Search Results (500)

As a core ground-based coronal observation facility in the low-latitude and high-altitude regions of China, the Lijiang Coronagraph takes advantage of the natural endowments of the Lijiang Astronomical Observation Station, such as an altitude of 3200 m and low atmospheric turbulence. It has gone through a complete development process from introduction through Chinese–Japanese cooperation to independent innovation and iteration. This paper systematically summarizes the core technological innovation achievements of this facility, including the upgrade of the automatic operating system, the integration of the dual-band observation system, the stray light suppression technology based on the image difference method before and after cleaning, and the high-precision image calibration and registration technology. These innovations have significantly improved observation efficiency and data quality, laying a solid foundation for high-quality observations. At the scientific research level, the observation data reveal that 1.1 R_⊙ (solar radius) is a highly correlated region between coronal green line brightness and magnetic field intensity. This study also confirms a strong correlation between the coronal green line and the SDO/AIA 211 Å extreme ultraviolet band (correlation coefficient: 0.89–0.99), which can support the research on early warning of Coronal Mass Ejections (CMEs). These achievements provide key data support for the verification of coronal heating mechanisms and the exploration of the origin of the slow solar wind. The technical experience accumulated from the Lijiang Coronagraph has not only laid a solid foundation for the research and development of China’s next-generation large-aperture coronagraphs, but also facilitated and accelerated substantial progress in China’s technical capabilities for low coronal observation, enabling the country to establish internationally parallel competitive capabilities in this field. This system has also become an important part of the global coronal observation network. Full article

The spatial distribution of oyster reefs is an important indicator for assessing environmental changes in nearshore fishery habitats. However, due to tidal fluctuations, images of oyster reef distribution acquired under low-light conditions such as early morning or evening often exhibit common issues such as bright spots and shadows. Thermal infrared (TIR) images, which are unaffected by external lighting conditions, can effectively address this problem. Aerial imaging of Liya Mountain, Haimen, Jiangsu Province, China, was conducted in this study. Based on unmanned aerial vehicles (UAVs) imagery acquired in 2025 using multispectral and TIR sensors, the total oyster reef area was estimated to be 6.61 ha. When compared with the oyster reef distribution derived from visible light aerial imagery collected in 2023 under favorable environmental conditions, this represents a decrease of 0.36 ha (5.4%), with the largest individual reef measuring 3388.17 m². To demonstrate the improvement in extraction accuracy achieved by integrating TIR data with multispectral imagery, the research team compared the extraction accuracy for oyster reefs of different sizes: a 1.91% improvement was observed for small reefs, a 9.02% improvement for middle reefs, and an 18.98% improvement for large reefs. Experimentally, the emissivity of oyster reefs was determined to be 0.982 ± 0.002 using an isothermal method in the laboratory. The emissivity derived from in situ measurements showed similar values, supporting the reliability of the laboratory result and providing a crucial parameter for the inversion of reef surface temperature. Experimental results demonstrate that the TIR band can effectively enhance the spatial accuracy of oyster reef measurements under low-light conditions. Full article

Replacing inefficient lighting with energy-efficient alternatives is a proven way to reduce urban energy use, yet evaluating such policies remains challenging. For example, in 2013, New York City (NYC) initiated a program to replace 250,000 high-pressure sodium (HPS) streetlights with light-emitting diodes (LEDs) by 2017, but no subsequent evaluation was published. Here, we employ ground-based hyperspectral imaging (HSI; 0.4–1.0 microns, ∼850 bands) observations from the “Urban Observatory” (UO), obtained in 2013 and 2018, to quantitatively characterize this technological transition. Following co-registration, artifact removal, and source identification, we classified individual light source technologies using both a maximum correlation approach with spectral templates of known lighting types and a one-dimensional Convolutional Neural Network (1D-CNN) trained on 1321 manually labeled spectra, achieving an average precision of ∼92% for the 2013 data and ∼94% for the 2018 data across technology classes. Scene-level mixture modeling indicates a reduction in the HPS-to-LED brightness ratio from 1.15 (2013) to 0.27 (2018), demonstrating the capability of longitudinal HSI for evaluating urban lighting policy outcomes. Full article

Characterizing both the CO₂ distribution and wind dynamics in the Martian mesosphere and lower thermosphere is vital for planetary atmospheric science and mission planning. In this work, we propose a novel dual-channel passive limb-viewing imaging system designed to simultaneously observe partial CO₂ column density and line-of-sight (LOS) wind speed from ultraviolet and visible airglow emissions under dayside and terminator illumination conditions. A dichroic beam splitter separates the ultraviolet and visible channels, ensuring high optical throughput and independent optimization of both subsystems. The ultraviolet channel targets O(¹S) 297.2 nm emission, a well-established Martian limb emission driven by CO₂ photodissociation under solar Lyman-α flux. By applying narrow-band imaging and brightness inversion, this channel provides quantitative constraints on CO₂ column density with a stable and well-defined response function. In the visible channel, we introduce a lens array-based compact static Michelson interferometer optimized for the O(¹S) 557.7 nm green line emission, which has been observed in the Martian dayside limb, providing Doppler wind measurements in the 60–180 km altitude range. Radiative transfer simulations using Mars Climate Database indicate retrieval precisions of ±6~8% for CO₂ column density and better than ±5 m/s for wind speed within the primary emission layer (approximately 60–160 km) under representative dayside limb conditions. This dual-parameter remote sensing concept simultaneously constrains the composition and dynamics of the Martian mesosphere and lower thermosphere region, addressing a long-standing observational gap. The compact and modular design of the system makes it well suited for future Mars orbiter payloads under nominal dayside and terminator observation geometries, providing critical data for validating global circulation models and supporting future entry, descent, and landing system design. Full article

This study proposes a method for retrieving ocean sea surface salinity (SSS) using C/X-band ocean emissivities in coastal regions, aiming to verify the performance of these unconventional frequencies for SSS retrieval in warm, high-salinity-variation coastal oceans. Since C/X-band brightness temperatures are less sensitive to sea surface salinity than L-band brightness temperatures, it becomes particularly important to develop a sophisticated and effective method for extracting salinity-related signals from C/X-band brightness temperatures. To this end, a wind effect correction process is developed to remove rough sea surface emissivity contributions from total emissivity and derive calm sea emissivity from WindSat’s brightness temperatures. The wind-induced effects are modeled with a third-order polynomial. Then, based on emissivity analysis, a weighted combination of C/X-band calm sea emissivities (with parameter λ) is introduced to reduce SST sensitivity. This λ-based combination is used to retrieve SSS in the Bay of Bengal. Based on the triple-match method and buoy data, the salinity retrieval results are verified and compared with the Soil Moisture Active Passive (SMAP) SSS and Argo in situ SSS. The results show that the use of parameter λ reduces the RMS error of SSS by 0.1–0.2 psu. The RMSE of SSS retrieval is about 0.64 psu, which is comparable to the error of SMAP data. Simultaneously, the SSS retrieval accuracy is significantly influenced by offshore distance. At an offshore distance of 100 km, the salinity retrieval error exceeds 1 psu, while when the offshore distance exceeds 500 km, the salinity retrieval error is better than 0.6 psu. Full article

Lignin from Phaleria macrocarpa (Mahkota Dewa) fruit, a bioactive-rich cultivated medicinal biomass, was employed as a renewable precursor for lignin quantum dots (LQDs). A simple, aqueous, catalyst-free two-step route (lignin to lignin nanoparticles to LQDs) is demonstrated, enabling the valorization of non-food lignin into photoluminescent nanomaterials. The resulting LQDs were predominantly amorphous with short-range graphitic ordering and a narrow particle size distribution (3–5 nm). Structural and chemical analyses indicated a partially graphitized carbon framework enriched with oxygenated surface functionalities, which is consistent with their bright blue–green emission (λ_em of 490 nm; average fluorescence lifetime of 4.51 ns). Hydrothermal carbonization induced a blue shift in the UV–Vis absorption profile, resulting in a main band at 288 nm with a shoulder at 312 nm. The LQDs exhibited high cytocompatibility toward L929 mouse fibroblasts (93.1 ± 6.5% viability at 24 h) and were readily internalized by cells, facilitating green fluorescence live-cell imaging as a proof-of-concept. Antibacterial activity was observed against both Gram-positive and Gram-negative strains, supporting dual biofunctional performance. Overall, this study established a green and scalable route for converting P. macrocarpa fruit lignin into multifunctional LQDs, with potential applications in circular-bioeconomy such as antimicrobial/active coatings and optical sensing in agro-industrial contexts. Full article

The Southwest China vortex (SWV) is a high-impact mesoscale cyclonic vortex that typically originates over Sichuan Province, China, and frequently produces hazardous rainfall. Yet systematic knowledge of the structural and microphysical properties of SWV precipitation remains insufficiently quantified. Using Global Precipitation Measurement Dual-frequency Precipitation Radar (GPM/DPR) observations from 2014 to 2022, this study investigates the vertical structure and macro- and microphysical characteristics of SWV precipitation, and quantifies their differences across life-cycle stages and precipitation types. The mature stage is characterized by higher echo tops, stronger radar reflectivity, higher strong-echo altitudes, and larger near-surface rainfall, together with a clearer melting-layer bright band and a stronger post-melting shift toward larger drops and lower number concentrations. The developing stage is weakest and shows the largest fraction of coalescence–breakup balance signatures, whereas the dissipating stage features enhanced evaporation- and breakup-related signals. Among precipitation types, deep strong convection exhibits the greatest vertical extent with enhanced ice/mixed-phase growth; stratiform precipitation produces stronger radar echoes and higher rainfall rates than deep weak convection despite similar echo-top heights; and shallow precipitation is characterized by smaller drops, higher concentrations, and active warm-rain spectral evolution. These findings provide satellite-based constraints for microphysics parameterization evaluation and improved numerical prediction of SWV-related rainfall over complex terrain. Full article

We present a supervised machine learning classification of sources from the Javalambre Physics of the Accelerating Universe Astrophysical Survey (J-PAS) Pathfinder datasets: miniJPAS and J-NEP. Leveraging crossmatches with spectroscopic and photometric catalogs, we construct a robust labeled dataset comprising 14,594 sources classified into extended (galaxies) and point-like (stars and quasars) objects. We assess dataset representativeness using UMAP analysis, confirming broad and consistent coverage of feature space. An XGBoost classifier, with hyperparameters tuned using automated optimization, is trained using purely photometric data (60-band J-PAS magnitudes) and combined photometric and morphological features, with performance thoroughly evaluated via ROC and purity–completeness metrics. Incorporating morphology significantly improves classification, outperforming the baseline classifications available in the catalogs. Permutation importance analysis reveals morphological parameters, particularly concentration, normalized peak surface brightness, and PSF, alongside photometric features around 4000 and 6900 Å, as crucial for accurate classifications. We release a value-added catalog with our models for star-galaxy classification, enhancing the utility of miniJPAS and J-NEP for subsequent cosmological and astrophysical analyses. Full article

Floods and droughts have increased in Northern Eurasia, probably caused by hydrological changes in other regions. We explore such hypothetical teleconnections by investigating environmental changes in two contrasting harsh environments: the Arctic Kara Sea and the arid Aral–Caspian region. Using long-term data from daily remote microwave sensing, we describe seasonal dynamics of temperature and moisture regimes in the two regions and hypothesize their inter-relationships from new analyses of wind data. For the first time, daily L-band satellite data were used to determine open water in the Kara Sea and long-term seasonal dynamics of brightness temperatures were used to relate variations in the ongoing aridization of the Aral Sea area and abnormal spring floods in the south of Western Siberia. Using soil moisture and Ocean Salinity satellite data, we discovered a previously unrecorded 4-year cyclicity of open-water periods for the Arctic seas and northern parts of the Caspian and Aral Seas. This cyclicity could impact climate forecasting in Northern Eurasia with significant societal implications. The main aim of this paper is to present new analyses that suggest possible mechanisms for teleconnections between the two contrasting harsh environments of Northern Eurasia. The hypothetical teleconnections now need to be tested. Full article

High-resolution TIR missions require sustained and well-characterized radiometric accuracy to support applications such as land surface temperature retrieval, drought monitoring, and surface energy budget analysis. To address this need, we develop an operational and automated ground-based vicarious radiometric calibration framework for TIR sensors and demonstrate its performance using the Wide-swath Thermal Infrared Imager (WTI) onboard Gaofen-5 01A (GF-5A). Three arid Gobi calibration sites were selected by integrating Moderate Resolution Imaging Spectroradiometer (MODIS) cloud products, Shuttle Radar Topography Mission (SRTM)-derived topography, and WTI-based radiometric uniformity metrics to ensure low cloud cover, flat terrain, and high spatial homogeneity. Automated ground stations deployed at Golmud, Dachaidan, and Dunhuang have continuously recorded 1 min contact surface temperature since October 2023. Field-measured emissivity spectra, Integrated Global Radiosonde Archive (IGRA) radiosonde profiles, and MODTRAN (MODerate resolution atmospheric TRANsmission) v5.2 simulations were combined to compute top-of-atmosphere (TOA) radiances, which were subsequently collocated with WTI imagery. After data screening and gain-stratified regression, linear calibration coefficients were derived for each TIR band. Based on 189 scenes from February–July 2024, all four bands exhibit strong linearity (R-squared greater than 0.979). Validation using 45 independent scenes yields a mean brightness–temperature root-mean-square error (RMSE) of 0.67 K. A full radiometric-chain uncertainty budget—including contact temperature, emissivity, atmospheric profiles, and radiative transfer modeling—results in a combined standard uncertainty of 1.41 K. The proposed framework provides a low-maintenance, traceable, and high-frequency solution for the long-term on-orbit radiometric calibration of GF-5A WTI and establishes a reproducible pathway for future TIR missions requiring sustained calibration stability. Full article

Organic optoelectronic devices receive appreciable attention due to their low cost, ecology, mechanical flexibility, band-gap engineering, brightness, and solution process ability over a broad area. In this study, we designed and studied organic light-emitting diodes (OLEDs) consisting of an assembly of natural dyes, extracted from noble fir leaves (evergreen) and blue hydrangea flowers mixed with poly-methyl methacrylate (PMMA) as light emitters. We experimentally demonstrate the effective conversion of blue light emitted by an inorganic laser/photodiode into longer-wavelength red and green tunable photoluminescence due to the excitation of natural dye–PMMA nanostructures. UV-visible absorption and photoluminescence spectroscopy, ellipsometry, and Fourier transform infrared methods, together with optical microscopy, were performed for confirming and characterizing the properties of light-emitting diodes based on natural dyes. We highlighted the optical and physical properties of two different natural dyes and demonstrated how such characteristics can be exploited to make efficient LED devices. A strong pure red emission with a narrow full-width at half maximum (FWHM) of 23 nm in the noble fir dye–PMMA layer and a green emission with a FWHM of 45 nm in blue hydrangea dye–PMMA layer were observed. It was revealed that adding monolayer MoS₂ to the nanostructures can significantly enhance the photoluminescence of the natural dye due to a strong correlation between the emission bands of the inorganic–organic emitters and back mirror reflection of the excitation blue light from the monolayer. Based on the investigation of two natural dyes, we demonstrated viable pathways for scalable manufacturing of efficient hybrid OLEDs consisting of assembly of natural-dye polymers through low-cost, purely ecological, and convenient processes. Full article

Conventional High Dynamic Range (HDR) image fusion algorithms generally require two or more original images with different exposure times for synthesis, making them unsuitable for real-time processing scenarios such as video streams. Additionally, the synthesized HDR images have the same bit depth as the original images, which may lead to banding artifacts and limits their applicability in professional fields requiring high fidelity. This paper utilizes a Field Programmable Gate Array (FPGA) to support an image sensor operating in Clear HDR mode, which simultaneously outputs High Conversion Gain (HCG) and Low Conversion Gain (LCG) images. These two images share the same exposure duration and are captured at the same moment, making them well-suited for real-time HDR fusion. This approach provides a feasible solution for real-time processing of video streams. An adaptive adjustment algorithm is employed to address the requirement for high fidelity. First, the initial HCG and LCG images are fused under the initial fusion parameters to generate a preliminary HDR image. Subsequently, the gain of the high-gain images in the video stream is adaptively adjusted according to the brightness of the fused HDR image, enabling stable brightness under dynamic illumination conditions. Finally, by evaluating the read noise of the HCG and LCG images, the fusion parameters are adaptively optimized to synthesize an HDR image with higher bit depth. Experimental results demonstrate that the proposed method achieves a processing rate of 46 frames per second for 2688 × 1520 resolution video streams, enabling real-time processing. The bit depth of the image is enhanced from 12 bits to 16 bits, preserving more scene information and effectively addressing banding artifacts in HDR images. This improvement provides greater flexibility for subsequent image processing tasks. Consequently, the adaptive algorithm is particularly suitable for dynamically changing scenarios such as real-time surveillance and professional applications including industrial inspection. Full article

In the field of smart agriculture, soil property data at the field scale drives the precision decision-making of agricultural inputs such as seeds and chemical fertilizers. However, soil texture has significant spatial variability at the field scale, and traditional remote sensing monitoring methods have certain data intermittency, which limits small-scale prediction research. In this study, based on the Google Earth Engine platform, soil synthetic images were generated according to different time intervals using mean compositing and median compositing modes, image bands were extracted, and spectral indices were introduced; combined with the random forest algorithm, the effects of different compositing time windows, compositing modes, and compositing data types on prediction accuracy were evaluated; and three partitioning strategies based on crop growth, soil synthetic image brightness, and soil type were adopted to conduct local partitioning regression of soil texture. The results show that: (1) The use of mean compositing of multi-year May images from 2021 to 2024 can improve prediction accuracy. When this method is combined with the “band reflectance + spectral indices” dataset, compared with other compositing methods, the R² of clay particles, silt particles, and sand particles can be increased by 8.89%, 9.50%, and 2.48% on average. (2) Compared with using only image band data, the introduction of spectral indices can significantly improve the prediction accuracy of soil texture at the field scale, and the R² of clay particles, silt particles, and sand particles is increased by 4.58%, 3.43%, and 4.59% on average, respectively. (3) Global regression is superior to local partitioning regression; however, the local partitioning regression strategy based on soil type has good accuracy performance. Under the optimal compositing method, the average R² of soil particles of each size fraction is only 1.08% lower than that of global regression, which has great application potential. This study innovatively constructs a comprehensive strategy of “moisture spectral indices + specific compositing time window + specific compositing mode + soil type partitioning”, providing a new paradigm for soil texture prediction at the field scale in Northeastern China, and lays the foundation for data-driven water and fertilizer decision-making. 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

The Fengyun-4 microwave satellite provides high-temporal-frequency observations at the 183 GHz band, providing unprecedented data for all-weather, three-dimensional measurements of atmospheric parameters. It is of importance to establish a simulated brightness temperature (BT) dataset for this band prior to launch, which can support the relevant quantitative applications significantly. Compared with clear-sky conditions, the accuracy of BT simulations under cloudy ones is considerably lower, primarily due to the influence of the adopted ice particle models. Up until now, few studies have systematically investigated ice particle model selection for different cloud types at the 183 GHz frequency band. In this paper, multi-sensor observations from Cloud Profiling Radar (CPR), Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), and Visible Infrared Imaging Radiometer Suite (VIIRS) were used as realistic atmospheric profiles. Using the high-precision radiative transfer model Atmospheric Radiative Transfer Simulator (ARTS), BT simulations at 183 GHz were performed to explore the optimal ice particle models for seven classical cloud types. The main conclusions are given as follows: (1) The sensitivity of simulated cloud radiances to ice particle habits differs with respect to different cloud phases. For altocumulus (Ac), stratocumulus (Sc), and cumulus (Cu) clouds, the different choices of ice particle model have little impacts on the simulated brightness temperatures (<1 K), with RMSEs below 3 K across multiple models, indicating that various models can be applied directly for such simulations. (2) For some mixed-phase clouds, including altostratus (As), nimbostratus (Ns), and deep convective (Dc) clouds, the Small Block Aggregate (SBA) and Small Plate Aggregate (SPA) models demonstrate good performance for As clouds, with RMSEs below 2.5 K, while the SBA, SPA, and Large Column Aggregate (LCA) models exhibit similarly good performance for Ns clouds, also achieving RMSEs below 2.5 K. For Dc clouds, although the SBA model yields RMSEs of approximately 10 K, it still provides a substantial improvement over the spherical model, whereas for cirrus (Ci) clouds, any non-spherical ice particle models are applicable, with RMSEs below 2 K. (3) Within the 183 GHz frequency band, channels with the higher weighting-function peaks are less sensitive to variable adoptions of ice particle models. These results offer valuable references for accurate radiative transfer simulations on 183 GHz frequency. Full article