Search Results (8,973)

Conventional multi-mission altimetry fusion tends to attenuate short-wavelength sea surface height anomaly (SLA) signals when high-density two-dimensional SWOT observations are incorporated into a single smoothing framework. To address this limitation, this study proposes a scale-separated, scale-wise fusion framework for high-resolution SLA reconstruction that jointly exploits multi-mission nadir altimetry and SWOT wide-swath observations. Multi-mission Level-3 observations from Sentinel-3A/B, HY-2B, SARAL/Altika, and SWOT are first harmonized through quality control, spatiotemporal reference unification, and cross-calibration referenced to Jason-3; Jason-3 was not used as a fusion input; instead, it served as the cross-calibration reference and as an external validation source after excluding calibration-involved samples. The SWOT-observed SLA field is then decomposed using an 80 km Lanczos filter—chosen as a practical working scale reflecting SWOT’s effective resolution rather than a universal physical boundary—into a large-scale background component and a mesoscale–submesoscale perturbation component. The large-scale component is reconstructed using adaptive optimal interpolation with latitude-dependent covariance scales, whereas the mesoscale–submesoscale component is refined through a physically regularized Transformer-based learning branch that recovers organized sub-80 km variability as a relative enhancement with respect to the AVISO/CMEMS reference. The two components are finally recombined on a 0.08° × 0.08° grid to generate a global SLA product. Validation from August 2023 to August 2024 shows that the proposed product maintains strong large-scale consistency with AVISO/CMEMS, with a mean daily spatial correlation of approximately 0.85. Sample-independent cross-validation against concurrent Jason-3 along-track observations yields a mean daily RMSE of 4.9 cm. Regional case studies in the Kuroshio Extension and the Scotia Sea further show that, relative to a conventional unified fusion scheme, the proposed framework better preserves organized sub-80 km structures, including fronts, eddy boundaries, and filamentary features, without degrading the large-scale background. Two specific technical contributions are (i) a reproducible scale-separated workflow that decouples large-scale OI mapping from fine-scale learning-based reconstruction, and (ii) a physically regularized loss formulation that constrains spatial gradients and Laplacian smoothness to suppress nonphysical artifacts during small-scale enhancement. These results suggest that scale-separated fusion provides an effective and operationally practical strategy for next-generation high-resolution SLA products and for improved observation of dynamically significant short-wavelength ocean variability. Full article

This study investigates the dyke swarms of the Um Dwiela area in the southern Egyptian Shield through a combined approach of remote sensing, field investigations and laboratory analyses, including mineralization and radioactive prospecting. Radioelements laboratory measurements and optical remote sensing datasets are combined to detect the bostonite rocks and their radioactive mineralization. The processing of Landsat-8, Sentinel-2 and ASTER data effectively delineated the country rocks, bostonite dykes and structural elements. Field observations indicate that the dykes trend NE-SW, extending approximately 12 km with widths ranging from 1 to 13 m. These dykes have experienced multiple alteration phases, pointing to the influence of hydrothermal fluids. Uranium mineralization is structurally controlled, occurring within fractures at the contact between bostonite and metasedimentary rocks. Average measurements obtained using a NaI(Tl) analyzer reveal elevated and variable radionuclide concentrations [²³²Th (442.25 Bq/kg), ²³⁸U (608.43 Bq/kg), and ⁴⁰K (1141.41 Bq/kg)], all exceeding internationally permissible safety limits. Multiple radiological hazard indices further indicate a substantial radiation risk, with all values classified as high according to global standards. Consequently, the associated gamma radiation exposure poses an elevated radiological hazard concern. Full article

Phthalic acid esters (PAEs), ubiquitous plasticizers and recognized endocrine-disrupting chemicals, pose a protracted threat to aquatic ecosystems and biodiversity. However, current ecotoxicological assessments often focus on isolated chemicals at exceedingly high laboratory doses, failing to reflect true environmental risks. This review systematically evaluates and compares the multisystemic toxicological effects of six priority PAEs (DEHP, DBP, BBP, DNOP, DEP, and DMP) using the zebrafish biological model. The synthesized evidence reveals a distinct structure–activity relationship, where long-chain and highly hydrophobic congeners exhibit substantially higher toxicity than their short-chain counterparts. Exposure to these PAEs induces severe developmental, cardiovascular, neurobehavioral, and reproductive anomalies. Specifically, DBP and BBP display the most potent cardiotoxic and neurotoxic effects, while DEHP and DBP drive profound reproductive decline and endocrine disruption at concentrations as low as 0.5–20 μg/L. Crucially, comparative environmental relevance assessments indicate that real-world PAE concentrations in industrial hotspots frequently meet or exceed these laboratory-derived lowest observed effect concentrations. These findings underscore the severe ecological risks posed by PAE contamination and position the zebrafish as a vital biological sentinel. Future ecotoxicological evaluations must prioritize chronic, low-dose mixture exposures and transgenerational toxicity to fully characterize the protracted legacy of these pollutants on zebrafish populations. Full article

Tailings storage facility (TSF) failures have caused severe casualties and economic losses. This study used Enhanced Small Baseline Subset InSAR (E-SBAS-InSAR) and 88 Sentinel-1A images to retrieve the 2022–2024 surface deformation time series of the Shiguilong TSF, located in the Fe–Cu polymetallic metallogenic belt of the middle–lower Yangtze River. The reliability of the results was assessed through consistency comparisons with Small Baseline Subset InSAR (SBAS-InSAR) and Persistent Scatterer InSAR (PS-InSAR). A time-series decomposition model was applied to extract seasonal deformation components and analyze their lagged responses to temperature and intense rainfall events. The results show that: (1) E-SBAS-InSAR achieved a monitoring-point density nearly 7 times higher than SBAS-InSAR, enabling dense and long-term deformation characterization; (2) subsidence at Shiguilong continued to increase, with cumulative subsidence reaching −76.8 mm and a maximum annual mean subsidence rate of −22.78 mm/yr; (3) deformation was mainly controlled by long-term consolidation of loose tailings and creep of dam–tailings materials, while seasonal factors induced stage-dependent fluctuations; (4) seasonal deformation showed lagged responses of 6 days to temperature variations and 2 days to intense rainfall events, with rainfall exerting a more pronounced influence. This work is significant for TSFs monitoring under complex surface conditions. Full article

Background/Objectives: Standard gastrectomy with lymph node dissection up to D1+ achieves good oncologic control for early gastric cancer not amenable to endoscopic submucosal dissection, yet it frequently leads to post-gastrectomy syndromes and long-term nutritional impairment. Local resection of the stomach reduces post-gastrectomy syndrome; however, the extent of lymph node dissection should be limited beyond D1+ in such cases. This review evaluates the safety of local resection for early gastric cancer reported in East Asia. Methods: We reviewed current concepts and clinical evidence regarding (i) the limitations of preoperative nodal staging, (ii) sentinel node biopsy and function-preserving gastrectomy, and (iii) functional outcomes and procedure-specific complications following local resection, with a focus on delayed gastric emptying. Results: Conventional imaging and biomarkers are inadequate for reliable preoperative identification of node-negative disease. Conversely, sentinel node biopsies demonstrate high intraoperative diagnostic accuracy. Large prospective studies have revealed that, when indications are strictly adhered to, sentinel node biopsy-guided function-preserving gastrectomy can yield survival outcomes comparable to those of standard gastrectomy. The indications for local resection include solitary submucosal tumors below 4 cm in size, diagnosed as node-negative by sentinel node biopsy. Although the available quality-of-life data are generally favorable, there is risk of delayed gastric emptying in local resection with limited lymph node dissection in cases of early gastric cancer. Postoperative gastric deformity following closure was identified as the primary cause. Conclusions: Local resection for submucosal gastric cancer guided by sentinel node biopsy may be oncologically acceptable and function-preserving; however, the prevention of gastric deformity is crucial for its safe implementation. Full article

Introduction: International studies have demonstrated a positive impact on air quality associated with the presence of green areas in urban conglomerates. However, in Brazil, studies addressing the impacts of urban green areas on air quality are still incipient and are predominantly focused on large urban centers. The objective of this study was to investigate the relationship between urban green areas, surface temperature (LST), and air quality across 15 medium-sized Brazilian cities. Methods: Concentrations of particulate matter fractions (PM₁, PM_2.5, and PM₁₀) were monitored from January 2023 to May 2024 using second data from low-cost sensors. The NDVI and both daytime and nighttime LST profiles were extracted via Google Earth Engine within a 1 km buffer zone surrounding each station via the Sentinel-2 and MODIS 11A1 satellite data, respectively. Spatial–temporal co-variation patterns were explored using principal component analysis (PCA). To model these dynamics while controlling for spatial dependencies, a multi-criteria framework compared linear models (simple linear regression (LM) and linear mixed (LMM)) and generalized models (generalized additive (GAM) and generalized additive mixed (GAMM)). Results: The results revealed a positive relationship between NDVI and PM_2.5 and PM₁₀ fractions in specific regions, while surface temperatures showed a direct association with finer particles (PM₁ and PM_2.5). The regression coefficient showed the significant association of PM2.5 with NDVI and nighttime LST (β = 1.330; IC 95%: [0.397; 2.270]; p = 0.005). The GAMM was the best-fitting model for all particle fractions, demonstrating that incorporating monitoring stations as random intercepts successfully controls for unmeasured local heterogeneity, while penalized splines accurately capture non-linear environmental factors. Conclusions: Although many studies have shown that green areas in temperate regions typically act as consistent sinks for particulate matter, our study revealed localized and seasonal responses in tropical urban landscapes. It should be noted that our study is conducted on a national scale and that the use of low-cost sensors and remote sensing does not allow us to distinguish between the localized microclimatic benefits of vegetation and the long-range transport of regional pollutants. Full article

Soil surface moisture (SSM) is a critical indicator of agricultural drought, yet high-resolution projections under climate change remain scarce. This study develops a machine learning framework to predict and project SSM at 1 km resolution across five European Living Labs (LLs), encompassing vineyards, olive groves, and fruit tree systems. Historical Sentinel-1 SSM observations (2014–2024) were used to train ensemble models (Random Forest, XGBoost, ExtraTrees, LightGBM) incorporating climate variables, soil texture, topography, and land use. Tree-based models achieved R² values of 0.63–0.87. Vineyards showed the highest predictability (R² ≈ 0.87), reflecting their sensitivity to short-term atmospheric demand and surface water availability, whereas olive groves were the least predictable (R² ≈ 0.63–0.68), consistent with deeper rooting systems and greater drought buffering capacity. When forced with bias-corrected CMIP6 projections under SSP1-2.6 and SSP5-8.5 for 2041–2070, models indicate minimal changes under SSP1-2.6 but pronounced SSM declines of 8–24% under SSP5-8.5, with historically wetter regions experiencing the largest absolute losses. SHAP analysis confirmed precipitation and potential evapotranspiration as dominant predictors across all crops. This framework provides spatially explicit, crop-relevant SSM projections to support climate adaptation in European agricultural landscapes. Full article

Marine-terminating glaciers are major contributors to sea-level rise, yet their frontal ablation—the combined loss from ice discharge and terminus retreat—remains poorly constrained. This study presents a monthly time series of ice discharge for 40 marine-terminating glaciers in Alaska from 2015 to 2021, derived from Sentinel-1 velocity data, and reconstructed ice thickness information. Frontal ablation was calculated as the sum of ice discharge and terminus mass loss, from manually delineated terminus positions between 2015 and 2020. The mean annual ice discharge was 11.81 ± 5.35 Gt a⁻¹, dominated by Hubbard, Columbia and Yahtse glaciers, which together accounted for ~70% of Alaska’s total ice discharge. Terminus retreat contributed an additional 1.30 ± 0.07 Gt a⁻¹, resulting in a total frontal ablation of 13.11 ± 5.35 Gt a⁻¹. Most glaciers exhibited late-summer velocity minima indicating seasonal changes in subglacial drainage efficiency, while the strongest relationship was found with regional ocean temperature. These findings confirm that Alaska’s marine-terminating glaciers currently lose relatively little mass through frontal retreat compared to their regional mass balance. Our observations are consistent with previous studies suggesting that many Alaskan marine-terminating glaciers have passed their phase of rapid retreat. The presented analysis also provides fundamental information for refining sea-level rise projections. Full article

Rice cultivation commonly employs the continuous flooding (CF) method, which depends heavily on water availability creating anaerobic conditions for methane (CH₄) emissions. Rainfed rice areas rely on precipitation for irrigation, making the system sensitive to climatic variability. This study examines associations between ENSO phases and satellite-observed atmospheric XCH₄ variability over Thailand using GOSAT as the primary long-term dataset from 2012 to 2022, with Sentinel-5P/TROPOMI used as a supporting dataset for recent spatial patterns. The analysis conducted covers three cropping seasons: (1) January–April, (2) May–August, and (3) September–December. The results indicate comparable average atmospheric methane concentrations of 1787.94 ± 11.50 XCH₄ (ppb) during El Niño, 1788.8 ± 11.22 XCH₄ (ppb) in neutral conditions, and 1793.45 ± 10.93 XCH₄ (ppb) during La Niña. The obtained data indicate a seasonal variability, with the highest satellite-observed XCH₄ values found during September–December, corresponding to the main growing period of wet-season rice. The results suggest that climate change amplifies these anomalies through altered precipitation patterns and water availability. Current rice cultivation practices warrant reconsideration, in particular the alternate wetting and drying (AWD) method, offering reduced CH₄ emissions while conserving water resources. This underscores the importance of water management strategies for sustainable rice production and resilience to climate variability. Full article

The continuous analysis of ground deformation is essential for both the assessment of natural hazards and the monitoring of human-induced activities. In this study, we present the results of a Persistent Scatterer Interferometry (PSI) analysis of ground deformations in the region of Cluj-Napoca, Romania. The PSI was performed using more than 10 years of Sentinel-1 ascending and descending Synthetic Aperture Radar data from 2014 to 2025, using a dual master approach. Results show significant displacements at many locations, including recently built-up areas at the edges of the city, often caused by the combined effect of anthropogenic activities and geological conditions. In this study, we highlight three case studies: the surroundings of a reclaimed mine, subsidence induced by dewatering, and a large-area, slow landslide, wherein we examined natural and anthropogenic influences. The accurately mapped and quantified ground deformations can be used for a better understanding of the geological processes and assessing the risk of the urban development in the area. Full article

Olive (Olea europaea L.) orchards on the hyper-arid Peruvian coast (Tacna, ${18}^{\circ }$ S) suffered >70$\%$ yield collapses in the 2016 and 2024 El Niño seasons against a non-failure mean of $\sim 6$ t ha⁻¹ and a 2022 La Niña bumper harvest, raising the question of whether insufficient winter chilling is the binding climate constraint. We combined in situ daily meteorology (2015–2025) with yield records from eleven Sevillana–Ascolana parcels (88 parcel-years over eight seasons) and fitted a year-level log-OLS model with mean chill-window and fruit-growth temperatures, validated by year-block bootstrap, permutation, a closed-form Bayesian posterior, and a parcel-year mixed model. The model achieves $R_{log}^2=0.65$, and the chill slope ($\beta =-0.82$) is robust across three independent tests: one-sided permutation $p=0.036$; Bayesian posterior with 99.8% of mass below zero (Savage–Dickey BF₁₀ = 15.9); parcel-year mixed model $p<{10}^{-14}$. Counterfactual restoration of chill-window temperature to its non-failure climatology recovers the full collapse in both years, whereas restoring fruit-growth temperature recovers nothing. CMIP6 delta-method projections identify a chill-collapse threshold at $\Delta T_{\mathrm{winter}}\approx +{1.25 }^{\circ }$C; SSP1-2.6 alone reduces mid-century mean yield by $\sim 52\%$, and SSP5-8.5 reaches $-89\%$ by 2051–2070. Tacna emerges as a chill-sentinel system where winter warmth, not summer heat, is the binding constraint and the transition to the failure regime lies on a near-term adaptation horizon. Full article

Accurate time series forecasting is fundamental to geohazard early warning, yet remains a major challenge. Conventional in situ geotechnical monitoring remains costly and spatially constrained, whereas deep learning applied to remote sensing data has become increasingly prevalent but often suffers from opacity of model decision-making. To address this issue, we propose a Transformer-based forecasting framework, namely PatchTST-Fusion, adapted for multivariate Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) time series. The framework integrates model interpretability analysis through TimeSHAP, providing temporal and feature-level attributions across the input sequence. Landslide deformation time series are first derived from Copernicus Sentinel-1 SAR data. Variational Mode Decomposition is then applied to decompose the non-linear signals into trend, seasonal, and noise components. The denoised displacement series are modeled and forecast using the proposed PatchTST-Fusion, which incorporates rainfall and reservoir water level fluctuations as feature-level drivers. Application to the Daping landslide cluster in the Three Gorges Reservoir Area in China demonstrates that our method captures both the long-term and transient non-linear coupling between deformation and its triggers, surpassing state-of-the-art models including CNN-BiGRU-Attention, Informer and original PatchTST with 7–55% improvements in MAE and 10–52% improvements in RMSE. Beyond predictive gains, feature attribution of environmental triggers via TimeSHAP reveals that rainfall and reservoir regulation exert temporally distinct influences on slope kinematics, with high relative importance concentrated in specific periods and characteristic lagged responses. This interpretable framework provides both enhanced forecasting accuracy and process-based insights, offering a broadly applicable tool for landslide early warning in reservoir regions. Full article

Water hyacinth (Eichhornia crassipes) is among the world’s most invasive aquatic macrophytes, yet quantitative models of shoreline preference remain absent for Lake Victoria. This study developed a distance-based quantitative framework for spatial distribution and decay modelling to quantify seasonal nearshore accumulation dynamics in Winam Gulf, Kenya, using Sentinel-2 imagery. A Support Vector Machine classifier with polygon-mean feature extraction achieved 94–96% accuracy, supported by strong spectral separability (Jeffries–Matusita distance > 1.9 in six bands). During peak dry season, water hyacinth covered 405.81 km² (27.1% of gulf area) and occurred significantly closer to shore than open water (mean preference = 687.9 m; 95% CI: 616.6–753.7 m; p < 0.001). Water hyacinth was 3.10 times more likely than open water to occur within 100 m of shoreline, with 48% of biomass concentrated within 2 km. A power-law decay model of odds ratio with shoreline distance provided superior fit (R² = 0.870, F = 10.06, p = 0.047) compared to exponential decay (R² = 0.477, p = 0.378). Critically, pronounced nearshore preference occurred only during dry-season conditions (+687.9 m to +1946.6 m), while wet–dry transition periods showed no significant preference (−124.2 m; p = 1.00), supporting wind-driven Stokes drift as the dominant transport mechanism and enabling seasonal prioritization of nearshore management interventions. Full article

The purpose of this work is to investigate the use of high-resolution ($~100$ m) surface soil moisture (SSM) maps derived from Sentinel-1 (S-1) and Sentinel-2 (S-2) data to identify irrigation events occurring in the Riaza irrigation district (Castilla y León region, Spain) from 2017 to 2021. The proposed method is based on the application of the Constant False Alarm Rate (CFAR) algorithm, which is an adaptive and unsupervised thresholding algorithm traditionally used for target detection in SAR images. This algorithm uses a sliding window approach that allows an adaptive threshold estimate for each pixel of the image, depending on the distribution of the surrounding pixels. The analysis was carried out on fields cultivated with maize, sugar beet and sunflower. Results show that the Overall Accuracy (OA) of the detection mainly depends on the time span (TS) between the S-1 passage and the irrigation event, the acquisition timing and the development stage of the vegetation. Indeed, the OA reaches a mean of $78\%$ and $70\%$, respectively, for the $6$ a.m. and $6$ p.m. acquisitions, when the irrigation events occur within $36$ h before the S-1 passage, and it follows a downward trend as the TS increases. On the other hand, when the vegetation reaches the mature stage, the mean OA decreases respectively to $56\%$ and $52\%$. Stemming from the event detection, the study explored the estimation of the total irrigated area in the early growing season, showing promising agreement with in situ data, as evidenced by the low Relative Error ($E_r\approx 5.6\%$). Additionally, the analysis revealed a significant correlation between field-scale mean SSM and irrigation depths ($R=0.89$). Full article

Accurate estimation of crop yield from remote sensing remains challenging due to the crop-specific nature of yield drivers and the difficulty of interpreting spectral indicators across agronomic systems. While many studies prioritise predictive accuracy through complex models, fewer explicitly examine the stability and physiological relevance of individual spectral and phenological indicators under controlled analytical conditions. This study investigates yield–spectral relationships in wheat and cotton using a unified Sentinel-2 indicator framework applied across multiple growing seasons in a Mediterranean agricultural environment. A consistent set of spectral and thermal indicators was derived from two phenologically targeted Sentinel-2 acquisitions per season and analysed using correlation analysis, univariate regression, constrained multivariate modelling, and recurrence analysis within an identical workflow for both crops. Distinct crop-specific patterns were observed. Wheat yield was most strongly associated with water-sensitive and canopy-related indicators, with NDWI-based metrics reaching Pearson correlations up to r = 0.85 and multivariate models explaining a substantial proportion of yield variability (up to R² ≈ 0.70) under controlled analytical conditions. In contrast, cotton yield variability was dominated by thermal accumulation, with growing degree day indicators showing correlations up to |r| = 0.59 and multivariate performance reaching R² = 0.74. Recurrence analysis indicated consistent recurrence of these indicator families across analytical stages under the examined conditions. Overall, the results indicate that parsimonious, physiologically interpretable indicator combinations can account for a meaningful proportion of yield variability without reliance on highly complex or high-dimensional modelling approaches, supporting crop-aware indicator selection for precision agriculture applications. Full article