Search Results (305)

Soil particle size fractions (PSFs)—sand, silt, and clay—are fundamental determinants of soil hydrological behavior, nutrient retention, and erodibility, yet their spatial prediction remains challenging due to the compositional nature of the data, unquantified prediction uncertainty, and limited interpretability of machine learning models. This study develops an integrated compositional mapping framework incorporating multi-source Sentinel-1/2 and topographic covariates, coupling the isometric log-ratio (ILR) transformation with Quantile Regression Forests (QRFs), a Monte Carlo simulation (MCS)-based latent-to-physical space uncertainty propagation strategy, and a Wrapper-SHAP attribution method to jointly address these challenges. The framework was evaluated across regional croplands in the central Shandong mountain-hilly region of China, using an elevation-stratified spatial cross-validation. Validations achieved R² values of 0.72, 0.61, and 0.59 for sand, silt, and clay, respectively, and a global Aitchison distance of 0.34. Critically, the MCS error propagation strategy effectively compensated for the probability distribution shift introduced by non-linear ILR back-transformation. This ensured that all predicted compositions strictly satisfied compositional closure and the [0, 100%] constraint, while aligning the prediction interval coverage probability (PICP) of each fraction closely with the 90% nominal level. Wrapper-SHAP overcame direct attribution limitations in compositional models, revealing the predictive associations of these multi-source covariates: high remote sensing-derived Bare Soil Index (BSI) and Moisture Stress Index (MSI) values primarily exhibited strong predictive associations with sand enrichment, whereas their lower values, combined with elevated Normalized Difference Moisture Index (NDMI), Enhanced Vegetation Index (EVI), and anthropogenic indicators, favored silt and clay accumulation. The proposed framework provides a transferable methodological reference for remote sensing-integrated compositional soil mapping with reliable uncertainty estimates and interpretable driver identification at regional scales. Full article

Timely and accurate assessment of rice productivity, encompassing grain yield, aboveground biomass (AGB), and harvest index (HI), is essential for harvest planning, supply chain coordination, and food security. This study evaluates the feasibility of predicting all three productivity components using satellite and weather time series data while examining trade-offs between forecast accuracy and operational lead time. Five machine learning models (CatBoost, Gaussian Process Regression (GPR), Random Forest, Ridge regression, and TabPFN) were compared across six in-season prediction windows (December to May) using Sentinel-2 vegetation indices (Normalized Difference Vegetation Index (NDVI), Chlorophyll Index Red Edge 2 (CIRE2), Land Surface Water Index (LSWI)), weather variables (minimum and maximum temperature and radiation), and agronomic records from 256 commercial and experimental rice fields in southern New South Wales, Australia, over four growing seasons (2022–2025) using leave-one-year-out cross-validation. Rolling in-season forecasts were evaluated across December–May; March was selected for further analysis as a practical window that balances accuracy and timeliness for decision-making, with minimal additional error reduction in later months closer to harvest. TabPFN had the lowest RMSE for yield prediction (RMSE = 1.85 t ha⁻¹, $r=0.72$), Ridge had the lowest RMSE for AGB (RMSE = 3.05 t ha⁻¹, $r=0.77$), while tree-based models yielded the lowest RMSE for derived HI (RMSE ≈ 0.07). HI prediction showed weak regional relationships, with direct prediction yielding $\left|r\right|\approx 0.24$ and derived HI (predicted yield divided by predicted AGB) showing $r\approx 0$. Although strong correlations ($r>0.9$) between HI and vegetation indices were observed within individual site-seasons, consistent with other studies, these relationships were highly variable across site-seasons, reflecting the difficulty of inferring HI from canopy reflectance when biotic and/or abiotic stresses decouple AGB accumulation from grain filling. Both direct and derived HI approaches yielded comparable errors, indicating that satellite and weather data lack information content for regional-scale HI prediction. These findings support satellite-based yield and AGB forecasting for operational use. Full article

This study investigates the Mw 7.1 earthquake that struck the Southern Tibetan Plateau (Xizang) on 7 January 2025, using joint Interferometric Synthetic Aperture Radar (InSAR) observations and inverse modeling to characterize the fault geometry and slip distribution. Coseismic interferograms derived from Sentinel-1 and ALOS-2 data reveal complex surface deformation patterns, indicating rupture along four distinct fault segments. This configuration provides a more detailed fault segmentation than proposed in previous studies, featuring predominantly normal faulting on a north–south-trending structure consistent with regional extensional tectonics. Integrated analysis of coseismic deformation, source modeling, and Coulomb Failure Function (ΔCFF) stress changes suggests that the three secondary fault segments were potentially activated synchronously with the mainshock, in addition to the principal rupture. The results underscore the complexity of the seismic source and document the activation of an antithetic fault segment, for which the InSAR observations provide compelling quantitative evidence. Full article

Urban areas are increasingly vulnerable to climate-related stresses such as heatwaves, flooding, and resource inefficiencies, requiring integrated, data-driven strategies to enhance resilience and sustainability. This study presents a modular assessment and planning framework that combines Geographic Information Systems (GIS), Building Information Modeling (BIM), City Information Modeling (CIM), microclimate simulations (ENVI-met, SWMM), Life Cycle Assessment (LCA), and remote sensing within a unified decision support interface (DSI). The framework operates across multiple spatial scales—from individual buildings to entire cities—to assess climate vulnerability, support evidence-based urban regeneration, and inform sustainable renovation strategies. It enables the identification of multifunctional interventions that reduce climate risks while improving energy efficiency, resource management, and environmental quality. Urban areas are classified based on their exposure and sensitivity to climate stressors, providing a systematic basis for prioritizing adaptation and mitigation measures. The approach is validated through a case study in Daegu, Republic of Korea, a city facing an aging building stock and increasing climatic pressures. The framework is presented as a conceptual design operating at Technology Readiness Level (TRL) 3–4, indicating that it has passed its proof-of-concept, with key components including ENVI-met microclimate simulations and Sentinel-2/Landsat remote sensing processing demonstrably operational for the Daegu context. Illustrative performance benchmarks drawn from the peer-reviewed literature demonstrate that framework-guided interventions can achieve urban heat island reductions of 1.5–4.0 °C via green roof and reflective surface combinations; stormwater runoff reductions of 30–60% through sustainable urban drainage systems; and building energy savings of 25–45 kWh/m²/yr from deep façade renovation. Its modular and transferable design ensures applicability across diverse urban contexts with similar climatic and infrastructural challenges. Full article

Food security is a growing global concern, and accurate crop mapping in major grain-producing regions like China’s Sanjiang Plain—which contributes approximately 7% of national grain output—is essential for agricultural resource management. However, crop classification in this area is hindered by frequent cloud cover, complex phenological rhythms, and spatial heterogeneity. To address these challenges, this study proposes Spatial-Temporal Attention U-Net (STA-UNet), a crop classification model based on time-series Sentinel-2 imagery, incorporating four key modules: Convolutional Block Attention for enhanced sensitivity to parcel boundaries, Temporal Attention Encoder for adaptive capture of temporal dependencies under cloud interference, Dynamic Upsampling for improved boundary recovery of small parcels, and Adaptive Feature Fusion for bridging semantic gaps between heterogeneous features. Extensive experiments on rice, maize, and soybean classification demonstrate that STA-UNet achieves an overall accuracy of 93.61% and an F1-score of 0.925, outperforming state-of-the-art methods. In spatial generalization tests, STA-UNet maintains overall accuracy above 85.02% in the left-subregion transfer setting and achieves the best three-year average OA of 81.34% in the rice-dominated right-subregion stress test, while temporal generalization tests confirm limited inter-annual performance degradation. These results indicate that STA-UNet provides a robust and effective framework for crop mapping in cloud-prone, phenologically complex agricultural regions. Full article

Leydig cells (LCs) represent a somatic testicular population responsible for testosterone synthesis, a hormone essential for spermatogenesis and male fertility. The obesity condition impairs LC steroidogenic activity, contributing to testicular oxidative stress and male reproductive dysfunctions. Using a high-fat-diet (HFD) murine model, we investigated the regulatory role of the nuclear factor of activated T cells 5 (NFAT5s) in the obesity-induced LC damage and the resulting alterations in intergenerationally inherited sperm circRNA cargo. Our findings reveal a significant upregulation of both circNFAT5 and NFAT5 protein levels in HFD testis. This molecular signature correlated with decreased antioxidant defense system, increased LC apoptosis, and impaired steroidogenesis. In vitro experiments, performed in TM3 cells, confirmed that NFAT5 nuclear shuttling drives proapoptotic gene activation, while NFAT5 silencing promotes LC survival. The analysis of HFD progeny (F1H) revealed a full recovery of testis oxidative status and LC apoptosis, linked with the recovery of NFAT5 expression. However, a steroidogenic deficiency persisted in F1H offspring. Notably, HFD and F1H epididymides exhibited NFAT5 overexpression concomitantly with impaired sperm morphology, motility, viability, and altered sperm circRNA profiles alongside a deregulated 4-hydroxy-2-nonenal (4HNE) profile, a marker of sperm oxidative stress. Lastly, an enhanced FUS-related amplification of circRNA perturbations was highlighted in F1H spermatozoa. Collectively, our findings reveal a dual functional role of NFAT5 as a testicular regulator of LC fate and an epididymal sentinel of metabolic stress, in turn linking paternal obesity to the persistent transmission of sperm epigenetic anomalies across the offspring. Full article

As an important component of forest ecosystem processes, leaf litterfall plays a key role in nutrient cycling and ecosystem functioning. However, monitoring litterfall dynamics in subtropical forests remains challenging due to complex community structures and asynchronous leaf phenology, which limit the applicability of remote sensing approaches developed for temperate forests. As a critical linkage between vegetation and soil carbon pools, leaf litterfall directly influences forest carbon sequestration by providing carbon inputs in the form of litter. Unlike the concentrated autumn leaf fall in temperate forests, subtropical forests exhibit complex community structures with concurrent leaf abscission and new leaf growth, limiting the applicability of temperate-focused remote sensing techniques. To address this, we collected annual leaf litterfall data from 18 plots in eastern China’s subtropical forests and integrated these with high-resolution Sentinel-2 imagery using supervised machine learning models to develop a novel monitoring method. Our results indicated that subtropical forests exhibited clear seasonal leaf litterfall peaks during spring, summer, and autumn. Sentinel-2 satellite imagery combined with supervised machine learning algorithms can effectively monitor forest leaf litterfall dynamics. Temporal models, which use multi-date monthly spectral differences (R²_adj = 0.70, RMSE = 0.46, RPD = 1.86), significantly outperformed instantaneous models based on single-date canopy states (R²_adj = 0.33, RMSE = 0.85, RPD = 1.24). Following variable selection, model performance improved, with R² increasing by more than 2% in most models and the number of variables reduced by over 44%. Robustness analysis indicated that the model was spatially robust (no significant bias among sites), and despite seasonal intercept differences, the slopes were consistent, enabling reliable tracking of litterfall dynamics. Among the examined spectral indices and canopy characteristics, those reflecting canopy greenness, pigments, and structure contributed over 65%, with WV-VI, MCARI2, and LAI being most influential. Incorporating drought-sensitive water indices and soil exposure-related mineral indices further enhanced model performance. These indices may partially reflect drought stress or seasonal canopy opening. Our findings provide a new method for monitoring leaf litterfall dynamics in structurally complex subtropical forests and offer a critical theoretical basis for accurately assessing leaf fall dynamics. Our findings provide a novel and effective method for monitoring leaf litterfall dynamics in structurally complex subtropical forests, improving seasonal litterfall assessment and supporting vegetation monitoring, with potential implications for ecosystem- and carbon-related studies. Full article

Subsidence is the lowering of the ground surface caused by both natural processes, such as geological and tectonic dynamics, and anthropogenic activities related to land and resource use. Identifying and monitoring this phenomenon is essential for several reasons, including ensuring public safety, supporting the sustainable management of subsurface resources, and mitigating potential economic impacts. This study investigates ground deformation in an underexplored sector of the Calabria Region (Southern Italy), namely the Sant’Eufemia Plain. To this end, long-term Sentinel-1 datasets were processed using multi-temporal Synthetic Aperture Radar Interferometry techniques. Significant subsidence, reaching locally up to −17 mm/yr, was detected in the industrial area of San Pietro Lametino. Historical SAR datasets (ERS, ENVISAT) and optical imagery were used to reconstruct the long-term evolution of deformation since the 1990s. Satellite observations were integrated with rainfall records, piezometric data, and geotechnical modelling. A spatially distributed comparison between groundwater level variations and InSAR-derived deformation, supported by local time-series analysis, highlights weak and inconsistent correlations, indicating that groundwater fluctuations alone do not linearly control subsidence. The results suggest that subsidence is primarily associated with long-term consolidation processes affecting highly compressible Holocene deposits, likely enhanced by anthropogenic loading, while groundwater variations may contribute by modifying effective stress conditions within the subsoil. The relative contribution of these processes remains unquantified, highlighting the need for coupled hydro-mechanical investigations. Full article

In order to ensure agricultural productivity and, implicitly, food security, irrigation is indispensable in regions with extensive agricultural areas subject to water stress. In such areas, approaches are needed to optimize water resources and better understand irrigation needs, aspects that cannot be captured exclusively through administrative reporting. This study analyzed the annual dynamics of irrigation in Galați County, Romania, during 2017–2025 using satellite data, spatial analysis techniques, and public administrative records. Using a Random Forest classifier applied to multitemporal Sentinel-2 and Landsat data, annual maps of irrigated areas were generated and their distribution inside and outside official irrigation systems was analyzed. Satellite-detected irrigated area varied from 19.1 thousand ha in 2017 to 41.8 thousand ha in 2023, broadly consistent with hydroclimatic variability. Agreement between satellite data and official reports was moderate at the aggregate level (r = 0.782, R² = 0.612), but strong at the irrigation-scheme level (r = 0.907, R² = 0.822). Spatial analysis further showed that 60.1% of the cropland-filtered outside-system irrigated area was located within 500 m of the nearest potential surface water source. The results indicate that satellite-based analysis can serve as a useful complementary tool for irrigation monitoring, spatial assessment, and county-scale irrigation auditing. Full article

Antimicrobial resistance (AMR) is one of the most significant threats to healthcare systems, particularly in low- and middle-income nations where infection prevention and control, antimicrobial stewardship, and laboratory surveillance might not be optimal. Acinetobacter baumannii is a high-risk nosocomial pathogen that has a strong capacity to develop extreme resistance phenotypes. Still, the degree to which extensively drug-resistant (XDR) and pandrug-resistant (PDR) phenotypes reflect the cumulative impact of antimicrobial pressure at unit and system levels in Iraqi hospitals is not fully described. This was a cross-sectional surveillance study that was a laboratory-based investigation done in public hospitals in the Governorate of Kirkuk between January 2024 and January 2025. The BD Phoenix system identified 80 non-duplicate A. baumannii isolates that were obtained in high-risk hospital units. The interpretation of antimicrobial susceptibility testing was done according to CLSI guidelines. Internationally recognized definitions were adjusted to local therapeutic availability to classify isolates as XDR or PDR. Unadjusted odds ratios and Fisher’s exact test were used to assess the associations between the PDR phenotype and the chosen clinical or unit-level variables. Among the 80 isolates, 60 (75%) were XDR and 20 (25%) were PDR. Burn units and wound-related infections were disproportionately represented by PDR isolates. There were significant associations between the PDR phenotype and burn unit admission, wound infection, exposure to invasive devices, long hospitalization (greater than 14 days), and previous exposure to broad-spectrum antibiotics. ICU admission and respiratory infection were not significantly related. Cefepime had in vitro activity only in a subset of XDR isolates. Extreme resistance phenotypes can be used as convenient sentinel measures of cumulative antimicrobial pressure and system-level stress in resource-limited environments. There is an urgent need to strengthen infection prevention and control, antimicrobial stewardship, and laboratory surveillance to preserve the remaining therapeutic options. Full article

Microplastic (MP) and nanoplastic (NP) pollution has emerged as a pervasive and still insufficiently quantified pressure on terrestrial ecosystems, yet its consequences for wild herbivores remain incompletely understood. As key links between primary producers and higher trophic levels, wild herbivores occupy a critical ecological position and may serve both as exposed receptors and as biological vectors of plastic contamination. This manuscript presents a narrative review that synthesizes recent advances in understanding the physiological, behavioural, and ecological implications of MP and/or NP exposure in free-ranging herbivorous mammals, integrating evidence from field surveys, experimental studies, ecological modelling, and supportive mechanistic findings from livestock and experimental mammalian systems. Available evidence indicates that MPs and NPs are consistently detected in wild herbivores from both human-modified and protected landscapes, demonstrating widespread terrestrial exposure. Reported biological effects include oxidative stress, digestive dysfunction, inflammatory and immune responses, altered gut microbial communities, impaired nutrient assimilation, and organ-level damage, although much of the mechanistic evidence derives from controlled laboratory or livestock-based studies rather than direct wildlife investigations. Behavioural responses remain comparatively underexplored, particularly in large-bodied herbivores, with limited evidence for altered foraging, habitat use, and stress-related behaviours. At the ecosystem level, emerging studies suggest that herbivores may contribute to the landscape-scale redistribution of MPs and NPs through movement and faecal deposition, with potential downstream effects on soil processes, nutrient cycling, and plant–herbivore interactions. However, the current evidence base is constrained by major methodological and conceptual limitations, including the lack of standardized detection and reporting protocols, limited ecological realism in exposure studies, taxonomic and geographic biases, and poor resolution of long-term population-level and food-web consequences. Overall, the available literature indicates that MP and NP pollution represent a multifaceted and emerging risk to wild herbivores and the ecosystems they inhabit. Future research should prioritize standardized contamination-controlled monitoring, non-invasive faecal surveillance, ecologically realistic chronic exposure studies, and integrated conservation frameworks that recognize wild herbivores as sentinel species for terrestrial plastic pollution. Full article

Ground deformation monitoring is pivotal for enhancing urban resilience and mitigating geohazards. This study presents a synergistic monitoring framework integrating 26 Sentinel-1A (C-band) and 16 LuTan-1 (L-band) SAR scenes acquired between December 2023 and August 2025 to characterize the deformation dynamics in Beijing. Utilizing SBAS-InSAR, we first established a regional deformation baseline using Sentinel-1A observations, identifying critical subsidence and uplift zones in the eastern plains. Subsequently, high-resolution (3 m) LT-1 data were leveraged to achieve refined spatiotemporal characterization of these deformation hotspots. Validation against ground leveling benchmarks confirmed that both satellites yield high accuracy. LuTan-1 (RMSE = 3.810 mm/a) shows slightly better agreement with the ground leveling data than Sentinel-1A (RMSE = 4.853 mm/a). Analysis of the spatiotemporal patterns derived from InSAR revealed that the study area is characterized by widespread gene uplift (averaging ~10 mm/a), interspersed with acute localized subsidence exceeding 40 mm/a. Correlation analysis demonstrates a high spatiotemporal coupling between the extent and rate of surface uplift and groundwater level recovery. To further investigate these dynamics, Terzaghi’s effective stress principle is employed to quantify the contribution of groundwater level fluctuations to the observed surface deformation. A Parametric Harmonic Model was implemented to decouple elastic and trend components, and attribution analysis confirms that the continuous recovery of groundwater levels is the fundamental driver of the regional surface uplift. The inverted elastic skeletal storativity (S_ke), ranging from 1.587 × 10⁻³ to 9.184 × 10⁻³, reveals that regional surface uplift is predominantly driven by the elastic rebound of aquifer systems following groundwater recovery. In contrast, localized subsidence anomalies observed at large-scale engineering construction sites, landfill facilities, major expressway corridors, and high-density residential areas are independent of groundwater fluctuations, instead they are primarily attributed to anthropogenic stressors. This study elucidates a dual-drive mechanism, which comprising macroscopic hydrogeological rebound and localized anthropogenic disturbance, providing a robust scientific basis for differentiated urban hazard management. Full article

Neuroblastoma remains a formidable pediatric malignancy characterized by profound metabolic plasticity and limited therapeutic responsiveness in high-risk disease. Emerging evidence positions the interplay between Reactive Oxygen Species (ROS) and the metabolic sentinel AMP-activated protein kinase (AMPK) as a critical regulator of tumor metabolic stress and apoptotic susceptibility, with additional implications in the systemic pathology of Cancer Cachexia. Building on our previous work demonstrating that 1,1-Diethoxyethane (1,1-DEE; Biosacetalin), a volatile aroma compound inhibits mitochondrial complex I, induces ROS production, and activates AMPK-PGC1α-mediated mitochondrial biogenesis accompanying enhancement of aerobic respiration, leading to anti-Warburg effect. We identify 1,1-DEE as a previously unrecognized metabolic modulator with potent antitumor activity. 1,1-DEE triggers ROS-induced AMPK activation, leading to apoptotic elimination of neuroblastoma cells (SH-SY5Y), robust suppression of tumor growth, and significant prolongation of survival (median survival 77 days) in tumor-bearing NSG mice. Strikingly, 1,1-DEE simultaneously alleviates cancer-associated cachexia by preserving body weight. Mechanistically, our findings reveal a ROS–AMPK–centered signaling axis through which 1,1-DEE integrates tumor-selective cytotoxicity with systemic metabolic protection, highlighting a unified therapeutic strategy for targeting both tumor progression and cachexia in neuroblastoma. Full article

The rapid expansion of seaweed aquaculture has profound impacts on coastal ecosystems, yet the lack of long-term, high-precision spatiotemporal monitoring methods has constrained systematic understanding of aquaculture dynamics and their environmental effects. This study integrated Landsat (1984–2025) and Sentinel-2 (2015–2025) imagery with an attention-enhanced U-Net deep learning model to achieve 41 years of continuous monitoring of seaweed aquaculture in the Dongtou Archipelago, Zhejiang Province, China. The model achieved high extraction accuracy for both Landsat and Sentinel-2 aquaculture areas (F1 scores of 0.972 and 0.979, respectively). On this basis, the cultivation zones were further classified into Porphyra sp. and Sargassum fusiforme cultivation areas by incorporating local aquaculture planning and field survey data. Results showed that the aquaculture area underwent three developmental stages: slow initiation (1984–2000, <3 km²), rapid expansion (2001–2015, 3–8 km²), and high-level fluctuation (post-2015, typically 8–20 km²), reaching a peak of ~30 km² during 2018–2019. Long-term retrieval of water quality parameters revealed that the decline in total suspended matter (from ~80 to 60 mg/L) and chlorophyll (from ~3 to 2 μg/L) within aquaculture zones was significantly greater than that in non-aquaculture areas, providing direct observational evidence for local water quality improvement by appropriately scaled aquaculture. Meanwhile, sea surface temperature showed a sustained increasing trend, with extremely high-temperature days (≥25 °C) exhibiting strong interannual variability, posing potential thermal stress risks to cold-preferring seaweed species. The NDVI (Normalized Difference Vegetation Index) and FAI (Floating Algae Index) indices effectively captured aquaculture phenology (seeding, growth, maturation, harvest), with their interannual peaks exhibiting an inverted U-shaped correlation with corresponding yields (R = 0.82 and 0.79, respectively, based on quadratic regression fitting), preliminarily demonstrating the potential of remote sensing in indicating density-dependent effects. This study systematically demonstrates the comprehensive capability of multi-source satellite remote sensing in long-term dynamic monitoring, environmental effect assessment, and yield relationship analysis of seaweed aquaculture, providing key technical support and scientific basis for aquaculture carrying capacity management and ecological risk prevention in island waters. Full article

Pitahaya (Hylocereus spp.) production is increasingly affected by climatic factors, as well as by phytopathogens and abiotic stress, leading to delays in agronomic interventions and reduced productivity. The objective was to design, implement, and validate a multimodal system (CARYPAR) that enables early disease detection and agile decision-making, characterized by low latency and reduced dependence on cloud connectivity. The methodology integrates climate reanalysis from NASA POWER, biophysical remote sensing variables derived from Sentinel-1/2, and proximal computer vision captured via mobile devices using a late fusion architecture and an optimized convolutional neural network, EfficientNet-V2B0, which discriminates between optimal and pathological conditions in vegetative tissues and fruit. The results of the experimental validation carried out in 160 georeferenced units achieved an overall accuracy of 80.0% and an F1 score of 0.8645 for Bad Fruit. The McNemar test and the operational agreement with agro-industrial experts yielded a Cohen’s Kappa index of κ = 0.6831, with an inference latency reduced to 22.00 ms. It is concluded that the multimodal integration of satellite bio-environmental data with edge computer vision achieves substantial agreement with agronomic expert judgment under heterogeneous field conditions (Cohen’s κ = 0.6831), supporting its role as a decision-support tool rather than a replacement for expert assessment. Therefore, its adoption can enhance real-time irrigation management and crop protection, while contributing to traceability and sustainable resource management in agricultural regions with limited connectivity. Full article