Search Results (286)

Xylosalsola chiwensis (Popov) Akhani & Roalson is listed in the Red Data Book of Kazakhstan as a rare species with a limited distribution, occurring in small populations in Kazakhstan, Uzbekistan, and Turkmenistan. The aim of this study is to deepen the understanding of the ecological conditions of its habitats, the floristic composition of its associated plant communities, the species’ morphological and anatomical characteristics, and its molecular phylogeny, as well as to identify the main threats to its survival. The ecological conditions of the X. chiwensis habitats include coastal sandy plains and the slopes of chinks and denudation plains with gray–brown desert soils and bozyngens on the Mangyshlak Peninsula and the Ustyurt Plateau at altitudes ranging from −3 to 270 m above sea level. The species is capable of surviving in arid conditions (less than 100 mm of annual precipitation) and under extreme temperatures (air temperatures exceeding 45 °C and soil surface temperatures above 65 °C). In X. chiwensis communities, we recorded 53 species of vascular plants. Anthropogenic factors associated with livestock grazing, industrial disturbances, and off-road vehicle traffic along an unregulated network of dirt roads have been identified as contributing to population decline and the potential extinction of the species under conditions of unsustainable land use. The morphometric traits of X. chiwensis could be used for taxonomic analysis and for identifying diagnostic morphological characteristics to distinguish between species of Xylosalsola. The most taxonomically valuable characteristics include the fruit diameter (with wings) and the cone-shaped structure length, as they differ consistently between species and exhibit relatively low variability. Anatomical adaptations to arid conditions were observed, including a well-developed hypodermis, which is indicative of a water-conserving strategy. The moderate photosynthetic activity, reflected by a thinner palisade mesophyll layer, may be associated with reduced photosynthetic intensity, which is compensated for through structural mechanisms for water conservation. The flow cytometry analysis revealed a genome size of 2.483 ± 0.191 pg (2n/4x = 18), and the phylogenetic analysis confirmed the placement of X. chiwensis within the tribe Salsoleae of the subfamily Salsoloideae, supporting its taxonomic distinctness. To support the conservation of this rare species, measures are proposed to expand the area of the Ustyurt Nature Reserve through the establishment of cluster sites. Full article

By integrating EarthCARE W-band doppler cloud radar observations with GPM Ku/Ka-band dual-frequency precipitation radar data, this study constructs a novel global “pseudo tripe-frequency” radar coincidence dataset comprising 2886 coincidence events (about one-third of the events detected precipitation), aiming to systematically investigating band-dependent responses to cloud and precipitation structure. Results demonstrate that the W-band is highly sensitive to high-altitude cloud particles and snowfall (reflectivity < 0 dBZ), yet it experiences substantial signal attenuation under heavy precipitation conditions, and with low-altitude reflectivity reductions exceeding 50 dBZ, its probability density distribution is more widespread, with low-altitude peaks increasing first, and then decreasing as precipitation increases. In contrast, the Ku and Ka-band radars maintain relatively stable detection capabilities, with attenuation differences generally within 15 dBZ, but its probability density distribution exhibits multiple peaks. As the precipitation rate increases, the peak value of the dual-frequency ratio (Ka/W) gradually rises from approximately 10 dBZ to 20 dBZ, and can even reach up to 60 dBZ under heavy rainfall conditions. Several cases analyses reveal clear contrasts: In stratiform precipitation regions, W-band radar reflectivity is higher above the melting layer than below, whereas the opposite pattern is observed in the Ku and Ka bands. Doppler velocities exceeding 5 m s⁻¹ and precipitation rates surpassing 30 mm h⁻¹ exhibit strong positive correlations in convection-dominated regimes. Furthermore, the dataset confirms the impact of ice–water cloud phase interactions and terrain-induced precipitation variability, underscoring the complementary strengths of multi-frequency radar observations for capturing diverse precipitation processes. Full article

Fixed automated (unmanned) above-water radiometric measurements are subject to unavoidable sky conditions and surface perturbations, leading to significant uncertainties in retrieved water surface remote sensing reflectances (R_rs(λ), sr⁻¹). This study evaluates various above-water R_rs(λ) glint correction methods using a comprehensive dataset collected at the Royal Netherlands Institute for Sea Research (NIOZ) Jetty Station located in the Marsdiep tidal inlet of the Dutch Wadden Sea, the Netherlands. The dataset includes in-situ water constituent concentrations (2006–2020), inherent optical properties (IOPs) (2006–2007), and above-water hyperspectral (ir)radiance observations collected every 10 min (2006–2023). The bio-optical models were validated using in-situ IOPs and utilized to generate glint-free remote sensing reflectances, R_rs,ref(λ), using a robust IOP-to-R_rs forward model. The R_rs,ref(λ) spectra were used as a benchmark to assess the accuracy of glint correction methods under various environmental conditions, including different sun positions, wind speeds, cloudiness, and aerosol loads. The results indicate that the three-component reflectance model (3C) outperforms other methods across all conditions, producing the highest percentage of high-quality R_rs(λ) spectra with minimal errors. Methods relying on fixed or lookup-table-based glint correction factors exhibited significant errors under overcast skies, high wind speeds, and varying aerosol optical thickness. The study highlights the critical importance of surface-reflected skylight corrections and wavelength-dependent glint estimations for accurate above-water R_rs(λ) retrievals. Two showcases on chlorophyll-a and total suspended matter retrieval further demonstrate the superiority of the 3C model in minimizing uncertainties. The findings highlight the importance of adaptable correction models that account for environmental variability to ensure accurate R_rs(λ) retrieval and reliable long-term water quality monitoring from hyperspectral radiometric measurements. Full article

High-precision wave data serve as a foundation for investigating the wave characteristics of the East China Sea (ECS) and wave energy development. Based on the simulating waves nearshore (SWAN) model, this study uses the ERA5 (ECMWF Reanalysis v5) reanalysis wind field data and ETOPO1 bathymetric data to perform high-precision simulations at a resolution of 0.05° × 0.05° for the waves in the area of 25–35° N and 120–130° E in the ECS from 2009 to 2023. The simulation results indicate that the application of the whitecapping dissipation parameter Komen and the bottom friction parameter Collins yields an average RMSE of 0.374 m and 0.369 m when compared to satellite-measured data, demonstrating its superior suitability for wave simulation in shallow waters such as the ESC over the other whitecapping dissipation parameter, Westhuysen, and the other two bottom friction parameters, Jonswap and Madsen, in the SWAN model. The monthly average significant wave height (SWH) ranges from 0 to 3 m, exhibiting a trend that it is more important in autumn and winter than in spring and summer and gradually increases from the northwest to the southeast. Due to the influence of the Kuroshio current, topography, and events such as typhoons, areas with significant wave heights are found in the northwest of the Ryukyu Islands and north of the Taiwan Strait. The wave energy flux density in most areas of the ECS is >2 kW/m, particularly in the north of the Ryukyu Islands, where the annual average value remains above 8 kW/m. Because of the influence of climate events such as El Niño and extreme heatwaves, the wave energy flux density decreased significantly in some years (a 21% decrease in 2015). The coefficient of variation of wave energy in the East China Sea exhibits pronounced regional heterogeneity, which can be categorized into four distinct patterns: high mean wave energy with high variation coefficient, high mean wave energy with low variation coefficient, low mean wave energy with high variation coefficient, and low mean wave energy with low variation coefficient. This classification fundamentally reflects the intrinsic differences in dynamic environments across various maritime regions. These high-precision numerical simulation results provide methodological and theoretical support for exploring the spatiotemporal variation laws of waves in the ECS region, the development and utilization of wave resources, and marine engineering construction. Full article

Estimating wheat traits based on spectral reflectance measurements and machine learning remains challenging due to the large datasets required for model training and testing. To overcome this limitation, a simulated dataset was generated using the radiative transfer model (RTM) PROSAIL and inverted based on an artificial neural network (ANN). Field experiments were conducted in Eastern Austria to measure spectral reflectance and destructively sample plants to measure the wheat traits plant area index (PAI), nitrogen yield (NY), canopy water content (CWC), and above-ground dry matter (AGDM). Four ANN-based RTM inversion models were setup, which varied in their spectral resolution, hyperspectral or multispectral, and the inclusion or exclusion of background soil spectra correction. The models were also compared to a simple vegetation index approach using Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red-Edge (NDRE). The RTM inversion model with hyperspectral input data and background soil spectra correction was the best among all tested models for estimating wheat traits during the vegetative developmental stages (PAI: R² = 0.930, RRMSE = 17.9%; NY: R² = 0.908, RRMSE = 14.4%; CWC: R² = 0.967, RRMSE = 17.0%) as well as throughout the whole growing season (PAI: R² = 0.845, RRMSE = 27.7%; CWC: R² = 0.884, RRMSE = 20.0%; AGDM: R² = 0.960, RRMSE = 13.7%). Many models presented in this study provided suitable estimations of the relevant wheat traits PAI, NY, CWC, and AGDM for application in agronomy, breeding, and crop sciences in general. Full article

The multi-band atmospheric correction algorithms, now referred to as remote sensing reflectance (R_rs) algorithms, have been implemented on a NASA computing facility for global remote sensing of ocean color and atmospheric aerosol parameters from data acquired with several satellite instruments, including the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi spacecraft platform. These algorithms are based on the 2-band version of the SeaWiFS (Sea-Viewing Wide Field-of-View Sensor) algorithm. The bands centered near 0.75 and 0.865 μm are used for atmospheric corrections. In order to obtain high-quality R_rs values over Case 1 waters (deep clear ocean waters), strict masking criteria are implemented inside these algorithms to mask out thin clouds and very turbid water pixels. As a result, R_rs values are often not retrieved over bright Case 2 waters. Through our analysis of VIIRS data, we have found that spatial features of bright Case 2 waters are observed in VIIRS visible band images contaminated by thin cirrus clouds. In this article, we describe methods of combining cirrus and aerosol corrections to improve spatial coverage in R_rs retrievals over Case 2 waters. One method is to remove cirrus cloud effects using our previously developed operational VIIRS cirrus reflectance algorithm and then to perform atmospheric corrections with our updated version of the spectrum-matching algorithm, which uses shortwave IR (SWIR) bands above 1 μm for retrieving atmospheric aerosol parameters and extrapolates the aerosol parameters to the visible region to retrieve water-leaving reflectances of VIIRS visible bands. Another method is to remove the cirrus effect first and then make empirical atmospheric and sun glint corrections for water-leaving reflectance retrievals. The two methods produce comparable retrieved results, but the second method is about 20 times faster than the spectrum-matching method. We compare our retrieved results with those obtained from the NASA VIIRS R_rs algorithm. We will show that the assumption of zero water-leaving reflectance for the VIIRS band centered at 0.75 μm (M6) over Case 2 waters with the NASA R_rs algorithm can sometimes result in slight underestimates of water-leaving reflectances of visible bands over Case 2 waters, where the M6 band water-leaving reflectances are actually not equal to zero. We will also show conclusively that the assumption of thin cirrus clouds as ‘white’ aerosols during atmospheric correction processes results in overestimates of aerosol optical thicknesses and underestimates of aerosol Ångström coefficients. Full article

Land surface reflectance is a basic physical parameter in many quantitative remote sensing models. However, the existing reflectance conversion techniques for drone-based (or UAV-based) remote sensing need further improvement and optimization due to either cumbersome operational procedures or inaccurate results. To tackle this problem, this study proposes a novel method to mathematically implement the separation of direct and scattering radiation using a self-developed multi-angle light intensity device. The verification results from practical experiments demonstrate that the proposed method has strong adaptability, as it can obtain accurate surface reflectance even under complicated conditions where both illumination intensity and component change simultaneously. Among the six selected typical land cover types (i.e., lake water, slab stone, shrub, green grass, red grass, and dry grass), green grass has the highest error among the five multispectral bands with a mean absolute error (MAE) of 1.59%. For all land cover types, the highest MAE of 1.01% is found in the red band. The above validation results indicate that the proposed land surface reflectance conversion method has considerably high accuracy. Therefore, the study results may provide valuable references for quantitative remote sensing applications of drone-based multispectral data, as well as the design of future multispectral drones. Full article

Soil degradation is a critical challenge to global agricultural sustainability, driven by intensive land use, unsustainable farming practices, and climate change. Conventional soil monitoring techniques often rely on invasive sampling methods, which can be labor-intensive, disruptive, and limited in spatial coverage. In contrast, non-invasive geophysical techniques, particularly ground-penetrating radar, have gained attention as tools for assessing soil properties. However, an assessment of ground-penetrating radar’s applications in agricultural soil research—particularly for detecting soil structural changes related to degradation—remains undetermined. To address this issue, a systematic literature review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines. A search was conducted across Scopus and Web of Science databases, as well as relevant review articles and study reference lists, up to 31 December 2024. This process resulted in 86 potentially relevant studies, of which 24 met the eligibility criteria and were included in the final review. The analysis revealed that the ground-penetrating radar allows the detection of structural changes associated with tillage practices and heavy machinery traffic in agricultural lands, namely topsoil disintegration and soil compaction, both of which are important indicators of soil degradation. These variations are reflected in changes in electrical permittivity and reflectivity, particularly above the tillage horizon. These shifts are associated with lower soil water content, increased soil homogeneity, and heightened wave reflectivity at the upper boundary of compacted soil. The latter is linked to density contrasts and waterlogging above this layer. Additionally, ground-penetrating radar has demonstrated its potential in mapping alterations in electrical permittivity related to preferential water flow pathways, detecting shifts in soil organic carbon distribution, identifying disruptions in root systems due to tillage, and assessing soil conditions potentially affected by excessive fertilization in iron oxide-rich soils. Future research should focus on refining methodologies to improve the ground-penetrating radar’s ability to quantify soil degradation processes with greater accuracy. In particular, there is a need for standardized experimental protocols to evaluate the effects of monocultures on soil fertility, assess the impact of excessive fertilization effects on soil acidity, and integrate ground-penetrating radar with complementary geophysical and remote sensing techniques for a holistic approach to soil health monitoring. Full article

Optically shallow waters (OSW) enable water depth retrieval through passive optical remote sensing, benefiting from the relatively low attenuation of the water column. However, in OSW environments, remote sensing reflectance is influenced by multiple optical parameters beyond water depth. Comprehensive quantification of these influences remains necessary. This study utilizes numerical simulations to systematically categorize optical parameter ranges and perform variance-based sensitivity analysis. The results indicate that OSW parameter characteristics differ between extremely shallow and moderately shallow waters. In extremely shallow waters, water depth and bottom reflectance are the primary determinants of remote sensing reflectance, with longer wavelengths exhibiting greater sensitivity to depth variations. Bathymetric models utilizing long wavelength combinations demonstrate high accuracy in extremely shallow waters. In moderately shallow waters, sensitivity contributions for shallow water features are concentrated in the blue-green bands, whereas reflectance above 600 nm is primarily influenced by deep-water reflectance and noise, limiting the retrieval of depth and bottom reflectance. Simulations across OSW parameter ranges further reveal that the absolute error in depth estimation increases with depth, whereas relative error is more pronounced in extremely shallow waters compared to moderately shallow waters. Full article

In an effort to monitor ascidian recruitment in mussel aquaculture facilities, a series of settlement plates (20 × 20 cm) were placed in a mussel farm located in the Amvrakikos Gulf (Ionian Sea). The plates were vertically deployed on floating facilities in the water column at regular intervals (depths of 0.2 m, 1.5 m, and 3 m) to monitor the settlement and proliferation of ascidians. Furthermore, measurements of seawater physicochemical parameters such as temperature, salinity, dissolved oxygen, and chlorophyll-a concentration were conducted together with the record of ascidian species in each sampling from January 2021 to November 2021. The correlation of these parameters with ascidian species provides information on their effect on the periodicity of ascidians’ recruitment. The results demonstrated a significant correlation between ascidian presence and water temperature. The potential influence of other important environmental parameters such as chlorophyll-a was not revealed, likely due to the limited number of values and samples included in the analyses. While increased chlorophyll levels, reflecting increased primary productivity or nutrient availability, are associated with increased growth and reproduction of all ascidian species, the effect of temperature was more potent and species-specific. Ciona robusta, Styela plicata, Microcosmus squamiger, and Phallusia mammillata were mainly detected at temperatures below 25 °C, whereas Clavelina oblonga was prevalent at temperatures above 25 °C. The absence of most ascidians at temperatures above 25 °C was possibly attributed to decreased settlement success and to the increased competition from C. oblonga at higher temperatures. The deployment of settlement plates in correlation with seawater physiochemical parameters can provide valuable data on ascidian settlement dynamics and support the development of targeted management practices for biofouling control. Full article

Due to the significant impedance mismatch between water and air, two types of pipeline silencers designed for the same target frequency with different filling materials often have significantly different thickness. This increases the difficulty in designing silencers for multiple pipelines with different filling materials in narrow spaces. The paper reports a metamaterial design paradigm based on a Helmholtz cavity for low-frequency sound absorption for both air pipelines and water-filled pipelines. An asymmetric absorption metamaterial with coupled Helmholtz resonators is proposed to reduce the impact of low-frequency noise in air pipelines. By coupling the absorption mode and reflection mode, the asymmetric absorption metamaterial with a thickness of 71 mm achieves 95.6% absorption at 403 Hz. The tunable absorbing performance in broadband is confirmed by a finite element simulation. Additionally, a composite metamaterial constructed with HRs associated with a rubber layer is proposed for low-frequency broadband sound absorption in water-filled pipelines. An average absorptance of above 0.8 is achieved over the range of 380–508 Hz by coupling four basic composite metamaterial units with a thickness of 31.5 mm. The proposed design paradigm can reduce the complexity of designing multiple pipelines silencers with different filling materials because the muffler should have similar thickness in the same paradigm. Full article

Furnas volcano, one of the three active central volcanoes of São Miguel (the Azores archipelago), hosts mineral waters with significant special variations, divided into hyperthermal (89.4–95.4 °C), thermal (29.9–70.0 °C), and cold (14.2–21.4 °C) waters. Groundwaters are classified as Na-HCO₃, with a neutral to slightly acidic pH, except one SO₄-Na acidic sample. The major elements are primarily influenced by rock leaching and volcanic input, patterns also reflected in the trace elements, including the rare earth elements. The major cations, along with lithium, iron, aluminum, rubidium, and strontium, indicate the influence of water–rock interactions. Some samples depict a higher influence in this input, shown by the similar REE behavior between them and the local rock behavior. The volcanic input is distinguished into two environments: an acid sulfate boiling pool, formed by steam heating, and neutral HCO₃-Cl waters, where bicarbonate-rich waters mix with a neutral chloride fluid from a deep reservoir. The deeper reservoir also provides boron, arsenic, antimony, and tungsten, also seemingly associated with a positive spike in europium due to rock dissolution at temperatures above 250 °C or a reducing environment. This interpretation is corroborated by the stability of the strontium isotopes between samples. Full article

Sewer networks are essential in supporting the sustainable development of rural settlements. However, many municipalities face difficulties due to inadequate or missing sanitation systems. Thus, the contamination of municipal water supplies has become a pressing issue. In the present study, the process of the sewer network establishment and its impacts on groundwater was assessed in the case of a Hungarian settlement. It was found that, following the realization of wastewater agglomeration in 2015, 85% of households in the municipality were connected by 2023. Results indicate that uninsulated septic tanks used before the sewer system resulted in a high rate of sewage leakage. 3D models using RockWorks software show a groundwater dome of more than 1 m in the vicinity of the septic tank. The discharge had a significant impact on groundwater quality; high NH₄⁺ concentrations (>90 mg/L) within the vicinity of the tank and above the limit value (0.5 mg/L) in the total area were detected. Na⁺ and NO₃⁻ concentrations above the contamination limit also reflect the severe impact of wastewater discharge. Significant positive changes in groundwater quality have been detected following sewerage. Our results highlight the importance of similar investments and draw attention to the positive changes that can be achieved. However, a very significant decrease in water levels was detected both in the vicinity of the septic tank and at the municipal level, which, in addition to precipitation patterns, is mainly attributable to the cessation of sewage outflow. Between 2013 and 2022, the average groundwater level declined by 3.8 m in the settlement. Therefore, long-term monitoring of the investment is essential. Full article

Coffea canephora genotypes adopt distinct strategies to cope with drought and rehydration. We hypothesized that the greater drought tolerance of genotype ‘3V’ compared to ‘A1’, previously reflected in physiological and anatomical leaf traits after two water-stress (WS) cycles, could also be observed in P–V curve responses, root and branch anatomy, leaf midrib elongation (CVL), and root distribution. The ‘3V’ and ‘A1’ plants were grown under well-watered (WW) conditions and two cycles of water stress (WS). The ‘3V’ was more sensitive to WS, with reduced branch xylem vessel density (BXVD), while ‘A1’ demonstrated increased BXVD. Root xylem vessel area (RXVA) decreased to a greater extent in ‘3V’ than in ‘A1’, and both genotypes showed increased bulk elastic modulus. Regardless of water conditions, ‘A1’ maintained a higher relative leaf water content at the turgor loss point (RWC_TLP). Morphological acclimation did not occur in the second WS cycle. The ‘3V’ plants developed greater root mass in deeper soil layers than ‘A1’ under the WS condition. These findings suggest that ‘A1’ follows a conservative drought-avoidance strategy with lower physio-morphological plasticity, while ‘3V’ exhibits greater drought tolerance. Such responses highlighted coordinated physiological, morphological, and anatomical adaptations of the above- and below-ground organs for resource acquisition and conservation under WS. Full article

Methane (CH₄) emissions in coal-energy-rich regions are characterized by hidden emission point sources and highly variable emission rates. While the Matched Filter (MF) method for detecting the CH₄ point source using hyperspectral satellite sensors has been validated for high-emission concentrations, the accurate inversion of low-concentration emissions in complex environments remains challenging. In this study, an ‘end-to-end’ experiment—from emission simulations to satellite spectra and inversion results—has been designed to quantify the impact of internal payload parameters and environmental parameters for CH₄ emission inversions, and perform real-scenario calculations. The study reveals several key findings: (1) Under ideal conditions, 15% of satellite spectral noise contributes to a 13% bias in CH4 detection inversion, and a spectral resolution of 10–14 nm allows the detection of CH₄ emissions with concentrations as low as 350 ppb, above the background level of 1900 ppb. (2) For near-surface aerosols at 2100 nm, an aerosol optical depth (AOD) of 0.1 leads to a low bias of −51.6% with water-soluble aerosols and a strong bias of −69.2% with black carbon aerosols, while dust aerosols induce a medium bias of up to −60.7%. (3) The height of the aerosol layer affects the accuracy of methane inversion, which is up to 7.3% higher under aerosol conditions at 3 km than under aerosol conditions near the ground. (4) When the CH₄ emission source and its diffuse plume are located above a high-reflectance (bright) surface, while the background CH₄ concentration is associated with a low-reflectance (dark) surface, the significant reflectance contrast between the two surfaces leads to a rapid degradation in inversion accuracy. This contrast makes it impossible to effectively extract CH₄ signals when the reflectance difference reaches 0.2. (5) Under harsh conditions, where multiple parameters are present (AOD = 0.2, albedo = 0.2, aerosol layer height (ALH) = 2), the MF method is still able to detect CH₄ emissions, but with a significant error of 74.65%. (6) External environmental variables, particularly atmospheric pressure and water vapor content, significantly influence the inversion accuracy of methane (CH₄) concentrations. Variations in atmospheric pressure induce deviations in the CH₄ concentration distribution, resulting in an average inversion error of −12.06%. Similarly, elevated water vapor levels can lead to a maximum error of −16.2%. These findings highlight the substantial challenges in accurately detecting low-concentration CH₄ emissions. The results offer critical insights for refining CH₄ detection algorithms and enhancing the precision of satellite-based inversions for low-concentration CH₄ point-source emissions. Full article