Search Results (2,588)

Seasonal basin management requires knowing when ecosystem services coincide and how their configurations differ among subbasins, information that annual summaries can obscure. We integrated monthly remote sensing, meteorological, soil, and terrain data to estimate carbon fixation (CF), water conservation (WC), soil retention (SR), and wind erosion prevention (WEP) in 18 Yongding River subbasins in 2018, 2020, and 2023. Seasonal association networks and a self-organizing map characterized 648 subbasin–month service profiles and their bundle histories. Of 648 monthly bundle labels, 237 (36.6%) differed from the label of the corresponding annual mean service vector. The high-CF–WC–SR bundle occurred in 26 monthly profiles, including 19 of 54 July observations, whereas subbasin 4 retained a WEP-oriented bundle in 30 of 36 sampled months. In the three-year mean seasonal profiles, 12 of 18 subbasins shared a summer CF-enhanced pathway; the other six followed alternative pathways. CF, WC, and SR were generally higher in summer, while WEP peaked in April and was more prominent in western and southwestern subbasins. Seasonal networks showed the strongest screened association for winter SR–WC, while the summer WC–WEP confidence interval crossed zero. Together, monthly bundle histories and seasonal association networks identify shared summer assessment windows and subbasin-specific priorities for spring wind exposure and service-bundle transition monitoring, providing spatially and temporally explicit evidence for differentiated seasonal management in the Yongding River Basin. Full article

Accurate road extraction from high-resolution satellite imagery is important for transportation planning, infrastructure monitoring, and geospatial updating, but blurred boundaries and fragmented segments still limit practical large-area applications. This study proposes EORENet, an edge-optimized road extraction network for 2 m GF-1 imagery and large-area road product generation. EORENet decouples road-surface segmentation and road-edge delineation into task-specific branches, introduces deformable convolution for adaptive spatial aggregation, and uses a dual-task cross-feature fusion module to exchange semantic and boundary cues. A GF-1 road sample set, the Cangling Road Dataset, was constructed and evaluated together with the Ottawa and DeepGlobe Road datasets. Compared with eight representative methods, EORENet achieved the best overall IoU, F1 score, and edge-buffered F1 score; on the Cangling dataset, it reached 55.11% IoU, 71.06% F1, and 56.21% edge-buffered F1. The trained model was then applied to a spatially independent large-area GF-1 inference collection. Scene-stratified product-level assessment with 1200 independently annotated 512 × 512 patches showed systematic scene-dependent differences, with F1 scores ranging from 62.80% in mountainous forest areas to 74.57% in high-density urban cores and a balanced scene-wise mean F1 of 69.48%. City-scale extraction over four metropolitan regions further visually illustrated the workflow’s potential for large-area road-surface product generation. Full article

Rice is one of the world’s most important staple crops, sustaining food security and rural livelihoods across many tropical countries. Accurate mapping of rice crop systems is therefore essential for monitoring agricultural production, supporting irrigation management, and assessing seasonal cultivation patterns. Reliable discrimination between crop systems depends not only on the quality of satellite observations but also on the time-series representation’s ability to preserve crop phenological characteristics. This study compares representative image encodings for rice crop system classification using Sentinel-2 Enhanced Vegetation Index (EVI) time series over Suphan Buri province, Thailand. The EVI profiles were reconstructed into daily observations using a Savitzky–Golay filter and encoded as images by the Continuous Wavelet Transform (CWT), Synchrosqueezing Transform (SST), Gramian Angular Field (GAF), Markov Transition Field (MTF), Recurrence Plot (RP), and Short-Time Fourier Transform (STFT), with the unencoded one-dimensional (1D) series as the baseline. To ensure a fair comparison, all representations were classified using the same convolutional neural network architecture and identical training samples, and evaluated on 200 independent ground truth points (with 50 samples per class). Overall accuracy ranged from 0.788 to 0.899 across the seven representations. STFT was the most accurate at 0.899, followed by CWT at 0.871, with a statistically significant difference between them. Confusion between the two most intensive systems accounted for about seven-tenths of the difference in accuracy between the best encoding and the baseline. Encoding alone was insufficient, since CWT did not significantly exceed the baseline, and SST, RP, GAF, and MTF fell below it. Encodings that preserve the interval between growth cycles are therefore preferable when classes are defined by cycle count rather than by signal amplitude. Full article

This study provides a technology-level reference for the future development, implementation, and validation of intelligent O&M systems for high-end drilling equipment. Intelligent operation and maintenance systems for high-end drilling equipment provide crucial technological support for addressing the challenges posed by extreme environments in deep-sea and deep-earth drilling and are intended to facilitate the transition from traditional reactive maintenance toward predictive maintenance. This study adopts a narrative technology review approach combined with a conceptual framework synthesis. The review draws on 51 sources: 36 academic publications or conference papers, 7 technical standards or official technical documentation sources, 1 institutional technical report, and 7 company or platform documents. Searches covered Web of Science, Engineering Village, and Google Scholar. Sources were selected for their direct relevance to the review scope, technical representativeness, methodological or engineering value, and ability to support specific technical claims. The reference set primarily covers 2019–2026, while earlier sources were retained when they provided foundational concepts, established standards, or directly relevant engineering evidence. The review examines user requirements, system architecture, data acquisition, application scenarios, and functional modules in deep-sea and deep-earth drilling contexts; synthesizes key technologies including data integration and transmission, edge-cloud collaboration, digital twins, data-driven and physics-informed modelling, and intelligent O&M decision support; and compares representative industrial platforms and related O&M capabilities. The review identifies distinct technical requirements and challenges for the two application contexts, particularly regarding communication constraints, environmental adaptability, data acquisition, data governance, cybersecurity, and lifecycle validation. Based on the reviewed evidence and identified engineering requirements, a conceptual reference architecture integrating edge-cloud collaboration, digital-twin-based state representation, data-driven and physics-informed modelling, and intelligent O&M decision support is synthesized. The architecture is presented as a conceptual framework rather than an implemented or field-validated system and provides a technology-level reference for the future development, implementation, and validation of intelligent O&M systems for high-end drilling equipment. Full article

Surface soil moisture (SSM) regulates land–atmosphere water and energy exchanges over the Tibetan Plateau (TP); yet, passive microwave SSM products, such as those from the Soil Moisture Active Passive (SMAP) mission, are limited to coarse spatial resolutions of tens of kilometers. Although various downscaling approaches have been developed using radar backscatter, thermal infrared observations, and optical–thermal indicators, the value of shortwave infrared (SWIR) reflectance—whose moisture-dependent absorption characteristics offer fine-scale soil moisture information—remains insufficiently assessed across contrasting environments. To address this gap, we developed a 1 km SSM product for 2017–2020 by downscaling 36 km MCCA-SMAP estimates over the TP, combining the normalized shortwave difference soil moisture index (NSDSI) derived from MODIS reflectance with a first-order Taylor expansion and daily sensitivity calibration. The product was evaluated against ground observations from five monitoring networks spanning diverse climatic and land surface conditions using spatial, temporal, regional-scale, and spatiotemporal comparison frameworks, and was benchmarked against three published 1 km downscaled products based on radar backscatter, diurnal temperature amplitude, and soil evaporation efficiency, respectively. Compared with the original MCCA-SMAP SSM, the spatial regression slopes of the NSDSI-based downscaled product moved closer to unity at all five networks, with spatial correlations increasing at four networks particularly at SMN-WDL (from −0.04 to 0.59) and Shiquanhe (from 0.12 to 0.44), although the near-unity slopes at Shiquanhe were sensitive to unusually high estimates at SQ11. The NSDSI-based downscaled product showed lower mean station-level temporal correlations across the five networks, whereas regional-scale temporal correlations remained nearly unchanged. Relative to the existing 1 km products, the NSDSI-based product had spatial slopes closer to unity at all networks except Maqu, although correlation and error rankings varied among networks. At Maqu, where the vegetation cover is relatively high, the NSDSI-based downscaled SSM had spatial correlations below 0.2 and exhibited lower correlations and a higher ubRMSD than the radar- and thermal inertia-based downscaled products. The resulting downscaled SSM maps preserved the broad west-to-east moisture gradient and showed finer spatial detail. These multi-network results identify improved spatial pattern representation as the principal benefit of the evaluated SWIR-assisted downscaling approach, while demonstrating that gains in spatial agreement do not necessarily improve temporal performance or reduce ubRMSD. Full article

Grasshopper outbreaks severely threaten pastoral agriculture and grassland ecosystems in Inner Mongolia, China. Identifying suitable habitats and understanding their monthly dynamics are critical for early warning and targeted control. This study integrated multi-source remote sensing data (Moderate Resolution Imaging Spectroradiometer land surface temperature (MODIS LST), normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI)), meteorological data (precipitation and temperature), soil and topographic variables, and field survey data (May–July 2023) to construct a coupled Random Forest (RF) and Maximum Entropy (MaxEnt) model for the dominant grasshopper species Dasyhippus barbipes. Key objectives were to identify environmental variables at different developmental stages, map monthly suitable habitats, quantify inter-monthly spatiotemporal-shift patterns of suitable habitats using a suitability class transition matrix, and analyze association patterns of NDVI, precipitation, and LST with suitability transitions. The results showed the following: (1) Key drivers varied monthly—vegetation/soil types, precipitation, and FVC dominated in May; FVC, SOC, slope, and NDVI increased in June; and soil, vegetation, aspect, and Max. The temperature of Warmest Month (Bio5) dominated in July. (2) Highly suitable areas expanded eastward from central Xilingol League and Chifeng City, following west-to-east and center-to-north-and-south diffusion patterns. (3) Vegetation green-up, precipitation (as a limiting moisture driver), and LST (threshold effects on development rates) jointly formed a vegetation–water–heat synergistic system. (4) The coupled Random Forest (RF) and Maximum Entropy (MaxEnt) model achieved high accuracy (AUC > 0.91), with variable importance matching observed spatiotemporal patterns. This study elucidates the monthly scale evolution of D. barbipes-suitable habitats and multi-factor associations between environmental predictors and habitat suitability, providing a scientific basis for refined monitoring, early warning, and green control in Inner Mongolia’s typical steppes. Full article

The NASA–ISRO Synthetic Aperture Radar (NISAR) mission provides L-band soil moisture at 200 m, but validating its early products depends on retrieval flags, overpass collocation and sensor depth. We evaluated the beta Level-3 product against seven U.S. in situ networks (October 2025–January 2026) and the provisional product at 21 National Ecological Observatory Network sites (June–July 2026). Beta retrievals with flag 0, which averages all three candidate algorithms, were matched to in situ data within 60 min, merged by orbit and averaged over 0–10 cm sensors, yielding 261 site overpasses from 117 sites. Beta NISAR had a preliminary bias of 0.003 m³ m⁻³, a root mean square error (RMSE) of 0.081 m³ m⁻³ (95% interval 0.066–0.098) and $R=0.578$. Accepting all flag classes increased coverage ninefold and RMSE to 0.096 m³ m⁻³. On common site overpasses, deeper sensors did not have significantly higher RMSE than 5 cm sensors. The exploratory provisional assessment found a wet bias of 0.110 m³ m⁻³, concentrated at very dry reference sites, with a pooled R of −0.038 but an anomaly R within sites of 0.259. These estimates rest on few sites and short records; validation of early NISAR products should report flag classes, overpass construction and matched depths and separate pooled from temporal agreement. Full article

Civil Air Patrol (CAP) imagery, often the first post-disaster observation, is designed for 2D interpretation rather than multi-temporal volumetric measurement. We build post-storm digital surface and terrain models from uncontrolled CAP imagery with structure-from-motion in western North Carolina’s open Swannanoa Valley and forested Hickory Nut Gorge. Surfaces are co-registered to pre-event LiDAR using roads as pseudo ground control, corrected for terrain-correlated bias, and thresholded with a spatially variable, calibrated Level of Detection. Before bias correction, bare-earth surfaces match withheld post-event LiDAR on stable terrain to 0.68 and 0.82 m normalized median absolute deviation. Volumes are 2017-to-2024 change, lower bounds in coverage, and conditional on the bias model, residual screen, and detection window. Corrected valley surfaces record 125,000 m³ of erosion and 123,000 m³ of deposition, and a sampled image check dates about 13% of that change to the sortie. Under the same limit, the uncorrected surface reports five times the deposition, and the alternative bias model gives gross terms 3.6 to 4.2 times larger. The gorge at Chimney Rock recorded 76,000 m³ of erosion and 84,000 m³ of positive change, just under half dating to the sortie, including land deposition partly cleared before the post-event LiDAR flew. CAP-derived surfaces can capture transient storage that later surveys cannot. Full article

Mobile-mapping systems (MMSs) are moving from sensor-centric survey platforms toward intelligence-driven components of the remote sensing ecosystem. This critical narrative review analyzes rather than catalogs that shift, organizing it around four structural trends, each stated as a claim and examined against supporting and contradicting evidence: the growing role of learned, feed-forward geometry alongside, not in place of, optimization-based simultaneous localization and mapping, arranged along a latency–fidelity axis; tokenization economics—how points become, and are merged out of, attention tokens—proposed as an interpretive framework for the efficiency of 3D perception; the migration of radiance-field reconstruction from visualization toward measurement, where explicit representations such as 3D Gaussian Splatting yield updatable digital twins whose metric reliability still has to be established against independent reference data; and the transition from perception toward agency, coupling MMS evidence to Earth foundation models. Each trend is tested against the two environments where mobile mapping is most deployed, city and forest, whose contrasting geometry and satellite visibility expose how far each advance generalizes. Seven recurring pitfalls are then consolidated, among them efficiency claims measured on heterogeneous hardware, parameter count used as an efficiency proxy, and architecture rankings assumed to transfer between domains as dissimilar as urban corridors and forest plots. A controlled same-hardware benchmark of four point-cloud backbones supplies measured evidence for the efficiency pitfalls, and a research agenda for the photogrammetry and remote sensing communities follows. The review has stated limits: the literature was selected purposively rather than systematically, forest-specific evidence is thinner than urban evidence, and the benchmark covers four backbones, two datasets, and one GPU at inference only, so its conclusions are bounded to those configurations. Full article

Satellite-AOD-based visibility estimation has largely focused on daily-scale estimates or limited overpass times, while its diurnal variations are less understood and discussed. This study therefore investigates surface visibility estimations over China during daytime at 00, 03, 06, and 09 UTC (i.e., 08:00, 11:00, 14:00, and 17:00 China Standard Time, UTC+8), using aerosol optical depth (AOD) observations from the geostationary FY-4B satellite and ERA5 meteorological data. A physically informed framework combines a first-order physical visibility estimate based on AOD, an empirically calibrated relative humidity (RH) adjustment, and XGBoost residual learning. The complete framework, evaluated against ground-based NCEI visibility observations, achieved R = 0.84, RMSE = 2.80 km, and MAE = 2.28 km under sample-based five-fold cross-validation. Independent validation from July 2024 to June 2025 yielded R = 0.79, RMSE = 3.10 km, and MAE = 2.56 km. Visibility-estimation accuracy varied across daytime observation times, with relatively lower errors at 00 and 03 UTC and the largest errors at 06 UTC in both the first-order physical estimate and the final model. Although the complete framework yielded RMSE values of 2.63–3.68 km across the four time slots, these diurnal differences remained evident. The physical estimate also exhibited RH-dependent bias, overestimating visibility at lower RH and underestimating it at higher RH, with a transition near 50%. An empirical RH-conditioned adjustment further reduced estimation errors. These findings highlight the importance of accounting for diurnal and RH-dependent errors in simplified AOD-to-visibility conversion and demonstrate the potential of repeated geostationary observations for daytime surface visibility estimation. Full article

Accurate precipitation estimation is crucial for water management and flood reconstructions, yet remains challenging due to sparse ground observations and systematic satellite biases. Despite the rise of deep learning in multi-source fusion, controlled comparisons under a unified framework remain scarce, and the roles of window size and precipitation product combinations remain underexplored. This study evaluated five deep learning architectures, namely 1D CNN, 2D CNN, 1D–2D CNN, 3D CNN, and Transformer, for fusing three precipitation products (IMERG, MSWEP, and ERA5-Land) to estimate station-scale daily precipitation in the Yellow River Basin, China. Results showed that deep learning models consistently outperformed individual products, weighting methods, and machine learning methods, with Transformer achieving the best overall performance, followed by 3D CNN; MSWEP led the standalone products. Spatial improvements were more pronounced in densely gauged areas, but Transformer also performed well in the sparse western regions. All products and fusion models tended to overestimate no-rain and light-rain events and underestimate moderate-to-heavy, and extreme events, with Transformer yielding the largest reductions in conditional RMSE and BIAS for heavy and extreme events. Enlarging the spatial or temporal window individually improved performance, and combining both worked even better. Ablation experiments revealed that IMERG contributed little unique information, whereas incorporating more products generally improved Transformer performance. Overall, Transformer demonstrated superior performance in fusing precipitation products and enhancing estimation accuracy. Full article

Critical raw material (CRM) exploration increasingly combines Earth Observation, geophysics, geochemistry, mineral prospectivity mapping, probabilistic modelling, and autonomous sensing. However, these capabilities are commonly developed or deployed as separate components, while the explicit coupling of observation, uncertainty-aware geological inference, and subsequent exploration decisions remains comparatively underdeveloped. This paper presents Closed-Loop Mineral Intelligence (CLMI) as a conceptual and methodological framework for organising these capabilities within an iterative Observe–Model–Decide process. The contribution does not propose a new stand-alone learning algorithm or claim an operationally validated exploration system. Instead, it formalises a reference architecture in which heterogeneous observations update probabilistic geological representations, uncertainty informs the prioritisation of subsequent observations, and expert oversight remains part of the decision loop. The framework is examined through an illustrative lithium-bearing pegmatite scenario that specifies the intended information flow, candidate data sources, modelling functions, decision points, and human intervention without reporting empirical performance. A validation roadmap is further defined to make the framework testable in future case studies, including requirements for benchmark baselines, uncertainty calibration, information-gain assessment, exploration-efficiency metrics, computational feasibility, and human–AI interaction. By distinguishing established technological components from the proposed system-level integration, the paper positions CLMI as a research framework and agenda for adaptive, uncertainty-aware mineral exploration rather than as a demonstrated replacement for current professional practice. Full article

Equitable science education not only requires access to schooling but also to the cultural tools and pedagogies through which contemporary science is practised. Astronomy is a revealing case because it demands complex visuospatial reasoning and is often taught through static textbook diagrams, particularly in resource-constrained settings. This study examines whether an integrated embodied digital astronomy module can broaden Grade 9 students’ epistemic access in such settings. The module combined whole-body role plays, guided gestures, low-cost physical models, workbooks for student-generated diagrams, and carefully designed Hindi-medium simulations and games focused on the solar system and the Earth–Moon–Sun system. A quasi-experimental design was implemented in 14 rural Hindi-medium government schools in Rajasthan, with seven schools (169 students) using the module and seven schools (118 students) continuing with regular chalk-and-talk instruction. Pre- and post-tests assessed astronomy content knowledge, with an emphasis on visuospatial items, as well as attitudes and beliefs. Average scores of students from intervention schools increased from 33% to 47%, while those for students from non-intervention schools showed minimal gains (from 32.07% to 32.98%). The difference in gain scores for intervention schools was statistically significant. Improvements were strongest on spatial and explanatory items aligned with the embodied digital activities, and in the interventions students reported slightly more positive attitudes toward science and astronomy. Deeply held astrological beliefs showed little change over the short intervention. Classroom observations indicated a shift in intervention lessons from teacher-dominated recitation toward role plays, student diagrams, and interactive discussions around simulations, while non-intervention classrooms remained textbook-driven. The findings suggest that coordinated embodied digital pedagogy, designed for resource-constrained settings, can enhance both learning and participation in astronomy education. By providing access to interactive visualizations and collaborative activities traditionally unavailable in such contexts, the module offers a practical strategy for broadening epistemic access and addressing educational inequity, with potential applicability beyond the specific setting of this study. Full article

The accuracy and resolution of digital elevation models (DEMs) determine the reliability of outputs used in hydrological modeling. However, an assessment of their suitability should not be limited to vertical accuracy alone. Particularly in small, highly fragmented catchments, uncertainty in elevation representation can propagate to subsequent derivatives depending on the local topographic structure and, consequently, influence the interpretation of runoff organization and sediment transport. The aim of this study is to determine how differences between multi-source elevation models propagate from the underlying elevation representation through slope and runoff organization to the drainage network and the spatial pattern of sediment connectivity. In the Stocki Creek catchment in the Lublin Uplands (SE Poland), ASTER GDEM, SRTM, EU-DEM, and a local TIN model were compared. Aerial laser scanning (ALS) data were used as a high-resolution reference for validating elevation and derived outputs. Vertical accuracy was assessed using RMSE and NMAD, and then the representation of relief, slope, hydrologic routing outputs, the drainage network, and the Sediment Connectivity Index (SCI) were compared. The smallest discrepancies relative to ALS were obtained for the TIN (RMSE 1.48 m; NMAD 0.41 m), and the largest for ASTER (6.78 m; 2.37 m). Errors were highest in forested areas. All models reproduced the general elevation pattern of the watershed relatively well, whereas differences were much more pronounced in products dependent on local gradients and topographic continuity. Satellite models smoothed out the steep slopes of gullies, generalized short runoff lines, and led to a simplification of the spatial pattern of sediment connectivity. The results show that the usefulness of elevation models in hydrological applications should be assessed not only by their vertical accuracy but also by the propagation of uncertainty to subsequent processed products. Validation against ALS therefore allows for an assessment of the reliability of multi-source Earth Observation (EO) products throughout the entire analytical chain: from elevation, through local morphometry and runoff routing, to the drainage network and sediment connectivity. Full article

Newly discovered theropod footprints are described from collapsed rock blocks derived from the Lower Cretaceous Guantou Formation at the Shangantian tracksite in Dongyang City, Zhejiang Province, eastern China. The specimens are preserved in at least 18 natural molds on a single siltstone slab. The footprint assemblage is composed of minute-sized tridactyl tracks with lengths ranging between 6.8 cm and 8.8 cm. Morphologically, these tracks exhibit a relatively wide divarication angle between digits II and IV, a low length/width ratio, weak mesaxony, and a prominent, elongate metatarsophalangeal pad associated with digit IV aligned along the long axis of digit III. Based on these diagnostic traits, the new tracks are tentatively assigned to Jialingpus isp. The reduced mesaxony and incomplete expression of the medial metatarsophalangeal area behind digit II represent morphometric and preservation-driven variations characteristic of Cretaceous Jialingpus material. Kinematic estimates from three distinct single-step trackways indicate that the trackmakers were moving at slow walking speeds of 0.4–0.8 m/s (1.4–2.9 km/h). This discovery enriches the mid-Cretaceous dinosaur track diversity in Zhejiang Province and provides important ichnological insights into the evolution and morphometric variation of Jialingpus across the Jurassic–Cretaceous boundary. Full article