Atmospheric and Oceanic Sciences

Abstract. A set of 11 aerosol turbidity profiles (ATP) and 2 aerosol extinction profiles (AEP) at λ=0.55 µm, observed with searchlight in New Mexico at 32° N, has been digitized from plots in scientific articles. They cover the period February to June 1963 and September 1965 to May 1975, complementing the already rescued and previously published 105 individual AEP, corresponding to 36 days, between December 1963 and December 1964. Eleven AEP are calculated (AEPc) from the ATP, and the corresponding stratospheric aerosol optical depth (sAOD) between 12 and 25 km is also derived. Estimates of the digitization errors for the AEPc and the sAOD are also calculated using information available in the literature. The combined set of rescued AEP reported here and the earlier rescued set of AEP from searchlight observations, are the only AEP dataset covering the period between the 1963 Mt Agung and the 1974 Fuego eruptions at northern midlatitudes. In this regard two relevant features identified in the AEP and the sAOD are described here. The first, using AEPc from March and April 1963 identified what could be the date of arrival of the stratospheric aerosols from the Mt. Agung first eruption on 17 March 1963. This would challenge the accepted criteria that the stratospheric aerosols from Mt Agung arrived at the northern hemisphere midlatitudes in the second half of 1963. The second feature evidences two anomalous increases of the sAOD during a period supposed to be the decay of the sAOD from Mt. Agung eruption. They show our limited knowledge and understanding of the 1963 Mt Agung volcanic stratospheric aerosol transport. Finally, we describe evidences found in the literature pointing to the possible existence of the original searchlight raw signals and its processing software. The dataset described in this work is available at: https://doi.org/10.1594/PANGAEA.992616 (Antuña-Marrero et al., 2026a).

Water Resources Plans (WRPs) are foundational policy instruments globally, yet implementation rates remain persistently low. Without consistent action classification, policymakers cannot define what to measure, track outcomes systematically, or generate evidence for adaptive learning. This study develops and validates a comprehensive typology of water resources actions, positioning it as a foundational framework for systematic performance measurement and international transferability. The typology was constructed through a rigorous multi-phase methodology: initial consolidation and unification of actions from Ceará’s hydrographic plans (serving as a methodological foundation due to the state’s comprehensive participatory water resources planning process, 2021–2024), expert consensus via focal group discussions, and empirical validation across the entire Brazilian national context. Validation encompassed 53 Water Resources Plans (20 Brazilian state plans, 14 Brazilian river basin plans, and 19 international plans), achieving 99.6% applicability. The typology operationalizes action classification through 13 first-order categories and 160 subtypes, organized around the concept of ‘deliverable’—a governance-neutral principle that permits instantiation across diverse institutional arrangements. The identified action categories reflect universal principles of water management maturity recognized in international planning contexts (European Water Framework Directive, Turkish and Moroccan water governance systems), demonstrating that the typology captures generalizable patterns of adaptive planning behavior rather than Brazil-specific peculiarities. Furthermore, the typology’s governance-agnostic design—based on deliverable-centered logic rather than institutional-specific procedures—enables its adaptation to diverse water governance models, from highly decentralized (Brazil’s basin committees) to centralized systems (as in other countries). By offering a structured and comprehensive categorization, this typology functions as a valuable menu of action types for future Water Resources Plans development, ensuring a holistic consideration of potential interventions. Its dual role—as a precursor to robust indicator development and as a guide for future planning—underscores its transformative potential for both assessing past actions and informing prospective water management.

Forest Fires alter vegetation structure, photosynthetic activity, and regeneration dynamics. However, the thermal effects on nearby unburned vegetation remain poorly understood. Heat radiation generated during fires spreads in all directions and may cause dehydration, physiological stress, and foliar damage in surrounding plant communities. In the Pantanal, one of the largest wetlands on Earth and an ecosystem naturally poorly adapted to fire, these indirect impacts have received little scientific attention. Therefore, this study aimed to quantify and delineate the spatial gradient of the indirect impacts of Forest Fires on adjacent unburned vegetation, using NDVI as an indicator of changes in photosynthetic vigour between 1985 and 2022. A database integrating vegetation indices, burned areas, and land use and land cover information was assembled. Burn scars mapped by MapBiomas were surrounded by concentric buffer zones ranging from 50 to 250 metres to evaluate NDVI variation before and after fire events. Kruskal-Wallis and Dunn tests combined with K-means clustering were applied to identify spatial and ecological response patterns among vegetation types. Significant differences (p < 0.05) were detected up to 100 metres beyond burn scars, indicating measurable indirect fire effects. Forest and wetlands showed greater resilience while grassland formations exhibited slower recovery overall.

Remote sensing image semantic segmentation plays a pivotal role in converting complex image data into quantifiable geographic spatial information, underpinning applications such as disaster assessment, urban planning, and agricultural resource investigation. Fully supervised semantic segmentation methods rely heavily on labour-intensive pixel-level annotations, prompting a shift towards weakly supervised semantic segmentation (WSSS) that utilizes image-level annotations. However, remote sensing images are characterized by dense, intricate targets and the absence of distinct backgrounds, leading to challenges, such as sparse activation of local regions, incomplete localization in class activation maps (CAMs), and noisy, rough boundaries in pseudo-labels generated from image-level supervision. To address these issues, we propose a deep learning method with pixel relationship constraints for WSSS in remote sensing images. Specifically, we design an image reconstruction (IR) loss function to provide pixel-level supervision, enhancing the completeness of CAMs; a pixel relationship constraint (PRC) module to strengthen the global correlation of target regions and improve detailed information extraction; and an intersection optimization strategy (IOS) based on the Segment Anything Model (SAM) to refine pseudo-labels and segmentation results by mitigating noise. Here, we show that our method achieves mean Intersection over Union (mIoU) values of 63.85%, 71.20%, and 40.96% on the Vaihingen, Potsdam, and iSAID datasets, respectively, reaching 89.25%, 89.02%, and 65.66% of the performance of fully supervised methods. This work advances WSSS for remote sensing images by addressing key limitations of CAM-based pseudo-labels generation, offering a cost-effective alternative to fully supervised approaches and facilitating broader applications in geographic information science and earth observation. The code is available at https://github.com/CHENDL-SHEN/PRCCAM.

The rapid growth of renewable-based distributed generation (DG) and electric vehicles (EVs) poses significant operational challenges for distribution systems (DSs), particularly under uncertainties in renewable output, load demand, and EV charging behavior. Distribution system operators must therefore evaluate and enhance both DG hosting capacity (DG-HC) and EV hosting capacity (EV-HC) while maintaining voltage security and reducing losses. This study presents a stochastic, multi-objective optimization framework that jointly coordinates smart inverter (SI)-based Volt/VAR control and EV charging scheduling to simultaneously maximize DG-HC and EV-HC and minimize active power losses and voltage deviation. The framework integrates active power management through EV charging coordination and reactive power support via optimally deployed SIs. The resulting multi-objective problem is solved using the Starfish Optimization Algorithm (SFOA) and benchmarked against three established metaheuristics. The methodology is validated on the IEEE 33-bus system and a real 59-bus distribution network in Cairo, Egypt. Results show that coordinated SI-EV control increases DG-HC and EV-HC by up to 74% and 89%, respectively, and achieves voltage deviation reductions of 55% in the IEEE 33-bus system and 11% in the Cairo DS. Comparative analysis confirms that SFOA provides superior convergence and solution quality relative to the competing techniques.

An interdisciplinary network of scientists and stakeholders is working to understand how saltwater intrusion and sea level rise are affecting rural communities and to help address the consequences.

Fluid injection into the subsurface can induce seismicity by reactivating shear rupture, which typically produces larger earthquake magnitudes than tensile rupture. In laboratory shear rupture experiments, pressurization of the entire fault is often limited because large unconfined samples allow fluid to leak at free surfaces. In this study, we investigated shear fault reactivation by directly injecting fluid into a PMMA fault (760 mm long, 76 mm high) formed as the interface between two separate PMMA blocks. To prevent leakage in the 76 mm dimension, we made a low permeability barrier by coating the outer edges of the fault with Teflon tape. Fluid pressure then extended along the 760 mm dimension, resulting in seismicity migration away from the injection well. Changes in injection rate and fluid viscosity revealed two mechanisms: (1) Low injection rate or low-viscosity fluid caused seismicity migration governed by pressure diffusion, and (2) high injection rate or high-viscosity fluid caused seismicity migration proportional to injected volume. Simulations with a 2D poroelastic model showed that seismicity migrated with the fluid pressure front in the volume-driven regime, whereas fluid pressure advanced well ahead of seismicity in the pressure-diffusion-driven regime. These results highlight that Teflon tape effectively sealed faults and controlled fluid flow, and that injection rate and fluid viscosity have a strong impact on fault slip and induced seismicity.

This study extends the Absement Method to leaky-aquifer pumping-test analysis by time integrating the Hantush–Jacob governing equation and deriving four complementary operators. Time integrating the Hantush–Jacob equation yields S·s = T∇2A − C·A, with storativity S, drawdown s, transmissivity T, the time (t)-integrated drawdown A(t) (absement), and leakance C. The four operators, A(t), time-averaged A(t)/t, windowed ΔA/Δt, and the normalized absement derivative (NAD), are applied jointly across all available observation wells. In a homogeneous aquifer, the fitted operators and NAD diagnostic provide mutually consistent parameter and flow-regime signatures. In a heterogeneous aquifer, systematic differences between operators become part of the interpretation: T-related variation appears as changes in the ΔA/Δt sliding profile across wells, whereas the leakage factor B = √(T/C)-related variation is identified by divergent A(t)/t asymptotes and NAD type-curve crossing. Monte Carlo assessment under composite noise (N = 50) confirms near-zero parameter bias, with T and S standard deviations approximately 3–4 times smaller for A(t)/t and ΔA/Δt than for A(t). The three field cases are identified: a 14% outward T decline with spatially uniform B (sandstone aquifer); approximately homogeneous T with outward-declining B flagged by NAD type-curve crossing before fitting (sandy aquifer); and T–B coupling resolution through the windowed ΔA/Δt profile (medium-grained sandstone aquifer). The outputs supported sustainable-yield assessment directly from routine pumping-test records.

Abstract. The ensemble Kalman filter (EnKF) is widely used for state estimation in chaotic dynamical systems, including atmospheric and oceanic flows. One of the fundamental questions is how many samples are required for accurate long-term performance of the EnKF. In this study, we introduce a notion of time-asymptotic filter accuracy based on the scaling of the analysis error with respect to the observation noise level. This formulation provides a qualitative distinction between convergent and divergent filtering behavior, beyond standard criteria based on time-averaged RMSE at a fixed noise level. We investigate the minimum ensemble size m* required for this filter accuracy and relate it to intrinsic instability of dynamical systems. Using the Lyapunov exponents (LEs), which quantify asymptotic exponential growth rates of infinitesimal perturbations, we characterize degrees of instability by the number of positive exponents N+. Because spanning the unstable directions by a limited ensemble is essential for long-term accuracy, we propose an ensemble spin-up and downsizing strategy. Numerical experiments with the EnKF applied to the Lorenz 96 model indicate that the minimum ensemble size required for this filter accuracy satisfies m*=N++1. These results provide a practical guideline for ensemble-size selection based on a priori dynamical information and bridge idealized theoretical requirements with feasible numerical implementations via the ensemble downsizing method.

Abstract Fault slip inversions based on geodetic observations deepen our understanding of earthquake source processes. Previous attempts to simultaneously estimate the fault geometry and slip distribution have typically assumed a homogeneous half‐space owing to the prohibitively high computational costs of conventional numerical approaches. Here, we develop a simultaneous inversion method considering heterogeneous crustal structures. First, an infinite‐dimensional problem of fault geometries is reduced to finite dimensions by introducing a dislocation potential based on fault geometry invariance. Second, physics‐informed deep learning is used to provide differentiable solutions for the dislocation potential, allowing efficient Bayesian inversions. Numerical investigations on 2‐D inplane problems show that incorporating suitable heterogeneous structures substantially improves the stability and accuracy of fault and slip estimations compared with biased results obtained under homogeneous assumptions. This represents a significant advance in finite fault inversion, as it addresses heterogeneous crustal structure and uncertain fault geometries by overcoming a computational constraint.

Crop distribution mapping is critical for safeguarding national food security and optimizing agricultural management strategies. However, accurately distinguishing spectrally similar crops while maintaining precise boundaries remains a significant challenge. To address this, we propose an enhanced High-Resolution Network, termed HRNet-ICoT-CNN. The key innovation of this model is the Improved Contextual Transformer (ICoT) module, which dynamically integrates local structural details with global temporal contexts, thereby enhancing multi-scale spatiotemporal feature extraction without compromising spatial resolution. Applied to Sentinel-2 time-series NDVI data from Xinhe County in the Aksu region of Xinjiang, China, the model accurately delineated the distributions of key crops – cotton, wheat, and maize – and was evaluated against leading existing models. Experimental results indicate that HRNet-ICoT-CNN outperforms baseline models across all major evaluation metrics, achieving an overall accuracy (OA) of 94.91%, average accuracy (AA) of 81.81%, and Intersection over Union (IoU) values of 0.8507 for cotton, 0.6467 for wheat, and 0.5513 for maize. Generalizability tests further affirmed the model’s robustness, yielding an OA of 81.47% and an AA of 73.20% in two untrained counties, with IoUs of 0.8750, 0.9065, and 0.6314 for the three crops, respectively. These results highlight the model’s strong transferability and reliability, offering a robust scientific foundation for crop mapping via remote sensing in arid regions.

Abstract Natural and man-made cellulosic textile fibres and microfibres have been viewed as environmentally benign due to their biological origins. This assumption has caused natural and man-made cellulosic microfibres to be overlooked and underrepresented in research despite findings demonstrating their abundance in environmental systems, compared to synthetic, microplastic fibres. This systematic literature review critically examines how natural and man-made cellulosic fibres undergo multiple chemically intensive processing stages within the textile manufacturing processes, resulting in materials that differ substantially from their raw counterparts, exposing critical gaps in understanding across environmental science, toxicology and textile production. These chemical processes influence fibre degradation, increase the potential for pollution adsorption, and contribute to the environmental persistence and impact of microfibre shedding. Across studies, natural and man-made cellulosic microfibres are frequently reported in high abundance in environmental systems yet remain underrepresented in environmental monitoring which predominantly focus on synthetic microplastics.&amp;#xD;By adopting a textiles perspective, which highlights viewing information through a textile-focussed lens rather than from an environmental science perspective which has dominated this field. This review challenges the assumption that natural and man-made cellulosic microfibres pose no harm to the environment, suggesting that “natural” fibres can cause comparable harm to the environment compared to their synthetic, microplastic counterparts. This demands a reframing of current narratives that position natural and man-made cellulosic textiles as environmentally benign, recognising their potential for environmental harm. The findings further support the inclusion of chemically processed natural and man-made cellulosic microfibres in environmental monitoring, policy and sustainable textile design.

This study numerically investigates how a finite, circular patch of emergent vegetation alters microplastic (MP) transport, concentration, and retention in open-channel flow. A validated numerical model was developed to represent the vegetation patch as a porous zone and simulate MP transport. The framework was validated against laboratory data for two configurations: a low-blockage case and a high-blockage case. After validation, 36 MP cases, comprising four polymer densities, three particle diameters ranging from 0.1 to 0.5 mm, and two categories of shape factors (elongated and spherical), were released upstream and tracked over 180–420 s. Results show that vegetation density, represented by the blockage parameter and solid volume fraction, primarily controls the interception of microplastics. Dense patches create persistent recirculation and low-velocity zones that increase residence time and trapping, whereas sparse patches induce only transient disturbances, allowing rapid downstream advection. Quantitatively, retention in the dense configuration was ≈62% for the smaller MP sizes (0.1–0.2 mm) versus ≈35% in the sparse configuration at 300 s. Polymer density, particle shape, and particle size had only secondary effects under the tested moderate flow conditions. Smaller microplastics and elongated particles showed slightly higher retention. The findings identify dense vegetation as a selective hydrodynamic filter, demonstrating that vegetation-induced flow restructuring is the dominant control on MP fate. These effects should be considered in river restoration and pollution mitigation strategies.

Accurate hyperspectral image classification is fundamental to geospatial applications but is often constrained by annotation scarcity. To achieve high classification performance under small-sample conditions, we propose the Memory-Guided Adaptive Spectral–Spatial Perception model, which incorporates a three-level globalization strategy. At the single-sample level, an adaptive perception Transformer combines deformable and dilated convolutions with a Transformer encoder to capture global context from individual samples. At the intra-batch level, we introduce a metric learning strategy that explicitly captures structural dependencies and feature relationships among samples within each mini-batch, enabling comprehensive feature aggregation in a localized context. At the cross-batch level, a memory-guided strategy constructs a dynamic memory bank to store and retrieve features from same-class samples across training iterations, bridging past and present distributions to enhance generalization. Using only 1% of the SaliLMSS, Pavia University and Kennedy Space Center datasets and 0.5% of the WHU-LongKou dataset as training samples, our method achieves outstanding overall accuracy of 96.15%, 97.81%, 89.22% and 99.32%, respectively, outperforming existing methods.

Anthropogenic CO2 emissions have perturbed the global carbon cycle and increased atmospheric carbon concentrations to critical levels, making carbon capture and storage (CCS) a key strategy for mitigating climate warming. Natural carbon sequestration has operated continuously in marine environments throughout Earth history. Here, we investigate the growth mechanisms and carbon-sink significance of calcite concretions in the Mesoproterozoic Xiamaling Formation from the Zhaojiashan section and the Zhenzhuquan section in the North China Craton, using petrographic, elemental geochemical and C-O-Re-Os isotopic evidence. The presence of erosional surfaces and local truncation of host-rock laminae suggests that these concretions formed synsedimentarily or during early diagenesis near the sediment-water interface. The δ13C values (−5.05‰ to 1.54‰) of samples, together with δ18O-δ13C relationships, indicate a marine carbonate affinity and suggest that dissolved inorganic carbon was the dominant carbon source. In addition, the concretions display initial 187Os/188Os ratios as low as 0.136, close to the mantle Os end-member, implying a contribution from mantle-derived material during concretion formation. The middle rare earth element and yttrium (MREYs)-enriched patterns and slight positive Ce anomalies further indicate that concretion growth occurred mainly within the Mn- and Fe-reduction zones. We estimate that the calcite-concretion-bearing interval of the Xiamaling Formation sequestered 70.24 Gt C, equivalent to 257.56 Gt CO2, serving as an archive of marine carbon burial in the Mesoproterozoic ocean. Microbially mediated carbonate precipitation may represent an effective carbon immobilization mechanism in marine sediments and has potential implications for the development of subseafloor carbon storage strategies, especially where biocatalysts and/or brine could accelerate seawater CO2 mineral trapping to industrially relevant rates.

Conventional remote sensing faces challenges in regions characterized by complex terrain, frequent cloud and fog cover, image scarcity, and mixed pixels. This study develops an integrated framework that sequentially combines spatiotemporal fusion, water masking, feature engineering, and machine learning to enable long-term SSC retrieval. Key results include: (1) RASTFM achieved the highest spectral fidelity among four fusion algorithms; (2) development of an adaptive machine learning model for independent hydrological stations and a water body masking strategy; (3) mainstream SSC decreased by 21.2% temporally and a 93.8% longitudinally from 2006 to 2020, generating 15-year along-channel mainstream SSC data for the Three Gorges Reservoir area.

Hydropower systems operate under complex hydraulic, mechanical, and electrical conditions, and their operational reliability is closely related to equipment safety and maintenance efficiency. With the increasing availability of monitoring data, intelligent algorithms have been gradually introduced into hydropower fault prediction and condition assessment. This review summarizes recent developments in machine learning, deep learning, physics-informed neural networks, and digital twin-assisted approaches for hydropower fault analysis. Representative studies and typical applications are discussed, with attention given to their diagnostic performance, data dependence, and applicability under different operating conditions. Existing studies indicate that conventional machine learning methods still perform effectively in limited-sample scenarios, while deep learning models are more suitable for extracting complex features from multi-source monitoring signals and time-series data. Hybrid approaches combining physical mechanisms with data-driven analysis have shown potential for improving model robustness and reliability. In addition, digital twin frameworks provide a possible way to integrate real-time monitoring, fault diagnosis, and operational assessment within a unified platform. Despite recent progress, several challenges remain, including limited fault data, model interpretability, and differences in operating conditions among hydropower stations. Future studies are expected to place greater emphasis on multi-source data fusion, improved model adaptability, and the integration of physical knowledge with intelligent algorithms, supporting more reliable fault analysis and condition monitoring in hydropower systems.

Traditional methods for measuring the soil-water characteristic curve (SWCC) are time‑consuming and prone to disturbing soil structure, whereas resistivity testing offers rapid, non‑destructive advantages. However, its applicability to compacted silty clays under drying‑wetting cycles has not been systematically validated. In this study, three types of silty clay (HZ, WY, ZJ) used for dam construction were compacted to degrees ranging from 92% to 100%. Under both drying and wetting paths, resistivity (two‑electrode method), volumetric water content, and matric suction (filter paper method) were measured simultaneously. A resistivity synthesis parameter (Re), derived from Archie’s structural factor and the shape factor, was introduced to establish an empirical relationship between matric suction and Re. The results show that the Pearson correlation coefficients between resistivity and matric suction range from 0.895 to 0.979 (p &amp;lt; 0.01), and partial correlation analysis confirms that water content is the dominant controlling factor. At the same water content, resistivity during the drying path is consistently higher than that during the wetting path, exhibiting clear path dependency. A higher compaction degree reduces the rate at which resistivity decreases with increasing water content. The quadratic model fits the h‑Re relationship well, with R² &amp;gt; 0.95 for all soils under both paths. The predicted matric suctions are in close agreement with those fitted by the classical van Genuchten model, with most data points lying near the 1:1 line. These findings confirm that resistivity parameters can effectively capture the hydraulic hysteresis of silty clays. The proposed empirical h‑Re equation provides a rapid, non‑destructive approach for estimating matric suction under reservoir water level fluctuations and holds practical value for seepage stability monitoring and early warning of earth‑rock dams.

Archaeological feature detection at complex, mixed-environment sites requires accurate, efficient methods for identifying subtle morphological signatures. This study presents a semi-automated remote sensing pipeline for the detection and delineation of archaeological earthworks at Cahokia Mounds (Illinois, USA), a major Mississippian urban center and UNESCO World Heritage Site. Three LiDAR datasets, two collected via UAV-mounted sensors and one from a piloted aircraft survey, were processed into Digital Terrain Models and transformed into Local Relief Models (LRM). K-means clustering was applied to segment the LRMs into feature classes, followed by contour bounding using the OpenCV library to outline mounds and borrow pits. Additionally, SAR-derived Local Incidence Angle (LIA) rasters from PALSAR-3 and Sentinel-1 were processed through angular deviation mapping to identify slope anomalies associated with archaeological features. Results across all five datasets demonstrate the complementary strengths of LiDAR and SAR: LiDAR excels at resolving elevation-defined features such as mound footprints, while LIA captures directional slope behavior that highlights mound edges, borrow pit rims, and linear features such as causeways. Comparative analysis of LiDAR acquisition frequencies reveals minimal differences in archaeological feature recovery between pulse settings, suggesting that sensor platform choice matters more than power-density tradeoffs for this application. Despite the need for human review to filter modern disturbances and natural false positives, the integrated workflow meaningfully accelerates prospection and reduces interpretive subjectivity. The methods are scalable, site-invariant, and work with open-access data, making them applicable to archaeological landscapes worldwide.

Abstract Enhanced geothermal systems rely on increasing permeability and pore surface area in rock. Cyclic thermal shocking can achieve both by inducing thermal cracks through repeated rapid cooling. Laboratory experiments subjected micritic limestone, granodiorite, and trachybasalt to up to 10 thermal shock cycles, while tracking crack evolution qualitatively using time‐lapse electron microscopy and quantifying pressure‐dependent permeability and elastic wave velocities. This work advances prior efforts focused primarily on crack initiation by demonstrating how lithology‐specific microstructures govern the cyclic evolution, persistence, and efficiency of pressure‐dependent permeability enhancement during cyclic thermal shocking. This reframes microstructure as a key design variable controlling permeability enhancement and monitoring during geothermal stimulation. Contrasting mineral thermal properties, large mineral grains, and irregular vugs promote the greatest permeability enhancement. Velocity reductions were most pronounced &lt;10 MPa effective pressure ( P eff ) and diminished with increasing cycle number, indicating that velocity‐based monitoring in geothermal systems must account for P eff and cycle number.

3D Gaussian Splatting (3DGS) offers efficient explicit rendering, but large-scale remote-sensing use remains fragmented across UAV/aerial photogrammetry, satellite reconstruction, large-scene scaling, surface modeling, and geospatial evaluation. We present a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-informed scoping review based on 55 core studies identified through Web of Science, Scopus, IEEE Xplore, and supplementary searches completed on 3 June 2026. A faceted taxonomy organizes the literature by platform, sensor model, scalability strategy, and geometric supervision. The synthesis shows that partitioning, hierarchy, compression, and feed-forward inference improve scalability but do not guarantee metric geometry. Reliable deployment additionally requires sensor-consistent projection, geometric or georeferencing constraints, explicit supervision labels, and product-level evaluation. In control-point-free settings, internal consistency should be distinguished from independently validated accuracy. We therefore propose a platform-aware benchmark framework that jointly records visual fidelity, computational cost, metric geometry, product utility, failure behavior, and reproducibility metadata for UAV/aerial, satellite, and hybrid settings.

Hyperspectral unmixing aims to extract pure endmembers and their corresponding abundance from mixed pixels. Existing deep learning-based unmixing methods predominantly rely on convolutional neural networks (CNNs) or Transformer architectures. However, CNNs suffer from limited receptive fields and struggle to capture long-range spectral dependencies across the entire spectral sequence. While Transformers possess global modeling capabilities, they are constrained by quadratic computational complexity and lack the ability to adaptively filter redundant noise in consecutive spectral bands. To address these limitations, this paper proposes a dynamic hyperspectral unmixing network integrating a spectral sequence Mamba with local spatial–spectral awareness. Specifically, the network features a novel asymmetric dual-stream collaborative architecture. The first branch, the spectral sequence Mamba, models hyperspectral data as a one-dimensional continuous sequence and employs the selective state space model to perform global scanning with linear complexity. This adaptively filters redundant spectral bands to accurately extract high-purity global spectral semantics. The second branch, dedicated to local spatial–spectral awareness, uses an attention-augmented CNN to capture local continuous spectral variations and spatial textures, providing fine-grained geometric boundary constraints for abundance estimation. Furthermore, a spatially adaptive gated fusion module is designed to dynamically balance global spectral semantics and local spatial–spectral details according to the pixel mixing complexity of varying spatial regions. Extensive experiments on multiple public hyperspectral datasets demonstrate that the proposed method achieves significant improvements in unmixing accuracy over comparative methods.

Introduction This study examines the dynamics and characteristics of seasonal fish migration into the floodplain of a large lowland river. Methods For continuous recording of the number of fish migrating upstream and downstream, and their size composition, the hydroacoustic complex “NetCor” was used. The system was permanently installed in spring 2022 on a floating platform in the Varpak River (a small tributary of the Irtysh River), mounted in a side-scan setup from the left bank. Results The dominant role of water level and temperature for abundance dynamics of fish migrating into the floodplain is demonstrated. The earlier onset of high values for these factors positively correlate with the abundance of migratory fish. Their influence accounts for approximately 70%. On a daily basis, migration dynamics are determined by the daily variation in illumination. The abundance of upstream migrant fish correlated with diurnal variations in illumination. Migration intensity increases during periods preceding morning and, to a lesser extent, evening twilight. The lowest proportion of migratory fish was observed during the daytime, when illumination was highest. Discussion The influence of water temperature, water level and light on the dynamics of seasonal fish migration is revealed. At the same time, a number of specific features have been noted for seasonal migration to the floodplain: synchronous increases and decreases in the intensity of fish movements up and downstream are simultaneously recorded, while the general trend of migration to the floodplain is maintained; in the initial phase of the flood, the largest fish are the first to migrate into the floodplain, which is an adaptive behavioral response. The diurnal distribution pattern of migratory fish is a strategy which they use to counter visually oriented predatory fish species—zander, perch, and pike—to avoid optimal prey search conditions.