Search Results (158)

Retrieving farmland soil water content with both high accuracy and physical interpretability remains a significant challenge, particularly for winter wheat. To bridge the gap between purely empirical data-driven approaches and mechanistic scattering models, this study proposed a novel hybrid framework that integrates an improved water cloud model (IWCM) with machine learning algorithms. Multi-modal unmanned aerial vehicle (UAV) experiments were conducted during the heading stage of winter wheat over two consecutive years (2024–2025) using a synchronized system equipped with a miniature synthetic aperture radar (MiniSAR) and a multi-spectral sensor. The core innovation of the proposed framework lies in the IWCM, which explicitly decouples vegetation and soil scattering contributions by incorporating fractional vegetation cover, thereby deriving physically meaningful soil backscatter coefficients from complex microwave signals. Unlike traditional methods that treat remote sensing variables as black box inputs, our approach employed these physics-derived features to guide data-driven modeling. Four feature input schemes including spectral reflectance, vegetation indices, MiniSAR polarimetric parameters, and their multi-source fusion were systematically evaluated using back propagation neural network (BPNN) and random forest (RF) regressors. The results demonstrated that the proposed framework significantly enhances retrieval performance. Notably, the RF model driven by spectral band reflectance within this physically constrained architecture achieved optimal accuracy, with a coefficient of determination (R²) of 0.865, a mean absolute error (MAE) of 0.0152, and a root mean square error (RMSE) of 0.0197. Compared to purely empirical approaches, the IWCM significantly improved the physical interpretability of microwave polarimetric characteristics, enabling the multi-source data fusion to better represent the interactions among vegetation, soil, and microwave scattering. This study demonstrated that integrating mechanistic models with multi-source UAV remote sensing data not only improves soil water content retrieval accuracy in winter wheat fields but also provides a valuable reference for developing operationally applicable and physically interpretable farmland soil water content monitoring systems. Full article

Background/Objectives: Citrus limon leaf essential oil (EO) is traditionally used for its calming and cognitive-enhancing properties. Although the chemical composition of C. limon leaf essential oils (EOs) obtained by means of hydrodistillation (HD) and solvent-free microwave extraction (SFME) has been previously characterized, the influence of the extraction method on their neuroprotective efficacy and dose–response effects remains insufficiently explored. In the present study, EOs obtained by means of HD (CEH) and SFME (CEM) were compared for their behavioral, biochemical, and in silico neuroprotective effects against scopolamine (SCOP)-induced cognitive and anxiety-like impairments in adult zebrafish. Methods: Adult Tübingen zebrafish were exposed to CEH or CEM via immersion at 10, 100, and 150 µL/L for 19 days prior to SCOP challenge (100 µM). Cognitive performance was evaluated using the Y-maze and novel object recognition (NOR) tests, while anxiety-like behavior was assessed using the novel tank test (NTT) and novel approach test (NAT). Brain acetylcholinesterase (AChE) activity and oxidative stress markers were quantified. Molecular docking analyses were conducted to investigate interactions between major EO constituents and AChE and monoamine oxidase A (MAO A). Results: Both CEH and CEM significantly attenuated SCOP-induced memory deficits, improved spontaneous alternation and NOR discrimination, and reduced anxiety-like behaviors. These effects were associated with AChE inhibition and restoration of redox balance. Notably, CEM generally exhibited stronger neurobehavioral and biochemical effects at comparable doses. In silico analyses supported these findings, revealing favorable binding affinities of key EO constituents toward cholinergic and monoaminergic targets. Conclusions: This study demonstrates that the extraction method influences the neuroprotective efficacy of C. limon leaf EOs. While both CEH and CEM exert antioxidant and cholinergic modulatory effects, CEM shows enhanced neuroprotective potential in a zebrafish model of SCOP-induced cognitive impairment, supporting the relevance of extraction-dependent biological profiling in EO-based neurotherapeutic research. Full article

The upper atmospheric microwave sounding channels data are important for atmospheric data assimilation and retrieval. However, radiative transfer simulation accuracy is constrained by the precise characterization of the Zeeman splitting effect. This study investigates key influencing factors in upper-atmospheric microwave radiance simulations, focusing on the geomagnetic field parameters and the Zeeman splitting absorption coefficients. A three-dimensional (3D) atmosphere-magnetic coupling dataset is constructed using the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) version 2.0 Level 2A atmospheric profiles and the International Geomagnetic Reference Field (IGRF-13) as input for the microwave Line-by-Line (LBL) model. Observations from Special Sensor Microwave Imager/Sounder (SSMIS) channels 19 and 20 are used to quantitatively compare the effects of 2D and 3D geomagnetic fields on simulations and evaluate the impact of updated Zeeman splitting coefficients. Quantitative analysis reveals that the average vertical attenuation rate of geomagnetic field strength between 50 and 0.001 hPa is 2.98%, and using 3D magnetic field parameters improves the observation and simulation bias (O-B) for SSMIS channels 19 and 20 by approximately 3.67% and 3.52%, respectively. The updated microwave LBL model, incorporating molecular self-spin interactions and higher-order Zeeman effects, reduces the mean absolute error (MAE) and root mean square error (RMSE) of the SSMIS channel 20 by approximately 2.7% and 2.25%, respectively. Experimental results indicate that the 7⁺ line within a 2 MHz frequency shift is sensitive to moderate magnetic field strength (0.35–0.55 Gauss), while the 1⁻ line is sensitive to strong magnetic fields (0.5–0.7 Gauss). This study demonstrates that optimizing geomagnetic field representation and Zeeman splitting coefficients can improve upper atmospheric microwave radiance simulation accuracy by detailed comparison with observations. Full article

In this study, biocarbon adsorbents were obtained from fennel and caraway seeds through microwave-assisted chemical activation with sodium carbonate. The activation process involved carbonizing the raw material at 300 °C for 30 min., followed by impregnation with sodium carbonate at a precursor-to-activator mass ratio of 1:2. Activation was performed at two distinct temperatures—500 °C and 600 °C—with an activation time of 15 min. The structural, textural, and surface chemical characteristics of the obtained biocarbons were investigated using complementary analytical techniques, including low-temperature nitrogen adsorption–desorption isotherms, X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), X-ray diffraction (XRD), Boehm titration, and pH analysis of aqueous extracts. The resulting adsorbents demonstrated low development of specific surface area (109–154 m²/g) and limited sorption capacity for methylene blue (20–32 mg/g). Adsorption experiments indicated that the Freundlich isotherm model most accurately described the data, suggesting multilayer adsorption on heterogeneous surfaces. Thermodynamic evaluations showed the adsorption to be both spontaneous and endothermic. The adsorption mechanism is primarily governed by electrostatic interactions between the cationic dye and surface functional groups, π–π interactions with the carbon structure, and diffusion within mesopores. This study provides a comparative evaluation of microwave-assisted Na₂CO₃ activation of fennel and caraway seed waste and assesses the potential of these biochars for dye removal from aqueous solutions. Full article

The Water Cloud Model (WCM) provides a simple and effective framework for quantifying vegetation canopy absorption and scattering components and has been widely applied to active microwave-based soil moisture retrieval on vegetated areas. However, under conditions of dense vegetation, the WCM neglects soil–vegetation interaction scattering, which limits its retrieval accuracy. To mitigate this limitation, this study analyzes the active microwave radiative transfer process under vegetated conditions and proposes an approach to explicitly quantify soil–vegetation interaction scattering by incorporating the first-order soil–vegetation-scattering component into the WCM, thereby enhancing the performance of the WCM at high vegetation coverage. The effectiveness of the proposed model is validated using in situ observations from three study areas with different vegetation characteristics: (a) a pure farmland area, (b) a mixed landscape with small forest and shrubland patches and large cropland areas, and (c) a mixed landscape with large forest and shrubland patches and small cropland areas. Data from 2020–2022 were used for model training and parameter calibration, while independent datasets from 2023 and 2024 were employed to validate the model performance. In both the model training and validation phases, the proposed model improved the soil moisture retrieval accuracy across all study areas while exhibiting slight differences in the backscatter simulation performance. During the model training period, the root-mean-square error (RMSE) between simulated and measured backscatter in study area (a) increased slightly by 1.9%, whereas it decreased by 2.79% and 2.0% in study areas (b) and (c), respectively. In terms of soil moisture retrieval, the RMSEs in study areas (a), (b), and (c) decreased by 6.66%, 1.18%, and 6.03%, respectively. In the validation experiments, for the year 2023, the RMSEs of simulated versus observed backscatter in study areas (a), (b), and (c) were reduced by 9.6%, 1.51%, and 4.35%, respectively, while the corresponding soil moisture retrieval RMSEs decreased by 12.6%, 4.53%, and 7.24%. For the year 2024, the backscatter RMSE in study area (a) increased by 6.07%, whereas it decreased by 2.17% and 6.47% in study areas (b) and (c), respectively; meanwhile, the soil moisture retrieval RMSEs were reduced by 2.81%, 3.69%, and 9.45%, respectively. In summary, this study improves the accuracy of active microwave remote sensing-based soil moisture retrieval in areas with different vegetation cover by explicitly quantifying soil–vegetation interaction scattering. Full article

As a key parameter of the Earth’s ecosystem, soil moisture significantly influences land-atmosphere interactions and has important applications in meteorology, hydrology, and agricultural studies. However, existing passive microwave remote sensing products of soil moisture are limited by their discontinuous temporal coverage and relatively coarse spatial resolution (typically 25–55 km), which cannot meet the requirements for fine-scale applications. This study developed and compared four machine learning-based downscaling approaches to improve the spatiotemporal resolution of MCCA SMAP soil moisture products. The methodology involved establishing complex nonlinear relationships between soil moisture and various high-resolution surface parameters including albedo, evapotranspiration, precipitation, and soil properties. High-resolution soil moisture maps were generated by leveraging the scale-invariant characteristics between soil moisture and surface parameters, followed by comprehensive evaluation using in situ ground observations and triple collocation analysis. The results demonstrated that all downscaling models showed excellent consistency with original MCCA SMAP observations (R > 0.93, RMSE < 0.033 m³ m⁻³), while successfully providing enhanced spatial details. The Random Forest (RF) model exhibited superior performance, showing higher correlation coefficients and lower biases when compared with in situ measurements. Uncertainty analysis revealed relatively low uncertainty levels for all models except Backpropagation Neural Network (BPNN) model. The RF-downscaled products accurately tracked temporal variations of soil moisture and showed good responsiveness to precipitation patterns, demonstrating their potential for fine-scale hydrological applications and regional environmental monitoring. Full article

This paper presents a thermal management solution for a Ka-band gyrotron traveling wave tube (gyro-TWT) with non-superconducting magnets. At present, the miniaturization and non-superconductivity of gyro-TWT have become a trend, but miniaturization leads to a significant increase in power density and a severe limitation in heat sink volume, which critically limits power capacity. To address this challenge, a joint microwave–thermal management evaluation model is used to investigate the heat transfer process and identify the crucial factors constraining the power capacity. A cylindrical heat sink with narrow rectangular grooves is introduced. Based on this, the cooling efficiency has been enhanced through structural optimization. The beam–wave interaction, electrothermal conversion, and heat conduction processes of the interaction circuit are analyzed. The compact heat sink achieves a 1.2-fold increase in coolant utilization and reduces the overall volume by 27.4%. Meanwhile, this heat sink improves the cooling performance and power capability of the gyro-TWT effectively. At 29 GHz, the gyro-TWT achieves a pulse power of 150 kW. Simulation results show that the maximum temperature is 348 °C at a 45% duty cycle, reduced by 159 °C. The power capacity of the Ka-band gyro-TWT increases by 40.6%. Full article

Sea ice concentration (SIC) is crucial to the global climate. In this study, a new single-channel SIC retrieval algorithm utilizing spaceborne L-band brightness temperature (TB) measurements is developed based on a microwave radiative transfer model. Additionally, its four uncertainties are quantified and constrained: (1) variations in seawater reference TB under warm water conditions, (2) variations in sea ice reference TB under extremely low-temperature conditions, (3) the freeze–thaw dynamics of sea ice captured by Diurnal Amplitude Variation (DAV) signals, and (4) Land mask imperfections. It is found that DAV has the most pronounced effect: eliminating its influence reduces RMSE from 10.51% to 8.43%, increases R from 0.92 to 0.94, and minimizes Bias from -0.68 to 0.13. Suppressing all four uncertainties lowers RMSE to 7.42% (a 3% improvement). Furthermore, the algorithm exhibits robust agreement with the seasonal variability of SSM/I SIC, with R mostly exceeding 0.9, RMSE mostly below 10%, and Biases mostly within 5% throughout the year. Compared to ship-based and SAR SIC data, the new L-band algorithm’s Bias and RMSE are only 2% and 2% (ship-based)/2% and 1% (SAR) higher, respectively, than those of the SSM/I product. Future algorithms can integrate the DAV signal more effectively to better understand sea ice freeze–thaw processes and ice-atmosphere interactions. Full article

This review article is the third of a three-article introductory series on virtual cathode oscillators. The first article has laid the theoretical ground for understanding the physical properties of the virtual cathode, and the second article has provided a numerical tool for studying virtual cathode oscillation. This third article focuses on the interaction between the electron beam and electromagnetic field. The virtual cathode oscillator has been studied for decades with the aim of developing it as high-power microwave source. The beam-field interaction has been one of the core issues that always perplexes both experimentalists and theorists. Using the physical model established in the first article and the numerical method described in the second article, this article is an attempt to answer some of the key questions based on a more comprehensive description of the device and its interaction process. This article is expected to serve as a reference for young researchers and students working on high-power microwaves and pulsed particle beams. Full article

Reliable land surface temperature (LST) data are required for monitoring climate variability, hydrological processes, and land–atmosphere interactions. Yet existing satellite-derived LST products, such as those from thermal infrared (TIR) sensors, are limited by gaps due to clouds, while passive microwave (PMW) observations, though less affected by atmospheric interference, suffer from coarse resolution and larger uncertainty. This study presents the first validated clear-sky merged LST product for the USA and combines downscaled PMW data from AMSR-E and AMSR2 with MODIS TIR observations, using a modified U-Net deep learning network. The merged dataset covers 2004–2021 at 5 km resolution, providing a compromise between spatial detail and robustness. The model performs well, with low mean squared errors and $R^2$ values of 0.80 (day) and 0.75 (night). The merged time series captures seasonal trends and shows a marked reduction in cloud-contamination artefacts compared to MODIS and AMSR signals. Spatially, the product is consistent across sensor transitions and reduces artefacts from TIR cloud contamination. Validation against ground stations shows results between those of TIR and PMW, with better accuracy at night and moderate positive biases influenced by land cover and terrain. Although the merged product does not match the fine resolution of TIR data by choice, it enhances spatial coverage over AMSR alone and temporal completeness over MODIS alone, where single-sensor products are limited. Residual temporal and seasonal biases are moderate, with systematic warm and cold deviations linked to land cover, propagation of emissivity errors, and sampling differences. Strong positive biases remain over terrain with complex surface properties as the downscaled AMSR is closer to MODIS temperatures. Results demonstrate the combined benefits of PMW’s broader coverage and cloud tolerance with TIR’s spatial detail. Overall, results demonstrate the potential of sensor fusion for producing spatially consistent LST records suitable for long-term environmental and climate monitoring. Full article

Passive microwave remote sensing of soil moisture is crucial for monitoring the Earth’s water cycle and surface dynamics. The penetration depth during this process is significant, as it influences the accuracy of retrieved soil moisture data. Within L-band remote sensing, tools such as the τ-z model interpret microwave emissions to estimate soil moisture, taking into account the complex interactions between soil and radiation. However, in validating these models against high-temporal-resolution, ground-based measurements, especially from extensive networks like the Terrestrial National Ecological Observatory Network (NEON), further research and validation efforts are needed. This study comprehensively validates the τ-z model’s ability to estimate the soil temperature sensing depth (z_Teff) using data from the NEON and Soil Moisture Active Passive (SMAP) satellite missions. A harmonization process was conducted to align the spatial and temporal scales of the two datasets, enabling rigorous validation. We compared soil optical depth (τ)—a parameter capable of theoretically unifying sensing depth representations across wet soil (~0.05 m) to extreme dry/frozen conditions (e.g., up to ~1500 m in ice-equivalent scenarios)—and geometric depth (z) frameworks against outputs from the τ-z model and NEON’s in situ profiles. The results show that: (1) for the profiles that satisfy the monotonic assumption by the τ-z model, z_Teff fits the prediction well at about 0.2 τ for the average; (2) Combining SMAP’s soil moisture, the τ-z model achieves high accuracy in estimating z_Teff, with RMSD (0.05 m) and unRMSD (0.03 m), and correlations (0.67) between estimated and observed values. The findings are expected to advance remote sensing techniques in various fields, including agriculture, hydrology, and climate change research. Full article

Global Navigation Satellite System Reflectometry (GNSS-R) provides all-weather, high-resolution ocean wind speed monitoring that offers additional benefits for forecasting tropical cyclones and severe weather events. However, existing GNSS-R wind retrieval models often lack interpretability and suffer accuracy degradation during high wind conditions. To address these limitations, we leverage a mathematical equivalence between Transformers and graph neural networks (GNNs) on complete graphs, which provides a physically grounded interpretation of self-attention as spatiotemporal influence propagation in GNSS-R data. In our model, each GNSS-R footprint is treated as a graph node whose multi-head self-attention weights quantify localized interactions across space and time. This aligns physical influence propagation with the computational efficiency of GPU-accelerated Transformers. Multi-head attention disentangles processes at multiple scales—capturing local (25–100 km), mesoscale (100 km–500 km), and synoptic (>500 km) circulation patterns. When applied to Level 1 Version 3.2 data (2023–2024) from four Asian sea regions, our Transformer–GNN achieves an overall wind speed RMSE reduction of 32% (to 1.35 m s⁻¹ from 1.98 m s⁻¹) and substantial gains in high-wind regimes (winds >25 m s⁻¹: 3.2 m s⁻¹ RMSE). The model is trained on ERA5 reanalysis 10 m equivalent-neutral wind fields, which serve as the primary reference dataset, with independent validation performed against Stepped Frequency Microwave Radiometer (SFMR) aircraft observations during tropical cyclone events and moored buoy measurements where spatiotemporally coincident data are available. Interpretability analysis with SHAP reveals condition-dependent feature attributions and suggests coupling mechanisms between ocean surface currents and wind fields. These results demonstrate that our model advances both predictive accuracy and interpretability in GNSS-R wind retrieval. With operationally viable inference performance, our framework offers a promising approach toward interpretable, physics-aware Earth system AI applications. Full article

Carbon dots (CDs) are promising for agro-environmental applications; however, clear connections between synthesis, photophysical properties, size, and biosafety are often not well established. In this study, we map these relationships for glucose–arginine CDs (GA-CDs). By using microwave and hydrothermal routes at precursor ratios of 1:3, 1:9, and 1:15, we produced sub-10 nm nanoparticles (analyzed by dynamic light scattering and atomic force microscopy) that exhibit tunable absorption and emission properties, as well as surface properties (demonstrated through UV–Vis spectroscopy, 3D photoluminescence, and FTIR analysis). The hydrothermal 1:9 condition yielded the narrowest size distribution and red-shifted photoluminescence. Across biological models spanning plants, insects, plant-growth-promoting bacteria (PGPR), and human cells, GA-CDs were well tolerated, with no adverse changes detected in plant stress markers, aphid feeding behavior or fecundity, or PGPR growth. In A549 cells, viability remained stable up to a concentration of 0.125 mg mL⁻¹, while exposure to 0.5 mg mL⁻¹ reduced viability, establishing a practical operating range. These results provide a clearer picture of how the structure and properties of carbon dots derived from arginine and glucose are correlated to their safety. The GA-CDs are, therefore, useful, and traceable tools for agro-environmental research. The findings support their use as biocompatible nanomaterials for studying interactions among plants, insects, and microbes in agriculture. Full article

This work aims to maximize the Hawking emission temperature arising in the optical analog model of the event horizon of an astrophysical black hole. A weak probe wave interacts with an intense ultrashort optical pulse via the Kerr effect in a photonic crystal fiber. This interaction causes the probe wave to experience an effective spacetime geometry characterized by the presence of an optical event horizon, where the analogous Hawking radiation effect arises. Here we refer to the simulated or classical version of the analog of Hawking radiation. This study considers four distinct types of photonic crystal fibers with anomalous dispersion curves that allow for maximizing the effect. Our first three numerical simulations indicate that a Hawking emission temperature of up to 361 K can be achieved with a photonic crystal fiber with two zero-dispersion wavelengths, while the emission temperature values in the original investigation are lower than 244 K. And in the fourth, we can see that we have a configuration in which the temperature can be improved up to 1027 K. Moreover, these results also emphasize the feasibility of using analog models to test the quantum effects of gravity, such as Hawking radiation produced by typical black holes, whose magnitude is far below the temperature of the cosmic microwave background (2.7 K). Full article

The eutrophication of natural water is a severe environmental risk faced by coastal marine ecosystems, and the excess nitrogen and phosphorus in aquaculture effluent are one of the main sources of environmental pollution. Effectively reducing as well as controlling the phosphorus content in aquaculture effluent is of great importance for alleviating eutrophication and the governance of coastal environments. This study focuses on addressing phosphorus pollution by developing novel bimetallic Ce-Al-MOFs adsorbents via the microwave-assisted rapid synthesis method, among which the monomer Ce₃Al₃-BDC₃ exhibits excellent phosphate adsorption capacity (136.99 mg P g⁻¹) and great removal efficiency over a wide pH range (2~10). Batch experiments reveal that the adsorption is followed by pseudo-second-order kinetics and the Langmuir isotherm model, indicating monolayer chemisorption. The MOFs material shows high selectivity for phosphorus even under the interference of co-existing anions, as well as excellent reusability, retaining over 65% removal efficiency after six adsorption–desorption cycles. Field tests in coastal areas and indoor aquaculture systems both achieve over 97% phosphate removal, meeting discharge standards. A series of characterization methods identify ligand exchange, electrostatic interactions and surface complexation as key adsorption mechanisms. The Ce-Al-MOFs present a promising solution for mitigating eutrophication and managing aquaculture wastewater sustainably. Full article