding phase to strengthen the representation of complex boundary regions through joint modeling of regional semantics and boundary information.
184. Optical, radar, and hybrid indices to detect farming practices in Europe
Core Problem: Although well-established methods exist for delineating the growing season using phenology and optical data, the detection of farming practices, particularly tillage, remains underexplored.
Key Innovation: Compared to the Copernicus High Resolution Layer Croplands product (HRL-Cropland), the proposed method based on HyBRIS time series improved sowing and harvest dates detection (MAE 26 and 23 days, respectively).
185. Assessing CWSI as an indicator of water stress in oak trees based on high-resolution thermal images
Core Problem: However, Tc-Ta is highly sensitive to variations in meteorological conditions.
Key Innovation: Using an extensive database of aerial images at high spatial and temporal resolution to record canopy temperature continuously at two locations, we developed a standardised baseline specifically for holm oak forests.
186. DeepGrass: A transformer-based approach for enhanced grassland management through Sentinel-2 time series spectral signature and vegetation indices forecasting
Core Problem: The availability of satellite observations in the input time series was crucial, especially for long-term forecasts, emphasizing the need to assess cloud coverage to evaluate model output reliability.
Key Innovation: In this study, we developed DeepGrass, an encoder-decoder transformer model designed to forecast all 10 Sentinel-2 spectral bands (SBs) and 28 vegetation indices (VIs) over the coming 15 days at field-level.
187. Satellite-observed legacy effects of autumn phenology improve predictions of spring green-up date
Core Problem: However, these shifts cannot be fully explained by preseason climate alone, as cross-seasonal legacy effects from the previous year's autumn phenology (i.e., date of foliar senescence, DFS) may also play a critical role.
Key Innovation: These findings underscore the importance of autumn legacy effects in regulating spring phenology of the following year and highlight the need to integrate such effects into phenology models to improve projections of growing season dynamics and terrestrial carbon uptake.
188. All-in-one texture restoration of building Façade with generative diffusion prior
Core Problem: Firstly, we design a practical façade degradation model that simulates real-world defects and enables network training solely on synthetic data, effectively addressing the challenge of acquiring paired HQ-LQ datasets.
Key Innovation: To further improve the network’s capability in handling and restoring complex defects, we design a dual-path feature guidance strategy, which leverages condition information from ControlNet and multi-scale defect features extracted from the defect probability map to enable staged control over the denoising process, facilitating accurate geometric alignment and detailed texture restoration.
189. Uncertainty-aware semi-supervised framework with supervised contrastive learning for sugarcane leaf disease detection
Core Problem: Sugarcane leaf diseases severely impact crop yield and quality, making accurate and timely detection critical for sustainable agriculture.
Key Innovation: Experimental evaluations on sugarcane leaf disease datasets, considered a representative case study, demonstrate that the proposed approach achieves performance close to that of fully supervised models while requiring far fewer annotated samples and consistently outperforms state-of-the-art supervised and semi-supervised approaches.
190. TMF: Temporal multi-modal fusion framework for estimating wheat yield from multi-source satellite and environmental data across the European Union
Core Problem: Accurate large-scale crop yield estimation is critical for agricultural management and advance warning of potentially compromised food security.
Key Innovation: Their integration reduced RMSE by 28-33% compared to climate-only inputs and improved prediction stability across the study domain.
191. Individual urban street tree detection and species-level mapping from street-view images and GIS data
Core Problem: Current street tree recognition methods using street-view images are limited by scarce labeled data and high manual annotation costs.
Key Innovation: Second, StreetTreeMapper reduces duplicate detections by integrating street-view image features with OpenStreetMap’s geographic constraints, achieving precise tree instance mapping.
192. Reproducible mapping of marine biodiversity using autonomous surface vehicles and deep learning
Core Problem: Monitoring shallow-water marine ecosystems requires precise, low-cost and reproducible methods, yet traditional diver-based surveys are labor-intensive, subject to observer bias and limited in spatial coverage.
Key Innovation: In this study, we present an autonomous framework that integrates deep learning techniques with open-source data acquisition platforms to overcome these challenges.
193. Feasibility of space-borne remote sensing to detect features related to CO2 and N2O exchange of boreal agricultural fields
Core Problem: The results also suggest that SAR data can potentially be used to fill cloud cover induced gaps in leaf area index (LAI) time series available from optical-range satellite imaging, improving the usefulness of satellite data in the monitoring of field management.
Key Innovation: The sites under investigation included two grassland fields on mineral soil and one on peat soil.
194. 8-day multisensor derived biophysical index groups and their ability to explain tree ring growth
Core Problem: Satellite-derived vegetation indices (VIs) can bridge this gap, yet most canopy-growth studies continue to rely on Normalized Difference Vegetation Index (NDVI), a greenness metric long known to saturate in high-biomass canopies and to decouple from stem growth under stress.
Key Innovation: Unlike previous studies relying on single vegetation indices and static seasonal metrics, we implemented an approach that integrates multiple vegetation indices across several accumulated temporal windows to capture intra-annual tree growth dynamics and stress responses.
195. Improving regional crop yield prediction by incorporating intra-regional phenological variations
Core Problem: However, spatial aggregation of remote sensing data-such as the simple averaging of pixel-wise vegetation index (VI) time series-ignores intra-regional phenological variations.
Key Innovation: To address this critical limitation, we therefore proposed a novel yield prediction framework that explicitly incorporates intra-regional phenological heterogeneity.
196. Evaluating phenological and machine learning approaches for estimating field-scale rice planting and harvest dates in Arkansas
Core Problem: However, satellite-based estimation of PD and HD across Arkansas, which produces 47.5% of U.S.
Key Innovation: Phenological and machine learning approaches can support the development of spatially explicit PD and HD datasets.
197. An interpretable stacking ensemble model for monitoring wheat stripe rust severity by integrating solar-induced chlorophyll fluorescence and hyperspectral indices
Core Problem: Wheat stripe rust represents a major threat to global wheat production and food security, rendering early diagnosis and accurate severity assessment essential for containing its spread and minimizing yield losses.
Key Innovation: Using SIF alone as input yielded an R2 of 0.511, whereas integrating SIF, VIs, and canopy physiological parameters increased R2 to 0.878, corresponding to an absolute gain (ΔR2) of 0.367 and a relative increase of 71.8%, demonstrating the added value of integrating multi-source spectral and physiological information.
198. Experimental study on mechanical properties of double-layer damped liquid storage tanks with various isolation devices
Core Problem: Results reveal that while both isolation systems significantly reduce base shear and overturning moments-with limited influence on liquid sloshing height-the composite rolling isolation device offers distinct advantages: it exhibits superior self-centering capacity and provides effective control over isolation-layer displacement.
Key Innovation: The study introduces an innovative composite isolation device that uniquely integrates isolation and damping functions within a single system.
199. Marble waste particles enabling structural support and microbial immobilization in MICP-reinforced sandy soil
Core Problem: However, its application is limited by bacterial loss, insufficient CaCO3 precipitation, and weak interfacial bonding.
Key Innovation: The study introduced calcium carbonate-based marble particles as functional additives to modify the granular medium of MICP-treated sand.
200. Application of geopolymers with different binding materials for the stabilization of sulfate-rich soils
Core Problem: Sulfate-rich soils (SRS) exhibit poor engineering properties, and studies focusing on the stabilization of these soils remain limited in the existing literature.
Key Innovation: The proposed method aims to improve the resistance of sulfate-rich soils (SRS) to deformation by increasing their bearing capacity and compressive strength.
201. Swelling characteristics of compacted bentonite with gaps filling by sands
Core Problem: Gaps, also known as technical voids, are considered inevitable during the installation of bentonite buffer material in the deep geological disposal project.
Key Innovation: To investigate the effect of gap filling sands on the swelling characteristics of bentonite, one-dimensional swelling pressure tests were conducted on specimens composed of a compacted bentonite layer, and sand layer in which water was infiltrated from the sand.