Search Results (1,041)

Electrohydrodynamic (EHD) printing offers mask-free, high-resolution deposition across a broad range of ink viscosities, yet combining void-free filling of high-aspect-ratio through-glass vias (TGVs) with ultrafine drop-on-demand (DOD) line printing on the same platform requires balancing conflicting requirements: for example, high field strengths to drive ink into deep and narrow vias; sufficiently high ink viscosity to prevent gravity-induced leakage; and stable meniscus dynamics to avoid satellite droplets and charge accumulation on the glass surface. By coupling electrostatic field analysis with transient level-set simulations, we establish a dimensionless regime map that delineates stable cone-jetting regime; these predictions are validated by high-speed imaging and surface profilometry. Operating within this window, the platform achieves complete, void-free filling of 200 µm × 1.52 mm TGVs and continuous 10 µm-wide traces in a single print pass. Demonstrating its capabilities, we fabricate transparent Ku-band substrate-integrated waveguide antennas on borosilicate glass: the printed vias and arc feed elements exhibit a reflection coefficient minimum of –18 dB at 14.2 GHz, a –10 dB bandwidth of 12.8–16.2 GHz, and an 8 dBi peak gain with 37° beam tilt, closely matching full-wave predictions. This physics-driven, all-in-one EHD approach provides a scalable route to high-performance, glass-integrated RF devices and transparent electronics. Full article

To enable accurate evaluation of satellite laser communication terminals under solar outage interference, this paper presents the design and implementation of a solar radiation simulation system targeting the 1540–1560 nm communication band. The system reconstructs co-propagating interference conditions through standardized and continuously tunable output, based on high irradiance and spectral uniformity. A compound beam homogenization structure—combining a multimode fiber and an apodizator—achieves 85.8% far-field uniformity over a 200 mm aperture. A power–spectrum co-optimization strategy is introduced for filter design, achieving a spectral matching degree of 78%. The system supports a tunable output from 2.5 to 130 mW with a 50× dynamic range and maintains power control accuracy within ±0.9%. To suppress internal background interference, a BRDF-based optical scattering model is established to trace primary and secondary stray light paths. Simulation results show that by maintaining the surface roughness of key mirrors below 2 nm and incorporating a U-shaped reflective light trap, stray light levels can be reduced to 5.13 × 10⁻¹² W, ensuring stable detection of a 10⁻¹⁰ W signal at a 10:1 signal-to-background ratio. Experimental validation confirms that the system can faithfully reproduce solar outage conditions within a ±3° field of view, achieving consistent performance in spectrum shaping, irradiance uniformity, and background suppression. The proposed platform provides a standardized and practical testbed for ground-based anti-interference assessment of optical communication terminals. Full article

Turbidity is a crucial parameter for assessing the ecological health of aquatic ecosystems, particularly in shallow tropical lakes that are subject to climatic variability and anthropogenic pressures. Lake Chapala, the largest freshwater body in Mexico, has experienced persistent turbidity and sediment influx since the 1970s, primarily due to upstream erosion and reduced water inflow. In this study, we utilized Landsat satellite imagery in conjunction with near-synchronous in situ reflectance measurements to monitor spatial and seasonal turbidity patterns between 2023 and 2025. The surface reflectance was radiometrically corrected and validated using spectroradiometer data collected across eight sampling sites in the eastern sector of the lake, the area where the highest rates of horizontal change in turbidity occur. Based on the relationship between near-infrared reflectance and field turbidity, second-order polynomial models were developed for spring, fall, and the composite annual model. The annual model demonstrated acceptable performance (R² = 0.72), effectively capturing the spatial variability and temporal dynamics of the average annual turbidity for the whole lake. Historical turbidity data (2000–2018) and a particular case study in 2016 were used as a reference for statistical validation, confirming the model’s applicability under varying hydrological conditions. Our findings underscore the utility of empirical remote-sensing models, supported by field validation, for cost-effective and scalable turbidity monitoring in dynamic tropical lakes with limited monitoring infrastructure. Full article

This study presents a comprehensive analysis of the microphysical structures of Meiyu heavy rainfall (near-surface rainfall intensity > 8 mm/h) across different life stages in the Yangtze-Huaihe River Valley (YHRV). We classified the heavy rainfall events into three life stages of developing, mature, and dissipating using ERA5 reanalysis and IMERG precipitation estimates, and examined vertical microphysical structures using Dual-frequency Precipitation Radar (DPR) data from the Global Precipitation Measurement (GPM) satellite during the Meiyu period from 2014 to 2023. The results showed that convective heavy rainfall during the mature stage exhibits peak radar reflectivity and surface rainfall rates, with the largest near-surface mass weighted diameter (D_m ≈ 1.8 mm) and the smallest droplet concentration (dBN_w ≈ 38). Downdrafts in the dissipating stage preferentially remove large ice particles, whereas sustained moisture influx stabilizes droplet concentrations. Stratiform heavy rainfall, characterized by weak updrafts, displays narrower particle size distributions. During dissipation, particle breakups dominate, reducing D_m while increasing dBN_w. The analysis of the relationship between microphysical parameters and rainfall rate revealed that convective heavy rainfall shows synchronized growth of D_m and dBN_w during the developing stage, with D_m peaking at about 2.1 mm near 70 mm/h before stabilizing in the mature stage, followed by small-particle dominance in the dissipating stage. In contrast, stratiform rainfall exhibits a “small size, high concentration” regime, where the rainfall rate correlates primarily with increasing dBN_w. Additionally, convective heavy rainfall demonstrates about 22% higher precipitation efficiency than stratiform systems, while stratiform rainfall shows a 25% efficiency surge during the dissipation stage compared to other stages. 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

Machine learning (ML) has gained significant attention for unraveling the complex, nonlinear relationships between soil moisture (SM) and various predictive variables, including remote sensing (RS; reflectance, brightness temperature, backscatter coefficients) and biophysical (topographic, soil, vegetation, and weather) variables. We reviewed the literature to extract and synthesize ML algorithms, reliable input features, and challenges in SM estimation using RS data. We analyzed results from 144 articles published from 2010 to 2024. Random forest (40 out of 67 studies), support vector regressor (13 out of 39 studies), and artificial neural networks (12 out of 27 studies) often outperformed other algorithms to estimate SM using RS datasets. Multi-source RS data often outperformed single-source data in SM estimation. Satellite-derived features, such as vegetation indices and backscattering coefficients, provided critical information on surface SM (SSM) variability to estimate SSM. For root zone SM estimation, soil properties and SSM generally were more reliable predictors than surface information derived solely from RS. Two recent advances—the use of semi-empirical models and L-band SAR to mitigate vegetation effects, and transfer learning to improve model transferability—have shown promise in addressing key challenges in SM estimation. Full article

This study utilized Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite level-2 data with high-confidence cloud–aerosol discrimination (|CAD| > 70) to investigate the optical properties, vertical distributions, seasonal variations, and aerosol interactions of near-surface cloud layers (cloud base height < 2.5 km) over Australia from 2006 to 2021. This definition encompasses both traditional low clouds and part of mid-level clouds that extend into the lower troposphere, enabling a comprehensive view of cloud systems that interact most directly with boundary-layer aerosols. The results showed that the optical depth of low clouds (CODL) exhibited significant spatial heterogeneity, with higher values in central and eastern regions (often exceeding 6.0) and lower values in western plateau regions (typically 4.0–5.0). CODL values demonstrated clear seasonal patterns with spring peaks across all regions, contrasting with traditional summer-maximum expectations. Pronounced diurnal variations were observed, with nighttime CODL showing systematic enhancement effects (up to 19.29 maximum values compared to daytime 11.43), primarily attributed to surface radiative cooling processes. Cloud base heights (CBL) exhibited counterintuitive nighttime increases (41% on average), reflecting fundamental differences in cloud formation mechanisms between day and night. The geometric thickness of low clouds (CTL) showed significant diurnal contrasts, decreasing by nearly 50% at night due to enhanced atmospheric stability. Cloud layer number (CN) displayed systematic nighttime reductions (18% decrease), indicating dominance of single stratiform cloud systems during nighttime. Regional analysis revealed that the central plains consistently exhibited higher CODL values, while eastern mountains showed elevated cloud heights due to orographic effects. Correlation analysis between cloud and aerosol layer properties revealed moderate but statistically significant relationships (|R| = 0.4–0.6), with the strongest correlations appearing between cloud layer heights and aerosol layer heights. However, these correlations represent only partial influences among multiple factors controlling cloud development, suggesting measurable but modest aerosol effects on cloud properties. This study provides comprehensive observational evidence for cloud optical property variations and aerosol–cloud interactions over Australia, contributing to an improved understanding of Southern Hemisphere cloud systems and their climatic implications. Full article

Understanding and mapping ephemeral gullies (EGs) are vital for enhancing agricultural productivity and achieving food security. This study proposes an upscaling-based strategy to refine the predictive mapping of EGs, utilizing high-resolution Pléiades Neo (0.6 m) and medium-resolution Sentinel-2 (10 m) satellite imagery, alongside ground-truth EGs mapping in Niagara Region, Canada. The research involved generating spectral feature maps using Blue, Green, Red, and Near-infrared spectral bands, complemented by indices indicative of surface wetness, vegetation, color, and soil texture. Employing the Random Forest (RF) algorithm, this study executed three distinct strategies for EGs identification. The first strategy involved direct calibration using Sentinel-2 spectral features for 10 m resolution mapping. The second strategy utilized high-resolution Pléiades Neo data for model calibration, enabling EGs mapping at resolutions of 0.6, 2, 4, 6, and 8 m. The third, or upscaling strategy, applied the high-resolution calibrated model to medium-resolution Sentinel-2 imagery, producing 10 m resolution EGs maps. The accuracy of these maps was evaluated against actual data and compared across strategies. The findings highlight the Variable Importance Measure (VIM) of different spectral features in EGs identification, with normalized near-infrared (Norm NIR) and normalized red reflectance (Norm Red) exhibiting the highest and lowest VIM, respectively. Vegetation-related indices demonstrated a higher VIM compared to surface wetness indices. The overall classification error of the upscaling strategy at spatial resolutions of 0.6, 2, 4, 6, 8, and 10 m (Upscaled), as well as that of the direct Sentinel-2 model, were 7.9%, 8.2%, 9.1%, 10.3%, 11.2%, 12.5%, and 14.5%, respectively. The errors for EGs maps at various resolutions revealed an increase in identification error with higher spatial resolution. However, the upscaling strategy significantly improved the accuracy of EGs identification in medium spatial resolution scenarios. This study not only advances the methodology for EGs mapping but also contributes to the broader field of precision agriculture and environmental management. By providing a scalable and accessible approach to EGs mapping, this research supports enhanced soil conservation practices and sustainable land management, addressing key challenges in agricultural sustainability and environmental stewardship. Full article

In recent years, spaceborne Global Navigation Satellite System reflectometry (GNSS-R) technology has made significant progress in the fields of Earth observation and remote sensing, with a wide range of applications, important research value, and broad development prospects. However, despite existing research focusing on the application of spaceborne GNSS-R L1-level data, the potential value of raw intermediate-frequency (IF) signals has not been fully explored for special applications that require a high accuracy and spatiotemporal resolution. This article provides a comprehensive overview of the current status of the measurement of raw IF signals from spaceborne GNSS-R in multiple application fields. Firstly, the development of spaceborne GNSS-R microsatellites launch technology is introduced, including the ability of microsatellites to receive GNSS signals and receiver technique, as well as related frequency bands and technological advancements. Secondly, the key role of coherence detection in spaceborne GNSS-R is discussed. By analyzing the phase and amplitude information of the reflected signals, parameters such as scattering characteristics, roughness, and the shape of surface features are extracted. Then, the application of spaceborne GNSS-R in inland water monitoring is explored, including inland water detection and the measurement of the surface height of inland (or lake) water bodies. In addition, the widespread application of group delay sea surface height measurement and carrier-phase sea surface height measurement technology in the marine field are also discussed. Further research is conducted on the progress of spaceborne GNSS-R in the retrieval of ice height or ice sheet height, as well as tropospheric parameter monitoring and the study of atmospheric parameters. Finally, the existing research results are summarized, and suggestions for future prospects are put forward, including improving the accuracy of signal processing and reflection signal analysis, developing more advanced algorithms and technologies, and so on, to achieve more accurate and reliable Earth observation and remote sensing applications. These research results have important application potential in fields such as environmental monitoring, climate change research, and weather prediction, and are expected to provide new technological means for global geophysical parameter retrieval. Full article

This study utilizes EnMAP hyperspectral satellite data to map the mineralogical footprints of hydrocarbon microseepage systems induced in the Upper-Red Formation (URF), a clastic Upper Miocene sedimentary sequence in the Qom region (Iran) affected by petroleum leakage from the underlying Alborz reservoir. The Level 2A surface reflectance product of EnMAP was processed using spectral matching and polynomial fitting techniques to characterize diagnostic absorption features associated with microseepage-induced alteration minerals. The identified mineralogical changes include partial to complete bleaching of hematite from redbeds, the formation of secondary goethite, and the development of montmorillonite, calcite, and Fe²⁺-bearing chlorite across the affected zones. Compared to previous studies conducted using ASTER and Sentinel-2 multispectral data, EnMAP demonstrated superior performance in identifying mineralogy and delineating petroleum-affected zones, with results aligning closely with field observations and laboratory spectroscopy. This study highlights the advantages of EnMAP hyperspectral data for mapping diagenetic mineralogical alterations induced in sedimentary strata, facilitating remote sensing-based detection of microseepage, and advancing petroleum exploration in exposed terrains. Full article

The escalation in the population of livestock coupled with inadequate precipitation has caused a reduction in pasture biomass, thereby resulting in diminished grassland carrying capacity (GCC) and pasture degradation. In this research, net primary productivity (NPP) data, sourced from the Global Land Surface Satellite (GLASS) and Moderate Resolution Imaging Spectroradiometer (MODIS) datasets from 1982 to 2020, were initially transformed into aboveground biomass (AGB) estimates. These estimates were subsequently utilized to evaluate and assess the long-term trends of GCC across Mongolia. The MODIS data indicated an upward trend in AGB from 2000 to 2020, whereas the GLASS data reflected a downward trend from 1982 to 2018. Between 1982 and 2020, climatic analysis uncovered robust positive correlations between AGB and precipitation (R > 0.80) and negative correlations with temperature (R < −0.60). These climatic alterations have led to a reduction in AGB, further impairing the regenerative capacity of grasslands. Concurrently, livestock numbers have generally increased since 1982, with a decrease in certain years due to dzud and summer drought, leading to the increase in the GCC. GCC assessment found that 37.5% of grasslands experienced severe overgrazing and 31.9–40.7% was within sustainable limits. Spatially, the eastern region of Mongolia could sustainably support current livestock numbers; the western and southern regions, as well as parts of northern Mongolia, have exhibited moderate to critical levels of grassland utilization. A detailed analysis of GCC dynamics and its climatic impacts would offer scientific support for policymakers in managing grasslands in the Mongolian Plateau. Full article

This study presents a systematic analysis of the optical-physical properties of low clouds and their vertical interaction mechanisms with aerosols over three African sub-regions (A: North African Desert; B: Congo Basin; C: Southeastern Plateau and Coastal Zone) using CALIPSO satellite vertical observations taken between 2006 and 2021. The results revealed distinct spatiotemporal variations: For example, the low-cloud aerosol optical depth (AOD) in Region A peaked during December–February, while Regions B and C exhibited higher values from June to November, with elevated dry-season and daytime levels. A positive correlation emerged between low-cloud AOD and its fractional contribution. Regional contrasts in low-cloud vertical structure were evident, with Region C showing the highest seasonal mean cloud base/top heights and Region A the lowest. The depolarisation ratio of low clouds was higher in desert areas (Region A) but lower in rainforest regions (Region B), while the SRlc (Low-cloud spectral reflectance ratio) was maximised in the Congo Basin (Region B), with wet-season and daytime enhancements. The near-surface aerosol AOD in Regions A and B was positively correlated with low-cloud AOD proportion (PAOD_lc). Across all regions, the near-surface aerosol layer top height showed positive correlations with the low-cloud base height and vertical extent, while the height of the bottom of the near-surface aerosol layer was positively aligned with the low-cloud base height. For Region C, there were negative correlations between near-surface aerosol layer heights and PAOD_lc, whereas the springtime aerosol parameters in Region A exhibited positive PAOD_lc correlations. These findings advance the current understanding of aerosol sources and ecosystem impacts, and provide critical insights for refining aerosol and low-cloud parameterisations in climate models. Full article

This study evaluates the effectiveness of combining remote sensing techniques with the Random Forest algorithm for estimating the Periodic Annual Increment (PAI) in a dry tropical forest located within the Caatinga biome in northeastern Brazil. The analysis integrates forest inventory data collected from permanent plots monitored between 2011 and 2019 with Landsat satellite imagery processed through the Google Earth Engine platform. By incorporating surface reflectance and vegetation indices, the approach significantly improved the accuracy of productivity estimates while reducing the costs and efforts associated with traditional field-based methods. The Random Forest model achieved a strong performance (R² = 0.8867; RMSE = 0.87), and its predictions were further refined using post-processing correction factors. These results demonstrate the potential of data-driven modeling to support forest monitoring and sustainable management practices, especially in ecosystems vulnerable to the impacts of climate change. Full article

High-precision hyperspectral remote sensing reflectance measurement of water bodies serves as the fundamental technical basis for accurately retrieving spatiotemporal distribution characteristics of water quality parameters, providing critical data support for dynamic monitoring of aquatic ecosystems and pollution source tracing. To address the critical issue of water surface glint interference significantly affecting measurement accuracy in aquatic remote sensing, this study innovatively developed a novel sensor system based on multi-field-of-view Kohler-polarization technology. The system incorporates three Kohler illumination lenses with exceptional surface uniformity exceeding 98.2%, effectively eliminating measurement errors caused by water surface brightness inhomogeneity. By integrating three core technologies—multi-field polarization measurement, skylight blocking, and high-precision radiometric calibration—into a single spectral measurement unit, the system achieves radiation measurement accuracy better than 3%, overcoming the limitations of traditional single-method glint suppression approaches. A glint removal efficiency (GRE) calculation model was established based on a skylight-blocked approach (SBA) and dual-band power function fitting to systematically evaluate glint suppression performance. Experimental results show that the system achieves GRE values of 93.1%, 84.9%, and 78.1% at ±3°, ±7°, and ±12° field-of-view angles, respectively, demonstrating that the ±3° configuration provides a 9.2% performance improvement over the ±7° configuration. Comparative analysis with dual-band power-law fitting reveals a GRE difference of 2.1% (93.1% vs. 95.2%) at ±3° field-of-view, while maintaining excellent consistency (ΔGRE < 3.2%) and goodness-of-fit (R² > 0.96) across all configurations. Shipborne experiments verified the system’s advantages in glint suppression (9.2%~15% improvement) and data reliability. This research provides crucial technical support for developing an integrated water remote sensing reflectance monitoring system combining in situ measurements, UAV platforms, and satellite observations, significantly enhancing the accuracy and reliability of ocean color remote sensing data. Full article

Today, open-source Cloud Computing platforms are valuable for geospatial image analysis while the combination of the Google Earth Engine (GEE) platform and new satellite launches greatly facilitate the monitoring of national-scale lake Water Quality (WQ). The main aim of this research is to assess the transferability and performance of published general, natural-only and artificial-only lake WQ models (Chl-a, Secchi Disk Depth-SDD- and Total Phosphorus-TP) across Greece’s WFD (Water Framework Directive) lake sampling network. We utilized Landsat (7 ETM +/8 OLI) and Sentinel 2 surface reflectance (SR) data embedded in GEE, while subjected to different atmospheric correction (AC) methods. Subsequently, Carlson’s Trophic State Index (TSI) was calculated based on both in situ and modelled WQ values. Initially, WQ models employed both DOS1-corrected (Dark Object Subtraction 1; manually applied) and GEE-retrieved respective SR data from the year 2018. Double WQ values per lake station were inserted in a linear regression analysis to harmonize the AC differences, separately for Landsat and Sentinel 2 data. Yielded linear equations were accompanied by strong associations (R² ranging from 0.68 to 0.98) while modelled and GEE-modelled TSI values were further validated based on reference in situ WQ datasets from the years 2019 and 2020. The values of the basic statistical error metrics indicated firstly the increased assessment’s accuracy of GEE-modelled over modelled TSIs and then the superiority of Landsat over Sentinel 2 data. In this way, the hereby adopted methodology was evolved into an efficient lake management tool by providing managers the means for integrated sustainable water resources management while contributing to saving valuable image pre-processing time. Full article