Visible Data Research Articles

Remote sensing through the fog of war: Infrastructure damage and environmental change during the Russian-Ukrainian conflict revealed by open-access data

The Russian-Ukrainian conflict spawned a high-intensity war that shattered decades of peace in Europe. The use of drones and social media elevates open-source intelligence as a critical strategic asset. However, information from these sources is sporadic, difficult to confirm, and prone to manipulation. Here, we use open-access spaceborne remote sensing data to probe the damage to infrastructure on and off the frontline at the city, region, and country-wide scales in Ukraine. Nighttime light data and Synthetic Aperture Radar images reveal widespread blackout and unveil the destruction of battleground cities, offering contrasted perspectives on the impact of the conflict. Optical satellite images capture extensive flooding along the Dnipro River in the aftermath of the breach of the Kakhovka dam. Leveraging visible, near-infrared, and microwave satellite data, we bring to light disruption of human activities, havoc in the environment, and the annihilation of entire cities during the protracted conflict. Open-source remote sensing can offer objective information about the nature and extent of devastation during military conflicts.

Hydrothermal synthesis of Cu2CoSnS4 nanoparticles: characterization and their applications of electrochemical, antibacterial and photocatalytic performances

Abstract A hydrothermal technique was used to successfully synthesize tetragonal Cu2CoSnS4 (CCTS) nanoparticles and investigate the effect of various thiourea concentrations on structural, morphological and optical properties. XRD analysis revealed the formation of tetragonal CCTS nanoparticles and the average crystallite size (nm) varied from 26 to 40 nm. The Raman studies confirmed the vibrational modes of the CCTS nanoparticles. The FE-SEM images revealed that the thiourea concentrations induced morphological changes in the CCTS nanoparticles, which exhibited the nanosheets changing into spherical structures. TEM images indicated that the CCTS sample had a spherical structure and the SAED pattern demonstrated a polycrystalline nature. The valance states of metallic species, such as Cu+ and Co2+ were further confirmed by XPS. The optical band gap (1.53 eV) was calculated from UV–Visible data and the obtained bandgap value from the literature. The electrochemical measurements of the CCTS TU-10 electrode exhibited pseudocapacitive behavior with a notable specific capacitance of 198 Fg−1 at a scan rate of 10 mV/S along with favorable electrocatalytic activity. In the photocatalytic application, when compared to MB dye (78 %), the CCTS catalyst was found to have a higher degradation efficiency toward CV dye (84 %). For antibacterial studies, the Vibrio parahaemolyticus bacteria exhibited the maximum zone of inhibition at 20 mm for 100 μg/ml. Finally, the experimental results suggested that the synthesized CCTS nanoparticles had better electrochemical, photocatalytic, and antibacterial properties.

Noninvasive Early Detection of Nutrient Deficiencies in Greenhouse-Grown Industrial Hemp Using Hyperspectral Imaging

Hyperspectral imaging is an emerging non-invasive technology with potential for early nutrient stress detection in plants prior to visible symptoms. This study evaluated hyperspectral imaging for early identification of nitrogen, phosphorus, and potassium (NPK) deficiencies across three greenhouse-grown industrial hemp plant cultivars (Cannabis sativa L.). Visible and near-infrared spectral data (380–1022 nm) were acquired from hemp samples subjected to controlled NPK stresses at multiple developmental timepoints using a benchtop hyperspectral camera. Robust principal component analysis was developed for effective screening of spectral outliers. Partial least squares discriminant analysis (PLS-DA) and support vector machines (SVM) were developed and optimized to classify nutrient deficiencies using key wavelengths selected by variable importance in projection (VIP) and interval partial least squares (iPLS). The 16-wavelength iPLS-C-SVM model achieved the highest precision of 0.75 to 1 on the test dataset. Key wavelengths for effective nutrient deficiency detection spanned the visible range, underscoring the hyperspectral imaging sensitivity to early changes in leaf pigment levels prior to any visible symptom development. The emergence of wavelengths related to chlorophyll, carotenoid, and anthocyanin absorption as optimal for classification, highlights the technology’s capacity to detect subtle impending biochemical perturbations linked to emerging deficiencies. Identifying stress at this pre-visual stage could provide hemp producers with timely corrective action to mitigate losses in crop quality and yields.

Structure, dielectric, vibrational, and optical properties of lead-free double perovskite Bi2FeMnO6

The solid-state reaction technique was executed to form a hexagonal double perovskite Bi2FeMnO6 nanopowder. X-ray diffraction (XRD) analysis gives information regarding the product’s average crystallite size of 27.9 nm and lattice strain of 0.00125, respectively. The Ultra-Violet (UV) visible spectroscopy data provides a band gap energy of 2.49 eV, which may be a suitable range for photovoltaic applications. Fourier Transform Infrared (FTIR) spectroscopy data analysis reveals the presence of all constituent atomic vibration bands. The dielectric measurements confirmed the presence of the Maxwell-Wanger type of dispersion. The negative temperature coefficient of resistance (NTCR) character is well understood from impedance spectroscopy. The electrical modulus results indicate the presence of a non-Debye-type of relaxation mechanism in the sample. Analyzing ac conductivity data reveals the thermally activated conduction process. The sample’s semi-conducting character was confirmed by both Nyquist and Cole-Cole plots.

Incorporating weather in counts and trends of migrating Common Nighthawks.

Effective conservation planning for species of concern requires long-term monitoring data that can accurately estimate population trends. Supplemental or alternative methods for estimating population trends are necessary for species that are poorly sampled by traditional breeding bird survey methods. Counts of migrating birds are commonly used to assess raptor population trends and could be useful for additional taxa that migrate diurnally and are difficult to monitor during the breeding season. In North America, the Common Nighthawk (Chordeiles minor) is challenging to detect during comprehensive dawn surveys like the North American Breeding Bird Survey and is considered a species of conservation concern because of steep population declines across its range. We conducted standardized evening counts of migrating Common Nighthawks at a fixed survey location along western Lake Superior each autumn from 2008 to 2022. To document peak migration activity, counts spanned ~3 hours each evening from mid-August to early September for a mean of 19.4 ± 2.4 days. These count data were then used to assess the effects of weather on daily counts and high-count days and to calculate population trends over this 15-year period. We used generalized linear mixed effects models to determine the relationship between daily counts and high-count days (i.e., ≥1000 migrating nighthawks) and weather variables. Additionally, using our 15-year dataset, we calculated a geometric mean passage rate that accounted for annual differences in weather to estimate count trends. Annual counts averaged ~18,000 (min = 2514, max = 32,837) individuals and high-count days occurred 56 times throughout the course of the study. Model results indicated lighter, westerly winds and warmer temperatures were associated with higher daily counts and greater probability of a large migratory flight. Results from the trend analyses suggest stable or non-significantly increasing trends for Common Nighthawks during this monitoring period; however, the trend models explained a relatively low percentage of the variation in the counts. Results from a power analysis suggest that continued monitoring efforts and adjustments with weather covariates will be necessary to effectively use visible migration count data to estimate Common Nighthawk trends. Establishing annual monitoring programs that use standardized visual counts to document Common Nighthawk migration at key sites across North America may provide supplemental information useful for population trend estimates of this species. Therefore, we advocate for the use of visible migration counts to monitor Common Nighthawks in North America and emphasize the value of long-term monitoring efforts.

Assessment of Data Representation in Scratch Via the SOLO Taxonomy

The work addresses the significance of robotics in education, emphasizing its role in enhancing STEM skills through programming and sensory feedback. Scratch, a multimedia programming environment, is highlighted as a tool for robotic projects. Within Scratch, this work discusses data representation, distinguishing between visible and transparent data. The current work focuses on the visible data. Variables in Scratch are made tangible, helping users understand their function. Developers define their own data, such as values, variables, lists, and call parameters, while Scratch also provides system data. This system data can be numeric, alphanumeric, or logical, and its representation in code varies. To evaluate how data are used in Scratch by novice programmers, this work also proposes an evaluation framework using the Structure of the Observed Learning Outcome (SOLO) taxonomy. This evaluation framework can be used by the teacher as a tool either to evaluate with measurable criteria the students’ code (on issues related to the way the data indicates their presence in Scratch) or to develop their personal teaching paths, thus creating mental scaffolds that assist students to master new knowledge.

RDCP: A Real Time Sea Fog Intensity and Visibility Estimation Algorithm

A number of accidents at sea are primarily caused by low visibility due to sea fog. Therefore, it is important to estimate sea fog intensity and visibility in real-time in the ocean environment. Visibility meters utilize optical sensors rather than visional information, so that the measured visibility data occasionally includes errors. Moreover, visibility meters have significant costs so that it is not viable to install them at various locations. Therefore, this paper proposes an algorithm called RDCP (Reduced Dark Channel Prior), which provides reliable estimation at a low cost by processing images captured from cameras, since they receive identical information as human eyes. For the estimation, the RDCP algorithm firstly acquires dark channels from an ocean image, then applies an optimized threshold value to the dark channels and crops out the sky region in the ocean image. For the estimation performance evaluation, 320 raw images captured from cameras at four different ports in Republic of Korea are used. Considering facilities in oceans, which are usually operated in power-limited environment, the processing time performance of PDCP is also examined. The experiments demonstrate that the RDCP algorithm provides reliable estimation performance in real-time.

Development of iron oxide nanoparticles using egg peel (brown) extract as a useful tool for removing the MB dye

The development of iron oxide nanoparticles (IO-NPs) (Fe3O4 and γ-Fe2O3) synthesized from ferric chloride (FeCl3) with egg peel extract by the hydrothermal method at different temperatures for the removal of methylene blue dye (MB) (Fe3O4 and γ-Fe2O3) NPs were diagnosed using (XRD) analysis, (FE-SEM) analysis, (TEM) images, (VSM) instrument, (UV–Vis) spectra, and (FTIR) spectroscopy. XRD measurements explained a crystallinity size of about 15 nm and a face center cubic structure (FCC) for Fe3O4 synthesized from egg peel (brown) extract at 200 °C, while for γ-Fe2O3 NPs prepared using egg peel (brown) extract at 400 °C, the crystal peak structure referred to a cubic structure (maghemite). The average grain size of Fe3O4 NPs was 18.61 to 27.91 nm; however, it was 31.25 to 125.5 nm for γ-Fe2O3 NPs, according to FESEM. Average grain size and the morphology of Fe3O4 and γ-Fe2O3 NPs were (14.7 nm to 26.1 nm) and the spherical structure was determined by the TEM images. The saturation magnetization (Ms) values were 8.12 emu/g and 9.39 emu/g, respectively, for Fe3O4 and γ-Fe2O3 NPs measured with the VSM device. The UV–visible data indicated a blue shift to the band energy gap of 4.1 eV for Fe3O4 NPs but 2.5 eV for γ-Fe2O3 NPs. The absorption band for Fe3O4 NPs is robust at 681 cm−1 at 200 °C, whereas it gets weaker at 650 cm−1 for γ-Fe2O3 NPs at 400 °C, according to FTIR. A photocatalytic activity value explained (Fe3O4 and γ-Fe2O3) NPs affected the MB dye's removal. A results referred to the synthesized Fe3O4 (magnétite) synthesized at 200 °C showed a higher removal efficiency (95 %) at 40 min than the γ-Fe2O3 (maghemite) at 400 °C, for which, after 65 min, the removal efficacy was 90 %.

Exploring the optimal model for assessing SOC and TN in Zanthoxylum bungeanum forest on the Loess Plateau using VNIR spectroscopy

Zanthoxylum bungeanum forest is an important economic forest on the Loess Plateau. The rapid assessment of its soil organic carbon (SOC) and total nitrogen (TN) concentrations is critical for the evaluation of soil quality. Soil samples of different forest ages and soil depths were collected for laboratory analyses and spectral measurements, and the visible and near-infrared reflectance (VNIR) spectral data were processed by mathematical transformations, such as first-order derivative (FD) and second-order derivative, multiple scattering correction, and logarithm of the reciprocal. The importance bands of SOC and TN were screened on the basis of competitive adaptive reweighted sampling. In addition, partial least squares regression and support vector machine regression models were constructed, and the applicability of SOC and TN to the models were compared. Results showed that forest age and soil depth remarkably affect SOC and TN concentrations. Among the calibration and validation models, the combination of the SVMR model with Savitzky–Golay smoothing and FD estimated SOC and TN with the highest accuracy, and the validation accuracy of the surface SOC and TN was higher than that of the bottom depths. Meanwhile, the importance bands involved in the modeling were mainly distributed in 400–600 nm and 1000–2400 nm. The combination of the FD transformation and the SVMR model is suitable for determining SOC and TN concentrations in Z. bungeanum forests, and the model has great potential for the quantitative evaluation of soil quality in the Loess Plateau.

A Machine-Learning-Based Study on All-Day Cloud Classification Using Himawari-8 Infrared Data

Clouds are diverse and complex, making accurate cloud type identification vital in improving the accuracy of weather forecasting and the effectiveness of climate monitoring. However, current cloud classification research has largely focused on daytime data. The lack of visible light data at night presents challenges in characterizing nocturnal cloud attributes, leading to difficulties in achieving continuous all-day cloud classification results. This study proposed an all-day infrared cloud classification model (AInfraredCCM) based on XGBoost. Initially, the latitude/longitude, 10 infrared channels, and 5 brightness temperature differences of the Himawari-8 satellite were selected as input features. Then, 1,314,275 samples were collected from the Himawari-8 full-disk data and cloud classification was conducted using the CPR/CALIOP merged cloud type product as training data. The key cloud types included cirrus, deep convective, altostratus, altocumulus, nimbostratus, stratocumulus, stratus, and cumulus. The cloud classification model achieved an overall accuracy of 86.22%, along with precision, recall, and F1-score values of 0.88, 0.84, and 0.86, respectively. The practicality of this model was validated across all-day temporal, daytime/nighttime, and seasonal scenarios. The results showed that the AInfraredCCM consistently performed well across various time periods and seasons, confirming its temporal applicability. In conclusion, this study presents an all-day cloud classification approach to obtain comprehensive cloud information for continuous weather monitoring, ultimately enhancing weather prediction accuracy and climate monitoring.

A 3D U-Net Based on a Vision Transformer for Radar Semantic Segmentation.

Radar data can be presented in various forms, unlike visible data. In the field of radar target recognition, most current work involves point cloud data due to computing limitations, but this form of data lacks useful information. This paper proposes a semantic segmentation network to process high-dimensional data and enable automatic radar target recognition. Rather than relying on point cloud data, which is common in current radar automatic target recognition algorithms, the paper suggests using a radar heat map of high-dimensional data to increase the efficiency of radar data use. The radar heat map provides more complete information than point cloud data, leading to more accurate classification results. Additionally, this paper proposes a dimension collapse module based on a vision transformer for feature extraction between two modules with dimension differences during dimension changes in high-dimensional data. This module is easily extendable to other networks with high-dimensional data collapse requirements. The network's performance is verified using a real radar dataset, showing that the radar semantic segmentation network based on a vision transformer has better performance and fewer parameters compared to segmentation networks that use other dimensional collapse methods.

ProxyVis—A Proxy for Nighttime Visible Imagery Applicable to Geostationary Satellite Observations

Abstract Visible satellite imagery is widely used by operational weather forecast centers for tropical and extratropical cyclone analysis and marine forecasting. The absence of visible imagery at night can significantly degrade forecast capabilities, such as determining tropical cyclone center locations or tracking warm-topped convective clusters. This paper documents ProxyVis imagery, an infrared-based proxy for daytime visible imagery developed to address the lack of visible satellite imagery at night and the limitations of existing nighttime visible options. ProxyVis was trained on the VIIRS day/night band imagery at times close to the full moon using VIIRS IR channels with closely matching GOES-16/17/18, Himawari-8/9, and Meteosat-9/10/11 channels. The final operational product applies the ProxyVis algorithms to geostationary satellite data and combines daytime visible and nighttime ProxyVis data to create full-disk animated GeoProxyVis imagery. The simple versions of the ProxyVis algorithm enable its generation from earlier GOES and Meteosat satellite imagery. ProxyVis offers significant improvement over existing operational products for tracking nighttime oceanic low-level clouds. Further, it is qualitatively similar to visible imagery for a wide range of backgrounds and synoptic conditions and phenomena, enabling forecasters to use it without special training. ProxyVis was first introduced to National Hurricane Center (NHC) operations in 2018 and was found to be extremely useful by forecasters becoming part of their standard operational satellite product suite in 2019. Currently, ProxyVis implemented for GOES-16/18, Himawari-9, and Meteosat-9/10/11 is being used in operational settings and evaluated for transition to operations at multiple NWS offices and the Joint Typhoon Warning Center. Significance Statement This paper describes ProxyVis imagery, a new method for combining infrared channels to qualitatively mimic daytime visible imagery at nighttime. ProxyVis demonstrates that a simple linear regression can combine just a few commonly available infrared channels to develop a nighttime proxy for visible imagery that significantly improves a forecaster’s ability to track low-level oceanic clouds and circulation features at night, works for all current geostationary satellites, and is useful across a wide range of backgrounds and meteorological scenarios. Animated ProxyVis geostationary imagery has been operational at the National Hurricane Center since 2019 and is also currently being transitioned to operations at other NWS offices and the Joint Typhoon Warning Center.

A multi‐modal fusion YoLo network for traffic detection

AbstractTraffic detection (including lane detection and traffic sign detection) is one of the key technologies to realize driving assistance system and auto drive system. However, most of the existing detection methods are designed based on single‐modal visible light data, when there are dramatic changes in lighting in the scene (such as insufficient lighting in night), it is difficult for these methods to obtain good detection results. In view of multi‐modal data can provide complementary discriminative information, based on the YoLoV5 model, this paper proposes a multi‐modal fusion YoLoV5 network, which consists of three key components: the dual stream feature extraction module, the correlation feature extraction module, and the self‐attention fusion module. Specifically, the dual stream feature extraction module is used to extract the features of each of the two modalities. Secondly, input the features learned from the dual stream feature extraction module into the correlation feature extraction module to learn the features with maximum correlation. Then, the extracted maximum correlation features are used to achieve information exchange between modalities through a self‐attention mechanism, and thus obtain fused features. Finally, the fused features are inputted into the detection layer to obtain the final detection result. Experimental results on different traffic detection tasks can demonstrate the superiority of the proposed method.

Copper Oxide Nanoparticles Synthesized from Indigofera linnaei Ali and This Plant’s Biological Applications

The leaf extract of Indigofera linnaei Ali, an Indian medicinal plant, was utilized in the synthesis of copper oxide nanoparticles (CuO-NPs). Green chemistry is a safe and cost-effective method for the synthesis of nanoparticles using plant extracts. The synthesis of CuO NPs was confirmed using ultraviolet–visible (UV-visible) spectrum λ-max data with two peaks at 269 and 337 nm. Different functional groups were identified using Fourier-transform infrared spectroscopy (FT-IR). X-ray diffraction (XRD) was used to confirm the crystalline structure of the CuO-nanoparticles. Scanning electron microscopy (SEM) and energy dispersive X-ray (EDX) analyses were performed to examine the surface morphology and elemental composition of the biosynthesized CuO-NPs. Furthermore, the synthesized CuO-NPs exhibited antibacterial activity against Staphylococcus aureus, Pseudomonas aeruginosa, Escherichia coli, and Enterococcus faecalis. Additionally, they exhibited a good insecticidal effect on Culex quinquefasciatus larvae, with low LC50 55.716 µg/mL and LC90 123.657 µg/mL values. The CuO-NPs inhibited human breast cancer cells in a concentration-dependent manner, with an IC50 value of 63.13 µg/mL.

MSGF-GLP: fusion method of visible and hyperspectral data for early detection of discolored standing trees

MSGF-GLP: fusion method of visible and hyperspectral data for early detection of discolored standing trees

Insights into the properties and possible bonding states of radiofrequency-magnetron-sputtered indium tin oxide thin films acquired using ultraviolet–visible spectroscopy data for cost-effective material characterization

Indium tin oxide (Sn-doped In2O3) is an optical material that is widely utilized in designing modern optoelectronic devices. To demonstrate the efficiency of ultraviolet–visible spectroscopy as a primary analysis tool for thin-film characterization, Sn-doped In2O3 films were deposited under untreated and annealed conditions. The optical parameters of these films were determined based on the transmission–reflection spectra recorded by an ultraviolet–visible spectroscopy laboratory setup. Through four-point probing, energy dispersive spectroscopy, and contact angle measurements, the electronic-band structures and chemical bonding states of the thin films were estimated. The transmission spectra confirmed that the post-annealing treatment promoted film homogeneity. Deeper insights into the optical parameters revealed that annealing encouraged Sn atom incorporation into the film structure and better stable phase formation. The incorporation of Sn atoms resulted in the substitution of Sn4+ into the In3+ sites of the In2O3 lattice and formation of the SnO phase. Notably, our evaluation of the film properties based on data acquired from ultraviolet–visible spectroscopy revealed good consistency with the data acquired from Hall-effect, X-ray diffractometry, and X-ray photoelectron spectroscopy investigations.

Correlations between laboratory line lists for FeH, CrH, and NiH and M-star spectra collected with ESPaDOnS and SPIRou

Context. Molecular bands of metal oxides and hydrides dominate the optical and near-infrared spectra of M dwarfs. High-resolution spectra of these bands have immense potential for determining many properties of these stars, such as effective temperature, surface gravity, elemental abundances, radial velocity, or surface magnetic fields. Techniques are being developed to do this but remain limited by the current availability and accuracy of molecular data and spectral line lists. Aims. This paper reports metal monohydride line lists selected from near-infrared and visible laboratory data to show that specific bands in several electronic transitions can be used to identify CrH, NiH, and FeH in M stars and to determine radial velocities from Doppler shifts. The possibility of measuring magnetic fields is also investigated for FeH and CrH. Methods. We used systematic cross-correlation analysis between unpolarised spectra from a selection of M stars and state-specific laboratory line lists. These lists were generated from a combination of existing data and new laboratory laser-excitation spectra recorded at Doppler-limited resolution, in zero-field conditions or in magnetic fields up to 0.6 tesla. Results. We show that transitions at visible wavelengths in FeH and NiH, usually neglected in the analysis of the spectra of M-type stars, do in fact contribute to the spectra, and we demonstrate the influence of magnetic sensitivity on selected transitions in CrH and FeH. Conclusions. Although the new line lists focus on transitions recorded at temperatures significantly lower than those of stellar objects, they remain pertinent because they cover some band-head regions of high spectral density. FeH bands can provide a useful supplement to atomic lines for the analysis of high-resolution optical and near-infrared spectra of M dwarfs. We demonstrate the influence of a magnetic field on CrH signatures around 862 nm.

First Detection of CO2 Emission in a Centaur: JWST NIRSpec Observations of 39P/Oterma

Centaurs are minor solar system bodies with orbits transitioning between those of trans-Neptunian scattered disk objects and Jupiter-family comets (JFCs). 39P/Oterma (39P) is a frequently active centaur that has recently held both centaur and JFC classifications and was observed with the JWST NIRSpec instrument on 2022 July 27 UTC while it was 5.82 au from the Sun. For the first time, CO2 gas emission was detected in a centaur, with a production rate of = (5.96 ± 0.80) × 1023 molecules s−1. This is the lowest detection of CO2 of any centaur or comet. CO and H2O were not detected down to constraining upper limits. Derived mixing ratios of Q CO/Q ≤ 2.03 and /Q ≥ 0.60 are consistent with CO2 and/or CO outgassing playing large roles in driving the activity, but not water, and show a significant difference between the coma abundances of 29P/Schwassmann–Wachmann 1, another centaur at a similar heliocentric distance, which may be explained by thermal processing of 39P’s surface during its previous JFC orbit. To help contextualize the JWST data we also acquired visible CCD imaging data on two dates in 2022 July (Gemini-North) and September (Lowell Discovery Telescope). Image analysis and photometry based on these data are consistent with a point-source detection and an estimated effective nucleus radius of 39P in the range of R nuc = 2.21–2.49 km.

Analysis of Spatio-Temporal intensity of Sea Fog in Yellow Sea Using Visibility Data of Ocean Research Stations (ORS) and Baengnyeongdo

While existing research on sea fog primarily relied on collecting data from ground observations in coastal areas, this study utilized municipal data from three Korea Ocean Research Stations (KORS) located in the Yellow Sea and Baengnyeongdo. The main objective was to analyze the strength of sea fog in specific locations within the Yellow Sea. The study employed the corrective distance as a measure of intensity and examined variations in sea fog strength according to the monthly and by time period of the study area. The research periods for each research station were as follows: Socheongcho Ocean Research Stations (SORS) from 2014 to 2022, Gageocho Ocean Research Stations (GORS) from 2014 to 2020, Ieodo Ocean Research Stations (IORS) from 2010 to 2015, and Baengnyeongdo from 2016 to 2022. In terms of season, the highest sea fog frequency in SORS and GORS were at 59% and 75% in spring, respectively. While in IORS, sea fog intensively occurred throughout the spring (46%) and summer (47%) seasons. On the other hand, in Baengnyeongdo, the sea fog frequency in summer, spring, autumn and winter were 41%, 25%, 17% and 17%, respectively. Between September and February the occurrence frequency was 19%, 8.1% and 7.3% in SORS, GORS and IORS, respectively which tends to decrease as it descends South of the Yellow Sea. In SORS, sea fog frequency with 0.2 km (strength 2) visibility was 49% while in the case of GORS and IORS it was 15% and 22%, respectively. Besides, the frequency of sea fog which visibility was between 0.2 and 0.5 km (strength 1) was 59% and 56% in GORS and IORS, respectively. In terms of frequency by time period, SORS and GORS showed a normalization maximum of 01 at 01:00 KST where IORS showed 02. Sea fog was not seen at 12:00 in SORS, at 13-17:00 in GORS and at 19-20:00 in IORS. In the future, we will analyze the spatial distribution of sea fog occurring in the Yellow Sea after sea fog verification using satellite images.

Unmasking air quality: A novel image-based approach to align public perception with pollution levels

In the quest to reconcile public perception of air pollution with scientific measurements, our study introduced a pioneering method involving a gradient boost-regression tree model integrating PM2.5 concentration, visibility, and image-based data. Traditional stationary monitoring often falls short of accurately capturing public air quality perceptions, prompting the need for alternative strategies. Leveraging an extensive dataset of over 20,000 public visibility perception evaluations and over 8,000 stationary images, our models effectively quantify diverse air quality perceptions. The predictive prowess of our models was validated by strong performance metrics for perceived visibility (R = 0.98, RMSE = 0.19), all-day PM2.5 concentrations (R: 0.77–0.78, RMSE: 8.31–9.40), and Central Weather Bureau visibility records (R = 0.82, RMSE = 9.00). Interestingly, image contrast and light intensity hold greater importance than scenery clarity in the visibility perception model. However, clarity is prioritized in PM2.5 and Central Weather Bureau models. Our research also unveiled spatial limitations in stationary monitoring and outlined the variations in predictive image features between near and far stations. Crucially, all models benefit from the characterization of atmospheric light sources through defogging techniques. The image-based insights highlight the disparity between public perception of air pollution and current policy implementation. In other words, policymakers should shift from solely emphasizing the reduction of PM2.5 levels to also incorporating the public's perception of visibility into their strategies. Our findings have broad implications for air quality evaluation, image mining in specific areas, and formulating air quality management strategies that account for public perception.