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1,709 results for “Reflectivity”
Natural product biosynthetic potential reflects macroevolutionary diversification within a widely distributed bacterial taxon
<p>Code and data for the article "Natural product biosynthetic potential reflects macroevolutionary diversification within a widely distributed bacterial taxon".</p>
Data from: Community-level canopy reflectance in grazed grasslands is linked to the habitat preferences of individual plant species
<p class="MsoNormal">Spectral remote sensing provides tools for biological monitoring and can be used to characterize habitat quality in grasslands. These data have been used to study the relationships between plant community composition and remotely sensed canopy reflectance in grazed grasslands. The study area is located on the island of Öland, Sweden, and the data were collected in grazed grasslands that represent a succession from previously arable fields to old semi-natural pastures. All included grassland sites have been assigned to three different classes of grassland age, defined by the grazing continuity in years: <span>young (5–14 years), intermediate-aged (15–49 years), and old (>50 years).</span></p> <p class="MsoNormal">The plant community data consist of presences/absences for 100 vascular plant species in 104 (4 m × 4 m) sample plots positioned in open grassland vegetation. Information on species' habitat preferences is included and has been used to explain the associations between plant species' occurrences and the variation in community-level canopy reflectance.</p> <p class="MsoNormal">The remote sensing data consist of 317 hyperspectral bands in the wavelength regions 414–1322 nm, 1496–1797 nm, and 2050–2351 nm, and show the mean reflectance in each spectral band for the 104 sample plots. The main gradient in the hyperspectral data is characterized by contrasting reflectance values between bands located in the NIR spectra and bands located in the red, blue, and SWIR spectra.</p> <p class="MsoNormal">Grassland canopy reflectance was able to explain variation in the occurrences of individual plant species, particularly those with distinct habitat preferences. Species' habitat preferences indicated that vegetation reflectance in the red, blue, SWIR, and NIR spectra was linked to the plant-availability of mineral nitrogen. In contrast, species' phosphorus preferences showed stronger associations with reflectance in the green and red-edge spectra.</p>
Predicting Pulsed-Laser Deposition SrTiO3 Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction - gaussian_fit_parameters - sample untreated_162nm
<p>RHEED raw dataset and Gaussia fitting parameter dataset for sample "untreated_162nm" in work "Predicting Pulsed Laser Deposition SrTiO<sub>3 </sub>Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction."</p>
Compressed Datasets for Work "Predicting Pulsed-Laser Deposition SrTiO3 Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction"
<p>Compressed version of RHEED image datasets and Gaussia fitting parameter datasets for samples "treated_213nm", "treated_81nm" and "untreated_162nm" in the work "Predicting Pulsed Laser Deposition SrTiO<sub>3 </sub>Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction."</p>
GOES 13 - visible reflectance
<p>This dataset is the GOES 13 visible reflectance provided by the Satellite and Meteorological Sensors Division of the National Institute of Space Research.</p> <p>Each file is the reflectance for a given observation time, varying from 0 to 1. The files are binary data in int16, big-endian.</p> <p>The data corresponds to the observations within 14 and 16 UTC for January, April, July and October from 2013 to 2017. It has a spatial resolution of 0.04 degree on a regular latitude-longitude grid of 1800x1800 pixels within latitudes 50°S to 21.96°N and longitudes 100°W to 28.04°W.</p> <p>S11235426_yyyymmddhhmm.ref</p> <p>yyyy - year</p> <p>mm - month</p> <p>dd - day</p> <p>hh - hour</p> <p>mm - minute</p>
Data from: Predator-prey interactions in the Arctic: DNA-metabarcoding reveals that nestling diet of snow buntings reflects arthropod seasonality
<p>Tundra arthropods are of considerable ecological importance as a seasonal food source for many arctic-breeding birds. Dietary composition and food preferences are rarely known, complicating assessments of ecological interactions in a changing environment. In our field study, we investigated nestling diet of snow buntings (<em>Plectrophenax nivalis</em> (L., 1758)) breeding in Svalbard. We collected faecal samples from 8-day-old nestlings and assessed dietary composition by DNA-metabarcoding. Simultaneously, the availability of potential prey arthropods was measured by pitfall-trapping. Molecular analyses of nestling faeces identified 31 arthropod taxa in the diet, whose proportions changed throughout the brood-rearing period. Changes in nestling diet matched varying abundances and emergence patterns of the tundra arthropod community. Snow buntings provisioned their offspring mainly with Diptera (true flies) based on both presence/absence and relative read abundance of diet items. At the beginning of the season in June, Chironomidae (non-biting midges) and the scathophagid fly <em>Scathophaga furcata</em> (Say, 1823) dominated the diet, whereas the muscid fly <em>Spilogona dorsata </em>(Zetterstedt, 1845) dominated the diet later in July. When accounted for availability, muscid flies were selected positively amongst the most often provisioned food taxa. Our study demonstrates the ecological role of the snow bunting as a generalist arthropod predator and highlights DNA-metabarcoding as a non-invasive technique for diet analyses with high taxonomical precision if sufficient DNA-sequence libraries are available.</p>
Reflection Of Video Assistant Referee Technology on Professional Football Discipline Board Decisions
<p>The aim of this study is to understand how Video Assistant Referee (VAR) technology is reflected in the decisions of Professional Football Disciplinary Board (PFDK) and to examine whether this technology creates any change. In accordance with this purpose, comparison of disciplinary violations detected in Super League competitions in general and direct send-off decisions to PFDK decisions was carried out within the scope of 2018-2019 and 2019-2020 seasons, the first two seasons where VAR technology was used in Super League, and 2016-2017 and 2017-2018 seasons, the last two seasons where VAR technology was not used. PFDK decisions for four seasons between 2016-2020 were collected by document analysis.</p>
Patterns of genetic variation reflect multiple introductions and pre-admixture sources of common ragweed (Ambrosia artemisiifolia) in China
<p>Informations about the locations and genetic diversity of <em>Ambrosia artemisiifolia</em> populations in published paper "Patterns of genetic variation reflect multiple introductions and pre-admixture sources of common ragweed (Ambrosia artemisiifolia) in China".</p>
Data - A Functionalized Monte Carlo 3D Radiative Transfer Model: Radiative Effects of Clouds over Reflecting Surfaces
<p>Data and scripts associated with the article "A Functionalized Monte Carlo 3D Radiative Transfer Model: Radiative Effects of Clouds over Reflecting Surfaces"</p>
Initial Sample of HYPERNETS Hyperspectral Surface Reflectance Measurements for Satellite Validation from the Barrax Site in Spain
<p>The HYPERNETS project (www.hypernets.eu) has the overall aim to ensure that high quality in situ measurements are available to support the (VNIR/SWIR) optical Copernicus products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu) dedicated to land and water surface reflectance validation with instrument pointing capabilities. In the prototype phase, the instrument is being deployed at 24 sites covering a range of water and land types and a range of climatic and logistic conditions. This dataset provides the first published data for the Gobabeb HYPERNETS site in Barrax, Spain (BASP). It is a subset of the complete data record which consists of the best quality BASP measurements which could be used for satellite validation over the three day test deployment period. </p> <p>The provided NetCDF files are the L2A hypernets products with surface reflectances, their associated uncertainties and error-correlation information. The reflectance in the L2A products is the Hemispherical-directional Reflectance Factor (HDRF) defined as: HDRF = π L / E where L is the directional upwelling radiance (with field of view of 5 degrees) and E is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance). These reflectances have dimensions of wavelength and series, where each series is a set of measurements for a given geometry (combination of viewing zenith and azimuth angle). In addition to variables for wavelength and bandwidth, the files also contain variables that provide for each series the acquisition time, viewing and solar angles, number of valid scans used, and quality flags (typically no flags are set in the data provided in this dataset). These NetCDF files also contain further relevant metadata as attributes. See https://hypernets-processor.readthedocs.io/ for further info.</p> <p>The BASP site was a temporary installation over the period of the 20<sup>th</sup> – 22<sup>nd</sup> July 2022 during the Surface Reflectance Intercomparison eXperiment (SRIX) campaign (https://frm4veg.org/srix4veg/) at the Las Tiesas experimental farm in Barrax, Spain. This location was selected due to its typical clear skies, flat terrain, and well-managed crops. The HYPSTAR®-XR (eXtended Range) was deployed in a small corn field next to the ongoing UAV experiment. The instrument was deployed on a 3.5m high pole with a short extended boom at 1.3m height from the crops, with measurements running every 30 minutes throughout the day (UTC+2) and measuring between viewing zenith angles of 0-60 degrees.</p> <p>The HYPSTAR®-XR instruments deployed at each land HYPERNETS site consist of a VNIR and a SWIR sensor and autonomously collect data between 380-1700 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with a FWHM of 3 nm and the SWIR sensor has 220 channels between 1000 and 1700 nm with a FWHM of 10 nm. The hypernets_processor (Goyens et al. 2021; De Vis et al. in prep.) automatically processes all this data into various products, including the L2A surface reflectance product provided here. All of the products have associated uncertainties (divided into random and systematic uncertainties, including error-correlation information) which were propagated using the CoMet toolkit (www.comet-toolkit.org). </p> <p>To obtain this dataset, we start from the full BASP data record and omit all the data that do not pass all of the quality checks performed as part of the hypernets_processor. In addition, an additional screening procedure was developed to remove outliers and only supply the best quality data suitable for satellite validation. To remove the outliers, a sigma-clipping method is used. First reflectances are extracted in separate 2 hour windows throughout the day (to account for BRDF differences due to different solar position) for 4 different wavelengths (500, 900, 1100 and 1600 nm). Outliers in these reflectances are then identified by iteratively calculating the mean reflectance trend with time (by binning the data per maximum 30 data points), calculating the standard deviation from this trend, and masking any data that is more than 3 standard deviations away from the trend. This process is repeated on the unmasked data until the standard deviation does not vary by more than 5% between two iterations. The masks for the 4 different wavelengths are then combined (keeping only measurements for which none of the 4 wavelengths is an outlier). The reflectances and associated uncertainties for any masked series (i.e. a geometry that is masked either by the sigma-clipping procedure or from the masks of the hypernets_processor) are replaced by NaNs. Any sequence that has more than half of its series masked is removed entirely. For BASP specifically, viewing zenith angles above 30 degrees have been removed, as well as any west-facing angles azimuth angles of 263,273 or 293 degrees) for viewing zenith angles of 5 degrees and 10 degrees. </p> <p>Note: In the accompanying .csv description file the measurement times are listed in (BST/ UTC+1) after UK time.</p>
Online classified adverts reflect the broader United Kingdom trade in turtles and tortoises rather than drive it.
<p>The accompanying file represents a flat dataset as used in the article: "Online classified adverts reflect the broader United Kingdom trade in turtles and tortoises rather than drive it."</p> <p>Any potentially personal or sensitive information was removed at the collection and processing stages and seller names or IDs (as listed on the original adverts) have been anonymised.<br> </p>
Impressive pan-genomic diversity of E. coli from a wild animal community near urban development reflects human impacts
<p>Data provided here support the findings of this study and code will allow the replication of analyses therein. To do so, first download and unzip the associated data files.</p> <p>For each analysis, the following files are required:</p> <p>PCAs: PCA.R, prokka_annotations.zip (and VirulenceFinder_results.csv for virulence factor PCA)</p> <p>Sankey diagram: Plasmid_AMR_Sankey.R, mlplasmids_results.zip, MOBsuite_results.zip, ResFinder_results.csv</p> <p>Pathotype assignment: VF_data_analysis.R, VirulenceFinder_results.csv</p> <p>TableS1_Ecoli_metadata.csv contains the isolate metadata (n=143) for all above analyses.</p>
Data from: Amazonian pollen assemblages reflect biogeographic gradients and forest cover
<p>Aim: We aim to assess i) how pollen assemblages vary across biogeographic and environmental gradients, ii) the source area of pollen assemblages from lake sediment samples, and iii) which pollen taxa can best be used to quantify deforested landscapes.</p> <p>Location: Amazonia</p> <p>Taxon: Plantae</p> <p>Methods: Pollen assemblages (N = 65) from mud-water interface samples (representing modern conditions) of lake sediment cores were analysed and compared with modern gradients of temperature, precipitation, and elevation. Pollen assemblages were also compared with local scale estimates of forest cover at 1, 2, 5, 10, 20, and 40 km buffers around each lake.</p> <p>Results: Over 250 pollen types were identified in the samples, and pollen assemblages were able to accurately differentiate biographic regions across the basin, corresponding with gradients in temperature and precipitation. Poaceae percentages had a significant negative relationship with forest cover estimates, which were stronger at the 1 km buffer and weaker as buffer sizes increased.</p> <p>Main conclusions: The diverse Amazonian pollen assemblages strongly reflect environmental gradients, and percentages of Poaceae best reflect local scale variability in forest cover. Our results of modern pollen-landscape relationships can be used to provide a foundation for quantitative reconstructions of climate and deforestation in Amazonian landscapes.</p>
Reflectance transformation imaging files of a selection of fossil arthropods (Carboniferous from Kaiping, China)
<p>Numerous arthropods (e.g. branchiopods, insects) collected during the first half of the 20th century by F.-F. Mathieu within the Pennsylvanian–Cisuralian (Moscovian–Asselian) succession of the Zhaogezhuang colliery (Hebei Province, China), which was romanised notably as Chao Ko Chwang and Chaokochuang in the literature, have been recently traced in the palaeontological collections of the Royal Belgian Institute of Natural Sciences (Brussels). This historical material from one of the classic localities of the Kaiping Coalfield includes a large amount of Blattodea wings, mainly from the Tangshan Formation and to a lesser extent from the Kaiping and Zhaogezhuang formations. The exceptional richness in insects of a particular horizon developed within the Moscovian part of the Tangshan Formation was already pointed out at the end of the 1920s by the French palaeoentomologist P. Pruvost, who provided the first account on the arthropods of Mathieu's collection from the Kaiping Coalfield. This singular Blattodea material was later studied by D. Laurentiaux in his unpublished Ph.D. In order to promote the revision of Mathieu's collection by specialists, we provide here a detailed scientific background and re-illustrate the arachnids (and formerly alleged ones: (Poliochera vel Curculioides) [sic] pustulatus Laurentiaux-Vieira & Laurentiaux)), branchiopods (Lioestheria? mathieui (Pruvost)), and insects from the Zhaogezhuang colliery, notably using the Reflectance Transformation Imaging methodology.</p>
Raw I/Q capture of GRAVES reflections caused by 2023 Perseids
<p>Raw I/Q capture of the radar reflections originating from the <em>Grand Réseau Adapté à la Veille Spatiale</em> (GRAVES) radar in the 143.050 MHz frequency, and caused by the meteor ionisation trails during 2023 Perseids meteor shower. The file format is 2 x float32 (cf64), little endian and the sample rate is <span class="math-tex">\(f_s=1.92\text{ Msps}\)</span>.</p> <p>The capture was obtained from an <a href="https://es.aliexpress.com/i/1005003181244737.html">AntSDR E310</a> in 2r2t mode, tuned to 143.3 MHz. The antenna consisted of a minimal array of two <span class="math-tex">\(\lambda/2\)</span> vertical dipoles, with a <span class="math-tex">\(\lambda/2\)</span> spacing between both, with a design frequency of 143.050 MHz. The location of the antenna was 40.45342176913288º N, -3.408497019619436º E</p> <p>The file was produced with SigDigger's "Pluto/ANTSDR 2RX combined source", provided by the <a href="https://github.com/BatchDrake/AntSDRPlugin">AntSDR plugin</a>. This custom signal source modulates the samples of RX1 and RX2 by <span class="math-tex">\(-f_s/4\)</span> and <span class="math-tex">\(f_s/4\)</span> respectively, and sums both. This results in a spectrum that, once visualised from SigDigger, seems duplicated around the central frequency.</p> <p>This representation enables simple manipulation of both coherent channels with the existing software infrastructure: if we wanted to compare the phases of a channel at frequency <span class="math-tex">\(f_0\)</span> coming from both antennas, we would see the signal coming from RX1 at <span class="math-tex">\(f_0-f_s/4\)</span> and the signal coming from RX2 at <span class="math-tex">\(f_0+f_s/4\)</span>. It would suffice by multiplying the samples by two NCOs that cycle over phases <span class="math-tex">\(0,\pm\pi/2,\pi,\mp\pi/2\)</span> and applying an appropriate low-pass filter and decimation to the output of both.</p>
Angle Dependent Spectral Reflectance Material Dataset based on 945 nm Time-of-Flight Camera Measurements
<p>The dataset contains angle dependent spectral reflectance measurements of materials in the infrared spectrum at a wavelength of 945 nm taken by a time-of-flight camera for an angle range of 0° to 80° in incremental steps of 10°, mounted on an adjustable angle measurement device.</p> <p>Each data file is structured in columns with two parameters: reflectance (%) and incidence angle (°).</p> <p>For each single material measurement a description file is attached in a separate ZIP-file.</p> <p>The extended dataset include new materials such as vehicle varnish and moss rubber. This new version further includes pictures for almost each of the measurements to depict the measurement setup and environmental conditions during the data acquisition. They are provided in a separate ZIP-file.</p> <p> </p> <p><strong>Note: </strong>Please always use the latest version!</p>
Data from: Coinfection with chytrid genotypes drives divergent infection dynamics reflecting regional distribution patterns
<p>By altering the abundance, diversity, and distribution of species — and their pathogens — globalization may inadvertently select for more virulent pathogens. In Brazil's Atlantic Forest, a hotspot of amphibian biodiversity, the global trade has facilitated the co-occurrence of previously isolated enzootic and panzootic lineages of the pathogenic amphibian-chytrid (<em>Batrachochytrium dendrobatidis</em>, 'Bd') and generated new virulent recombinant genotypes ('hybrid'). Epidemiological data indicate that amphibian declines are most severe in hybrid zones, suggesting that coinfections are causing more severe infections or selecting for higher virulence. We investigated how coinfections involving these genotypes shaped virulence and transmission. Overall, coinfection favored the more virulent and competitively superior panzootic genotype, despite dampening its transmission potential and overall virulence. However, for the least virulent and least competitive genotype, coinfection increased both overall virulence and transmission. Thus, by integrating experimental and epidemiological data, our results provide a mechanistic insight into how globalization can select for, and propel, the emergence of introduced hypervirulent lineages, such as the globally distributed panzootic lineage of Bd.</p>
Dataset: Magnetic and electric antennas synergy for partial discharge measurements in gas-insulated substations: Power flow and reflection suppression
<p>Data set for the publication named: Magnetic and electric antennas synergy for partial discharge measurements in gas-insulated substations: Power flow and reflection suppression. Each header corresponds to the figure and legend.</p>
PANTHYR hyperspectral water reflectance - VEIT
<p><strong>Introduction</strong> </p><p>This dataset contains water-leaving radiance reflectance (𝜌𝑤, variable names reflectance and reflectance_nosc) measurements made by an autonomous Pan and Tilt Hyperspectral Radiometer (PANTHYR, Vansteenwegen et al. 2019) installed at site VEIT. Data are provided in NetCDF format with information on processing settings provided in the NetCDF global attributes. This dataset contains measurements from the first two deployments (Oct. 2019—Oct. 2020 and Nov. 2020—Mar. 2022) that pass quality control, and have bounding calibration information, and ancillary wind speed available. For this site, the use of reflectance with the Similarity Spectrum offset correction (Ruddick et al. 2005, 2006) is recommended.</p><p><strong>Methods</strong> </p><p>A PANTHYR was deployed at Acqua Alta Oceanographic Tower, Adriatic Sea, VEIT, located at 45.3139°N, 12.5083°E for two deployments Oct. 2019—Oct. 2020 and Nov. 2020—Mar. 2022. </p><p>PANTHYR consists of a pair of TriOS RAMSES radiometers, one for measurement of radiance, and one with a cosine collector for measurement of irradiance, with custom control hard- and software, mounted on a pan and tilt head. The RAMSES spectral range is about 350—950 nm in 190 channels. The pan and tilt head allows the orientation of each radiometer in a specific direction. Using the standard protocol, a PANTHYR cycle consists of sequential measurements of downwelling irradiance (𝐸𝑑, 6 replicates), downwelling (sky) radiance (𝐿𝑑, 6 replicates), and upwelling radiance (𝐿𝑢, 11 replicates). Three 𝐸𝑑 and 𝐿𝑑 measurements are performed each before and after the 𝐿𝑢 measurements. Measurement cycles are performed every 20 minutes during daytime, at 90, 135, 225, and/or 270 degrees relative azimuth to the sun to minimize air-water interface reflectance (Mobley 1999, Ruddick et al. 2006). Platform pointing conditions are skipped by the definition of an absolute pointing azimuth keep-out zone. Each cycle takes between several minutes (first deployment) and less than a minute (second and later deployments). </p><p>Measurements are converted from digital counts to (ir)radiance using two laboratory instrument characterisations performed by Tartu Observatory (Estonia) before and after each deployment period. Calibration data for a specific scan are obtained from linear interpolation in time between pre-deployment and post-deployment instrument characterisation. The calibrated scan data are linearly interpolated from the instrument specific wavelengths to a common wavelength grid (355—900 nm, every 2.5 nm). Individual calibrated scans are subjected to quality control as in Ruddick et al. (2006), i.e. scans differing > 25% at 550 nm from their neighbouring scans are rejected. For the Ed measurements, this quality control step takes the change in sun zenith angle between the measurements into account.</p><p>If sufficient calibrated scans are available in the cycle, i.e. >=5/6 𝐸𝑑, >=5/6 𝐿𝑑, >=9/11 𝐿𝑢, the scans are mean averaged and the standard deviation is computed. The water-leaving radiance reflectance (𝜌𝑤, variable name reflectance_nosc) is then computed according to:</p><p>reflectance_nosc = 𝜋/𝐸𝑑 × (𝐿𝑢 - 𝜌𝐹 × 𝐿𝑑)</p><p>where 𝐸𝑑, 𝐿𝑢, and 𝐿𝑑 are the mean averaged values, and 𝜌𝐹 the effective Fresnel correction factor as determined from lookup tables provided by Mobley (1999). Ancillary wind speed is obtained fromthe GDAS1 0.25 degree global model 6 hourly nowcast archive, by interpolation of the model grid in time and space to the measurement average time, and site position. The used wind speed and 𝜌𝐹 are provided in the global attributes of each file.</p><p>In the present dataset, reflectance data are provided with and without a "nosc" suffix, indicating whether a residual correction for the air-water interface reflectance (Ruddick et al. 2005) is performed. The reflectance without the "nosc" suffix uses the Similarity Spectrum (Ruddick et al. 2006) to estimate a spectrally flat residual air-water interface reflectance error (𝜀) using the 720 and 780 nm combination:</p><p>reflectance = 𝜋/𝐸𝑑 × (𝐿𝑢 - 𝜌𝐹 × 𝐿𝑑) - 𝜀</p><p>𝜀 = (𝛼 × 𝜌𝑤 780 – 𝜌𝑤 720) / (𝛼 -1),</p><p>where 𝛼 is the Similarity Spectrum ratio between the two used wavelengths, i.e. 2.35 for 720:780 nm. The 𝜀 value is provided in the global attributes of each file. For this VEIT dataset, the use of reflectance with Similarity Spectrum correction is recommended.</p><p>The reflectance products are further quality controlled using the following criteria:</p><p>1) 𝐿𝑑/𝐸𝑑 at 750 nm < 5%, removing non-clear sky conditions</p><p>2) Variability (coefficient of variation) of water reflectance at 780 nm < 10%, removing highly variable water conditions</p><p>3) Water reflectance > 0 for 350—900 nm, removing spectra with negative reflectance retrievals</p><p>4) NIR water reflectance (840—900 nm) is assumed to be decreasing with wavelength, removing potentially contaminated spectra</p><p>5) Bright water spectra (average VIS reflectance 400—700 nm > 0.07 or average NIR reflectance 780—950 nm > 0.01) have a local maximum at around 810 nm (805—815 nm) due to the local minimum in pure water absorption, removing potentially contaminated spectra</p><p>6) Irradiance measurements in the range 860—885 nm are within 20% of the Gregg and Carder (1990) clear sky model with an aerosol optical depth of 0.1 at normal pressure, removing cloudy, shadowed, or very hazy conditions</p><p><strong>Acknowledgements</strong></p><p>The installation and maintenance of the PANTHYR at AAOT was carried out within the context of the HYPERNETS project funded by the European Union's Horizon 2020 research and innovation programme (Grant agreement n◦ 775983) and of the HYPERNETS-POP project funded by the European Space Agency (contract n◦ 4000139081/22/I-EF). The skipper and crew of the AAOT and R/V Litus and are also acknowledged.</p><p><strong>References</strong></p><p>Gregg, W.W. and Carder, K.L., 1990. A simple spectral solar irradiance model for cloudless maritime atmospheres. Limnology and oceanography, 35(8), pp.1657-1675.</p><p>Mobley, C.D., 1999. Estimation of the remote-sensing reflectance from above-surface measurements. Applied optics, 38(36), pp.7442-7455.</p><p>Ruddick, K., De Cauwer, V. and Van Mol, B., 2005, August. Use of the near infrared similarity reflectance spectrum for the quality control of remote sensing data. In Remote Sensing of the Coastal Oceanic Environment (Vol. 5885, p. 588501). SPIE.</p><p>Ruddick, K.G., De Cauwer, V., Park, Y.J. and Moore, G., 2006. Seaborne measurements of near infrared water‐leaving reflectance: The similarity spectrum for turbid waters. Limnology and Oceanography, 51(2), pp.1167-1179.</p><p>Vansteenwegen, D., Ruddick, K., Cattrijsse, A., Vanhellemont, Q. and Beck, M., 2019. The pan-and-tilt hyperspectral radiometer system (PANTHYR) for autonomous satellite validation measurements—Prototype design and testing. Remote Sensing, 11(11), p.1360.</p>
PANTHYR hyperspectral water reflectance - O1BE
<p><strong>Introduction</strong> </p><p>This dataset contains water-leaving radiance reflectance (𝜌𝑤, variable names reflectance and reflectance_nosc) measurements made by an autonomous Pan and Tilt Hyperspectral Radiometer (PANTHYR, Vansteenwegen et al. 2019) installed at site O1BE. Data are provided in NetCDF format with information on processing settings provided in the NetCDF global attributes. This dataset contains measurements from the first two deployments (Dec. 2019—Aug. 2020 and Feb. 2022—Nov. 2022) that pass quality control, and have bounding calibration information, and ancillary wind speed available. For this site, the use of reflectance without the Similarity Spectrum offset correction (Ruddick et al. 2005, 2006) is recommended.</p><p><strong>Methods</strong> </p><p>A PANTHYR was deployed at RT1 Blue Accelerator Platform (Oostende, Belgium), O1BE, located at 51.2464°N, 2.9193°E for two deployments Dec. 2019—Aug. 2020 and Feb. 2022—Nov. 2022. </p><p>PANTHYR consists of a pair of TriOS RAMSES radiometers, one for measurement of radiance, and one with a cosine collector for measurement of irradiance, with custom control hard- and software, mounted on a pan and tilt head. The RAMSES spectral range is about 350—950 nm in 190 channels. The pan and tilt head allows the orientation of each radiometer in a specific direction. Using the standard protocol, a PANTHYR cycle consists of sequential measurements of downwelling irradiance (𝐸𝑑, 6 replicates), downwelling (sky) radiance (𝐿𝑑, 6 replicates), and upwelling radiance (𝐿𝑢, 11 replicates). Three 𝐸𝑑 and 𝐿𝑑 measurements are performed each before and after the 𝐿𝑢 measurements. Measurement cycles are performed every 20 minutes during daytime, at 90, 135, 225, and/or 270 degrees relative azimuth to the sun to minimize air-water interface reflectance (Mobley 1999, Ruddick et al. 2006). Platform pointing conditions are skipped by the definition of an absolute pointing azimuth keep-out zone. Each cycle takes around a minute to complete.</p><p>Measurements are converted from digital counts to (ir)radiance using two laboratory instrument characterisations performed by Tartu Observatory (Estonia) before and after each deployment period. Calibration data for a specific scan are obtained from linear interpolation in time between pre-deployment and post-deployment instrument characterisation. The calibrated scan data are linearly interpolated from the instrument specific wavelengths to a common wavelength grid (355—900 nm, every 2.5 nm). Individual calibrated scans are subjected to quality control as in Ruddick et al. (2006), i.e. scans differing > 25% at 550 nm from their neighbouring scans are rejected. For the Ed measurements, this quality control step takes the change in sun zenith angle between the measurements into account.</p><p>If sufficient calibrated scans are available in the cycle, i.e. >=5/6 𝐸𝑑, >=5/6 𝐿𝑑, >=9/11 𝐿𝑢, the scans are mean averaged and the standard deviation is computed. The water-leaving radiance reflectance (𝜌𝑤, variable name reflectance_nosc) is then computed according to:</p><p>reflectance_nosc = 𝜋/𝐸𝑑 × (𝐿𝑢 - 𝜌𝐹 × 𝐿𝑑)</p><p>where 𝐸𝑑, 𝐿𝑢, and 𝐿𝑑 are the mean averaged values, and 𝜌𝐹 the effective Fresnel correction factor as determined from lookup tables provided by Mobley (1999). Ancillary wind speed is obtained fromthe GDAS1 0.25 degree global model 6 hourly nowcast archive, by interpolation of the model grid in time and space to the measurement average time, and site position. The used wind speed and 𝜌𝐹 are provided in the global attributes of each file.</p><p>In the present dataset, reflectance data are provided with and without a "nosc" suffix, indicating whether a residual correction for the air-water interface reflectance (Ruddick et al. 2005) is performed. The reflectance without the "nosc" suffix uses the Similarity Spectrum (Ruddick et al. 2006) to estimate a spectrally flat residual air-water interface reflectance error (𝜀) using the 720 and 780 nm combination:</p><p>reflectance = 𝜋/𝐸𝑑 × (𝐿𝑢 - 𝜌𝐹 × 𝐿𝑑) - 𝜀</p><p>𝜀 = (𝛼 × 𝜌𝑤 780 – 𝜌𝑤 720) / (𝛼 -1),</p><p>where 𝛼 is the Similarity Spectrum ratio between the two used wavelengths, i.e. 2.35 for 720:780 nm. The 𝜀 value is provided in the global attributes of each file. For this O1BE dataset, the use of reflectance without Similarity Spectrum correction is recommended.</p><p>The reflectance products are further quality controlled using the following criteria:</p><p>1) 𝐿𝑑/𝐸𝑑 at 750 nm < 5%, removing non-clear sky conditions</p><p>2) Variability (coefficient of variation) of water reflectance at 780 nm < 10%, removing highly variable water conditions</p><p>3) Water reflectance > 0 for 350—900 nm, removing spectra with negative reflectance retrievals</p><p>4) NIR water reflectance (840—900 nm) is assumed to be decreasing with wavelength, removing potentially contaminated spectra</p><p>5) Bright water spectra (average VIS reflectance 400—700 nm > 0.07 or average NIR reflectance 780—950 nm > 0.01) have a local maximum at around 810 nm (805—815 nm) due to the local minimum in pure water absorption, removing potentially contaminated spectra</p><p>6) Irradiance measurements in the range 860—885 nm are within 20% of the Gregg and Carder (1990) clear sky model with an aerosol optical depth of 0.1 at normal pressure, removing cloudy, shadowed, or very hazy conditions</p><p><strong>Acknowledgements</strong></p><p>The Flanders Marine Institute (VLIZ) and POM West-Vlaanderen are thanked for access to the RT1 Blue Accelerator Platform (Oostende, Belgium) and installation support.</p><p><strong>References</strong></p><p>Gregg, W.W. and Carder, K.L., 1990. A simple spectral solar irradiance model for cloudless maritime atmospheres. Limnology and oceanography, 35(8), pp.1657-1675.</p><p>Mobley, C.D., 1999. Estimation of the remote-sensing reflectance from above-surface measurements. Applied optics, 38(36), pp.7442-7455.</p><p>Ruddick, K., De Cauwer, V. and Van Mol, B., 2005, August. Use of the near infrared similarity reflectance spectrum for the quality control of remote sensing data. In Remote Sensing of the Coastal Oceanic Environment (Vol. 5885, p. 588501). SPIE.</p><p>Ruddick, K.G., De Cauwer, V., Park, Y.J. and Moore, G., 2006. Seaborne measurements of near infrared water‐leaving reflectance: The similarity spectrum for turbid waters. Limnology and Oceanography, 51(2), pp.1167-1179.</p><p>Vansteenwegen, D., Ruddick, K., Cattrijsse, A., Vanhellemont, Q. and Beck, M., 2019. The pan-and-tilt hyperspectral radiometer system (PANTHYR) for autonomous satellite validation measurements—Prototype design and testing. Remote Sensing, 11(11), p.1360.</p>
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