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Sample of HYPERNETS Hyperspectral Surface Reflectance Measurements for Satellite Validation from the Gobabeb Site in Namibia

The HYPERNETS project (www.hypernets.eu; Ruddick et al. 2024) has the overall aim to ensure that high quality in situ measurements are available to support the (VNIR/SWIR) optical satellite products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu; Kuusk et al. 2024) dedicated to land and water surface reflectance validation with instrument pointing capabilities. This instrument has been deployed over various sites covering a range of water and land types and a range of climatic and logistic conditions. Here, we provide the first fully quality-checked data for the Gobabeb HYPERNETS site in Namibia (GHNA). The HYPERNETS data products were processed using the HYPERNETS_processor (De Vis et al. 2024b). The provided NetCDF files are the L2B hypernets products with surface reflectances, their associated uncertainties and error-correlation information. The reflectance in these products is the Hemispherical-conical Reflectance Factor (HCRF) defined as: HCRF = π L / E where L is the conical 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. The GHNA site has minimal daily variation in surface cover and weather conditions and is an ideal location for sustained, homogeneous measurements. The site is well characterised as it is very close to an instrument already recognised as a radiometric calibration site (GONA) as part of the RadCalNet network (Bialek et al. 2016). The HYPERNETS site itself (23.60153 degrees S, 15.12589 degrees E) is 650 m from the RadCalNet site, and is located on a gravel plain near a dry riverbed which separates it from the neighbouring dune sea. The HYPSTAR®-XR sensor was installed May 2022 at the top of a 9m mast on an extended 1 m horizontal boom to minimise interruption of the field of view. Data are collected every 30 minutes between 9am and 6pm local time (UTC+02) between viewing zenith angles of 0 and 60 degrees. No measurements are taken at 2pm and 2:30pm local time to avoid the hottest part of the day. 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 (De Vis et al. 2024b) 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). For an example using these data for satellite vicarious calibration, see De Vis et al. 2024a). To obtain this dataset, we start from the full GHNA data record and omit data that does not pass the relevant quality checks (QC). Some QC are performed during the near-real time processing done by the hypernets_processor (see https://hypernets-processor.readthedocs.io/en/latest/content/atbd/processing/quality_checks.html) to produce the L2A files. Then, a number of site-specific QC are performed as post-processing to produce the L2B files. These site-specific QC cover things such as removing flags in the L2A data, avoiding periods with bad deployment conditions, removing unsuitable viewing and solar angles, as well as poorly performing wavelength ranges and individual sequences. Any potential misalignment of the sensor is also corrected, affecting L1D irradiances, and L2B reflectances. These corrected data are then used in a more stringent clear sky check, and in a check that verifies the reflectances are within realistic ranges for a given angle and time of year for the given site.  There was a rain event in Gobabeb in March 2025, resulting in the growth of grass at the site. We expect the site will be back to its normal surface cover in the near future. Since the rain event, less data passed the site-specific QC. A dedicated QC will be developed for this period, as the data with grass surface cover will still be useful for satellite validation. These updated data will be made available in the future.

opencc-by-4.0Aug 2023View details →

Interlaboratory study: Testing reproducibility of solid biofuels component identification using reflected light microscopy

Submitted data was used to write an article: Drobniak, A., Mastalerz, M., Jelonek, Z., Jelonek, I., Adsul, T., Andol&scaron;ek, N., Ardakani, O.H., Congo, T., Demberelsuren, B., Donohoe, B.S., Douds, A., Flores, D., Ganzorig, R., Ghosh, S., Gize, A., Goncalves, P.A., Hackely, P., Hatcherian, J., Hower, J.C., Kalaitzidis, S., Kędzior, S., Knowles, W., Kuś, J., Lis, K., Lis, G., Liu, B., Luo, Q., Du, M., Mishra, D., Misz-Kennan, M., Mugerwa, T., O'Keefe, J., Park, J., Pearson, R., Petersen, H., Reyes, J., Ribeiro, J., Niedzwiedzkas, J.L., de la Rosa Rodriguez, G., Sosnowski, P., Valentine, B., Varma, A., Wojtaszek-Kalaitzidi, M., Xu, Z., Zdravkov, A.,  Ziemianin, K., Interlaboratory study: Testing reproducibility of biomass fuels component identification using reflected light microscopy. International Journal of Coal Geology 277, 104331. <a href="https://doi.org/10.1016/j.coal.2023.104331">https://doi.org/10.1016/j.coal.2023.104331</a>.   Funding acknowledgments: The project is co-financed by the Polish National Agency for Academic Exchange within the Polish Returns Programme (BPN/PPO/2021/1/00005/DEC/1), the National Science Center, Poland (2022/01/1/ST10/00024), and the research activities co-financed by the funds granted under the Research Excellence Initiative of the University of Silesia in Katowice, Poland.    Article Abstract: Considering global market trends and concerns about climate change and sustainability, increased biomass use for energy is expected to continue. As more diverse materials are being utilized to manufacture solid biomass fuels, it is critical to implement quality assessment methods to analyze these fuels thoroughly. One such method is reflected light microscopy (RLM), which has the potential to complement and enhance current standard testing, leading to improving fuel quality assessment and, ultimately, preventing avoidable air pollution. An interlaboratory study (ILS) was conducted to test the reproducibility of biomass fuels component identification using a reflected light microscopy technique. The exercise was conducted on thirty photomicrographs showing biomass and various undesired components (like plastics or mineral matter), which were purposely added (by the ILS organizers) to contaminate wood pellets and charcoal-based grilling fuels. Forty-six participants had various levels of difficulty identifying the marked components, and as a result, the percentage of correct answers ranged from 52.2 to 94.4%. Among the most difficult components to distinguish were petroleum products and inorganic matter. Various reasons led to the misidentification, including insufficient morphological descriptions of the components provided to participants, ambiguities of the nomenclature, limitations of the analytical and exercise method, and insufficient experience of the participants. Overall, the results indicate that RLM has the potential to enhance the quality assessment of biomass fuels. However, they also demonstrate that the petrographic classification used in this exercise requires further refinement before it can be standardized. While a new simplified classification of solid biomass fuels components was created as an outcome of this study, future research is necessary to refine the nomenclature, develop a microscopic morphological description of the components, and verify the accuracy of component identification with a follow-up ILS.

opencc-by-4.0Dec 2022View details →

Spectral reflectance data of Mercury's surface collected by the Mercury Atmospheric and Surface Composition Spectrometer (MASCS) instrument during orbital observations of the NASA MESSENGER mission between 2011 and 2015 resampled to a [55399 × 396] tabular data format.

MASCS is a three sensor point spectrometer with a spectral coverage from 200 nm to 1450 nm. Single spectra are resamples in to steps to a format useful for our ML application : a datacube with ~400 spectral channel covering the whole surface of Mercury. The final dataset has dimension [N×M] where N is the number of grid cells (360 × 180 = 64, 800) and M is the number of spectral features (396). Due to the incomplete coverage and data filtering, some grid cells are empty. After removing these empty cells, the size of the dataset is [55399 × 396]. This specific product is stored as a gzip compressed json, where each element is a grid cell. We are in the process to publish a complete pipeline to produce this product from RAW data on https://github.com/epn-ml/MESSENGER-Mercury-Surface-Cassification-Unsupervised_DLR/ . Spectral reflectance data of Mercury’s surface collected by the Mercury Atmospheric and Surface Composition Spectrometer (MASCS) instrument during orbital observations of the NASA MESSENGER mission between 2011 and 2015. MASCS is a three sensor point spectrometer with a spectral coverage from 200 nm to 1450 nm. Single spectra are resamples in to steps to a format useful for our ML application : a datacube with ~400 spectral channel covering the whole surface of Mercury. The final dataset has dimension [N×M] where N is the number of grid cells (360 × 180 = 64, 800) and M is the number of spectral features (396). Due to the incomplete coverage and data filtering, some grid cells are empty. After removing these empty cells, the size of the dataset is [55399 × 396]. 0. Pre-filtering We used the most recent dataset that had large-scale photometric corrections and thus was almost free from observation geometry effects. However, extreme geometry are still present and are typically associated with high noise and some residual instrumental effects. Based on our empirical tests, we filtered out observations with an emission/incidence angle ≥80∘. We also calculated the median value per wavelength and per cell grid when constructing the global hyperspectral data cube and filtered out observations falling under the 2nd percentile and above 99.9th percentile to clean some residual geometry effects. With this approach we create an effective noise filter while retaining enough observations to be able to analyse the entirety of the surface of the planet. 1. Spectral resmpling Unprocessed MASCS spectra could have 512 or 256 channes, depending on binning. We resampled the data in the spectral dimension to a common wavelength range from 260 nm to 1052 nm with a  4 nm resolution (2 nm spectral sampling), resulting in 396 spectral channels. This approach slightly oversamples the original 4.77 nm spectral resolution and removes some points from the original 200-1050 nm range. The resulting data matrix is expressed in tabular form, with each row representing a single grid cell or pixel on the surface. The elements of each row are the spectral reflectance values from the VIS instrument at 396 (resampled) wavelengths. 2. Spatial resmpling The whole dataset of &sim; 5 million spectra is resampled to a planet-wide rectangular grid of 1×1deg in the latitudinal band between ± 80. The cell longitudinal size varies between &sim; 40 km at the equator to a minimum of &sim; 10 km at ±80∘. Thus, the area spanned by each grid cell depends on the latitude. However, the same is true for the acquisition process, where higher spatial resolution is reached near the equator and lower resolution at the poles.

opencc-by-4.0Feb 2023View details →

Look-Up Table of A Prototype of Reconfigurable Intelligent Surface with Continuous Control of the Reflection Phase

Tabulated values (look-up table) of the magnitude (dB) and phase (degres) of the unit-cell versus voltage, experimentally characterized, for a Reconfigurable Intelligent Surface prototype based on varactors described in : R. Fara, P. Ratajczak, D. -T. Phan-Huy, A. Ourir, M. Di Renzo and J. de Rosny, "A Prototype of Reconfigurable Intelligent Surface with Continuous Control of the Reflection Phase," in IEEE Wireless Communications, vol. 29, no. 1, pp. 70-77, February 2022, doi: 10.1109/MWC.007.00345. also accessible here: https://arxiv.org/ftp/arxiv/papers/2105/2105.11862.pdf

Initial Sample of HYPERNETS Hyperspectral Surface Reflectance Measurements for Satellite Validation from the Bare soil at Marquardt, Germany

The HYPERNETS project (www.hypernets.eu) aims 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 ATB HYPERNETS site in Marquardt, Germany [52°27'59.40"N, 12°57'35.16"E] (ATGE). It is a subset of the complete data record, consisting of the measurements which could be used for satellite validation.  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 the field o, view of 5 dgrees), 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. The HYPSTAR®-XR sensor was installed on 11 Oct 2022 at the top of a 5m mast on an extended 5 m horizontal boom to minimise interruption of the field of view. The boom faces South at the right angle towards bare soil. The mast is located at 52.466778°N, 12.959778°E. Data are collected every 30 minutes between 9:00 and 17:00 (UTC) from different zenith and azimuth angle. 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 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).  To obtain this dataset, we start from the full ATGE data record and omit all the data that do not pass all quality checks performed as part of the hypernets_processor. In addition, an additional screening procedure was also developed to remove outliers and 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 positions) for four 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 of 30 data points), calculating the standard deviation from this trend, and masking any data that is more than three 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 four different wavelengths are then combined (keeping only measurements for which none of the four 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.

openCC (other)Mar 2025View details →

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Shifts in Bacterioplankton During Cyanobacterial Blooms Reflect Bloom Toxicity and lake Trophic State, OR 2019-2020

Harmful cyanobacterial blooms (cyanoHABs) typically occur in human-impacted eutrophic lakes suffering from nutrient pollution, but they also occur in lakes spanning the trophic and disturbance gradients. CyanoHABs change the bacterioplankton community structure with increases in specific cyanobacteria strains, as well as shifts in heterotrophic taxa. Bacterioplankton community shifts during cyanoHABs can be somewhat predictable but have been only studied in a limited number of lakes, most highly productive and in developed watersheds. The Cascade Mountains (USA) offer an unique area to study cyanotoxin variation and shifts in bacterioplankton composition across a productivity gradient in lakes with documented cyanoHABs but removed from most development. We explored associations of bacterioplankton communities with cyanoHABs and toxins within a season, as well as across lakes and years via physicochemical metrics, passive toxin samplers and 16S rRNA gene sequencing. The data set is a compilation of physicochemical, meteorological, biological as well as physical lake characteristics and sampling information. Water temperature was tracked continuously within a season for the three lakes while single point measurements of water temperature were taken for the other lakes in the spatial (n= 29) and intra-annual subset (n =12). Daily air temperature, precipitation and aerosol optical depth were extracted from the PRISM Gridded Climate data. Nutrient concentrations were measured for all lakes and analyzed for nitrogen and phosphorus via colorimetry in a flow analyzer. Chlorophyll-a concentrations were measured from filter samples via fluorimetry. Microcystin concentrations from grab and SPATT samples were analyzed via an ELISA kit for Microcystin-LR. Bacterioplankton diversity metrics were calculated from the processed 16S rRNA sequences along with the relative abundance of potentially toxigenic cyanobacteria. Bacterioplankton composition was and can be derived from the raw

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Vegetation indices calculated from reflectance spectra collected at LTER plots at Toolik Lake, Alaska during the 2007-2019 growing seasons.

Vegetation indices calculated from reflectance spectra collected at Arctic LTER experimental plots at Toolik Lake, Alaska during the 2007-2019 growing seasons. Long term experimental plots span several different vegetation types: Heath (HTH89), Moist Acidic Tussock (MAT89 and Low Fert), Moist Non-Acidic Tussock (MNAT), Non-Acidic Non-Tussock (NANT), Shrub (SHB), and Wet Sedge (WSG). Plots are differentiated by their experimental treatment and are located in replicate blocks.Canopy reflectance is measured by hand-held spectrophotometer and several indices of interest (NDVI, EVI, EVI2, PRI, WBI, and Chlorophyll index) are calculated.

openCC (other)Mar 2022View details →

opencc-by-4.0Dec 2018View details →

Mast-borne spectral reflectance measurements of boreal landscape during spring

This dataset contains mast-borne spectral reflectance measurements (350-2500 nm / 350-1000 nm) measured with an ASD Field Spec Pro JR spectroradiometer and digital images of the measurement areas from the time of the measurements. The measurement targets are a boreal sparse pine forest  and a forest opening located at the premises of the Arctic Space Centre of the Finnish Meteorological Institute in Sodankylä, northern Finland (N67.361833, E26.634154, WGS84). The dataset covers spring time periods during years 2010-2018 from the dry snow period until some time after the snow disappearance. The temporal coverage vary from year to year depending on the mounting date and due to technical problems. Measurements have been conducted every 30 min during fixed day-time period and based on set weather threshold values. The spectral reflectance data are organized in yearly CSV files the metadata information attached in the file header. Accordingly, the digital images from the measurement areas are organized in yearly folders and packed into zip files. For this version a data example plot (Data_example_mast.png) was added to have a quick visualisation of the sort of the data available. For further information contact Henna-Reetta Hannula (henna-reetta.hannula@fmi.fi) or Kirsikka Heinilä (kirsikka.heinila@ymparisto.fi)

opencc-by-4.0Dec 2019View details →

River Surface Reflectance Database (RiverSR)

RiverSR database (River Surface Reflectance) v1.1.0 This database contains Landsat 5, 7, and 8 Level 1 Collection 1 surface reflectance from all rivers in the contiguous USA that are ~60 meters wide or greater. The surface reflectance values across bands (red, green, blue, nir, swir1, swir1) represent the median reflectance of pixels detected as water within each Landsat scene that are within the boundaries of each reach represented by NHDPlusV2 centerlines. Surface reflectance is therefore geo-referenced to river center lines with network topology (NHDPlusV2) for quick geospatial analysis. Files: 1) Metadata (riverSR_v1.1_metadata.docx): Description of all data files associated with this repository.  2) Surface reflectance database (riverSR_usa_v1.1.feather). Feather files are text files readable in R and python with the feather package and this table is joinable to nhdplusv2_modified_v1.0.shp based on the "ID" column and to the original NHDplusV2 flowlines with the "COMID" column. 3) Shapefile of river centerlines to which the reflectance data can be attached (nhdplusv2_modified_v1.0.shp). 4) Shapefile of the reach polygons associated with each nhdplusv2_modified reach. (nhdplusv2_polygons.shp). 5) The reach IDs of original and new NHDplusV2 centerlines. (COMID_ID.csv).

opencc-by-4.0Dec 2020View details →

Radar measurements for the article "Dynamic differential reflectivity calibration using vertical profiles in rain and snow"

Dataset documentation The archives in hdf5 format provided at this link contain the datasets used in the manuscript Dynamic differential reflectivity calibration using vertical profiles in rain and snow, submitted to Remote Sensing (MDPI) by Alfonso Ferrone and Alexis Berne in 2020.   File content Each file is structured as a table, with each column referring to a specific variable and each row containing a different realization (in space or time). The set of available variables is campaign dependent, and the possibilities are: <ul> <li> idx, and integer index that starts at 1 for the first scan of the dataset and increases by 1 for every successive scan; </li> <li> t, the timestamp of the scan, in seconds since seconds since Jan 01, 1970; </li> <li> r or rg (depending on the file), the distance from the radar in meters; </li> <li> az, the azimuth angle in degrees; </li> <li> el, the elevation angle in degrees; </li> <li> zdr, the uncalibrated differential reflectivity, in dB; </li> <li> zh, the horizontal reflectivity, in dBZ; </li> <li> rhovh or rho, the co-polar correlation coefficient, unitless; </li> <li> snr_h or snr, the signal to noise ratio for the horizontal channel in dB; </li> <li> snrv, the signal to noise ratio for the vertical channel, in dB, </li> <li> ngates, the number of unique range gates. </li> </ul> For the comparison of the data collected by MXPol and DX50 during the PAYERNE campaign, two auxiliary variables were added to the archives: <ul> <li> x the horizontal distance from the current radar, computed on a line passing through the location of two radars; </li> <li> z the vertical distance from the current radar. </li> </ul>   Usage The dataset are provided in the the Hierarchical Data Format version 5 (HDF5), an open source file format, supported by several programming language. They archives were created using the vaex library for Python 3: https://github.com/vaexio/vaex The function vaex.open from the same library can be used for accessing the archives and converting them to vaex.DataFrame.   Campaign-specific information Some of the parameters associated to the variables included in the archives may change depending on the campaign. The following subsection provide a summary of these information.   dataframe_HYMEX_2013_from_20130907-040344_to_20131105-175944.hdf5 Contains PPI scans at 90° elevation for the HYMEX campaign. Location information: <ul> <li> Latitude: 44.61° N </li> <li> Longitude: 4.55° E </li> <li> Altitude: 604 m.a.s.l. </li> </ul> Technical information: <ul> <li> Radar Frequency: 9.41 GHz </li> <li> Theoretical 3 dB angular beamwidth of the antenna: 1.45° </li> </ul> Scan setup: <ul> <li> Range resolution: 75 m </li> <li> Range to the first gate: 204.3 m </li> </ul> Processsing parameters: <ul> <li> Clutter filtering: OFF </li> <li> Minimum rhohv: 0.6 </li> <li> Attenuation correction: Z-PHI method </li> <li> Note on reflectivity calibration: The original manufacturer calibration constant was 7.56 dBZ. The value used here derives from comparison with disdrometers during the HYMEX campaign. </li> </ul>   dataframe_PAYERNE_2014_from_20140321-160016_to_20140519-085808.hdf5 Contains PPI scans at 90° elevation performed by MXPol during the PAYERNE campaign. Location information: <ul> <li> Latitude: 46.81° N </li> <li> Longitude: 6.94° E </li> <li> Altitude: 496 m.a.s.l. </li> </ul> Technical information: <ul> <li> Radar Frequency: 9.41 GHz </li> <li> Theoretical 3 dB angular beamwidth of the antenna: 1.45° </li> </ul> Scan setup: <ul> <li> Range resolution: 75 m </li> <li> Range to the first gate: 204.3 m </li> </ul> Processsing parameters: <ul> <li> Clutter filtering: OFF </li> <li> Minimum rhohv: 0.6 </li> <li> Attenuation correction: None </li> </ul>   dataframe_DAVOS_2014_from_20140704-090224_to_20141231-105720.hdf5 Contains PPI scans at 90° elevation for the DAVOS campaign. Location information: <ul> <li> Latitude: 46.82° N </li> <li> Longitude: 9.82° E </li> <li> Altitude: 2220 m.a.s.l. </li> </ul> Technical information: <ul> <li> Radar Frequency: 9.41 GHz </li> <li> Theoretical 3 dB angular beamwidth of the antenna: 1.45° </li> </ul> Scan setup: <ul> <li> Range resolution: 75 m </li> <li> Range to the first gate: 204.3 m </li> </ul> Processsing parameters: <ul> <li> Clutter filtering: OFF </li> <li> Minimum rhohv: 0.6 </li> <li> Attenuation correction: None </li> </ul>   dataframe_APRES3_from_20151207-123944_to_20160129-125856.hdf5 Contains PPI scans at 90° elevation for the APRES3 campaign. Location information: <ul> <li> Latitude: 66.66 S </li> <li> Longitude: 140.00 E </li> <li> Altitude: 40 m.a.s.l. </li> </ul> Technical information: <ul> <li> Radar Frequency: 9.41 GHz </li> <li> Theoretical 3 dB angular beamwidth of the antenna: 1.45° </li> </ul> Scan setup: <ul> <li> Range resolution: 75 m </li> <li> Range to the first gate: 354.3 m </li> </ul> Processsing parameters: <ul> <li> Clutter filtering: OFF </li> <li> Minimum rhohv: 0.6 </li> <li> Attenuation correction: None </li> </ul>   dataframe_VALAIS_2016_from_20161104-154312_to_20170306-195912.hdf5 Contains PPI scans at 90° elevation for the VALAIS campaign. Location information: <ul> <li> Latitude: 46.12 N </li> <li> Longitude: 7.10 E </li> <li> Altitude: 460 m.a.s.l. </li> </ul> Technical information: <ul> <li> Radar Frequency: 9.41 GHz </li> <li> Theoretical 3 dB angular beamwidth of the antenna: 1.27° </li> </ul> Scan setup: <ul> <li> Range resolution: 30 m </li> <li> Range to the first gate: 226.95 m </li> </ul> Processsing parameters: <ul> <li> Clutter filtering: OFF </li> <li> Minimum rhohv: 0.6 </li> <li> Attenuation correction: None </li> </ul>   dataframe_DX50_from_20140501-020000_to_20140524-015752.hdf5 Contains PPI scans at 90° elevation performed by DX50 during the PAYERNE campaign. Location information: <ul> <li> Latitude: 46.84° N </li> <li> Longitude: 6.92° E </li> <li> Altitude: 450 m.a.s.l. </li> </ul> Technical information: <ul> <li> Radar Frequency: 9.459 GHz </li> <li> Theoretical 3 dB angular beamwidth of the antenna: 1.273° </li> </ul> Scan setup: <ul> <li> Range resolution: 75 m </li> <li> Range to the first gate: 0.0 m </li> </ul> Processsing parameters: <ul> <li> Clutter filtering: OFF </li> <li> Minimum rhohv: 0.0 </li> <li> Attenuation correction: None </li> </ul>   dataframe_DX50_RHI_201405.hdf5 Contains RHI scans from DX50 the PAYERNE campaign. The remaining information equal to the ones listed for dataframe_DX50_from_20140501-020000_to_20140524-015752.hdf5.   dataframe_MXPol_RHI_201405.hdf5 Contains RHI scans from MXPol the PAYERNE campaign. The remaining information is equal to the ones listed for dataframe_PAYERNE_2014_from_20140321-160016_to_20140519-085808.hdf5.

opencc-by-4.0May 2024View details →

Burned Area Maps based on MODIS Surface Reflectance

Burned area (BA) was classified using in-house algorithms, described in detail by Woźniak and Aleksandrowicz (2019). This method utilizes images acquired before and after fire events. All MODIS surface reflectance products MOD09A1 (tiles 24_03 and 25_03) for the period 2002 – 2021 were investigated. Since the study area is obscured by clouds or covered with snow for most of the year, only images from the time window that maximized the number of available frames across most years were selected. Hence, only images acquired between the 145th and 241st day of each year (corresponding to the spring-summer period) were retained for further processing.

opencc-by-4.0Jun 2024View details →

Synthetic datasets reflecting the shRNA-seq knockdown ENCODE data for HepG2 and K562 with coresponding GRN

Synthetic data correspond to the ENCODE data for cell lines HepG2 (https://www.encodeproject.org/biosamples/ENCBS282XVK/) and K562 (https://www.encodeproject.org/biosamples/ENCBS023XVB/). The data and networks were generated using GeneSPIDER (publicly available at https://bitbucket.org/sonnhammergrni/genespider/).   Table.1 Description of the files <table> <tbody> <tr> <td>data_HepG2like_SNR_L=0.0054699_diff=1.6188e-05.txt</td> <td>Synthetic gene expression knockdown (shRNA-seq) data immitating the ENCODE data for HepG2 cell line. Data size: 232 RBPs vs 464 experiments (2 replicates). SNR_L is the value of signal to noise ratio. Difference (diff) value tells the difference between replicate correlation coefficients of real and synthetic ENCODE data. Columns represent experiments, rows represent genes.</td> </tr> <tr> <td>data_K562like_SNR_L=0.0028692_diff=0.00017339.txt</td> <td>Synthetic gene expression knockdown (shRNA-seq) data immitating the ENCODE data for K562 cell line. Data size: 232 RBPs vs 464 experiments (2 replicates). SNR_L is the value of signal to noise ratio. Difference (diff) value tells the difference between replicate correlation coefficients of real and synthetic ENCODE data. Columns represent experiments, rows represent genes.</td> </tr> <tr> <td>network_HEPG2like_sparsity4.txt</td> <td>Synthetic scale-free gene regulatory network compatibile with data_HepG2like_SNR_L=0.0054699_diff=1.6188e-05.txt. Sparsity (average node degree) is 4 including selfloops. Direction should be read from columns to rows.</td> </tr> <tr> <td>network_K562like_sparsity4.txt</td> <td>Synthetic scale-free gene regulatory network compatibile with data_K562like_SNR_L=0.0028692_diff=0.00017339.txt. Sparsity (average node degree) is 4 including selfloops. Direction should be read from columns to rows.</td> </tr> <tr> <td>perturbations_HepG2&K562_2replicates.txt</td> <td>Perturbation matrix including information about knockeddown RBPs. Data size: 232 RBPs vs 464 experiments (2 replicates).</td> </tr> </tbody> </table>   Created by Garbulowski et al. (2024) as a part of the work entitled "Comprehensive analysis of the RBP regulome reveals functional modules and drug candidates in liver cancer"

opencc-by-4.0Mar 2018View details →

Reflection Spectra Repository for Cool Giant Planets

Supplementary material for <a href="http://iopscience.iop.org/article/10.3847/1538-4357/aabb05">Exploring H2O Prominence in Reflection Spectra of Cool Giant Planets</a> - ApJ 858, 69 (2018). This repository contains 65520 model reflection spectra of cool giant planets. The grid explores the influence of metallicity, gravity, effective temperature, and sedimentation efficiency on H2O absorption signatures in giant planet atmospheres. We also include two animations to visualise how the prominence of H2O absorption evolves over this parameter space. The included models range over: *m => 1-100 x solar (log(m) @ 0.0, 0.5, 1.0, 1.5, 1.7, 2.0 dex) <-- log(m) = 1.7 new for V2 of the database. *g => 1-100 m/s2 (evenly over log(g) in steps of 0.1 dex) *Teff => 150-400 K (linearly in steps of 10 K) *fsed => 1-10 (linearly in steps of 1) (V 1.0, March 30th 2018): <blockquote> Initial release of the reflection spectra repository.  </blockquote> (V 2.0, Oct 1st 2019):  <blockquote> The cool giant reflection spectra grid has been re-computed using the latest version of the PICASO albedo code (doi: <a href="https://arxiv.org/ct?url=https%3A%2F%2Fdx.doi.org%2F10.3847%2F1538-4357%2Fab1b51&v=77076c4a">10.3847/1538-4357/ab1b51</a>). This fixes a few bugs and adds new model features (e.g. Raman scattering, see Batalha+2019). The new grid is packaged as a HDF5 file with an accompanying python script 'Open_Albedo_Database.py'. The python script is provided to show how to open the albedo database, plot the spectra, and save spectra as a .txt file. The user need only change 4 lines (specifying log(m), log(g), Teff, fsed) and run the python script to produce a plot of the albedo spectra (both with and without H2O absorption). </blockquote> NEW: (V 2.1, Oct 3rd 2019):  <blockquote> Fixed a bug causing models with log(g) = 3.4 or 3.9 to not display cloud opacity. </blockquote>

opencc-by-4.0May 2023View details →

HYPSTAR hyperspectral water reflectance and derived water quality products (suspended particulate matter and chlorophyll-a concentration) at the Blankaart surface water reservoir (BE)

Hyperspectral Water Leaving Reflectance spectra (2988) measured between 2021-02-03 and 2022-08-03 at the Blankaart Surface Water Reservoir (Belgium, 50.98857N, 2.835213E) with the HYPSTAR® (ID: HYPSTAR_12120241). Detailed description of the data collection, processing and analysis can be found in Goyens et al.- Remote Sens. 2022 - 14(21)- 5607; https://doi.org/10.3390/rs14215607. <ol> <li>HYPSTAR_W_BSBE_L2A_REFL_20210203_20220803_v1.csv</li> </ol> Chlorophyll-a (Chl-a) concentration and Suspended Particulate Matter (SPM) were derived from the above dataset of hyperspectral water reflectance and estimated according to different algorithms found in the litterature, i.e., <ol> <li>HYPSTAR_W_BSBE_CHLA_SIMIS_20210203_20220803_v1.csv: Chlorophyll-a concentration estimated from the HYPSTAR® reflectance measurements and following the algorithm suggested by Simis et al. (2005; https://doi.org/10.4319/lo.2005.50.1.0237) with a variable absorption coefficient of phytoplankton per unit of Chl-a concentration as described in Goyens et al. (2022)</li> <li>HYPSTAR_W_BSBE_CHLA_CRAT_20210203_20220803_v1.csv: Chlorophyll-a concentration estimated with the HYPSTAR® reflectance measurements and following the algorithm suggested by Ruddick et al. (2001; https://doi.org/10.1364/AO.40.003575.) with a variable absorption coefficient of phytoplankton per unit of Chl-a concentration as described in Goyens et al. (2022)</li> <li>HYPSTAR_W_BSBE_SPM_20210203_20220803_v1.csv: Suspended particulate matter estimated with the HYPSTAR® reflectance measurements and following the algorithm suggested by Nechad et al. (2010) at 700 nm</li> </ol>

Initial Sample of HYPERNETS Hyperspectral Water Reflectance Measurements for Satellite Validation at Lake Garda, GAIT site (Italy)

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 in Lake Garda in Italy (GAIT). It is a subset of the complete data record which consists of the best quality GAIT measurements which could be used for satellite validation. 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:\(\rho_wnosc=\pi (Lu-\rho_FLd)/E_d\)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).For the GAIT site, the reflectance corrected for the NIR Similarity correction (epsilon, see Ruddick et al., 2006) is also provided:\(\rho_w=\pi (Lu-\rho_FLd)/E_d-\epsilon\)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.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.To obtain this dataset, we start from the full GAIT data record and omit all the data that do not pass all 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:1. The coefficient of variation in water reflectance is below 10% in the 580-600 nm range2. The water reflectance (after correction for the NIR similarity) at 500 nm is below 0.1

Initial Sample of HYPERNETS Hyperspectral Water Reflectance Measurements for Satellite Validation from the LPAR site (Argentina)

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 in Rio de La Plata, LPAR, in Argentina. It is a subset of the complete data record which consists of the best quality LPAR measurements which could be used for satellite validation.  The provided NetCDF files are the L2A hypernets products with water leaving radiance and reflectances (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 (referred to as reflectance_nosc in the file) defined as: ρwnosc=π(Lu−ρFLd)/Ed 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). 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. 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. To obtain this dataset, we start from the full LPAR 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: 1. The coefficient of variation in water reflectance is below 10% in the 400-900 nm range 2. The water reflectance at 500 nm is below 0.1

Initial Sample of HYPERNETS Hyperspectral Water Reflectance Measurements for Satellite Validation from the VEIT site (Italy)

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 Aqua Alta, Venice in Italy (VEIT). It is a subset of the complete data record which consists of the best quality VEIT measurements which could be used for satellite validation.  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: \(\rho_wnosc=\pi (Lu-\rho_FLd)/E_d\) 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). For the VEIT site, the reflectance corrected for the NIR Similarity correction (epsilon, see Ruddick et al., 2006) is also provided: \(\rho_w=\pi (Lu-\rho_FLd)/E_d-\epsilon\) 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. 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. To obtain this dataset, we start from the full VEIT 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: 1. The coefficient of variation in water reflectance is below 10% in the 580-600 nm range 2. The water reflectance (after correction for the NIR similarity) above 800 nm is below 0.01

Initial Sample of HYPERNETS Hyperspectral Water Reflectance Measurements for Satellite Validation at Berre coastal lagoon, BEFR site (France)

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 Etang de Berre in France (BEFR). It is a subset of the complete data record which consists of the best quality BEFR measurements which could be used for satellite validation.  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: ρwnosc=π(Lu−ρFLd)/Ed   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). For the BEFR site, the reflectance corrected for the NIR Similarity correction (epsilon, see Ruddick et al., 2006) is also provided: ρw=π(Lu−ρFLd)/Ed−ϵ   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. 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. To obtain this dataset, we start from the full BEFR 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: 1. The coefficient of variation in water reflectance is below 10% in the 500-600 nm range 2. The water reflectance (after correction for the NIR similarity) between 700-900 nm is below 0.01

Initial Sample of HYPERNETS Hyperspectral Water Reflectance Measurements for Satellite Validation at the mouth of the Gironde Estuary, MAFR site (France)

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 Gironde Estuary, MAGEST Network, in France (MAFR). It is a subset of the complete data record which consists of the best quality MAFR measurements which could be used for satellite validation.  The provided NetCDF files are the L2A hypernets products with water leaving radiance and reflectances 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: ρwnosc=π(Lu−ρFLd)/Ed 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). 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. 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. To obtain this dataset, we start from the full MAFR 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: 1. The coefficient of variation in water reflectance is below 10% in the 600-700 nm range

Initial Sample of HYPERNETS Hyperspectral Surface Reflectance Measurements for Satellite Validation from the IFEVA site in Argentina

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 IFEVA in Buenos Aires  Argentina (IFAR). It is a subset of the complete data record which consists of the best quality IFAR measurements which could be used for satellite validation.  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. The IFAR site is a temporary test site located in the Agronomy Faculty campus in Buenos Aires city, Argentina (34.592322°S, 58.479017°W). The venue is managed by the IFEVA (Agricultural Physiology and Ecology Research Institute) and characterized by natural pastures with different treatments distributed in 16 patches of 7mx7m. The HYPSTAR®-XR sensor has been deployed in June 2021 at the top of a 2.4 m high tripod that is pointing to one of the patches where the vegetation has no specific treatment (natural) and is cut regularly every year in February. Data is collected every 30 min between 14:00 and 18:00 hs UTC (11:00 to 15:00 local time). 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).  To obtain this dataset, we start from the full IFAR 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.