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1,709 results for “Reflectivity”
Initial Sample of HYPERNETS Hyperspectral Water Reflectance Measurements for Satellite Validation at the measurement tower MOW1, M1BE site (Belgium)
<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 HYPERNETS site at the measurement pole near the <em>Zeebrugge</em> harbour 3.65km from land, often called MOW1, in Belgium (M1BE). It is a subset of the complete data record which consists of the best quality M1BE measurements which could be used for satellite validation. </p> <p>The provided NetCDF files are the L2A hypernets products with water leaving radiance and reflectances, with and without NIR Similarity Correction (see Ruddick et al., 2006, DOI:<a href="http://dx.doi.org/10.2307/3841124">10.2307/3841124</a>). The reflectance in the L2A products is the Water Reflectance without NIR Similarity Correction (referred to as reflectance_nosc in the file) defined as:</p> <p><span class="math-tex">\(\rho_wnosc=\pi (Lu-\rho_FLd)/E_d\)</span></p> <p>where Lu is the upwelling radiance (at 40° zenith angle, and, 90° or 135° azimuth angle relative to the sun), Ld is the downwelling radiance (at 140° zenith angle, and, 90° or 135° azimuth angle relative to the sun). Ed is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance).</p> <p>For the M1BE site, the reflectance corrected for the NIR Similarity correction (epsilon, see Ruddick et al., 2006) is also provided:</p> <p><span class="math-tex">\(\rho_w=\pi (Lu-\rho_FLd)/E_d-\epsilon\)</span></p> <p>These reflectances have dimensions of wavelength and series, where each series is a set of measurements for the computation of a water reflectance measurement. 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, 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 HYPSTAR®-SR (Standard Range) instruments deployed at each land HYPERNETS site consist of a VNIR sensor and autonomously collect data between 380-1000 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. The hypernets_processor (Goyens et al. 2021, DOI: <a href="https://doi.org/10.1109/IGARSS47720.2021.9553738">10.1109/IGARSS47720.2021.9553738</a>; De Vis et al. in prep.) automatically processes all this data into various products, including the L2A surface reflectance product provided here. The current dataset is limited to the 400-900 nm range. Uncertainties are not yet included.</p> <p>To obtain this dataset, we start from the full M1BE 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 supply the best quality data suitable for satellite validation:</p> <p>1. The coefficient of variation in water reflectance is below 10% in the 500-600 nm range</p> <p>2. The water reflectance at 500 nm is below 0.1</p> <p>The data consists of 73 spectra ranging from 20230226T1431 till 20230429T1502.</p> <p>Coordinates of the site are the following:</p> <p>site_latitude = 51.360548<br> site_longitude = 3.118246</p> <p>The site is owned by Afdeling Kust (https://www.agentschapmdk.be/nl).</p> <p> </p>
Initial Sample of HYPERNETS Hyperspectral Surface Reflectance Measurements for Satellite Validation from the Wytham Woods site in the United Kingdom
<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 Wytham Woods HYPERNETS site in the United Kingdom (WWUK). It is a subset of the complete data record which consists of the best quality WWUK measurements which could be used for satellite validation. </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 WWUK site is a deciduous broadleaf forest comprised primarily of Oak, Hazel, Ash, Sycamore and Beech. It is located approximately 5 km North-West of Oxford, UK and has an extensive history of scientific research. The site follows the typical seasonal dynamics of a temperate forest with distinctive periods of leaf-off, green up and senescence across the growing season. The HYPERNETS site itself (51.777206 degrees N, 1.338494 W), is located at a height of 28 m upon a flux tower in the centre of the forest. The HYPSTAR®-XR sensor was installed in October 2021. Data are collected e very 30 minutes between 9am and 6pm local time between viewing zenith angles of 0 and 30 degrees.</p> <p>The HYPSTAR®-XR (eXtended Range) 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 WWUK 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, two additional screening procedures are developed to remove outliers and only supply the best quality data suitable for satellite validation. For Wytham wood, sequences are only supplied that match a typical vegetation spectrum. As such, data is only provided between April and October during the leaf-on period. Reflectances are then tested against three parameters to check that they are vegetation spectrum. Firstly, that there is a peak in the green portion of the visible wavebands (560 nm). Secondly, that a red edge is detected. Finally, the Normalized Difference Vegetation Index (NDVI) is calculated. Spectra with an NDVI of less than 0.42 are removed from the final data set.</p> <p>After the vegetation quality flag are applied, a sigma-clipping method is used to remove outliers. 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. </p>
Anaktuvuk River fire scar canopy reflectance spectra from the 2008-2014 growing seasons, North Slope Alaska.
The Anaktuvuk River Fire occurred in 2007 on the North Slope of Alaska. In 2008, three eddy covariance towers were established at sites represent ing unburned tundra, moderately burned tundra, and severely burned tundra. During the 2008-2014 growing seasons, canopy vegetation within the footprint of each of these towers was scanned with a handheld spectrophotometer several times throughout the growing season. Average reflectance spectra per site and collection day are presented here.
Vegetation indices calculated from canopy reflectance spectra at four sites along Imnavait Creek, AK during the 2008-2010 growing seasons.
A spectrophotometer was used to scan the canopy vegetation at four sites along Imnavait Creek in the Kuparuk Watershed near Toolik Lake LTER, Alaska. The resulting reflectance spectra were used to calculate average vegetation indices for each site and collection day.
Effects of a tropical stream poisoning: do they reflect effects of small-scale experiments?
Small-scale experiments in tropical streams have suggested that freshwater shrimps play a critical role in determining the quality and quantity of benthic organic matter and overall nutrient dynamics. We quantified the effects of a whole-reach shrimp poisoning event in the Sonadora, a second-order stream draining the Luquillo Experimental Forest in northeastern Puerto Rico. The illegal poisoning (for shrimp harvest) caused massive mortality of shrimps and aquatic insects. Atyid and xiphocaridid shrimp abundances in pools of the poisoned reach were reduced by ~95%, relative to abundances in an upstream reference reach. A survey of poisoned vs. reference pools, combined with a manipulative experiment (in which atyid and xiphocaridid shrimps were added to 3 poisoned pools), showed that reduced shrimp abundances due to the poisoning had strong impacts on benthic resources. The benthos of poisoned pools, where shrimp abundances were reduced, had 4 times more chlorophyll a, 6 times more algal biovolume, 4 times more fine particulate organic matter, 14 times more fine particulate inorganic matter, 5 times more carbon, and 4 times more nitrogen than did the benthos of pools in the reference reach. These increases in benthic resources were consistent with increases in algae, organic/inorganic matter, and nutrients in previous small-scale shrimp exclusion experiments conducted in the study river and tributaries. Effects of shrimp poisoning on the benthos varied by habitat, with riffles showing fewer significant differences than did pools. Compared to reference riffles, poisoned riffles had higher standing stocks of fine particulate inorganic matter, nitrogen, and biovolume of filamentous algae, and lower epilithic C:N ratios. Overall, previous small-scale exclusion experiments were highly predictive of the direction of effects due to large-scale shrimp removal by poisoning. Our study provides a tropical data set to add to the short list of stream studies that examine the pred
Field-Collected Spectral Reflectance of Dominant Vegetation at the Sevilleta National Wildlife Refuge
This dataset includes field-collected spectral reflectance of dominant vegetation species in grassland and shrubland at the Sevilleta National Wildlife Refuge collected monthly May – September 2019. A spectroradiometer was used to collect the percent spectral reflectance of electromagnetic radiation (range 400-2500nm) of a sample of dominant vegetation species ("spectra"), yielding a spectral curve for each species. At least ten individuals per species were sampled. These data form a spectral library which was used to calibrate a multiple-endmember spectral mixture analysis (MESMA) of satellite imagery of the Sevilleta NWR, as part of an ongoing collaboration between the LTER and the Center for the Advancement of Spatial Informatics Research and Education (ASPIRE). Ultimately, we aim to produce fractional images of green vegetation, non-photosynthetic vegetation, bare soil, and shade to form a synoptic thirty-year record of vegetation dynamics at the Refuge. The spectral library can be referenced by future researchers using remote sensing methods to examine vegetation dynamics at the Sevilleta NWR.
Non-personalized HRIR databases with and without floor reflections
<p>Non-personalized HRIR databases in SOFA format [1] with and without floor reflections. Floor reflections were simulated with a plywood board between a Head-And-Torso Simulator (HATS) and a dodecahedral loudspeaker. These recordings were captured at the anechoic chamber of the University of Aizu.</p> <p><strong>Apparatus</strong></p> <ul> <li><strong>Head and Torso Simulator (HATS):</strong> 5128-C (Brüel & Kjær—B&K, Denmark).</li> <li><strong>Preamplifier:</strong> NEXUS preamplifier (B&K).</li> <li><strong>Audio interface:</strong> Babyface (RME, Germany).</li> <li><strong>Software used:</strong> ScanIR [2].</li> <li><strong>Loudspeaker:</strong> Self-built regular dodecahedral loudspeaker (7.2 kg). This could be circumscribed by a sphere of 25 cm in diameter. Drivers (P800K—FOSTEX, Japan) were attached to 3 mm acrylic plates.</li> <li><strong>Audio source: </strong>A one-second sine-sweep tone sampled at 96 kHz generated with ScanIR.</li> <li><strong>Floor simulation: </strong>Plywood board 181x91x1.2 cm weighing 13 kg (density ρ = 658 kg/m3 i.e., a relatively firm board).</li> <li><strong>Locations:</strong> 72 azimuths from 0º to 355º in steps of 5º (counterclockwise measured) with a combination of elevation φ = [±60º, ±30º, 0º] at a distance of 153 cm from the center of the HATS’ head to the center of the loudspeaker.</li> </ul> <p>Other details on the procedure and how this was used in our research are found in [3]. HRIRs were capture with and without the plywood board. they are called here “echoic” and “anechoic,” respectively. In addition to the original sampling rate, we include here resampled versions at 44.1 and 48 kHz.</p> <p><strong>Filenames</strong></p> <p>For both anechoic and echoic databases, download:<br> AizuEle@[<em>sample rate</em>].zip</p> <p>Other SOFA files:<br> AizuEle[<em>XXX</em>]@[<em>sample rate</em>].sofa, replace ‘<em>XXX</em>’ with ‘WIF’ for echoic recordings and with ‘WOF’ for anechoic ones.</p>
Forward-modelled reflectance from spring and summer Baltic Sea specific inherent optical properties
<p>An extensive dataset of remote-sensing reflectance (R<sub>rs</sub>, units sr<sup>-1</sup>) spectra based on forward modelling of mean concentration-specific inherent optical properties (SIOPs) for both spring and summer optical conditions in the open Baltic Sea. The spectra are modelled using Hydrolight 5.2 for a wide range of Chlorophyll-a (Chla), Coloured Dissolved Organic Matter (CDOM), and Total Suspended Matter (TSM) concentrations as well as solar and viewing angles. The primary aim of providing this supplementary dataset is to aid evaluation of remote sensing algorithms for the Baltic Sea in future studies.</p>
XUV spectrum generated via HHG in neon, reflected by multilayer mirror
<p>XUV spectra with spatial resolution are generated via High Harmonic Generation in neon filled cell. The conditions are optimized for high XUV yield in the spectral region of interest (bandwidth of ∼6 eV FWHM around 94.4 eV).</p> <p>A laser pulse of 0.25 mJ energy, about 6 fs of duration and centered at 800 nm is focused by 50 cm focal length mirror in a gas cell of 2.5 mm length. The generated XUV beam is then focused by a multilayer Mo/Si mirror (bandwidth of ∼6 eV FWHM around 94.4 eV) into a krypton cell (1 mm long). The transmitted XUV spectra are then diffracted by a flat-field XUV concave grating with 1200 grooves per mm (Hitachi 001-0640) and acquired with a XUV camera model PI-SX:400 manufactured by Princeton Instruments. There is also a slit < 0.5 mm that is imaged by the XUV grating to the XUV camera.</p> <ul> <li>HHG_Ne is a spectrogram of XUV with the krypton cell evacuated.</li> <li>HHG_Ne_in_Kr is a spectrogram of XUV with the krypton cell filled. One can observe krypton absorption lines.</li> <li>HHG_lines is a resulting Kr absorption spectral lines with assigned shells. 5p denotes excitation to 5p, term 5/2 3/2, while 5p' denotes 5p, term 3/2 1/2.</li> </ul>
IODP Expedition 391 Color reflectance
Color reflectance data were measured on section halves using an integration sphere and a UV-VIS spectrophotometer mounted on the Section Half Multisensor Logger (SHMSL). Spectral counts are recorded in the range of 380 to 700 nm, covering the visible spectrum, and binned in ~2 nm bins. Spectral data are reduced from spectra and recorded in tristimulus XYZ values, CieLAB L*a*b* values, and other units.
IODP Expedition 397T Color reflectance
Color reflectance data were measured on section halves using an integration sphere and a UV-VIS spectrophotometer mounted on the Section Half Multisensor Logger (SHMSL). Spectral counts are recorded in the range of 380 to 700 nm, covering the visible spectrum, and binned in ~2 nm bins. Spectral data are reduced from spectra and recorded in tristimulus XYZ values, CieLAB L*a*b* values, and other units.
Ostwald_colour-atlas_reflectance-measurements
<p>This repository contains the results of visible reflectance spectroscopy that have been performed on three colour atlases in the 1920s made by Verlag Unesma under the supervision of Wilhelm Ostwald. These colour atlases are currently held at the Rijksmuseum Research Library in Amsterdam under the following inventory numbers (339D32 / 339D33 and GF386G3). </p><p>The atlases are physical representations of the colour space developed by Wilhelm Ostwald in the 1910s. They are composed of hundreds of small swatches of paint, where each one represents a specific colour and can be characterised by a hue number (ranging from F01 to F24) and a two-letter code that indicates the position of the swatch in the Ostwald colour space. </p><p>In addition to photographs stored in the zip file, each colour swatch were measured three times with a spectrophotometer from Konica Minolta (CM-2600d), where the UV radiation had been cut-off. Subsequently, the mean and standard deviation were calculated and stored inside the Ostwald_DB.csv file. The Lab values were calculated with the help of the Colour Science python package (https://www.colour-science.org/) according to a 10° observer and a D65 illuminant.</p><p>A Jupyter notebook along with a python script have been created to help users to manipulate the data contains in the Ostwald_DB.csv file.</p>
IODP Expedition 383 Color reflectance
Color reflectance data were measured on section halves using an integration sphere and a UV-VIS spectrophotometer mounted on the Section Half Multisensor Logger (SHMSL). Spectral counts are recorded in the range of 380 to 700 nm, covering the visible spectrum, and binned in ~2 nm bins. Spectral data are reduced from spectra and recorded in tristimulus XYZ values, CieLAB L*a*b* values, and other units.
IODP Expedition 378 Color reflectance
Color reflectance data were measured on section halves using an integration sphere and a UV-VIS spectrophotometer mounted on the Section Half Multisensor Logger (SHMSL). Spectral counts are recorded in the range of 380 to 700 nm, covering the visible spectrum, and binned in ~2 nm bins. Spectral data are reduced from spectra and recorded in tristimulus XYZ values, CieLAB L*a*b* values, and other units.
Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P75 (2019): Reflectances bands, NDVI and NDWI
<h2><strong>Data information</strong></h2> <p>This dataset provides the P75 (percentile 75) values of corresponding predictors for the year 2019. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI.</p> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P75 (2020): Reflectances bands, NDVI and NDWI
<h2><strong>Data information</strong></h2> <p>This dataset provides the P75 (percentile 75) values of corresponding predictors for the year 2020. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI.</p> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P75 (2011): Reflectances bands, NDVI and NDWI
<h2><strong>Data information</strong></h2> <p>This dataset provides the P75 (percentile 75) values of corresponding predictors for the year 2011. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI.</p> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P75 (2008): Reflectances bands, NDVI and NDWI
<h2><strong>Data information</strong></h2> <p>This dataset provides the P75 (percentile 75) values of corresponding predictors for the year 2008. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI.</p> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P75 (2002): Reflectances bands, NDVI and NDWI
<h2><strong>Data information</strong></h2> <p>This dataset provides the P75 (percentile 75) values of corresponding predictors for the year 2002. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI.</p> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P75 (2005): Reflectances bands, NDVI and NDWI
<h2><strong>Data information</strong></h2> <p>This dataset provides the P75 (percentile 75) values of corresponding predictors for the year 2005. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI.</p> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.