Search Results (981)

Leaf phenology is key to ecosystem functioning by regulating carbon, water, and energy fluxes and influencing vegetation productivity. Yet, detecting land surface phenology (LSP) in drylands using remote sensing remains particularly challenging due to sparse and heterogeneous vegetation cover, high spatiotemporal variability, and complex spectral signals. Unlike the Northern Hemisphere, these challenges are further compounded in the Southern Hemisphere (SH), where several regions experience year-round moderate temperatures. When combined with irregular rainfall, this leads to highly variable vegetation activity throughout the year. However, LSP dynamics in the SH remain poorly understood. This study presents a review of remote sensing-based phenology research in drylands, integrating (i) a synthesis of global methodological advances and (ii) a systematic analysis of peer-reviewed studies published from 2015 through April 2025 focused on SH drylands. This review reveals a research landscape still dominated by conventional vegetation indices (e.g., NDVI) and moderate-spatial-resolution sensors (e.g., MODIS), though a gradual shift toward higher-resolution sensors such as PlanetScope and Sentinel-2 has emerged since 2020. Despite the widespread use of start- and end-of-season metrics, their accuracy varies greatly, especially in heterogeneous landscapes. Yet, advanced products such as solar-induced chlorophyll fluorescence or the fraction of absorbed photosynthetically active radiation were rarely employed. Gaps remain in the representation of hyperarid zones, grass- and shrub-dominated landscapes, and large regions of Africa and South America. Our findings highlight the need for multi-sensor approaches and expanded field validation to improve phenological assessments in dryland environments. The accurate differentiation of vegetation responses in LSP is essential not only for refining phenological metrics but also for enabling more realistic assessments of ecosystem functioning in the context of climate change and its impact on vegetation dynamics. Full article

Transpiration is the dominant process driving water loss in crops, significantly influencing their growth, development, and yield. Efficient monitoring of transpiration rate (Tr) is crucial for evaluating crop physiological status and optimizing water management strategies. The three-temperature (3T) model has potential for rapid estimation of transpiration rates, but its application to low-altitude remote sensing has not yet been further investigated. To evaluate the performance of 3T model based on land surface temperature (LST) and canopy temperature (T_C) in estimating transpiration rate, this study utilized an unmanned aerial vehicle (UAV) equipped with a thermal infrared (TIR) camera to capture TIR images of summer maize during the nodulation-irrigation stage under four different moisture treatments, from which LST was extracted. The Gaussian Hidden Markov Random Field (GHMRF) model was applied to segment the TIR images, facilitating the extraction of T_C. Finally, an improved 3T model incorporating fractional vegetation coverage (FVC) was proposed. The findings of the study demonstrate that: (1) The GHMRF model offers an effective approach for TIR image segmentation. The mechanism of thermal TIR segmentation implemented by the GHMRF model is explored. The results indicate that when the potential energy function parameter β value is 0.1, the optimal performance is provided. (2) The feasibility of utilizing UAV-based TIR remote sensing in conjunction with the 3T model for estimating Tr has been demonstrated, showing a significant correlation between the measured and the estimated transpiration rate (T_r-3T_C), derived from T_C data obtained through the segmentation and processing of TIR imagery. The correlation coefficients (r) were 0.946 in 2022 and 0.872 in 2023. (3) The improved 3T model has demonstrated its ability to enhance the estimation accuracy of crop Tr rapidly and effectively, exhibiting a robust correlation with T_r-3T_C. The correlation coefficients for the two observed years are 0.991 and 0.989, respectively, while the model maintains low RMSE of 0.756 mmol H₂O m⁻² s⁻¹ and 0.555 mmol H₂O m⁻² s⁻¹ for the respective years, indicating strong interannual stability. Full article

The Advanced Scatterometer (ASCAT), Soil Moisture Active Passive (SMAP), and European Space Agency-Climate Change Initiative (ESA–CCI) soil moisture (SM) products are widely used in agricultural drought monitoring, water resource management, and climate analysis applications. However, the performance of these SM products varies significantly across regions and environmental conditions, due to in sensor characteristics, retrieval algorithms, and the lack of localized calibration. This study proposes a multi-source long short-term memory (MLSTM) for improving ASCAT, ESA–CCI, and SMAP SM products by combining in-situ SM measurements and four key auxiliary variables: precipitation (PRE), land surface temperature (LST), fractional vegetation cover (FVC), and evapotranspiration (ET). First, the in-situ measured data from four in-situ observation networks were corrected using the LSTM method to match the grid sizes of ASCAT (0.1°), ESA–CCI (0.25°), and SMAP (0.1°) SM products. The RPE, LST, FVC, and ET were used as inputs to the LSTM to obtain loss data against in-situ SM measurements. Second, the ASCAT, ESA–CCI, and SMAP SM datasets were used as inputs to the LSTM to generate loss data, which were subsequently corrected using LSTM-derived loss data based on in-situ SM measurements. When the mean squared error (MSE) loss values were minimized, the improvement for ASCAT, ESA–CCI, and SMAP products was considered the best. Finally, the improved ASCAT, ESA–CCI, and SMAP were produced and evaluated by the correlation coefficient (R), root mean square error (RMSE), and standard deviation (SD). The results showed that the RMSE values of the improved ASCAT, ESA–CCI, and SMAP products against the corrected in-situ SM data in the OZNET network were lower, i.e., 0.014 cm³/cm³, 0.019 cm³/cm³, and 0.034 cm³/cm³, respectively. Compared with the ESA–CCI and SMAP products, the ASCAT product was greatly improved, e.g., in the SNOTEL network, the Root Mean-Square Deviation (RMSD) values of 0.1049 cm³/cm³ (ASCAT) and 0.0662 cm³/cm³ (improved ASCAT). Overall, the MLSTM-based algorithm has the potential to improve the global satellite SM product. Full article

Rapid urbanization has exacerbated the urban heat island effect, posing a significant threat to human health and urban ecosystems. While numerous studies have demonstrated that urban morphology significantly influences land surface temperatures (LSTs), few have systematically explored the impact and contribution of urban morphology on LST across different functional zones. Therefore, this study takes Xi’an as a case and employs an interpretable CatBoost-SHAP machine learning model to evaluate the nonlinear influence of building landscape features on LST in different functional zones during summer. The results indicate the following: (1) The highest LST in the study area reached 52.68 °C, while the lowest was 21.68 °C. High-temperature areas were predominantly concentrated in the urban center and industrial zones with dense buildings, whereas areas around water bodies and green spaces exhibited relatively lower temperatures. (2) SHAP analysis revealed that landscape indicators exerted the most substantial impact across all functional zones, with green space zones contributing up to 62%. Among these, fractional vegetation coverage (FVC), as a core landscape factor, served as the primary cooling factor in all six functional zones and consistently demonstrated a negative effect. (3) Population density (POP) exhibited a generally high SHAP contribution across all functional zones, showing a positive correlation. Its effect was most pronounced in commercial zones, accounting for 16%. When POP ranged between 0 and 250 people, the warming effect was particularly prominent. (4) The mean building height (MBH) constituted a major influencing factor in most functional zones, especially in residential zones, where the SHAP value reached 0.7643. Within the range of 10–20 m, the SHAP value increased sharply, indicating a significant warming effect. (5) This study proposes targeted cooling strategies tailored to six functional zones, providing a scientific basis for formulating targeted mitigation strategies for different functional zones to alleviate the urban heat island effect. Full article

Microplastics (MP) have been found not only in the environment but also in living beings, including humans. As an initial step in MP detection, a method is proposed to measure the effective refractive index of a solution containing 5 µm diameter spherical polystyrene particles (SPSP) in distilled water, based on the surface plasmon resonance (SPR) technique and Mie scattering theory. The reflectances of the samples are obtained with their resonance angles and depths that must be normalized and adjusted according to the reference of the air and the distilled water, to subsequently find their effective refraction index corresponding to the Mie scattering theory. The system has an optical sensor with a Kretschmann–Raether configuration, consisting of a semicircular prism, a thin gold film, and a glass cell for solution samples with different concentrations (0.00, 0.20, 0.05, 0.50, and 1.00%). The experimental result provided a good linear fit with an R² = 0.9856 and a sensitivity of 7.2863 × ${10}^{-5}$ RIU/% (refractive index unit per percentage of fill fraction). The limits of detection (LOD) and limit of quantification (LOQ) were determined to be 0.001% and 0.0035%, respectively. The developed optomechatronic system and its applications based on the SPR and Scattering enabled the effective measurement of the refractive index and concentration of solutions containing 5 µm diameter SPSP in distilled water. Full article

Water and salt stresses reduce net CO₂ assimilation (A_N) primarily by restricting stomatal conductance (g_s) and mesophyll conductance (g_m), while altering leaf structure, anatomy, and cell wall composition. Although some reports observed relationships between these modifications and g_m, in others they remain less clear. Here, we compiled data on studies in which major cell wall components (cellulose; C, hemicellulose; H; pectins; P) were determined with photosynthetic, structural and anatomical features, obtaining a dataset presenting distinct species subjected to both stresses. Among parameters previously reported to affect g_m (leaf mass per area: LMA; chloroplast surface area exposed to intercellular air spaces per unit of leaf surface area: S_c/S; fraction of intercellular air spaces: f_ias; cell wall thickness: T_cw), pectins and the P/(C + H) ratio were the unique consistently varying in salt- and water-stressed plants. Despite no single trait correlated with g_m, it was positively linked with [P/(C + H) × S_c/S × f_ias]/[T_cw × Lignin × LMA] in studies in which all parameters were tested, suggesting that distinct traits may exert antagonistic influences on g_m. Although further experiments are needed to reinforce our findings, we hypothesize that increases in pectins under stress could limit larger g_m declines, improving g_m/g_s ratio and water use efficiency (WUE). Full article

Clouds modulate the net radiative flux that interacts with both shortwave (SW) and longwave (LW) radiation, but the uncertainties regarding their effect in polar regions are especially high because ground observations are lacking and evaluation through satellites is made difficult by high surface reflectance. In this work, sky conditions for six different polar stations, two in the Arctic (Ny-Ålesund and Utqiagvik [formerly Barrow]) and four in Antarctica (Neumayer, Syowa, South Pole, and Dome C) will be presented, considering the decade between 2010 and 2020. Measurements of broadband SW and LW radiation components (both downwelling and upwelling) are collected within the frame of the Baseline Surface Radiation Network (BSRN). Sky conditions—categorized as clear sky, cloudy, or overcast—were determined using cloud fraction estimates obtained through the RADFLUX method, which integrates shortwave (SW) and longwave (LW) radiative fluxes. RADFLUX was applied with daily fitting for all BSRN stations, producing two cloud fraction values: one derived from shortwave downward (SWD) measurements and the other from longwave downward (LWD) measurements. The variation in cloud fraction used to classify conditions from clear sky to overcast appeared consistent and reasonable when compared to seasonal changes in shortwave downward (SWD) and diffuse radiation (DIF), as well as longwave downward (LWD) and longwave upward (LWU) fluxes. These classifications served as labels for a machine learning-based classification task. Three algorithms were evaluated: Random Forest, K-Nearest Neighbors (KNN), and XGBoost. Input features include downward LW radiation, solar zenith angle, surface air temperature ($T_a$), relative humidity, and the ratio of water vapor pressure to $T_a$. Among these models, XGBoost achieved the highest balanced accuracy, with the best scores of 0.78 at Ny-Ålesund (Arctic) and 0.78 at Syowa (Antarctica). The evaluation employed a leave-one-year-out approach to ensure robust temporal validation. Finally, the results from cross-station models highlighted the need for deeper investigation, particularly through clustering stations with similar environmental and climatic characteristics to improve generalization and transferability across locations. Additionally, the use of feature normalization strategies proved effective in reducing inter-station variability and promoting more stable model performance across diverse settings. Full article

Cowpea (Vigna unguiculata L.) pods are an underexploited by-product of legume production with significant antioxidant potential. Their recovery and characterization support sustainable waste valorization in agri-food systems. This study aimed to optimize the extraction of phenolic compounds (PCs) with antioxidant capacity (AOC) from cowpea pods and identify key bioactives through experimental and theoretical approaches. First, high-intensity ultrasound extraction was optimized using response surface methodology with ethanol–water mixtures. Under optimal conditions (20% amplitude, 15 min, 50% ethanol), the ethanolic extract (Eo) showed higher total phenolic content (TPC) and AOC than the aqueous extract (Wo). Subsequently, fractionation by Sephadex LH-20 chromatography yielded fractions E2 and W2 with enhanced TPC and AOC. Phytochemical profiling showed that E2 was enriched in caftaric acid, p-coumaric acid, and morin, while W2 had higher levels of caftaric, p-coumaric, and caffeic acids. Finally, density functional theory was used to assess thermodynamic parameters linked to antioxidant mechanisms (HAT, SET-PT, SPLET), revealing morin as the most effective radical scavenger, followed by caffeic and caftaric acids. These findings show that AOC depends not only on phenolic concentration but also on molecular structure and solvent interactions. Thus, cowpea pod extracts and fractions hold promise for antioxidant-rich formulations in food, nutraceutical, or cosmetic applications. Full article

This study presents the first quantitative comparison of catastrophic phase inversion behavior of water-in-oil emulsions stabilized by nanocrystalline cellulose (NCC) and molecular surfactants with different headgroup charge types: anionic (sodium dodecyl sulfate referred to as SDS), cationic (octadecyltrimethylammonium chloride referred to as OTAC), nonionic (C12–14 alcohol ethoxylate referred to as Alfonic), and zwitterionic (cetyl betaine referred to as Amphosol). By using conductivity measurements under controlled mixing and pendant drop tensiometry, this study shows that NCC markedly delays catastrophic phase inversion through interfacial jamming, whereas surfactant-stabilized systems exhibit concentration-dependent inversion driven by interfacial saturation. Specifically, NCC-stabilized emulsions exhibited a nonlinear increase in the critical aqueous phase volume fraction required for inversion, ranging from 0.253 (0 wt% NCC) to 0.545 (1.5 wt% NCC), consistent with enhanced resistance to inversion typically associated with the formation of rigid interfacial layers in Pickering emulsions. In contrast, surfactant-stabilized systems exhibited a concentration-dependent inversion trend with opposing effects. At low concentrations, limited interfacial coverage delayed inversion, while at higher concentrations, increased surfactant availability and interfacial saturation promoted earlier inversion and favored the formation of oil-in-water structures. Pendant drop tensiometry confirmed negligible surface activity for NCC, while all surfactants significantly lowered interfacial tension. Despite its weak surface activity, NCC imparted strong coalescence resistance above 0.2 wt%, attributed to steric stabilization. These findings establish distinct mechanisms for governing phase inversion in particle- versus surfactant-stabilized systems. To our knowledge, this is the first study to quantitively characterize the catastrophic phase inversion behavior of water-in-oil emulsions using NCC. This work supports the use of NCC as an effective stabilizer for emulsions with high internal phase volume. Full article

The prediction of microstructure evolution during thermal processing plays a crucial role in tailoring the mechanical properties of metallic components. Therefore, this work presents a comprehensive, multiscale modelling approach to simulating phase transformations in large-diameter steel rings during cooling. A finite-element-based thermal model was first used to simulate transient temperature distributions in a large-diameter ring under different cooling conditions, including air and water quenching. These thermal histories were subsequently employed in two complementary phase transformation models of different levels of complexity. The Avrami model provides a first-order approximation of the evolution of phase volume fractions, while a complex full-field cellular automata approach explicitly simulates the nucleation and growth of ferrite grains at the microstructural level, incorporating local kinetics and microstructural heterogeneities. The results highlight the sensitivity of final grain morphology to local cooling rates within the ring and initial austenite grain sizes. Simulations demonstrated the formation of heterogeneous microstructures, particularly pronounced in the ring’s surface region, due to sharp thermal gradients. This approach offers valuable insights for optimising heat treatment conditions to obtain high-quality large-diameter ring products. Full article

Asbolane is a secondary source of cobalt (Co) and manganese (Mn), essential for battery and alloy production. Enhancing the utilization of low-grade ores, typically containing ~1.2% Co and 14.7% Mn, is vital for conserving high-grade resources. However, fine grinding for such ores presents challenges for conventional gravity separation. This study investigates the effectiveness of the Multi-Gravity Separator (MGS) in processing finely disseminated asbolane ore from Agios Ioannis, Greece. The study was conducted at the Mineral Processing Laboratory of UPC/Bases Manresa. Two size fractions, D₈₀ (−100 +50 µm and −50 µm), were tested under varying drum speeds, tilt angles, and wash water flows. Response surface methodology (RSM) was implemented using Python-optimized (version 3.15) process parameters. The results demonstrate that a concentrate with 2.6% Co and 32.5% Mn can be obtained, achieving 82.1% Co recovery. Independent and multi-objective optimizations confirm MGS as a viable method for recovering Co and Mn from complex low-grade ores, with reduced overgrinding-related energy losses essential for production. The study aimed to implement and enhance low-grade asbolane ore from a feed containing 2.6% Co and 32.5% Mn. Variables were optimized with a multi-objective target, demonstrating their effectiveness. Full article

The adhesive properties between rubber asphalt mastic and aggregate are crucial to rubber asphalt mixtures’ stability and moisture resistance. This paper employs surface free energy (SFE) theory and molecular dynamics (MD) to examine the bond strength and debonding behavior at the rubber asphalt mastic–aggregate interface. The results showed that the dispersion fraction of RC1.0 was 7.12 mJ/m² higher than that of RA, and the limestone mineral powder improved the adhesion properties of rubberized asphalt to aggregate and the anti-stripping properties. SiO₂ and CaCO₃ are contributors to the van der Waals and electrostatic forces between rubber asphalt–aggregate, respectively. The high concentration of mineral powder has a bridging effect in rubber asphalt mastic–aggregate. CaCO₃ filler is more pronounced in enhancing the adhesion properties of rubber asphalt–aggregate. CaCO₃ mineral powder mainly improves the anti-debonding ability of rubber asphalt–aggregate by reducing the thickness of water film between rubber asphalt–aggregate. Full article

Urbanization and cropland irrigation modify land surface water and energy budgets in different ways; however, few observational studies have explicitly quantified their contrasts. Using high-resolution observations from over 2000 surface weather stations and urban and irrigation fraction data, this study investigated the individual and combined effects of urbanization and cropland irrigation on surface air temperature over the Beijing–Tianjin–Hebei (BTH) region in China, where highly urbanized areas and heavily irrigated croplands exist together. The results indicate that (1) the daytime irrigation cooling (with surface air temperature decreasing by ~0.1–0.5 °C at irrigated stations) was non-negligible in late autumn, early winter, and later spring months, when winter wheat irrigation mainly occurred over the BTH region, while a slight warming was observed at many irrigated stations during the nighttime. By contrast, urban warming was most pronounced in the nighttime, especially in winter, and the daytime warming at urban sites was much weaker and comparable to the magnitude of cooling induced by concurrent irrigation for winter wheat. (2) Collectively, the vast stretches of irrigated croplands helped mitigate urban warming, and their combined effect on the daytime surface air temperature over the whole region resulted in a slight cooling of ~0.2 °C in some of the winter wheat-growing months. (3) The contrasting temperature changes due to urbanization and irrigation were spatially variable. Beijing was predominantly characterized by urban warming, while Shijiazhuang, with extensive irrigation, exhibited irrigation cooling (or slight warming) during the daytime (or nighttime) in most of the winter wheat-growing months, which could be a possible contributor to the daytime cooling (or stronger nighttime warming) at urban sites. This work highlights the temperature contrasts between urban areas and surrounding irrigated croplands, as well as the potential role of extensive irrigation in mitigating (or enhancing) daytime (or nighttime) urban warming. Full article

The rheological behavior of cementitious paste plays a pivotal role in determining the workability, pumpability, and uniformity of fresh concrete. Classical rheological models often struggle to capture the complex flocculation and hydration effects inherent in cement-based systems, and they typically depend on parameters that are difficult to measure directly, limiting their practical utility. This study presents a novel composition-based constitutive model that introduces a virtual maximum packing fraction (${\varphi }_{max}$) to account for interparticle flocculation and entrapped water effects. By establishing quantitative relationships between powder characteristics—such as particle size and specific surface area—and rheological parameters, the model enables physically interpretable and measurable predictions of yield stress and plastic viscosity. Our validation against 65 paste formulations with varying water-to-binder ratios, mineral admixture types and dosages, and superplasticizer contents demonstrates strong predictive accuracy (R² > 0.98 for plain pastes and >0.85 for blended systems). The influence of superplasticizers is effectively captured through modifications to ${\varphi }_{max}$, allowing the model to remain both robust and parameter efficient. This framework supports forward prediction of paste rheology from raw material properties, offering a valuable tool for intelligent mix design in high-performance concrete applications such as self-consolidating and 3D-printed concrete. Full article

This study aims to determine the Blaťácké zlato cheese in vitro antioxidant activity and its correlation with specific peptides. A general physicochemical evaluation was also conducted, considering possible differences between batches. The antioxidant activity focused mainly on the nitrogen fractions with the shortest-chain peptides. Other parameters were evaluated, including color, weight, size, moisture, dry matter, and texture analysis, which included the whole cheese hardness and the texture profile analysis. The ethanol soluble (EtOH-SN) and non-protein nitrogen (NPN) fractions were selected to evaluate antioxidant activity by the 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2’-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) methods, total phenol content (TPC), and peptide profiles. Our findings revealed significant differences between batches for NPN ABTS activity and EtOH-SN TPC. Significant differences were observed for water activity, moisture, dry matter, moisture on fat-free basis (MFFB), and pH in the central surface. DPPH and TPC showed a similar behavior, and NPN showed higher values than the EtOH-SN fraction. However, the opposite was observed for ABTS. Significant correlations were found for the biological activities with individual peaks of their corresponding HPLC peptide profiles. Finally, the principal component analysis separated the cheeses according to the batch, mainly due to specific peptides. Full article