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1,709 results for “Reflectivity”
Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2000-07-01/2000-08-31): Reflectance bands
<h2><strong>Data Information</strong></h2> <p>This dataset includes seven reflectance bands: blue, green, red, nir, swir1, swir2, and thermal, for the period 2000-07-01/2000-08-31.</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 - Bimonthly predictor (2022-11-01/2022-12-31): Reflectance bands
<h2><strong>Data Information</strong></h2> <p>This dataset includes seven reflectance bands: blue, green, red, nir, swir1, swir2, and thermal, for the period 2022-11-01/2022-12-31.</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 - Bimonthly predictor (2000-11-01/2000-12-31): Reflectance bands
<h2><strong>Data Information</strong></h2> <p>This dataset includes seven reflectance bands: blue, green, red, nir, swir1, swir2, and thermal, for the period 2000-11-01/2000-12-31.</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 - Bimonthly predictor (2000-01-01/2000-02-28): Reflectance bands
<h2><strong>Data Information</strong></h2> <p>This dataset includes seven reflectance bands: blue, green, red, nir, swir1, swir2, and thermal, for the period 2000-01-01/2000-02-28.</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 - Bimonthly predictor (2022-01-01/2022-02-28): Reflectance bands
<h2><strong>Data Information</strong></h2> <p>This dataset includes seven reflectance bands: blue, green, red, nir, swir1, swir2, and thermal, for the period 2022-01-01/2022-02-28.</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 - Bimonthly predictor (2022-07-01/2022-08-31): Reflectance bands
<h2><strong>Data Information</strong></h2> <p>This dataset includes seven reflectance bands: blue, green, red, nir, swir1, swir2, and thermal, for the period 2022-07-01/2022-08-31.</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 - Bimonthly predictor (2022-09-01/2022-10-31): Reflectance bands
<h2><strong>Data Information</strong></h2> <p>This dataset includes seven reflectance bands: blue, green, red, nir, swir1, swir2, and thermal, for the period 2022-09-01/2022-10-31.</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 - Bimonthly predictor (2000-03-01/2000-04-30): Reflectance bands
<h2><strong>Data Information</strong></h2> <p>This dataset includes seven reflectance bands: blue, green, red, nir, swir1, swir2, and thermal, for the period 2000-03-01/2000-04-30.</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>
Dataset for "Reflectance spectra of seven lunar swirls examined by statistical methods: A space weathering study"
<p>This archive corresponds to the source code, raw data, and results described in the article "Reflectance spectra of seven lunar swirls examined by statistical methods: A space weathering study" by Chrbolková et al. (2019) published in Icarus journal. See AA_README.txt for more information.</p>
First spectral Reflectance Dataset of Equisetum hyemale (Snake grass) Invasive Alien Plant
<p><em><span>This repository contains the first spectral reflectance dataset of <span>snakegrass</span> (Equisetum hyemale) invasive alien species recorded in South Africa. Spectral reflectance measurements were collected under lab conditions using the Spectral Evolution PSR-300 full-range spectrometer. Spectral pre-processing was performed in R statistical software to remove noisy spectra and regions and perform averaging per sample (code accessible: https://github.com/mkganyago/SpectralEvolutionFileReader).<br></span></em></p>
Multichannel Seismic Reflection Data from RV Pelagia during cruise 64PE-445 (SALTAX project)
<p>We present digital multichannel seismic reflection data from the central Red Sea. They were collected on RV Pelagia during cruise 64PE-445 as part of the SALTAX project (Augustin et al., 2019). A Delta Sparker system with 6 kJ and a dominant frequency of ~300 Hz was used as the seismic source. Seismic energy was recorded using a Microeel solid-state streamer with 24 channels and a length of 100 m. Data processing was carried out using VISTA software and comprised trace-editing, simple frequency filtering (50–2000 Hz), normal moveout correction (1500 m/s), common mid-point stacking, finite-difference post-stack migration, as well as top-muting and white noise removal. Interpretation of the seismic data was carried out using the KingdomSuite software of IHS.</p>
IODP Expedition 398 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.
REFLECTION CHARACTERISTICS OF RECONFIGURABLE INTELLIGENT SURFACES
<p>This dataset consists of 2 CSV files containing measurement results of the RIS reflectivity characteristics and 1 JSON file.</p> <p>The file <strong>24_04_ch_ka_3D_5_5Ghz_1_5m.csv</strong> consists of 5 columns of data. The first column contains the elevation angle, the second column contains the azimuth angle. The third column provides the number of the selected pattern – descriptions of the used patterns can be found in the file <strong>RIS_patterns_description.json</strong>. The fourth column contains the frequency at which the measurement was conducted, in Hertz.The fifth column contains the received power level measured at the receiving antenna in dBm.</p> <p> The structure of the file <strong>2D_2RIS_1_5m_16_06.csv</strong> is very similar but does not include the first column with the elevation angle.</p>
IODP Expedition 355 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.
GF1 WFV Surface Reflectance product
<p>GF1 WFV SR products are generated by the SR retrieval procedure (AirSR) and the GF1 WFV images received by the remote sensing satellite ground station of China. AirSR adopts a per-pixel atmospheric correction method based on the 6S radiative transfer model and MODIS AOD (Aerosol Optical Depth) spatial fusion. The MODIS AOD products used include MOD08D3, MOD09CMA, and MCD19A2, which are spatially fused using the Universal Kriging Method (UK) to obtain a complete coverage of AOD data. The required atmospheric water vapor content and ozone data are from MCD19A2 and MOD09CMG products, which can be downloaded from the MODIS product website (https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/). AirSR also considers the influences of solar zenith angle and satellite observation zenith angle.</p> <p>contacts: zhangzhaoming@aircas.ac.cn/zhangzm@radi.ac.cn</p>
IODP Expedition 356 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 353 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 359 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.
Development of a diffuse reflectance probe for in situ measurement of inherent optical properties in sea ice
<p>Included are the data presented in the publication entitled: <em>Development of a diffuse reflectance probe for in situ measurement of inherent optical properties in sea ice</em> accepted for publication in The Cryosphere Journal (2021). The data set includes Data and codes:</p> <p>1. Data (duplicated in .xlsx and .mat):</p> <p> </p> <p>1.1 Sites coordinates- (figure 5) -Geolocalisation of both sea ice sampling sites visited for this study (1 and 4)</p> <p> </p> <p>1.2 cumu_sg- (figure 6)- cumulative signal vs depth vs source-detector distance vs scattering coefficient obtained with Monte Carlo simulations</p> <p> —cumu_sg- cumulative signal (%)</p> <p> — depth (mm)</p> <p> —standard deviation on depth where signal is cumulated</p> <p> —ddet (mm)- radial distance between source and detection point </p> <p> — b (m^-1)-scattering coefficient</p> <p> </p> <p>1.3 validation-(figure 7)- Error on IOPs vs IOP value estimated measuring on microspheres solutions </p> <p> </p> <p>—vf (-)- microspheres volume fraction (in water)</p> <p> —a_theo (m^-1) - theoretical value of the absorption coefficient</p> <p> — mean_error_a(%) - error between theoretical value and measured value</p> <p> —std_error_a_x (%) - standard deviation on theoretical value (based on the standard deviation on microspheres diameter)</p> <p> —std_error_a_y (%) -standard deviation on error_a </p> <p> —rb_theo (m^-1) - theoretical value of the reduced scattering coefficient</p> <p> —mean_error_rb(%) - error between theoretical value and measured value</p> <p> —std_error_rb_x (%) - standard deviation on theoretical value (based on the standard deviation on microspheres diameter)</p> <p> —std_error_rb_y (%)) -standard deviation on error_rb </p> <p> —gamma_theo (-) - theoretical value of gamma</p> <p> —mean_error_gamma (%) - standard deviation on theoretical value (based on the standard deviation on microspheres diameter)</p> <p> —std_error_gamma (%) - standard deviation on error_gamma</p> <p> </p> <p>-1.4 T-S-(figure 8)- Vertical profiles of temperature and bulk salinity of sampled sea ice available at both snow covered site 1 and bare ice site 4</p> <p> </p> <p> —T (celsius) - ice temperature</p> <p> —S_si (ppt) - ice bulk salinity</p> <p> —depth (cm)</p> <p> </p> <p>1.5 Rmes-(figure 9)-Vertical profiles of spatially resolved diffuse Reflectance in sea ice using different covers to shade available at both snow covered site 1 and bare ice site 4</p> <p> </p> <p> —Rmes (-) - spatially resolved diffuse Reflectance</p> <p> —Rmes_nbg (-) - spatially resolved diffuse Reflectance with no background sunlight subtraction in calculation of Rmes</p> <p> —dmes (mm) - distance between source and detecting fibre (named rho in the paper)</p> <p> —depth (cm)</p> <p> — cover - cover used to shade from the sun: te=tent,nc= no cover, ta=tarp</p> <p> </p> <p>1.6 IOPprofiles-(figure 9)-Vertical profiles of reduced scattering coefficient in sea ice using different covers to shade available at both snow covered site 1 (ice+snow) and bare ice site 4</p> <p> </p> <p> —infferedrb (m^-1) - reduced scattering coefficient</p> <p> —infferedrb_nbg (m^-1) - reduced scattering coefficient with no background sunlight subtraction in calculation of Rmes</p> <p> —cr1 (binary)— criteria determining if the measurement is kept or not</p> <p> —depth (cm)- depth from the surface . **watch out** at site 1 , the measurments start from the surface of the snow. Substract 24 cm to get measurement from surface of the ice.</p> <p> — cover - cover used to shade from the sun: te=tent,nc= no cover, ta=tarp</p> <p> </p> <p>2. Code (written in .m with MATLAB_R2018b ®) :</p> <p> </p> <p>2.1 inversion algorithm—(figure 9 ) — used to find rb from Rmes (dmes) vertical profiles in sea ice</p> <p> </p> <p>— Main_vprofiles_Rtorb-qik2019_article.m - Main script of the inversion alorithm to get rb from Rmes (dmes)</p> <p>—importfiledata.m-subfunction to import data from .csv </p> <p>—importfiledatamay8.m-subfunction to import data from .csv (specific to may 8th because file was corrupted)</p> <p>—interp1lookup_HR_enlarged_bin10.mat - lookup table of Reflectance vs dmes vs a vs b’ vs gamma used in the inversion</p> <p>—calibjune6_ha_interp1_indcalib2.mat - calibration factor with microspheres as a reference</p> <p>—site1_c20-picture of the ice core taken at site 1</p> <p>—site4_c20-picture of the ice core taken at site 4</p> <p>—may8th+othertests_fixed.csv-raw data from may 8 (site1)</p> <p>—may9day3.csv-raw data from may 9 (site4)</p> <p> </p> <p> </p>
Baltic Sea shipborne Hyperspectral Reflectance data from 2016
<p>Hyperspectral Remote-sensing reflectance data collected by the Finnish Environment Institute (SYKE) within the BONUS FerryScope project, analysed (quality checks and spectral filtering) at the Plymouth Marine Laboratory. Methods initially described in:</p> <p>The data were collected from merchant vessels Finnmaid (Finnlines) and Transpaper (Transatlantic). This data set is limited to records for the year 2016. </p> <p>Field data collection and processing:</p> <p>Simis, S.G.H., & Olsson, J. (2013). Unattended processing of shipborne hyperspectral reflectance measurements. Remote Sensing of Environment, 135, 202–212</p> <p>Data quality control: </p> <p>Qin, P., Simis, S.G.H., & Tilstone, G.H. (2017). Radiometric validation of atmospheric correction for MERIS in the Baltic Sea based on continuous observations from ships and AERONET-OC. Remote Sensing of Environment, 200, 263-280</p> <p> </p> <p>Data specification</p> <p>lat, lon - geographical latitude/longitude coordinates in decimal degrees</p> <p>time - timestamp (date+time) in UTC following ISO 8601 notation. </p> <p>(The location and time fields correspond to the start of a measurement)</p> <p>Rrs_001_3233 .... Rrs_193_9536 - Remote-sensing reflectance (Rrs, units 1/sr). The sequential numbering (1-193) denotes band number, the last term is wavelength x 10 in nm. For example Rrs_001_3233 is the 1st band centred at 323.3 nm. The wavebands approximate the native resolution of the 3-sensor system (TriOS Ramses ARC + ACC units) used to collect radiance and irradiance spectra of the sea surface and sky. </p> <p>Contributions:</p> <p>Stefan Simis, Jenni Attila, Mikko Kervinen, Kari Kallio, Sampsa Koponen, Sakari Väkevä maintained the in situ system.</p> <p>Stefan Simis developed the code to process the (ir)radiance data to Remote-sensing reflectance.</p> <p>Mikko Kervinen and Stefan Simis maintained the operational processing system</p> <p>Ping Qin analysed multi-year observation records and developed quality-control filters</p> <p>Silvia Pardo and Gavin Tilstone analysed the data against satellite sensor records. </p>
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