Visible Channel Research Articles

Inferring iron oxides species content in atmospheric mineral dust from DSCOVR EPIC observations

Abstract. The iron-oxide content of dust in the atmosphere and most notably its apportionment between hematite (α-Fe2O3) and goethite (α-FeOOH) are key determinants in quantifying dust's light absorption, its top of atmosphere UV radiances used for dust monitoring, and ultimately shortwave dust direct radiative effects (DRE). Hematite and goethite column mass concentrations and iron-oxide mass fractions of total dust mass concentration were retrieved from the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) measurements in the ultraviolet–visible (UV–Vis) channels. The retrievals were performed for dust-identified aerosol plumes using aerosol optical depth (AOD) and spectral imaginary refractive index provided by the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm over six continental regions (North America, North Africa, West Asia, Central Asia, East Asia, and Australia). The dust particles are represented as an internal mixture of non-absorbing host and absorbing hematite and goethite. We use the Maxwell–Garnett effective medium approximation with carefully selected complex refractive indices of hematite and goethite that produce mass fractions of iron oxides species consistent with in situ values found in the literature to derive the hematite and goethite volumetric/mass concentrations from MAIAC EPIC products. We compared the retrieved hematite and goethite concentrations with in situ dust aerosol mineralogical content measurements, as well as with published data. Our data display variations within the published range of hematite, goethite, and iron-oxide mass fractions for pure mineral dust cases. A specific analysis is presented for 15 sites over the main dust source regions. Sites in the central Sahara, Sahel, and Middle East exhibit a greater temporal variability of iron oxides relative to other sites. Niger site (13.52° N, 2.63° E) is dominated by goethite over Harmattan season with median of ~2 weight percentage (wt.%) of iron-oxide. Saudi Arabia site (27.49° N, 41.98° E) over Middle East also exhibited surge of goethite content with the beginning of Shamal season. The Sahel dust is richer in iron-oxide than Saharan and northern China dust except in Summer. The Bodele Depression area shows a distinctively lower iron-oxide concentration (~1 wt. %) throughout the year. Finally, we show that EPIC data allow to constrain the hematite refractive index. Specifically, we select 5 out of 13 different number of hematite refractive indices widely variable in published laboratory studies by constraining the iron-oxide mass ratio to the known measured values. Provided climatology of hematite and goethite mass fractions across main dust regions of the Earth will be useful for dust shortwave DRE studies and climate modeling.

Beacons over bridges: hashtags, visibility, and sexual assault disclosure on social media

ABSTRACT Aside from #MeToo and similar campaigns, sexual assault survivors use social media to disclose personal experiences. This paper uses a mixed-methods approach and draws on network ethnography (Howard, 2002) to explore the visibility of these ‘everyday’ sexual assault disclosures and how such disclosures further survivors’ disclosure motivations. Social network analysis of 23 Twitter hashtags related to sexual assault shows minimal engagement with disclosive tweets, while interviews with survivors (N = 27) indicate that hashtagged disclosures can result in impactful interactions with other survivors, particularly via less visible social media channels, such as direct messages. In combination, results suggest (1) that survivor networks can and do form around mundane hashtags, and (2) that highly visible hashtagged disclosures act as beacons to other survivors and are employed in pursuit of motivations such as supporting other survivors and challenging stigma associated with sexual assault.

Pre-trained feature aggregated deep learning-based monitoring of overshooting tops using multi-spectral channels of GeoKompsat-2A advanced meteorological imagery

ABSTRACT Overshooting tops (OTs) play a crucial role in carrying tropospheric water vapor to the lower stratosphere. They are closely related to climate change as well as local severe weather conditions, such as lightning, hail, and air turbulence, which implies the importance of their detection and monitoring. While many studies have proposed threshold-based detection models using the spatial characteristics of OTs, they have shown varied performance depending on the seasonality and study areas. In this study, we propose a pre-trained feature-aggregated convolutional neural network approach for OT detection and monitoring. The proposed approach was evaluated using multi-channel data from Geo-Kompsat-2A Advanced Meteorological Imager (GK2A AMI) over East Asia. The fusion of a visible channel and multi-infrared channels enabled the proposed model to consider both physical and spatial characteristics of OTs. Six schemes were evaluated according to two types of data pre-processing methods and three types of deep learning model architectures. The best-performed scheme yielded a probability of detection (POD) of 92.1%, a false alarm ratio (FAR) of 21.5%, and a critical success index (CSI) of 0.7. The results were significantly improved when compared to those of the existing CNN-based OT detection model (POD increase by 4.8% and FAR decrease by 29.4%).

Increasing the spatial resolution of cloud property retrievals from Meteosat SEVIRI by use of its high-resolution visible channel: implementation and examples

Abstract. The modification of an existing cloud property retrieval scheme for the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument on board the geostationary Meteosat satellites is described to utilize its high-resolution visible (HRV) channel for increasing the spatial resolution of its physical outputs. This results in products with a nadir spatial resolution of 1×1 km2 compared to the standard 3×3 km2 resolution offered by the narrowband channels. This improvement thus greatly reduces the resolution gap between current geostationary and polar-orbiting meteorological satellite imagers. In the first processing step, cloudiness is determined from the HRV observations by a threshold-based cloud masking algorithm. Subsequently, a linear model that links the 0.6 µm, 0.8 µm, and HRV reflectances provides a physical constraint to incorporate the spatial high-frequency component of the HRV observations into the retrieval of cloud optical depth. The implementation of the method is described, including the ancillary datasets used. It is demonstrated that the omission of high-frequency variations in the cloud-absorbing 1.6 µm channel results in comparatively large uncertainties in the retrieved cloud effective radius, likely due to the mismatch in channel resolutions. A newly developed downscaling scheme for the 1.6 µm reflectance is therefore applied to mitigate the effects of this scale mismatch. Benefits of the increased spatial resolution of the resulting SEVIRI products are demonstrated for three example applications: (i) for a convective cloud field, it is shown that significantly better agreement between the distributions of cloud optical depth retrieved from SEVIRI and from collocated MODIS observations is achieved. (ii) The temporal evolution of cloud properties for a growing convective storm at standard and HRV spatial resolutions are compared, illustrating an improved contrast in growth signatures resulting from the use of the HRV channel. (iii) An example of surface solar irradiance, determined from the retrieved cloud properties, is shown, for which the HRV channel helps to better capture the large spatiotemporal variability induced by convective clouds. These results suggest that incorporating the HRV channel into the retrieval has potential for improving Meteosat-based cloud products for several application domains.

Cloud Detection Over Sunglint Regions With Observations From the Earth Polychromatic Imaging Camera

With the ability to observe the entire sunlit side of the Earth, EPIC data have become an important resource for studying cloud daily variability. Inaccurate cloud masking is a great source of uncertainty. One main region that is prone to error in cloud masking is the sunglint area over ocean surfaces. Cloud detection over these regions is challenging for the EPIC instrument because of its limited spectral channels. Clear sky ocean surface reflectance from visible channels over sunglint is much larger than that over the non-glint areas and can exceed reflectance from thin clouds. This paper presents an improved EPIC ocean cloud masking algorithm (Version 3). Over sunglint regions (glint angle ≤25°), the algorithm utilizes EPIC’s oxygen (O2) A-band ratio (764/780 nm) in addition to the 780 nm reflectance observations in masking tests. Outside the sunglint regions, a dynamic reflectance threshold for the Rayleigh corrected 780 nm reflectance is applied. The thresholds are derived as a function of glint angle. When compared with co-located data from the geosynchronous Earth orbit (GEO) and the low Earth orbit (LEO) observations, the consistency of the new ocean cloud mask algorithm has increased by 4∼10% and 4∼6% in the glint center and granule edges respectively. The false positive rate is reduced by 10∼17%. Overall global ocean cloud detection consistency increases by 2%. This algorithm, along with other improvements to the EPIC cloud masks, has been implemented in the EPIC cloud products Version 3. This algorithm will improve the cloud daily variability analysis by removing the artificial peak at local noon time in the glint center latitudes and reducing biases in the early morning and late afternoon cloud fraction over ocean surfaces.

Increase of precipitation by cloud seeding observed from a case study in November 2020 over Shijiazhuang, China

Human weather modification is a method to influence cloud developing process so that the precipitation becomes more beneficial to local human society. A long existing challenging issue is the quantification of the efficiency of cloud seeding. This study investigates the changes of cloud and precipitation properties after cloud seeding by using the observations from a comprehensive experiment carried out over Shijiazhuang, China on November 21, 2020. The precipitation events before the cloud seeding experiment make the atmospheric background with sufficient water vapor. Comparison of the cloud and precipitation properties between before and after the cloud seeding shows distinct changes in both cloud and precipitation properties based on observations from ground sites, aircraft, and satellite. The aircraft observations show that the cloud droplet number concentration and liquid water content decreased significantly, and the ice particle concentration and precipitation particle concentration increased significantly, after cloud seeding. The satellite remote sensing observations show a semi-circle with decreased cloud reflection in visible channel after cloud seeding. The ground weather radar shows increased radar reflectivity echos for clouds impacted by seeding, indicating the formation of precipitation. After cloud seeding, there are also two short-term precipitation events occurring in the periods of 04:54–04:56 and 05:26–05:34 UTC over ZhaoXian, and 05:05–05:14 and 06:13–06:22 UTC over NingJin on November 21, 2020, with total precipitation amount of 0.04 mm and 0.042 mm, respectively. All the research findings show robust evidences of conversion from liquid droplets to ice particles and increased precipitation by the cloud seeding during this experiment.

Dynamical inverse seesaw mechanism as a simple benchmark for electroweak breaking and Higgs boson studies

The Standard Model (SM) vacuum is unstable for the measured values of the top Yukawa coupling and Higgs mass. Here we study the issue of vacuum stability when neutrino masses are generated through spontaneous low-scale lepton number violation. In the simplest dynamical inverse seesaw, the SM Higgs has two siblings: a massive CP-even scalar plus a massless Nambu-Goldstone boson, called majoron. For TeV scale breaking of lepton number, Higgs bosons can have a sizeable decay into the invisible majorons. We examine the interplay and complementarity of vacuum stability and perturbativity restrictions, with collider constraints on visible and invisible Higgs boson decay channels. This simple framework may help guiding further studies, for example, at the proposed FCC facility.

Illumination-based adaptive saliency detection network through fusion of multi-source features

Salient object detection (SOD) tasks aim to outline the most concerned part of human vision, which is widely used in computer vision fields. Due to possibility of the insufficient illumination in the application environment (such as night or dim indoor environment), RGB images from visible channels usually lose most of their performance, while thermal images can improve the detection performance. Therefore, it is in urgent need of a robust saliency detection method, which can handle complex illumination conditions and take use of features from multiple sources intelligently. Accordingly, we propose our ‘illumination based multi-source fused salient object detection network’ (IAN-MF-SOD network). Taking the illumination condition as a quantitative reference, we guide features from two sources to fuse adaptively and intelligently, so that our method can enhance both of their advantages. For different illumination conditions, we distribute different fusion weights for each RGB–thermal image pair. Well fused images are generated as inputs to a trained SOD network to obtain saliency maps. Due to the analysis of our proposed IAN-score, our method performs favorably against traditional RGB-based SOD networks.

Effect of scattering phase function on underwater visible light communication channel models

Non-sequential ray tracing simulations are commonly employed to model underwater visible light communication (VLC) channels. The accuracy of such simulations highly depends on how well the optical properties of water (i.e., absorption and scattering) as well as scattering phase function (SPF) are modeled in the simulation. Existing empirical models are only a function of chlorophyll concentration and particle composition and are independent of refractive index, size and concentration of particles. In this paper, we carry out an underwater VLC channel modeling study using the Mie SPF which provides a full description of the scattering from phytoplankton particles which dominate the optical properties of most oceanic waters. We obtain the channel impulse response (CIR) based on an extensive non-sequential ray tracing study and calculate the fundamental channel parameters such as channel gain and delay spread. Comparison of CIRs reveals out that deployment of simplified SPF models results in the overestimation of path loss with respect to Mie SPF. Our results clearly demonstrate the importance of selecting realistic SPF models for an accurate underwater VLC channel modeling. While highlighting the channel models, we discuss adaptive modulation technique to maximize the data rate under the constraint of a targeted bit error rate. Besides, the maximum achievable distance is also determined both in terms of analytical guarantees and computer simulations. The results reveal that larger transmission distances can be achieved through Mie SPF channel model.

Testbed for Experimental Characterization of Indoor Visible Light Communication Channels

In this paper, we describe an experimental testbed designed to evaluate indoor visible light communications (VLC) in realistic scenarios. The system is based on a mockup where the location and orientation of the optical receiver can be modified with precision for a static configuration of walls and ceiling lamp arrangements. The system utilizes a timing synchronization method, which is based on evaluating the training sequence periods used for channel response estimation, which enables robust frame synchronization. In addition, an adaptive rate orthogonal frequency-division multiplexing (OFDM) scheme is used to assess the VLC performance throughout the receiver plane emulating a real communication. The preliminary results obtained with this prototype, considering a multiple-input single-output (MISO) scenario, demonstrate that reflection on walls yields a significant increase in data rates, which can be additionally improved if appropriate orientation of the receiver is implemented. However, vertical orientation upward of the optical receiver still constitutes a simple solution but efficient enough. Moreover, a good agreement between simulation and experimental results is observed, which confirms the suitability of the mockup as an experimental testbed for practical evaluation of indoor VLC systems, where system performance for different lamp arrangements and receiver designs, including multi-user communications, can be studied.

Calibration of the DSCOVR EPIC Visible and NIR Channels using Multiple LEO Radiometers

The first five years of operation of the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) at the Lagrange one point have produced results that uniquely complement the data from currently operating low orbit Earth-observing instruments. In this paper we describe an updated unified approach to EPIC calibration. In this approach, calibration coefficients and their trends were obtained by comparing EPIC observations to the measurements from polar orbiting radiometers. In this study L1B reflectances from Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua and Terra satellites, Multi-angle Imaging Spectroradiometer (MISR) onboard Terra and Visible Infrared Imaging Radiometer (VIIRS) onboard the Suomi National Polar-orbiting Partnership (Suomi NPP) spacecraft were used to infer calibration coefficients for four EPIC visible and near-infrared channels: 443 nm, 551 nm, 680 nm, and 780 nm. EPIC Version three measurements made between June 2015 and August 2020 were used for comparison. The calibration procedure identifies the most homogeneous low Earth orbit radiometer scenes matching scattering angles that are temporarily and spatially collocated with EPIC observations. These scenes are used to determine reflectance to count (R/C) ratios in spectrally analogous channels. Seasonal average R/C ratios were analyzed to obtain EPIC calibration gains and trends. The trends for the full dataset period are not statistically significant except in the 443 nm channel. No significant changes in calibration were found after the instrument’s exit from safe hold in March 2020. The R/C ratios were also used to determine the differences in EPIC gains resulting from separate calibrations: against MODIS Aqua or Terra, as well as against forward or aftward MISR cameras. Statistical tests indicate that the differences between the two datasets are not significant except in the 780 nm channels where Aqua-derived coefficients may be around 2% lower compared to Terra. The dependence of EPIC calibration gains on the instrument scattering angle and on DSCOVR-Earth distance were investigated. Lastly, model Low Earth Orbit (LEO) reflectances calculated to match the EPIC viewing geometry were employed to study how EPIC calibration coefficients depend on EPIC-LEO viewing geometry differences. The effect of LEO and EPIC angular mismatch on calibration was shown to be small.

Real-time Tropical Cyclone Intensity Estimation by Handling Temporally Heterogeneous Satellite Data

Analyzing big geophysical observational data collected by multiple advanced sensors on various satellite platforms promotes our understanding of the geophysical system. For instance, convolutional neural networks (CNN) have achieved great success in estimating tropical cyclone (TC) intensity based on satellite data with fixed temporal frequency (e.g., ~3 h). However, to achieve more timely (under 30 min) and accurate TC intensity estimates, a deep learning model is demanded to handle temporally-heterogeneous satellite observations. Specifically, infrared (IR1) and water vapor (WV) images are available within every 15 minute period, while passive microwave rain rate (PMW) is available about every 3 hours. Meanwhile, the visible (VIS) channel is severely affected by noise and sunlight intensity, making it difficult to be utilized. Therefore, we propose a novel framework that combines generative adversarial network (GAN) with CNN. The model utilizes all data during the training phase including VIS and PMW information and eventually uses only the high-frequent IR1 and WV data for providing intensity estimates during the predicting phase. Experimental results demonstrate that the hybrid GAN-CNN framework achieves comparable precision to the state-of-the-art models, while possessing the capability of increasing the maximum estimation frequency from 3 hours to less than 15 minutes. Please visit https://github.com/BoyoChen/CNN-GAN-TC for codes and implementation details.

Day and Night Clouds Detection Using a Thermal-Infrared All-Sky-View Camera

The formation and evolution of clouds are associated with their thermodynamical and microphysical progress. Previous studies have been conducted to collect images using ground-based cloud observation equipment to provide important cloud characteristics information. However, most of this equipment cannot perform continuous observations during the day and night, and their field of view (FOV) is also limited. To address these issues, this work proposes a day and night clouds detection approach integrated into a self-made thermal-infrared (TIR) all-sky-view camera. The TIR camera consists of a high-resolution thermal microbolometer array and a fish-eye lens with a FOV larger than 160°. In addition, a detection scheme was designed to directly subtract the contamination of the atmospheric TIR emission from the entire infrared image of such a large FOV, which was used for cloud recognition. The performance of this scheme was validated by comparing the cloud fractions retrieved from the infrared channel with those from the visible channel and manual observation. The results indicated that the current instrument could obtain accurate cloud fraction from the observed infrared image, and the TIR all-sky-view camera developed in this work exhibits good feasibility for long-term and continuous cloud observation.

Vertical Temperature Profile of Natural Lightning Return Strokes Derived From Optical Spectra

AbstractThis study reports on spectroscopy results from a high‐speed optical spectrograph of two naturally occurring lightning return strokes. The two strokes occurred near Melbourne, FL and were from two separate flashes that were about 10 min apart and had National Lightning Detection Network (NLDN) peak currents of −19 and −63 kA. The larger peak current stroke was from a dart leader and was the last stroke in a 5 return stroke flash, while the −19 kA stroke originated from a stepped leader and was the only stroke in that flash. From the flash spectra, the return stroke channel temperature was calculated using the neutral lines of 715.7 nm (OI) and 777.4 nm (OI). In addition to the use of the neutral emission lines, the use of novel instrumentation and image processing techniques allowed the temperature to be calculated for nearly the entire visible channel (several km) and for long durations (several hundred μs). This enables temperature estimates on an unprecedented spatial and temporal scale, which show that the vertical temperature profile is not uniform across the channel. The lower altitudes are significantly hotter than higher altitudes near the time of the return stroke, with temperature gradients along the channel as large as 12,000 K/km. The rate of cooling of the channel is also initially 3–4 times larger at lower altitudes in comparison with the segments at higher altitudes. The stroke with the larger peak current shows larger maximum temperatures, larger temperature gradients along the channel, and also cools quicker.

Introduction of the Advanced Meteorological Imager of Geo-Kompsat-2a: In-Orbit Tests and Performance Validation

Geo-Kompsat-2A (Geostationary-Korean Multi-Purpose SATtellite-2A, GK2A), a new generation of Korean geostationary meteorological satellite, carry state-of-the-art optical sensors with significantly higher radiometric, spectral, and spatial resolution than the Communication, Ocean, and Meteorological Satellite (COMS) previously available in the geostationary orbit. The new Advanced Meteorological Imager (AMI) on GK2A has 16 observation channels, and its spatial resolution is 0.5 or 1 km for visible channels and 2 km for near-infrared and infrared channels. These advantages, when combined with shortened revisit times (around 10 min for full disk and 2 min for sectored regions), provide new levels of capacity for the identification and tracking of rapidly changing weather phenomena and for the derivation of quantitative products. These improvements will bring about unprecedented levels of performance in nowcasting services and short-range weather forecasting systems. Imagery from the satellites is distributed and disseminated to users via multiple paths, including internet services and satellite broadcasting services. In post-launch performance validation, infrared channel calibration is accurate to within 0.2 K with no significant diurnal variation using an approach developed under the Global Space-based Inter-Calibration System framework. Visible and near infrared channels showed unexpected seasonal variations of approximately 5 to 10% using the ray matching method and lunar calibration. Image navigation was accurate to within requirements, 42 µrad (1.5 km), and channel-to-channel registration was also validated. This paper describes the features of the GK2A AMI, GK2A ground segment, and data distribution. Early performance results of AMI during the commissioning period are presented to demonstrate the capabilities and applications of the sensor.

Improved machine-learning-based open-water–sea-ice–cloud discrimination over wintertime Antarctic sea ice using MODIS thermal-infrared imagery

Abstract. The frequent presence of cloud cover in polar regions limits the use of the Moderate Resolution Imaging Spectroradiometer (MODIS) and similar instruments for the investigation and monitoring of sea-ice polynyas compared to passive-microwave-based sensors. The very low thermal contrast between present clouds and the sea-ice surface in combination with the lack of available visible and near-infrared channels during polar nighttime results in deficiencies in the MODIS cloud mask and dependent MODIS data products. This leads to frequent misclassifications of (i) present clouds as sea ice or open water (false negative) and (ii) open-water and/or thin-ice areas as clouds (false positive), which results in an underestimation of actual polynya area and subsequently derived information. Here, we present a novel machine-learning-based approach using a deep neural network that is able to reliably discriminate between clouds, sea-ice, and open-water and/or thin-ice areas in a given swath solely from thermal-infrared MODIS channels and derived additional information. Compared to the reference MODIS sea-ice product for the year 2017, our data result in an overall increase of 20 % in annual swath-based coverage for the Brunt Ice Shelf polynya, attributed to an improved cloud-cover discrimination and the reduction of false-positive classifications. At the same time, the mean annual polynya area decreases by 44 % through the reduction of false-negative classifications of warm clouds as thin ice. Additionally, higher spatial coverage results in an overall better subdaily representation of thin-ice conditions that cannot be reconstructed with current state-of-the-art cloud-cover compensation methods.

Radiometric Performance Evaluation of FY-4A/AGRI Based on Aqua/MODIS.

Fengyun-4A (FY-4A) is the first satellite of the Chinese second-generation geostationary orbit meteorological satellites (FY-4). The Advanced Geostationary Radiation Imager (AGRI), onboard FY-4A does not load with high-precision calibration facility in visible and near infrared (VNIR) channel. As a consequence, it is necessary to comprehensively evaluate its radiometric performance and quantitatively describe the attenuation while using its VNIR data. In this paper, the radiometric performance at VNIR channels of FY-4A/AGRI is evaluated based on Aqua/MODIS data using the deep convective cloud (DCC) target. In order to reduce the influence of view angle and spectral response difference, the bi-directional reflectance distribution function (BRDF) correction and spectral matching have been performed. The evaluation result shows the radiometric performance of FY-4A/AGRI: (1) is less stable and with obvious fluctuations; (2) has a lower radiation level because of 24.99% lower compared with Aqua/MODIS; 3) has a high attenuation with 9.11% total attenuation over 2 years and 4.0% average annual attenuation rate. After the evaluation, relative radiometric normalization between AGRI and MODIS in VNIR channel is performed and the procedure is proved effective. This paper proposed a more reliable reference for the quantitative applications of FY-4A data.

Ultraviolet vision aids the detection of nutrient-dense non-signaling plant foods

To expand our understanding of what tasks are particularly helped by UV vision and may justify the costs of focusing high-energy light onto the retina, we used an avian-vision multispectral camera to image diverse vegetated habitats in search of UV contrasts that differ markedly from visible-light contrasts. One UV contrast that stood out as very different from visible-light contrasts was that of nutrient-dense non-signaling plant foods (such as young leaves and immature fruits) against their natural backgrounds. From our images, we calculated color contrasts between 62+ species of such foods and mature foliage for the two predominant color vision systems of birds, UVS and VS. We also computationally generated images of what a generalized tetrachromat, unfiltered by oil droplets, would see, by developing a new methodology that uses constrained linear least squares to solve for optimal weighted combinations of avian camera filters to mimic new spectral sensitivities. In all visual systems, we found that nutrient-dense non-signaling plant foods presented a lower, often negative figure-ground contrast in the UV channels, and a higher, often positive figure-ground contrast in the visible channels. Although a zero contrast may sound unhelpful, it can actually enhance color contrast when compared in a color opponent system to other channels with nonzero contrasts. Here, low or negative UV contrasts markedly enhanced color contrasts. We propose that plants may struggle to evolve better UV crypsis since UV reflectance from vegetation is largely specular and thus highly dependent on object orientation, shape, and texture.

Evaluation of GSICS Correction for COMS/MI Visible Channel Using S-NPP/VIIRS

The Global Space-based Inter-Calibration System (GSICS) is an international partnership sponsored by World Meteorological Organization (WMO) to continue and improve climate monitoring and to ensure consistent accuracy between observation data from meteorological satellites operating around the world. The objective for GSICS is to inter-calibration from pairs of satellites observations, which includes direct comparison of collocated Geostationary Earth Orbit (GEO)-Low Earth Orbit (LEO) observations. One of the GSICS intercalibration methods, the Ray-matching technique, is a surrogate approach that uses matched, co-angled and colocated pixels to transfer the calibration from a well calibrated satellite sensor to another sensor. In Korea, the first GEO satellite, Communication Ocean and Meteorological Satellite (COMS), is used to participate in the GSICS program. The National Meteorological Satellite Center (NMSC), which operated COMS/MI, calculated the Radiative Transfer Model (RTM)-based GSICS coefficient coefficients. The L1P reproduced through GSICS correction coefficient showed lower RMSE and Bias than L1B without GSICS correction coefficient applied. The calculation cycles of the GSICS correction coefficients for COMS/MI visible channel are provided annual and diurnal (2, 5, 10, 14-day), but long-term evaluation according to these cycles was not performed. The purpose of this paper is to perform evaluation depending on the annual/diurnal cycles of COMS/MI GSICS correction coefficients based on the ray-matching technique using Suomi-NPP/Visible Infrared Imaging Radiometer Suite (VIIRS) data as reference data. As a result of evaluation, the diurnal cycle had a higher coincidence rate with the reference data than the annual cycle, and the 14-day diurnal cycle was the most suitable for use as the GSICS correction coefficient.

The Intellectual Lighting And Data Transmission System based On RGBW Light Emitting Diodes

The system of intellectual lighting data transmission via visible light is developed and manufactured. Spectral characteristics of a downlink which uses the red crystal of a RGBW light emitting diode for data transfer were studied. The DALI protocol-based radiation chromaticity control system which allows us to set different lighting scenarios with constant data transmission rate was developed. The radiation chromaticity range covers almost the entire colour gamut in the colour space. The system of high-frequency matching of system component impedances was developed and frequency characteristics of the suggested scheme were studied for development of the system. Optimal parameters of the signal for visual light communication such as carrier frequency, modulation type and band were determined. Observation of the constellation diagram which represents different values of the complex amplitude of the keyed signal in the form of a complex number on a quadrature plane (cosine and sine components of the carrying signal) and of fixation of the amplitude of the error vector magnitude (EVM) was selected as a method of study of the transmission channel quality. The value of EVM in the visible light transmission channel was significantly lower for signals with amplitude modulation than for phase-manipulated signals. When implementing different lighting change scenarios, radiation of other crystals of the light emitting diode crystals not used for transmission did not lead to increase of EVM by more than one percent.