Color Data Research Articles

Characterization and removal of striping artifacts in VIIRS-derived ocean color products

We revisit the methodology of the destriping method for deriving ocean color products from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the NOAA-20 satellite and provide an important update for removing striping artifacts in VIIRS-measured normalized water-leaving radiance nLw(λ) spectra and other related ocean color products. We show that an existing destriping methodology applied for VIIRS on the Suomi National Polar-orbiting Partnership (SNPP) during its early years of mission must be updated to effectively eliminate the striping noise in VIIRS-NOAA-20 ocean color data. We further review the importance of this change in nLw(λ) at various VIIRS-NOAA-20 spectral bands and show the corresponding impacts on the derived chlorophyll-a, water clarity, and spectral nLw(λ) quality assurance score in ocean color data products. In addition, we find similar, though less severe, striping artifacts in the VIIRS-NOAA-21-derived nLw(λ) imagery and show that some updates are required for the VIIRS-SNPP destriping approach. Finally, it is demonstrated that, with appropriately selected destriping parameters, we can effectively mitigate striping artifacts and significantly improve ocean color products from all three VIIRS sensors.

Point-of-Care-Testing NO3-N Detection Technology with Selected Transition-Metal-Based Colorimetric Sensor Arrays.

Nitrate-nitrogen (NO3-N) is a major contaminant in groundwater and seawater. Significant amounts of ammonia are oxidized to nitrate through nitrification, leading to an imbalance in the nitrogen cycle and causing nitrate pollution in water bodies. Controlling NO3-N levels is a significant challenge for both marine aquaculture and human health. Traditional measurement methods, such as ion chromatography and continuous flow analysis, require pretreatment steps to detect NO3-N in complex matrices, which is time-consuming. However, in this study, we developed a transition-metal-based sensor capable of measuring NO3-N concentrations on-site without the need for pretreatment. We analyzed the color change of transition-metal-based sensors over time and obtained color data by mixing transition metals (Mn, V, Fe, Co, Cr, Cu, and Ni) with solvents and additives at fixed ratios, and combining them with standard solutions of NO3-N at concentrations of 1, 2, 3, 5, 10, 20, 30, 40, 50, 75, and 100 ppm. We selected sensors that exhibited linearly increasing color velocity with increasing NO3-N concentrations and developed an array sensor using the selected sensors. The performance of the array was validated by comparing its results with those of hierarchical cluster analysis (HCA) based on color data and compositional analysis, confirming its ability to detect NO3-N in complex matrices. Additionally, by creating a large data set of color change patterns of the array sensor, we can develop selective array sensors for detecting specific substances, surpassing the capability of merely measuring the NO3-N concentration.

Acute Respiratory Distress Identification via Multi-Modality Using Deep Learning

Medical instruments are essential in pediatric intensive care units (PICUs) for measuring respiratory parameters to prevent health complications. However, the assessment of acute respiratory distress (ARD) is still conducted through intermittent visual examination. This process is subjective, labor-intensive, and prone to human error, making it unsuitable for continuous monitoring and early detection of deterioration. Previous studies have proposed solutions to address these challenges, but their techniques rely on color information, the performance of which can be influenced by variations in skin tone and lighting conditions. We propose leveraging multi-modality data to address these limitations. Our method integrates color and depth data using deep convolutional neural networks with a late feature fusion scheme. We train and evaluate our model on a dataset of 153 patients with respiratory illnesses, 86 of whom have ARD of varying severity levels. Experimental results demonstrate that multi-modality data combined with simple late fusion techniques are more effective with limited data, offering higher confidence scores compared to using color information alone. Our approach achieves an accuracy of 85.2%, a precision of 86.7%, a recall of 85.2%, and an F1 score of 85.8%. These findings suggest that multi-modality data provide a promising solution for improving ARD detection accuracy and confidence in clinical settings.

Remote sensing of the subsurface chlorophyll maximum depth in the North Pacific Ocean

ABSTRACT Estimation of the subsurface chlorophyll maximum (SCM) depth is critical to constructing the vertical profile of chlorophyll a, which in turn is important in accurately assessing phytoplankton distribution, especially in the extensive tropical oceans. Current ocean colour algorithms generally detect a limited area only or use a suite of environmental variables, part of which may be difficultly quantified. By using field in situ observations, a two-step approach, in which the first step used a relationship between sea surface temperature and surface chlorophyll to detect the occurrence of the SCM and the second step used an algorithm based on the ratio of remote sensing reflectance, was developed to remotely estimate the SCM depth in the North Pacific. Although this approach, with the inputs of easily available ocean colour data, is simple, the deviation in the remotely sensed SCM depth was only about ± 17%. Our study may provide a promising approach to assess the stratification status of the water column and to help profiling the vertical variation of chlorophyll in the North Pacific and even in the global oceans.

Colorimetric analysis of intraoral scans: A novel approach for detecting gingival inflammation.

Gingivitis, a widely prevalent oral health condition, affects up to 80% of the population. Traditional assessment methods for gingivitis rely heavily on subjective clinical evaluation. This study seeks to explore the efficacy of interpreting the color metrics from intraoral scans to objectively differentiate between healthy and inflamed gingiva. This study used the percentage of bleeding on probing (BOP%) as the clinical reference standard. Intraoral scans, obtained before and after gingivitis treatment using a scanner, were analyzed through a custom MATLAB script to quantify HSV (hue, saturation, value) and CIELAB (Commission Internationale de l'Eclairage L*a*b*) color coordinates. The region of interest was a 2-mm-wide gingival strip along the buccal margin of the maxillary anterior teeth. Linear regression analysis was performed to evaluate the relationship between photometric outcomes and continuous, dichotomous, and categorical BOP data. Diagnostic accuracy was assessed using the area under the receiver operating characteristic (ROC) curve (AUC), as well as sensitivity and specificity measures. The analysis included clinical and digital color data from 110 scans, adhering to the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines. The multilevel linear regression analysis underscored a significant correlation between the BOP% and digital color metrics, specifically the CIELAB a* (red-green chroma), CIELAB b* (yellow-blue chroma), and color saturation, with AUC performances of 70%, 79.5%, and 80.8%, respectively. Digital color analysis of intraoral scans has demonstrated a range of performance from acceptable to excellent in distinguishing sites with BOP. This innovative approach presents a promising tool for dentists and researchers in the accurate diagnosis, screening, and management of gingivitis. Our study focuses on finding a better way to detect gingivitis, a common gum disease affecting many people. Traditional methods rely on the dentist's visual inspection, which can be subjective. We explored the use of color measurements from digital intraoral scans to objectively identify healthy versus inflamed gums. We analyzed 110 scans from 55 participants, examining the color differences in the gums before and after treatment. By measuring specific color values, we achieved up to 80.8% accuracy in distinguishing between healthy and inflamed gums. This method could offer a more reliable tool for dentists and researchers to diagnose and manage gingivitis, leading to better oral health outcomes.

Assessing optical water types in Asian coastal ocean waters from space using GCOM-C/SGLI observations

ABSTRACT Human activities and climate change exert environmental and ecological pressures on the Asian coastal ocean waters through dramatic changes in optical water types. This necessitates the development of methods to observe and monitor optical water types with optimal spatiotemporal resolution, which can only be achieved through remote sensing techniques, particularly for optically detectable water properties. This study utilized 250-mresolution ocean colour data retrieved from the Second-Generation Global Imager (SGLI), covering waters across Asia, to develop a common optical water type algorithm for Asian coastal ocean waters. The algorithm is based on relationships among optical properties, threshold values, and criteria, classifying the waters into eight optical water types: turbid, high-coloured-dissolved organic matter (CDOM), mixed, oligotrophic, coccolithophore bloom, mesotrophic, diatom bloom, and dinoflagellate bloom waters. The values of remote sensing reflectance (Rrs) slope between 490 and 530 nm (3.0 × 10−6) and the Rrs at 490 nm (0.0013 sr−1) serve as optimal thresholds for distinguishing dinoflagellate blooms, diatom blooms, and high-CDOM waters. The proposed method performs well in classifying most water bodies, though some artefacts suggest that refinements are needed to improve the method’s robustness. The ability to classify optical water types provides a valuable tool for marine ecosystem and biogeochemical studies, especially in optically complex water bodies where land–ocean interactions are particularly strong.

An ensemble model of machine learning regression techniques and color spaces integrated with a color sensor: application to color-changing biochemical assays.

Non-destructive color sensors are widely applied for rapid analysis of various biological and healthcare point-of-care applications. However, existing red, green, blue (RGB)-based color sensor systems, relying on the conversion to human-perceptible color spaces like hue, saturation, lightness (HSL), hue, saturation, value (HSV), as well as cyan, magenta, yellow, key (CMYK) and the CIE L*a*b* (CIELAB) exhibit limitations compared to spectroscopic methods. The integration of machine learning (ML) techniques presents an opportunity to enhance data analysis and interpretation, enabling insights discovery, prediction, process automation, and decision-making. In this study, we utilized four different regression models integrated with an RGB sensor for colorimetric analysis. Colorimetric protein concentration assays, such as the bicinchoninic acid (BCA) assay and the Bradford assay, were chosen as model studies to evaluate the performance of the ML-based color sensor. Leveraging regression models, the sensor effectively interprets and processes color data, facilitating precision color detection and analysis. Furthermore, the incorporation of diverse color spaces enhances the sensor's adaptability to various color perception models, promising precise measurement, and analysis capabilities for a range of applications.

A new species of Namib Day Gecko (Gekkonidae: Rhoptropus Peters, 1869) from the Serra da Neve inselberg, southwestern Angola

The genus Rhoptropus comprises nine recognized species of diurnal geckos endemic to the arid regions of Angola and Namibia. Seven species occur in Angola, including the widespread R. boultoni and the poorly known Angolan endemics R. benguellensis and R. montanus, formerly recognized as subspecies of R. boultoni. While R. benguellensis is relatively widespread in the Angolan Escarpment highlands, R. montanus is geographically restricted to the Huíla Plateau. Recent fieldwork on the Serra da Neve inselberg, a herpetological diversity hotspot in southwestern Angola, revealed the presence of an undescribed species of Rhoptropus at an elevation of approximately 1600 m. An integrative taxonomic approach combining coloration, morphological and molecular data supports the recognition of this population as a distinct taxonomic unit with affinities to R. montanus, which we describe herein as Rhoptropus nivimontanus sp. nov. The discovery of a new species of Rhoptropus endemic to Serra da Neve is discussed in the biogeographic context of the region.

Penetapan Kadar Flavonoid Ekstrak Daun Kalapapa (Vitex Pinnata L.) dengan Tingkatan Fraksi

The background to this research is that one of the plants that is often used by people in the interior of Central Kalimantan, specifically in the village of Tumbang Talaken, is the Kalapapa plant. The Dayak tribe community in the Tumbang Talaken area of Central Kalimantan uses Kalapapa leaves as a trusted medicinal plant and is used as a traditional medicine to heal wounds using the simple method of crushing the leaves and applying them directly to the surface of the injured skin. Kalapapa leaves contain flavonoids, alkaloids, saponins and tannins. In the context of wound healing, flavonoids have an important role in increasing the speed of wound healing. The aim of the research was to determine the levels of total flavonoid compounds in the distilled water fraction, ethyl acetate fraction, and n-hexane fraction of kalapapa leaf extract (Vitex Pinnata L.). The research method used was a descriptive method by looking at the results of qualitative data, namely a reaction test. color and quantitative data, namely using Uv-Vis spectrophotometry to see total flavonoid levels. The results of the qualitative analysis research showed that the color test identification of kalapapa leaf extract (Vitex Pinnata L.) was positive for containing flavonoids. The results of quantitative analysis using Uv-Vis spectrophotometry showed that the total flavonoid content with 96% ethanol extract was 43.95 QE/g, distilled water with a value of 42.14 QE/g, ethyl acetate with a value of 196.8 QE/g, and n -hexane with a value of 54.42 QE/g or 96% 4.39% ethanol extract, distilled water 4.21%, ethyl acetate 19.68%, and N-hexane 5.44%. The highest total flavonoid levels were found in the ethyl acetate fraction. The research conclusion is that kalapapa leaf extract at fraction levels contains flavonoid compounds with total flavonoid levels in the ethanol extract of 96%, distilled water fraction, ethyl acetate fraction and n-hexane fraction of 43.95 mg QE/g or 4.39%; 42.14 mg QE/g or 4.21%; 196.8 mg QE/g or 19.68%; and 54.42 mg QE/g or 5.44%.

The production and characterization of PBT/PBS fiber dyeable with dispersed dye

The work aimed to study the color properties of poly(butylene terephthalate) (PBT) modified with various contents of poly(butylene succinate) (PBS). The PBT/PBS fibers were successfully prepared using a melt-spinning process. The PEO-g-MA, 2 wt%, was used as a coupling agent. A disperse dye: blue dye was applied as a dyeing material for PBT/PBS fibers. The fibers were dyed at the temperature of 98 °C (using the classic process for PBT fibers). The morphology, mechanical properties, thermal properties, crystal structure, and dyeability were also investigated. The fiber dyeing results are explored in CIELAB (position data of color coordinates L*, a*, b*) color systems. The results show the relationship between the dyeability of PBT/PBS fibers and the contents of PBS in the polymer matrix. The SEM image of the fiber of PBT100 and PBT:PBS blend after dyeing shows that the color dyeing on the fiber increased with PBS contents, supporting the result from spectroscopy.

Egg-Based Mousse Ice Cream Incorporated with Lavulu [<em>Pouteria campechiana</em> (Kunth) Baehni] Flour as a Colorant and an Antioxidant

This study aimed to produce egg-based mousse ice cream and to promote the food value of underutilized Lavulu (Pouteria campechiana (Kunth) Baehni) fruit. Lavulu flour (LF) was prepared by oven drying the ripened Lavulu fruit slices along with the peel at 60°C for 26 h (until moisture content reaches 5%) and grinding to a fine powder. Ice cream mix was prepared with constant levels of egg white foam (45%), whipped dairy cream (33%), sugar (18%) and varying levels of LF (0, 0.2, 0.4 and 0.6% w/w). LF was analyzed for total phenolics content, total flavonoids content, color, carotenoid profile, radical scavenging activity (DPPH assay), pH, water holding capacity, oil holding capacity, emulsifying activity, foaming activity, and proximate composition. The melting ratio, complete melting time, overrun, viscosity, hardness, total flavonoid content, total phenolic content, and sensory properties of ice cream samples were analyzed on day 2 of the frozen storage (-18°C). Radical scavenging activity (RSA), pH, color, and microbial analysis of ice cream samples were determined weekly during 1 month of frozen storage (-18°C). Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach was used to determine optimum LF incorporation level using sensory, RSA, and color data. Proximate analysis was carried out for the selected treatment (0.4% w/w LF). Sixteen carotenoid types were identified quantitatively and qualitatively in LF. Total carotenoid content and RSA of LF was 21.98 mg/g and 82.55±0.78%, respectively. Increasing LF percentage significantly increased (p<0.05) the viscosity, L* value, and complete melting time of ice cream. Total phenolic content, total flavonoid content, and RSA were significantly (p<0.05) higher in LF incorporated samples compared to the control. The present study revealed that LF can be incorporated in ice cream as a rich source of antioxidants, along with color, flavor and texture enhancer.

Enhancing Immunoglobulin G Goat Colostrum Determination Using Color-Based Techniques and Data Science.

Circulating immunoglobulin G (IgG) concentrations in newborn goat kids are not sufficient to protect the animal against external agents. Therefore, consumption of colostrum, rich in immune components, shortly after birth is crucial. Traditional laboratory methods used to measure IgG concentrations, such as ELISA or RID, are reliable but costly and impractical for many farmers. This study proposes a more accessible alternative for farmers to predict IgG concentration in goat colostrum by integrating color-based techniques with machine learning models, specifically decision trees and neural networks, through the development of two regression models based on colostrum color data from Majorera dairy goats. A total of 813 colostrum samples were collected in a previous study (June 1997-April 2003) that utilized multiple regression analysis as a reference to verify that applying data science techniques improves accuracy and reliability. The decision tree model outperformed the neural network, achieving higher accuracy and lower error rates. Both models provided predictions that closely matched IgG concentrations obtained by ELISA. Therefore, this methodology offers a practical and affordable solution for the on-farm assessment of colostrum quality (i.e., IgG concentration). This approach could significantly improve farm management practices, ensuring better health outcomes in newborn animals by facilitating timely and accurate colostrum quality evaluation.

Monitoring the coastal–offshore water interactions in the Levantine Sea using ocean color and deep supervised learning

Abstract. Understanding and tracking the surface circulation of the Levantine Sea present significant challenges, particularly close to the coast. This difficulty arises due to two main factors: the limited availability of in situ observations and the increasing inaccuracies in altimetry data close to the coastline. Here, we propose a new approach to monitor the interaction between offshore and coastal waters. In this approach, we develop a pattern detection model using deep learning by training the U-Net model on ocean color data to track the interactions between the coastal and offshore water in the Levantine Sea. The results showed the presence of notable variations in the behavior of coastal currents as they progress northward beyond 33.8° E. As these coastal currents become increasingly unstable, they exhibit continuous pinching-off events that are missed by conventional observational tools. These pinching-off events, especially observed along the Lebanese coast, manifest in various patterns that evolve simultaneously. Typically, these patterns have a relatively short lifespan of a few weeks, appearing and disappearing rapidly. However, these structures can evolve into larger eddies that endure for over 4 months in some years, especially in the northern part of the Lebanese coasts. Although these structures could be observed during all the seasons, spring consistently records the lowest activity of these structures. Overall, we showed that the pinching-off events were always observed in the eastern part of the Levantine Sea. On the contrary, in the southern part, along the Egyptian coasts, the coastal flow is more stable in the southern region, where these events are less frequently observed, with more than 63 % of the total observations not exhibiting any pinching-off events. Moreover, when these events occur in the south, their spatial extent is notably limited. This research not only sheds light on previously missed (or underestimated) coastal current dynamics in the Levantine Sea but also highlights the crucial need to increase in situ observations to advance our understanding of this region's complex oceanographic processes.

Effects of cloud and tree shading on surface temperature of different pavement materials for urban sidewalks.

The surface color and materials of sidewalk pavements exhibit different albedo characteristics, leading to varied surface urban heat island effects in subtropical regions. To quantify the effect of pavement surface color and material on SUHI, Prefabricated Concrete Structure brick (PCB), Granite brick (GB) and Dutch brick (DB) totaling 14 pavement samples in Hangzhou were placed under unshaded, cloud shaded and tree shaded conditions. CIELAB (International Commission on Illumination L*a*b*) color data, short-wave radiation (incoming and outgoing) and surface temperature were measured. Results showed that L*( lightness) value played a dominant role in the albedo of the pavement surface, and there was a positive correlation in summer and winter. The lower the L* value, the greater the cooling effect of the pavement under cloud and tree shaded conditions. Compared to unshaded condition, tree shade provided the highest cooling benefit of 32.2°C in summer. Among the 3 types of pavement materials, DB had the lowest average surface temperature in summer and the highest in winter. Therefore, in cities with cold winter and hot summer, it is advised that DB with a high surface L* value be employed. The use of pavement with a low L* value should be accompanied by continuous shading measures to cool the surface temperature. These findings provide a basis for selecting low-energy embodied pavement materials for urban streets and offer important technical support for mitigating the urban heat island effect.

The colours of the ocean using multispectral satellite imagery to estimate sea surface temperature and salinity in global coastal areas, the gulf of Mexico and the UK

Understanding and monitoring sea surface salinity (SSS) and temperature (SST) is vital for assessing ocean health. Interconnections among the ocean, atmosphere, seabed, and land create a complex environment with diverse spatial and temporal scales. Climate change exacerbates marine heatwaves, eutrophication, and acidification, impacting biodiversity and coastal communities. Satellite-derived ocean colour data provides enhanced spatial coverage and resolution compared to traditional methods, enabling the estimation of SST and SSS. This study presents a methodology for extracting SST and SSS using machine learning algorithms trained with in-situ and multispectral satellite data. A global neural network model was developed, leveraging spectral bands and metadata to predict these parameters. The model incorporated Shapley values to evaluate feature importance, offering insight into the contributions of specific bands and environmental factors. The global model achieved an R2 of 0.83 for temperature and 0.65 for salinity. In the Gulf of Mexico case study, the model demonstrated a root mean square error (RMSE) of 0.83°C for test cases and 1.69°C for validation cases for SST, outperforming traditional methods in dynamic coastal environments. Feature importance analysis identified the critical roles of infrared bands in SST prediction and blue/green colour bands in SSS estimation. This approach addresses the “black box” nature of machine learning models by providing insights into the relative importance of spectral bands and metadata. Key factors such as solar azimuth angle and specific spectral bands were highlighted, demonstrating the potential of machine learning to enhance ocean property estimation, particularly in complex coastal regions.

Chemometrics and digital image colorimetry approaches applied to paper-based analytical devices: A review.

Colorimetric paper-based analytical devices (CPADs) are cost-efficient and high-throughput technologies that use readily available materials for point-of-need (PON) applications by leveraging color changes in response to target analytes. However, the complexity of samples can limit the precision and accuracy of CPAD applications. Therefore, CPADs have been combined with chemometric approaches to enhance analytical performance and provide simple solutions to complex systems. The integration of formal optimization techniques, such as Design of Experiments (DoE), classification tools, quantification methods, and other advanced algorithms enables optimal experimental analytical conditions and extracting meaningful information from the complex colorimetric data generated by CPADs. These approaches facilitate robust calibration and prediction models, enabling reliable quantifications or sample differentiation. In addition, chemometrics combined with CPADs contributes to Green Analytical Chemistry once they have the potential to minimize the number of experiments, provide optimal designs and consumption of reagents, and decrease waste generation. The synergy between digital colorimetry using CPADs and smart devices with chemometric techniques holds great promise for portable analysis in resource-limited settings, with applications ranging from environmental monitoring to point-of-care diagnostics. Herein, we review recent advances in the development of CPADs, ranging from manufacturing methods to extraction of color patterns and data treatment using several chemometric tools, performance assessment, and potential transfer to onsite applications for relevant analytical problems.

Influence of the Kara Sea Surface Layer Parameters on the Accuracy of Chlorophyll-a Concentration Estimation by the Bio-optical Algorithms

The results of the hydro-optical model setup in the HydroLight software for three stations in the Kara Sea characterized by different vertical distributions of the values of bio-optical properties are presented. The selected distributions are typical for the areas of the Kara Sea influenced by the river runoff. The stations are located inside, outside and at the boundary of the surface desalinated layer. The model reproduces the spectra of remote sensing reflectance, vertical profiles of light attenuation coefficient of sea water and photosynthetically available radiation measured in expeditions with good accuracy (average relative error less than 10 %). The results obtained made it possible to evaluate the accuracy of different algorithms for determining chlorophyll-a concentration in the range of values typical for the Kara Sea. For the same purpose, similar calculations were performed for different values of the absorption coefficient of colored dissolved organic matter. It is shown that the IO RAS regional algorithm allows estimation of chlorophyll-a concentration in the range of values exceeding 0.5 mg/m3 with a significantly smaller relative error (less than 50 %) than the semi-analytical GIOP algorithm (more than 100 %). At the same time, variations in the yellow matter absorption coefficient have a significantly smaller impact on the results of the regional algorithm. The significant influence of the initial approximation of chlorophyll-a concentration values on the results of the semi-analytical GIOP algorithm makes it unsuitable for use in the area of influence of river runoff in the Kara Sea. A numerical method for determining the thickness of the layer forming 90 % of the water-leaving radiance was implemented, which will allow a more detailed assessment of the influence of surface layer parameters on the accuracy of chlorophyll-a concentration estimation in the Kara Sea based on satellite ocean color data in the future.

Features of Empirical Bio-Optical Algorithms for Estimating Chlorophyll-a Concen tration from Satellite Ocean Color Data in Waters around the Antarctic Peninsula

The features of the empirical bio-optical algorithm operation in the waters around the Antarctic Peninsula are analyzed based on a comparison of calibrated data from the shipborne flow fluorimeter and satellite data from the OLCI radiometer on Senti nel-3A and Sentinel-3B satellites during the Antarctic summers of January-February 2020 and 2022. It is shown that the standard OC4 bio-optical algorithm significantly underestimates satellite estimates of Chl-a concentration from ~1.5 to ~9 times (on aver age by a factor of ~3.1). The known regional OC4-SO algorithm provides acceptable errors of Chl-a concentration estimates and can be used for studies related to the analysis of Chl-a concentration in the waters around the Antarctic Peninsula. The developed in this work new regional algorithm OC4-AP has significantly lower error in comparison with the known standard and regional algorithms. It can be used if it is necessary to obtain a remote estimate of the concentration of Chl-a, as close as possible to the accumulated world experience in determining this value by standard extract spectrophotometric and fluorimetric methods. The observed underestimation of satellite estimates of Chl-a concentration using the standard empirical bio-optical OC4 algorithm can be attributed to at least three reasons typical for the studied water area: low relative CDOM content, high phycoerythrin content, and stronger effect of pigment packing in phytoplankton cells compared to the average values in the World Ocean.

Colorism and immigrant earnings in the United States, 2015-2024.

Using data from the Current Populations Survey 2015-2024 matched to skin color data in the New Immigrant Survey, this article shows that immigrants from countries with darker skin color face a substantial earnings penalty. The penalty is similar to that found using 2003 data on individual immigrants. Controls for extensive labor market characteristics and race and ethnicity does not eliminate the negative effect of darker skin tone on wages. Color discrimination lawsuits in light of the addition of a Middle Eastern and North African (MENA) reporting category for US government surveys may become more viable.

Adaptation of Satellite Remote Sensing Algorithms for Retrieval of Environmental Parameters in the Caspian Sea

The state of the Caspian Sea ecosystems causes serious concern due to anthropogenic (oil and gas production and transportation) and natural reasons (climatic changes, extreme meteorological and hydrological phenomena, natural hazards and their frequency of recurrence, etc.). Climatic changes have already led to a sharp drop in the Caspian Sea level, an increase in sea surface temperature, changes in ice conditions in the Northern Caspian, the intensity of phytoplankton blooms, especially in the Southern Caspian, an increase in turbidity in coastal areas due to river runoff, and changes in the frequency and intensity of wind patterns. As a rule, such phenomena have a significant specific (more often negative) impact on the natural systems and infrastructure of the coastal zone. To understand the current state and climatic changes in the main parameters of the marine environment in the Caspian Sea and to carry out its comprehensive analysis, it is necessary to use remote sensing data and to determine correctly different environmental parameters. Our experience shows that in many cases standard algorithms for retrieval of these parameters from the remote sensing data that are elaborated, calibrated and validated for the open ocean conditions, are not valid for inland seas which have their own specific physico-geographical, oceanographic and meteorological conditions. The paper presents a review of new algorithms and techniques developed during the past decade for correct retrieval of sea surface temperature, water turbidity, suspended particulate matter concentration, as well as determination of ice cover, river plumes, and wind surge areas based first of all on Ocean Color Data: MSI Sentinel-2A, -2B; OLI/TIRS Landsat-8 and OLI-2/TIRS-2 Landsat-9. These algorithms have been introduced into the specialized information and analytical system “See the Caspian Sea” (STCS), designed for collecting, analyzing and visualizing satellite and meteorological data for the Caspian Sea region.