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303 results for “hyperspectral”
Spectral data for direct detection and quantification of phycocyanin in sediments by hyperspectral imaging: Spiking Session 2 - Phycocyanin & Chlorophyll a
<p>Accompanying data to publication: <strong>Direct detection and quantification of phycocyanin in sediments by hyperspectral imaging</strong></p> <p>Hyperspectral data with all their processing steps - normalization, ROI, subsets, masking - resulting in RABD indices of Chlorophyll a and Phycocyanin pigments from spiking experiments on sediments.</p>
A dataset including 2-year eddy covariance and hyperspectral data at Yunxiao mangrove flux tower
<p>A dataset including 2-year eddy covariance and hyperspectral data at Yunxiao mangrove flux tower. The data is used for producing key findings in a manuscript under review.</p>
Hyperspectral Imaging of cake
<p>Excerpt of a hyperspectral image acquisition of a cake, including noramlization data. Used in the napari-sediment widget as an example dataset.</p>
Data from: Hyperspectral imaging reveals differential carotenoid and chlorophyll temporal dynamics and spatial patterns in Scots pine under water stress
<p>Data and codes associated with the manuscript '<span>Hyperspectral imaging reveals differential carotenoid and chlorophyll temporal dynamics and spatial patterns in Scots pine under water stress</span>'. </p>
Transmittance hyperspectral images of microalgae on well plates
<p>Images are stored in folders whose name indicate the imaging date as yyyy_mm_dd. These folders contain folders with an ID given by the imager (SpecimIQ, Specim, Finland). Inside these are the white and dark references and raw radiance images (folder: capture), transmittance images (REFLECTANCE, in file names, but transmittance in practice due to the imaging in transmission light) calculated by the SpecimIQ (folder: results) and information about the images (metadata.txt). Each rolling ID-folder contain also an RGB image of the target in png format.</p>
Selected clear-sky FloX hyperspectral data
<p>Zip - Folders containing calibrated reflectance, reflected radiance and incoming radiance retrieved from measurements with FloX field spectrometers over Alfalfa (2018 in Grosseto, Italy) and grass (2018 in Julich, Germany) during clear sky conditions. The incoming radiance is measured with optics of hemispherical (~180°) field of view and the reflected radiance with conical (~25°) field of view.</p> <p>The data is organized in sub-folders inside the zip-repositories which are named according to the date of recording, containing CSV-files which hold the hyperspecteral information. One file is associated to the integrated full-range (FULL) spectrometer between 340nm and 1000nm, and the fluorescence-range (FLUO) spectrometer between 650nm and 810nm, containing incoming radiance, reflected radiance or reflectance, respectively. The first collumn contains the associated central wavelengths, the first row the time of recording, each subsequent field of the table contains the calibrated radiance (in W m<sup>-2</sup> sr<sup>-1</sup> nm<sup>-1</sup>) or reflectance (unitless) values.</p>
Snow hyperspectral measurements in Terra Nova Bay (Antarctica) and supplementary materials
<p>SISpec is a database containing spectroradiometric, snow and ancillary (environmental and meteorological) data acquired in polar environments. The project is the result of the co-operation of different expertise, and its main objective is to contribute to the knowledge of the interaction between microphysics characteristic of the snow cover and its reflection properties of the solar incident radiation and to study glacial environment and particularly to monitor the snow/ice covers by multispectral remote sensing data. Field surveys were performed in Antarctica, in the region where the Italian research station of Terra Nova Bay is located, the climatic characteristics and the low human impact allow to study snow/ice surfaces without impurities and with different characteristics with respect to those of the Arctic and the Alpine regions, where seasonal melting of the snow cover occur.</p>
Event-based hyperspectral EELS: towards nanosecond temporal resolution
<p>Here we present the two data sets presented in the work <a href="https://arxiv.org/abs/2110.01706">Event-based hyperspectral EELS: towards nanosecond temporal resolution</a>. Data was processed using Rust.</p> <p>In ASI Cheetah Timepix3, we have two different kinds of events: electron and TDC events (little-endian). Data chunk package is a 8-byte data that begins with "TPX3". Electron hit is a 8-byte packet that contains "0xb" in 60-63 bits. TDC data packet is a 8-byte packet that contains "0x6" in 60-63 bits. 56-59 bits identify if it is comes from TDC Line 1 or 2 and also identifies if it is a falling or a rising edge.</p>
Data Set: Hyperspectral image unmixing with LiDAR data-aided spatial regularization
<p>Data set and matlab codes used for the experimental section of "Hyperspectral Image Unmixing With LiDAR Data-Aided Spatial Regularization"</p> <p>T. Uezato, M. Fauvel and N. Dobigeon, "Hyperspectral Image Unmixing With LiDAR Data-Aided Spatial Regularization," in <em>IEEE Transactions on Geoscience and Remote Sensing</em>, vol. 56, no. 7, pp. 4098-4108, July 2018.<br> doi: 10.1109/TGRS.2018.2823419<br> URL: <a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8347066&isnumber=8393475">http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8347066&isnumber=8393475</a><br> </p>
Hyperspectral imaging of exciton confinement within a moiré unit cell with a subnanometer electron probe
<p>Data and processing notebooks for article titled "Hyperspectral imaging of exciton confinement within a moiré unit cell with a subnanometer electron probe" published in Science in Decembner 2022.</p> <p>The `ADF Image.dm4` is the simultaneously acquired annular dark field (ADF-) scanning transmission electron microscopy (STEM) image. This was used to determine the structural reconstruction of the WSe2 / WS2 heterostructure.</p> <p>The `EELS Spectrum Image.dm4` is a spectrum image (one spectra per probe position). This was used to determine the extent of the localization of the exciton peak I.</p> <p>The two `ipynb` jupyter notebooks were used to do the analysis shown in the paper. The output is embedded in the notebooks. Also, each notebook was run and then exported as a static HTML file to retain the code and output together in a generally readable format.</p> <p>Contact Peter Ercius (percius@lbl.gov) regarding the code or data.</p>
Active and low-cost hyperspectral imaging for spectral analysis in low lighting environment
<p>Hyperspectral imaging can capture information beyond conventional RGB cameras; thus, it has many applications, such as material identification and spectral analysis. However, like many camera systems, most of the existing hyperspectral cameras are still passive imaging systems: they require external light sources to illuminate the objects to capture the spectral intensity. As a result, the collected images highly depend on the environment lighting, and the imaging system cannot function in a dark or low-lighting environment. This work develops a prototype system for active hyperspectral imaging, which actively emits different single-wavelength lights at different frequencies when imaging. This concept has several advantages: first, using the controlled lighting, the magnitude of the individual bands is normalized to extract reflectance information; second, the system is capable of collecting information at the desired spectral range by tailoring the light sources; third, an active system is mechanically easier to make, since it does not require complex band filters as used in passive systems; last, such a system may work under low light or dark environments, which greatly facilitate underground/subsurface sensing applications such as borehole based mining exploration. This prototype is achieved by using an array of low-cost and single-wavelength LED (Light Emitting Diode) lights, a remote control module controlling the LED illuminator, and the shutter of a full spectrum camera. We demonstrate that such design is feasible and could yield informative hyperspectral images for spectral analysis and machine learning-based object identification in low light or dark environments, having great potential to benefit both the academic and industry such as in geochemistry, earth science, subsurface energy, and mining.</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>
Data, research code, and metadata related to the Soils of the Upper Part of Itatiaia National Park (INP) for Pedology, Hyperspectral Soil Mapping, and Spectral studies
<p>Data, research code (in R language), and metadata related to the Soils of the Upper Part of Itatiaia National Park (INP) for Hyperspectral Soil Mapping</p> <p>Up to this point, this data, and code were used in the Doctoral thesis of Elias Mendes Costa and Yuri Andrei Gelsleichter, and the following publications.</p> <p>------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>Dados, código de pesquisa (em linguagem R) e metadados relacionados aos Solos e ao Mapeamento da Parte Superior do Parque Nacional de Itatiaia (PNI) para o Mapeamento Hiperespectral de Solos</p> <p>Até este momento, esses dados, e códigos foram utilizados nas teses de doutorado de Elias Mendes Costa e de Yuri Andrei Gelsleichter e nas seguintes publicações.</p> <p> </p> <p>Gelsleichter, Y. A., Costa, E. M., dos Anjos, L. H. C., & Marcondes, R. A. T. (2023). Enhancing soil mapping with hyperspectral subsurface images generated from soil lab vis-SWIR spectra tested in southern Brazil. Geoderma Regional, e00641.<br> doi: https://doi.org/10.1016/j.geodrs.2023.e00641<br> link: https://www.sciencedirect.com/science/article/pii/S2352009423000378</p> <p>Costa, E. M., Pinheiro, H. S. K., Anjos, L. H. C. dos, Marcondes, R. A. T., & Gelsleichter, Y. A. (2020). Mapping soil properties in a poorly-accessible area. Revista Brasileira de Ciência Do Solo, 44.<br> doi: https://doi.org/10.36783/18069657rbcs20190107<br> link: https://www.rbcsjournal.org/article/mapping-soil-properties-in-a-poorly-accessible-area/<br> <br> Costa, E. M., dos Anjos, L. H. C., Pinheiro, H. S. K., Gelsleichter, Y. A., & Marcondes, R. A. T. (2020). Spatial Bayesian belief networks: A participatory approach for mapping environmental vulnerability at the Itatiaia National Park, Brazil. Environmental Earth Sciences, 79, 1–13.<br> doi: https://doi.org/10.1007/s12665-020-09099-9<br> link: https://link.springer.com/article/10.1007/s12665-020-09099-9#citeas</p> <p>The code is arranged to deliver the outputs in their respective folders.</p>
Hyperspectral Placenta Dataset: Hyperspectral Image Acquisition, Annotations, and Processing of Biological Tissues in Microsurgical Training
<p>The dataset consists of 101 hyperspectral images of four fresh human placentas and six hyperspectral images of contrast dyes (i.e., indocyanine green and red and blue food colorant) that were captured in the range 515-900 nm, step = 5 nm. The hyperspectral images were manually annotated, delineating the key anatomical structures: arteries, veins, stroma, and the umbilical cord. Standard reference materials were used for flat-field correction. The dataset can be used to develop machine learning algorithms for the automated classification of biological structures, particularly the classification of superficial and deep vessels and transparent tissue layers.</p>
Hyperspectral dataset: Ceramic tiles in dry and underwater setting
<p>The objective of this experiment was to observe the changes in spectral information of a calibration tile when placed underwater. For this purpose, the SPECIM IQ hyperspectral camera was used which operates in Visible and near infra-red regions (400-1000nm) with a spectral resolution FWHM of 7nm resulting in 204 bands in a data cube.</p> <p>To establish a baseline for comparison, data was acquired before submerging the calibration tile into water. Then the tile was immersed in water and data was recorded under two settings i.e., outdoor and indoor. The latter provided a controlled environment where a halogen lamp was used as the light source. Lastly, the tile was removed and data was captured both outside and inside once it had dried off. The data was annotated to enable comparison of similar data points under different conditions.</p> <p> </p>
Forestry and Biodiversity monitoring in Lithuania with hyperspectral camera and UAV
<p>Acquisition dates: to be updated.</p> <p>Location: Scots pine and mixed forest in Lithuania</p> <p>Camera data: </p> <p>Spectral Range 400 – 1000 nm</p> <p>Spectral sampling 2.68 nm</p> <p>Spectral resolution 5.5 nm</p> <p>Fore lens focal length 15 mm</p> <p>Field of view 38 deg</p> <p>Spectral bands 224</p> <p>Spatial pixels 1024</p> <p>Flight altitude: 70 m</p> <p>Spatial resolution: 0.05 m/pixel</p> <p> </p> <p> The dataset consists of pine tree forest hyperspectral imaging data acquired with a UAV on several dates. </p> <p>The data from each UAV flight are given as a separate dataset. </p> <p>Each dataset consists of raw and processed hyperspectral imaging data. The raw data include calibration images of white reference and dark background, raw hyperspectral images, and information on the UAV flight path. </p> <p>TheSPECIM CaliGeoPRO software was used to process raw images into hyperspectral data cubes, which are provided in the format ENVI standard. </p> <p><strong>Each flight data will come as a separate hyperlink to the storage.</strong></p> <p><strong>zip file structure (folders):<br> calibration - holds the radiometric calibration ENVI type file (raster of size 1x1024)<br> capture - raw camera capture data, navigation files, log file.<br> metdata, results - config and empty folder<br> out - holds generated ENVI data cube raster file.</strong></p> <p><strong>Download:</strong></p> <p><a href="https://icaerus-data-1.s3.eu-central-1.amazonaws.com/Uzkresti_miskai_new_fl7_20230510_151005.zip">https://icaerus-data-1.s3.eu-central-1.amazonaws.com/Uzkresti_miskai_new_fl7_20230510_151005.zip</a></p>
emmaschiavon/hyperspectral-user-requirements: hyperspectral-user-requirements
<p>In this repository you will find data elicited from Italian and the NASA Surface Biology and Geology (SBG) Designated Observable users. the first page "read me first" provides a description of the first sheet "User requirements merged" where you will find the reqirements codified for both community of users.</p> <p>Other files include the codes used throug the R software to develop several useful figures.</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>
Image dataset: Applicability of hyperspectral imaging during salinity stress in rice for tracking Na+ and K+ levels in planta
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