Search Results (535)

Accurate estimation of forest soil organic carbon (SOC) is considered critical for understanding terrestrial carbon cycling and supporting climate change mitigation strategies. However, the canopy block, intricate vertical structure of forests, and the constraints of single-source remote sensing data have presented considerable obstacles for estimating forest SOC. This study proposes a forest SOC estimation and uncertainty analysis (ForSOC-UA) framework to enhance forest SOC estimation and quantify its uncertainty in the natural secondary forests of northern China by integrating hyperspectral imagery (ZY-1F), synthetic aperture radar data (Sentinel-1), and environmental covariates (such as topography, vegetation, and soil indices). The performance of traditional machine learning models (RF, SVM, and CNN), geographically weighted regression (GWR), and a geographically weighted random forest (GWRF) model was compared across three different soil depths (0–5 cm, 5–10 cm, and 10–30 cm). The results showed that GWRF consistently outperformed all other models across all soil depth layers, with the highest accuracy achieved using multi-source data (R² = 0.58, RMSE = 27.49 g/kg, rRMSE = 0.31). Analysis of feature importance revealed that soil moisture, terrain characteristics, and Sentinel-1 polarization attributes were the primary predictors, while spectral derivatives in the red and near-infrared bands from ZY-1F also played a significant role for forest SOC estimation. The uncertainty analysis indicated a forest SOC estimation uncertainty of 37.2 g/kg in the 0–5 cm soil layer, with a decreasing trend as depth increased. This pattern is associated with the vertical spatial distribution of the measured forest SOC. This integrated approach effectively captures spatial heterogeneity and nonlinear relationships between feature and forest SOC, while also assessing estimation uncertainty, so providing a robust methodology for predicting forest SOC. The ForSOC-UA framework addresses the uncertainty quantification of SOC estimation at different vertical depths based on machine learning, providing methodological enhancements for the assessment of large-scale forest SOC and the monitoring of carbon sinks within forest ecosystems. Full article

Perennial deciduous trees such as Prunus avium undergo seasonal transitions, culminating in bud dormancy establishment that involves coordinated physiological and metabolic adjustments. Dormancy monitoring in orchard systems still relies primarily on temperature-based models and forcing assays, which rarely incorporate physiological or biochemical indicators. Here, we tested whether seasonal metabolic dynamics associated with dormancy progression differ between sweet cherry genotypes and whether these physiological differences are reflected in canopy-scale vegetation indices derived from satellite observations. Field measurements were conducted in two genotypes with contrasting chilling behavior (‘Regina’ and ‘210’) during the transition from vegetative growth to dormancy. Leaf gas exchange and chlorophyll fluorescence were monitored across the season, polar metabolites in floral buds were profiled by gas chromatography-mass spectrometry, and satellite-derived vegetation indices were used to characterize canopy dynamics. Dormancy progression was associated with declines in CO₂ assimilation, transpiration, PSII photochemical efficiency, and electron transport rate, accompanied by increases in intercellular CO₂ concentration and non-regulated energy dissipation. Metabolomic analysis revealed that genotype explained a larger proportion of metabolite variation than dormancy stage (PERMANOVA R² = 0.483, p = 0.001), while principal component analysis accounted for 79.7% of total variance. Fructose showed the strongest genotype difference during paradormancy I, corresponding to an approximately 9.5-fold increase in ‘Regina’. Pathway enrichment analysis highlighted starch and sucrose metabolism and pyruvate metabolism as the most represented pathways during dormancy progression. Satellite-derived vegetation indices captured seasonal canopy decline and were significantly associated with several physiological variables. These results provide an integrated description of physiological and metabolic adjustments during dormancy establishment in sweet cherry and highlight the potential of combining metabolomics, plant physiology, and open-access satellite observations to monitor phenological transitions in orchard systems at scalable spatial and temporal resolutions. Full article

Paddy field water management and rice phenology strongly affect crop productivity and environmental processes, requiring continuous and quantitative monitoring. This study combined satellite synthetic aperture radar (SAR) observations and ground-based Global Navigation Satellite System (GNSS) interferometric reflectometry (GNSS-IR) over a paddy field to analyze their sensitivities to water-level variations and phenological dynamics. Sentinel-1 (C-band) and ALOS-2/PALSAR-2 (L-band) SAR time series were compared with continuous GNSS-IR observations acquired using geodetic-grade instrumentation. For GNSS-IR, Lomb–Scargle periodogram (LSP) analysis of SNR data was applied to derive two indicators: (i) the dominant spectral peak (f_water) frequency associated with the effective reflecting surface, and (ii) a normalized spectral integral (GNSS Phenology Indicator, GPI) representing vegetation-induced scattering and attenuation effects. The temporal evolution of LSP spectra exhibited systematic changes with rice phenological progression, including peak broadening and the emergence of multiple peaks as vegetation developed. For water level variations, L-band SAR co-polarized backscatter (VV and HH) and the GNSS-IR spectral peak exhibited comparable relationships with in situ water level, whereas C-band SAR showed weaker sensitivity. For phenological dynamics, GPI showed temporal behavior similar to that of the SAR polarization ratio (VH/VV), with clear responses around key growth stages, such as heading and harvest. These results suggest that SAR polarization-based indicators and GNSS-IR spectral characteristics can be interpreted within a consistent electromagnetic framework: co-polarized L-band SAR responses correspond to the water-surface-related GNSS-IR peak, whereas cross-polarized indicators correspond to GPI. This study demonstrated the potential of GNSS-IR as complementary information for physically interpreting SAR scattering mechanisms, highlighting a pathway toward more integrated microwave-based monitoring of land surface processes. Full article

Seasonal tropical wetlands such as the Brazilian Pantanal are increasingly threatened by climate variability and extreme hydrological events, creating a need for robust monitoring tools that capture flood dynamics at high spatial and temporal resolution. This study used Sentinel-1 Synthetic Aperture Radar (SAR) imagery to map and monitor flooding in the northern Pantanal, a Ramsar site renowned for its wildlife, between 2017 and 2020. Ground Range Detected (GRD) VV-polarized scenes were preprocessed using radiometric terrain normalization and speckle filtering (Lee filter, 5 × 5 window) to improve the separability of water and non-water surfaces. Flooded areas were initially extracted with Otsu’s histogram thresholding and validated using high-resolution optical imagery (PlanetScope and Landsat-8). A supervised Random Forest classifier then refined land-cover discrimination into three classes (open water/flood, open land/vegetation, and others), achieving an overall accuracy of 97.70% on the independent testing dataset (n = 6622), while temporal consistency was supported by Cuiabá River hydrological data. The results revealed strong interannual variability in flood extent, with inundation covering 34.7% of the reserve in March 2017 compared with 0.75% in March 2020 and reaching a peak of 79.9% in April 2017. Overall, Sentinel-1 SAR effectively delineated open water and flood-affected surfaces under persistent cloud cover, demonstrating its value for complementing existing products such as MapBiomas, strengthening wetland management, and supporting scalable flood monitoring in other tropical flood-prone Ramsar sites. Full article

Wildfires represent a significant global environmental challenge, necessitating advanced monitoring and assessment techniques. This study explores the integration of Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical data within a Google Earth Engine (GEE) framework to enhance wildfire detection, burned area estimation, and severity assessment. By leveraging SAR’s capability to penetrate atmospheric obstructions and optical data’s spectral sensitivity to vegetation changes, the proposed methodology addresses limitations of single-sensor approaches. The results demonstrate strong correlations between SAR-based indices, such as the Radar Vegetation Index (RVI) and Dual-Polarized SAR Vegetation Index (DPSVI), and traditional optical indices, including the Normalized Burn Ratio (NBR) and differenced NBR (ΔNBR). Despite challenges related to terrain influence, sensor resolution differences, and computational demands, the integration of multi-sensor data in a cloud-based environment offers a scalable and efficient solution for wildfire monitoring. During the peak of the fire events, significant atmospheric obstruction was technically verified using Sentinel-2 metadata and the QA60 cloud mask band, which confirmed persistent cloud cover and thick smoke plumes over the study areas. This interference limited the reliability of purely optical monitoring, further justifying the integration of SAR data. Future research should focus on refining data fusion techniques, incorporating additional datasets such as thermal infrared imagery and meteorological variables, and enhancing automation through artificial intelligence (AI). This study underscores the potential of remote sensing advancements in improving fire management strategies and global wildfire mitigation efforts. Full article

Polarimetric information is essential for scattering interpretation and target characterization in synthetic aperture radar (SAR) remote sensing, yet many resource-constrained platforms (e.g., small satellites and unmanned aerial vehicles (UAVs)) operate with limited polarization modes or even a single radio frequency (RF) chain, which limits full polarimetric scattering acquisition. To address this limitation, this paper proposes a single-channel framework for estimating the full polarization scattering matrix (PSM) enabled by time-varying polarization modulation. The transmit/receive polarization states are steered along predefined trajectories on the Poincaré sphere to generate time-varying polarization tags that are encoded into the received echoes through the target’s polarization-varying response. A compact observation model is then derived to relate the single-channel echoes, the known polarization tags, and the unknown PSM; based on this, the PSM is then estimated via a least squares formulation with a low-rank approximation. Simulation results demonstrate the robust reconstruction of the full polarimetric scattering matrix under diverse modulation trajectories. For arbitrarily chosen random point targets, when the signal-to-noise ratio (SNR) exceeds −20 dB, the polarimetric similarity coefficient approaches 1, and the estimation errors of Pauli power components converge toward zero. Furthermore, the method’s reliability is validated on distributed vegetation clutter. Quantitative metrics demonstrate near-perfect statistical consistency, with polarimetric entropy and alpha angle errors within 0.14%. Overall, the proposed approach provides a practical pathway to enhance the availability of full polarimetric scattering information under limited-observation conditions, confirming its feasibility for downstream analysis in complex natural scenes while maintaining a single radio frequency (RF) chain architecture augmented by a polarization modulator. Full article

The Normalized Difference Vegetation Index (NDVI) derived from polar-orbiting satellites is widely used for vegetation monitoring; however, its temporal continuity is often limited by cloud contamination and fixed revisit cycles. To address this limitation, this study investigates the feasibility of using geostationary satellite observations to enhance the spatial completeness of Sentinel-2 NDVI at its standard revisit intervals through cloud gap-filling applications. Geostationary Ocean Color Imager II (GOCI-II) data (250 m) was used as input, while Sentinel-2 Multispectral Instrument (MSI) NDVI (10 m) served as the reference dataset. To enable cross-sensor integration, a data-driven transformation framework was developed to convert GOCI-II NDVI into MSI-like NDVI while preserving dominant spatial variation patterns rather than pursuing strict pixel-level super-resolution. The transformed NDVI was assessed through spatial comparisons and statistical metrics, including correlation coefficient, mean absolute error, root mean square error (RMSE), normalized RMSE, and structural similarity index measure. Results show that geostationary-derived NDVI captures broad spatial organization and field-scale variability observed in MSI NDVI. Building on this cross-scale consistency, cloud gap-filling experiments demonstrate that temporally adjacent transformed NDVI scenes maintain consistent variation patterns, supporting their complementary use for compensating cloud-induced gaps. Although reduced contrast and magnitude-dependent biases remain, primarily due to the large spatial resolution difference and sub-pixel heterogeneity, an intermediate-resolution (80 m) sensitivity analysis indicates improved stability when the resolution gap is reduced. Overall, these findings highlight the practical potential of integrating geostationary and polar-orbiting observations to improve NDVI spatial continuity in cloud-prone regions. Full article

The growing demand for environmentally responsible lubricants motivates the use of bio-based base stocks and benign solid additives. This study assesses the tribological performance of two Amazonian vegetable oils, Carapa guianensis (andiroba) and Copaifera spp. (copaiba resin) and a paraffinic mineral oil (PNL30) formulated with different zinc oxide (ZnO) particles, namely nanocrystals and microcrystals, at 0.01, 0.05, and 0.10 wt.%. Reciprocating sliding tests, coupled with 3D profilometry, viscosity, and sedimentation analyses, were used to link dispersion stability with friction and wear responses. A preliminary stability screening constrained the practical loading window to ≤0.10 wt.% for reproducible suspensions. Performance depended on the interplay between particle type and base-oil chemistry. Andiroba exhibited the most pronounced gains, with ZnO microcrystals near 0.05 wt.% delivering the best friction outcomes and the largest wear reductions (up to ~35%). In copaiba resin oil, nanocrystals produced small, sometimes non-significant improvements, whereas microcrystals tended to worsen wear consistent with abrasive third-body effects in a less polar matrix. In PNL30, the overall benefits were modest: nanocrystal additions generally increased wear, whereas microcrystals particularly at the highest loading 0.10 wt.% achieved a 36.4% reduction in SWR, representing a measurable and statistically significant improvement in wear resistance. These results highlight that eco-efficient lubricant design should co-optimize particle characteristics and dosage with base-oil polarity and film-forming tendencies, prioritizing dispersion stability alongside tribological targets. Full article

Usnic acid (UA) is one of the most extensively studied specialized metabolites of lichens, attracting considerable interest due to its antimicrobial, anti-inflammatory, and cytotoxic properties. The efficiency of UA extraction from lichens depends on multiple interrelated biological and technological factors. This systematic review aims to synthesize and critically evaluate reported strategies for UA extraction from wild-grown lichen biomass, with particular emphasis on extraction efficiency, practicality, and application potential. This systematic literature review, based on the Scopus database was conducted by including original research articles reporting UA extraction from wild-growing lichens. The analysis covered species selection, sample pre-treatment, solvent type, and extraction methodology. A total of 117 studies were included. Due to the predominantly non-polar nature of UA, higher extraction efficiencies were generally achieved using solvents, including acetone, supercritical CO₂, vegetable oils, and lipophilic green solvent systems. Pre-treatment strategies such as grinding or flaking significantly enhanced extraction performance by improving mass transfer. Alongside conventional methods (maceration, reflux, Soxhlet), non-conventional techniques such as Supercritical Fluid Extraction (SFE), Ultrasound- (UAE), and Microwave-Assisted Extraction (MAE) enabled faster and more selective UA extraction with reduced solvent use. Notably, SFE have been reported as particularly promising in terms of selectivity, process control, and potential suitability for scale-up, with commercially available supercritical CO₂ extracts of Usnea species supporting the feasibility of this approach. This review provides a consolidated and application-oriented overview of UA extraction, highlighting strategies that balance efficiency, selectivity, sustainability, and practical implementation. Full article

Deep-fat frying is widely used, but high temperatures and complex food matrices promote oil deterioration and harmful substance formation, posing risks to food safety and oil quality. This study evaluated five vegetable oils—sunflower oil (SFO), canola oil (CNO), palm oil (PO), cottonseed oil (CSO), and soybean oil (SBO)—during deep-fat frying of crispy meat to elucidate oil deterioration and contaminant formation patterns. After 32 h of frying, total polar compounds (TPCs) of PO and CNO were 29.8% and 32.6%, significantly lower than the other oils. Similar trends were observed for total oxidation value (TOTOX), carbonyl value (CV), and polar polymers, suggesting higher oxidative stability of PO and CNO, as confirmed by principal component analysis (PCA). Initial monochloro-1,2-propanediol esters (MCPDEs) and glycidyl ester (GE) in PO were relatively high (e.g., 3-MCPDE: 3630 μg/kg) but decreased over time during frying, whereas levels in SFO, CSO, and SBO remained low. Pearson’s correlation analysis indicated diacylglycerols (DAG) and monoacylglycerols (MAG) were positively correlated with MCPDEs and GE (p < 0.05). L* and b* values were positively correlated with polar polymers and contaminants, indicating that color parameters may serve as rapid, non-invasive auxiliary indicators of oil quality but should be combined with other indices for accurate evaluation. Full article

In the context of accelerating urbanization, university students face mounting academic stress and increasingly severe psychological health challenges. University blue-green spaces are critical environments for fostering restorative experiences. They highlight the urgent need for targeted strategies to enhance their restorative potential. This study used three universities in Guangzhou as case studies, based on image collection and deep learning-based semantic segmentation methods, and employed the Perceived Restorativeness Scale (PRS) and Restoration Outcome Scale (ROS) to explore the hypothesized pathways and threshold characteristics through which visual elements of blue-green spaces are associated with university students’ psychological restoration within everyday campus environments. The results indicate: (1) the restorative effects of different space types follow a clear gradient: waterfront spaces > planar vegetation spaces > linear vegetation spaces > point vegetation spaces; (2) perceived restorativeness acts as a key mediator between visual elements and psychological restoration. The mediating pathways vary across space types. Waterfront spaces show polarized effects. Planar vegetation spaces rely on a dual pathway of being away and compatibility, supplemented by a secondary role of fascination. Linear vegetation spaces exhibit complex pathway patterns in which multidimensional positive support coexists with both positive and negative influences; (3) several visual elements display nonlinear threshold effects. This study deepens the understanding of the “environment–perception–psychology” pathway in the context of sustainable campus environments. It also proposes a three-level optimization framework (macro–meso–micro) that provides empirical references for evidence-informed planning and design of university blue-green spaces, with potential implications for sustainable campus environments and student well-being. Full article

Remote sensing-based water body extraction is essential for monitoring hydrological dynamics, particularly in reservoirs with pronounced seasonal variability. This study evaluates automated surface water identification using multi-sensor satellite data, focusing on validation against hydrological observations. The workflow was implemented in the Google Earth Engine environment using Sentinel-2 multispectral imagery acquired between 2018 and 2023 and filtered for cloud cover below 20%. Water extent was extracted using commonly applied spectral indices, including the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Automated Water Extraction Index (AWEI), and Water Ratio Index (WRI), and compared with water level records from the Veľká Domaša reservoir. The results show strong agreement between extracted water extent and water levels, with Spearman correlation coefficients ranging from 0.92 to 0.96 for all indices except AWEI_nsh, which exhibited higher variability likely due to sediment and vegetation influences. Maximum and minimum water extents (12.58 km² and 9.04 km²) were consistent with observed hydrological trends. Validation using Sentinel-1 SAR data achieved an average Overall Accuracy of 98.6%, with VH polarization outperforming VV. Comparison with high-resolution aerial orthophotos revealed surface area differences of 0.20–1.26%. Automated thresholding produced results comparable to manual delineation, with minor and consistent deviations, confirming its reliability for repeatable water body extraction. Overall, the study demonstrates the effectiveness of spectral indices and automated approaches for long-term reservoir monitoring. Full article

Continuous vegetation monitoring is essential for predicting crop varieties and yields; however, optical satellite data are frequently unavailable due to cloud cover. To overcome this limitation, this study proposes a method for generating pseudo-NDVI (Normalized Difference Vegetation Index) imagery from RVI (Radar Vegetation Index) derived from Synthetic Aperture Radar (SAR) data using Generative Adversarial Networks (GANs). Two architectures—pix2pixHD (supervised) and CycleGAN (unsupervised)—were evaluated using Sentinel-1 and Sentinel-2 data under identical conditions. By introducing RVI as an intermediate feature instead of directly converting SAR backscatter to NDVI, the proposed method enhanced physical interpretability and improved correlation with NDVI. Quantitative results show that pix2pix achieved higher accuracy (SSIM = 0.5667, PSNR = 22.24 dB, RMSE = 20.54) than CycleGAN (SSIM = 0.5240, PSNR = 19.54 dB, RMSE = 28.02), with further improvement when combining VV and VH polarization data. Although the absolute accuracy remains moderate, this approach enables continuous annual NDVI time series reconstruction for crop monitoring under persistent cloud conditions, demonstrating clear advantages over conventional direct SAR-to-NDVI conversion methods. Full article

This study used high-pressure extraction to obtain antioxidant-rich fractions from Desmodium canadense leaves harvested at five vegetation phases (intensive growing to end of blooming) and to evaluate their antioxidant activity and phytochemical profile. Supercritical CO₂ extraction recovered lipophilic compounds, with the highest yield at massive flowering. The remaining plant material was fractionated by pressurized liquid extraction (PLE) using acetone, ethanol, and water; the highest PLE yield was achieved with water (16.54 g/100 g DW) at the bud formation stage. Antioxidant capacity was measured using total phenolic content (TPC) and ABTS^•+, CUPRAC, and ORAC assays. Overall, ethanol PLE extracts showed the strongest antioxidant properties: maximum TPC (282.1 mg GAE/gE) and ABTS^•+ (1010 mg TE/gE) at massive flowering, and highest CUPRAC (853.3 mg TE/gE) and ORAC (1882 mg TE/gE) at bud formation. UPLC-Q-TOF-MS/MS profiling identified 37 compounds, mainly C-glycosyl flavones, flavonol O-glycosides, hydroxycinnamic acid derivatives, and low molecular weight organic acids. Water extracts were rich in low molecular weight organic acids, while acetone and ethanol extracts contained the highest flavonoid levels. Citric acid and vitexin were the most abundant compounds. The findings indicate that D. canadense leaves, especially harvested at budding through massive flowering, are a promising source of flavonoid-rich antioxidant extracts for nutraceutical and functional food applications. Full article

Previous studies on single-polarized InSAR-based sub-canopy topography inversion have mainly relied on simplified or empirical models that only consider the volume scattering process. In a boreal forest area, the canopy layer is often discontinuous. In such a case, the radar backscattering echoes are mainly dominated by ground surface and volume scattering processes. However, interferometric scattering models like Random Volume over Ground (RVoG) have been little utilized in the case of single-polarized InSAR. In this study, we propose a novel method for retrieving sub-canopy topography by combining the RVoG model with multi-baseline InSAR data. Prior to the RVoG model inversion, a SAR-based dimidiate pixel model and a coherence-based penetration depth model are introduced to quantify the initial values of the unknown parameters, thereby minimizing the reliance on external vegetation datasets. Building on this, a nonlinear least-squares algorithm is employed. Then, we estimate the scattering phase center height and subsequently derive the sub-canopy topography. Two frames of multi-baseline TanDEM-X co-registered single-look slant-range complex (CoSSC) data (resampled to 10 m × 10 m) over the Krycklan catchment in northern Sweden are used for the inversion. Validation from airborne light detection and ranging (LiDAR) data shows that the root-mean-square error (RMSE) for the two test sites is 3.82 m and 3.47 m, respectively, demonstrating a significant improvement over the InSAR phase-measured digital elevation model (DEM). Furthermore, diverse interferometric baseline geometries and different initial values are identified as key factors influencing retrieval performance. In summary, our work effectively addresses the limitations of the traditional RVoG model and provides an advanced and practical tool for sub-canopy topography mapping in forested areas. Full article