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opencc-by-4.0Jul 2022View details →

Training and validation data for artificial neural networks using three-dimensional partial convolutions to fill gaps in satellite image time series

This dataset contains training and validation data for artificial neural networks using three-dimensional partial convolutions to fill gaps in satellite image time series. The data have been derived from Sentinel-5P total column carbon monoxide observations, using the offline processing stream. Preprocessing The following operations have been applied on the original S5P imagery: <ol> <li>Images have been resampled to 0.1 by 0.1 degree spatial resolution</li> <li>Pixels with quality assessment value less than or equal to 0.5 have been set to NA</li> <li>Images have been aggregated by day of observation</li> <li>Images have been cropped to -60 to 60 degrees latitude</li> <li>Images have been devided into spatiotemporal blocks of size 128 x 128 pixels and 16 days</li> </ol> Imagery has been recorded between 2021-01-01 and 2021-11-25. Notice that both the training and the validation blocks have been randomly sampled from all available blocks. Data Format and Naming Conventions Input and output data blocks are stored as GeoTIFF files, where bands represent time. Notice the following file naming conventions: <ul> <li>Files starting with X represent input measurements for training, where artificial gaps have been added.</li> <li>Files starting with Y represent true measurements without artificially added gaps (but still containing gaps in many cases).</li> <li>Binary masks of input data where all pixels with valid measurements are 1 and others 0 are stored in files whose name starts with MASK</li> <li>Files starting with VALMASK contain a binary mask where only pixels that are available in Y but not in X are 1. The latter is used for validation on artificially removed pixels only.</li> </ul> Numbers in filenames encode spatial and temporal block indexes. In addition, the dataset contains prediction of the validation blocks from different models in the `predictions` directory. The subfolders contain output from different models: <ul> <li>mean refers to simple block-wise mean predictions.</li> <li>timeseries refers to simple linear time series interpolation.</li> <li>gapfill refers to the method proposed in [1].</li> <li>stmra refers to the method proposed in [2].</li> <li>STpconv refers to predictions passed on an artificial neural netowork with three-dimensional partial convolutions.</li> </ul> References [1] Gerber, F., de Jong, R., Schaepman, M. E., Schaepman-Strub, G., & Furrer, R. (2018). Predicting missing values in spatio-temporal remote sensing data. IEEE Transactions on Geoscience and Remote Sensing, 56(5), 2841-2853. [2] Appel, M., & Pebesma, E. (2020). Spatiotemporal multi-resolution approximations for analyzing global environmental data. Spatial Statistics, 38, 100465.

opencc-by-4.0Nov 2024View details →

Positioning of lightning electromagnetic radiation sources with satellite constellations: simulation and preliminary validation

The dataset contains simulation calculation codes, satellite trajectories, and transmission time estimates. The simulation calculation is based on the version of matlab2024a, The observation_data_processing file contains the satellite orbit information and time difference information.

opencc-by-4.0Jul 2021View details →

Survey response of the H2020 Water-ForCE expert meeting on In situ calibration and validation of satellite products of water quality and hydrology

These slides provide an overview of the expert survey held in advance of the H2020 Water-ForCE workshop: In situ calibration and validation of satellite products of water quality and hydrology which was held as three virtual meeting sessions on 17, 18 and 20th May 2021: <ol> <li>Data availability, Accessibility and quality gaps</li> <li>Emerging technologies to address current gaps</li> <li>Data harmonization and sharing</li> </ol> Source data have been removed from the graphs included in these slides but may be requested from the authors. The workshop and outcomes are part of the H2020 funded project Water-ForCE: Water Scenarios For Copernicus Exploitation. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101004186

opencc-by-4.0Jun 2023View details →

Initial Sample of HYPERNETS Hyperspectral Surface Reflectance Measurements for Satellite Validation from the Barrax Site in Spain

The HYPERNETS project (www.hypernets.eu) has the overall aim to ensure that high quality in situ measurements are available to support the (VNIR/SWIR) optical Copernicus products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu) dedicated to land and water surface reflectance validation with instrument pointing capabilities. In the prototype phase, the instrument is being deployed at 24 sites covering a range of water and land types and a range of climatic and logistic conditions. This dataset provides the first published data for the Gobabeb HYPERNETS site in Barrax, Spain (BASP). It is a subset of the complete data record which consists of the best quality BASP measurements which could be used for satellite validation over the three day test deployment period.  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 BASP site was a temporary installation over the period of the 20th – 22nd July 2022 during the Surface Reflectance Intercomparison eXperiment (SRIX) campaign (https://frm4veg.org/srix4veg/) at the Las Tiesas experimental farm in Barrax, Spain. This location was selected due to its typical clear skies, flat terrain, and well-managed crops. The HYPSTAR®-XR (eXtended Range) was deployed in a small corn field next to the ongoing UAV experiment. The instrument was deployed on a 3.5m high pole with a short extended boom at 1.3m height from the crops, with measurements running every 30 minutes throughout the day (UTC+2) and measuring between viewing zenith angles of 0-60 degrees. The HYPSTAR®-XR instruments deployed at each land HYPERNETS site consist of a VNIR and a SWIR sensor and autonomously collect data between 380-1700 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with a FWHM of 3 nm and the SWIR sensor has 220 channels between 1000 and 1700 nm with a FWHM of 10 nm. The hypernets_processor (Goyens et al. 2021; De Vis et al. in prep.) automatically processes all this data into various products, including the L2A surface reflectance product provided here. All of the products have associated uncertainties (divided into random and systematic uncertainties, including error-correlation information) which were propagated using the CoMet toolkit (www.comet-toolkit.org).  To obtain this dataset, we start from the full BASP data record and omit all the data that do not pass all of the quality checks performed as part of the hypernets_processor. In addition, an additional screening procedure was developed to remove outliers and only supply the best quality data suitable for satellite validation. To remove the outliers, a sigma-clipping method is used. First reflectances are extracted in separate 2 hour windows throughout the day (to account for BRDF differences due to different solar position) for 4 different wavelengths (500, 900, 1100 and 1600 nm). Outliers in these reflectances are then identified by iteratively calculating the mean reflectance trend with time (by binning the data per maximum 30 data points), calculating the standard deviation from this trend, and masking any data that is more than 3 standard deviations away from the trend. This process is repeated on the unmasked data until the standard deviation does not vary by more than 5% between two iterations. The masks for the 4 different wavelengths are then combined (keeping only measurements for which none of the 4 wavelengths is an outlier). The reflectances and associated uncertainties for any masked series (i.e. a geometry that is masked either by the sigma-clipping procedure or from the masks of the hypernets_processor) are replaced by NaNs. Any sequence that has more than half of its series masked is removed entirely. For BASP specifically, viewing zenith angles above 30 degrees have been removed, as well as any west-facing angles azimuth angles of 263,273 or 293 degrees) for viewing zenith angles of 5 degrees and 10 degrees.  Note: In the accompanying .csv description file the measurement times are listed in (BST/ UTC+1) after UK time.

opencc-by-4.0Jun 2023View details →

Validation of the satellite-estimated sedimentation rates in reservoirs using 10-m Sentinel-2 satellites and water level data

Overview: The database contains data used for the validation of satellite-based sedimentation rates in eight reservoirs across the central and western United States using 10-m Sentinel-2 imagery and in-situ level data. Additional validation of the results from combining Sentinel-2 imagery with simulated 27-day Sentinel-3 altimetry levels is also included.   This dataset includes: <ol> <li>Satellite-derived area-level duplets</li> <li>Bathymetry curves from satellite-based estimates</li> <li>Bathymetry curves from survey data</li> <li>Validation</li> </ol>

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Figure 1 in Validation of Wind Speed Calculated on Satellite Altimetry Data by Measurements on Weather Stations Located Along the White Sea Coast

Figure 1. Maps of the White Sea. Dashed lines show boundaries of the sea and their internal parts (Lebedev et al., 2011).

opencc-by-4.0Nov 2019View details →

GPM Ground Validation Satellite Overpasses IFloodS V1

The GPM Ground Validation Satellite Overpasses IFloodS dataset contains plots of satellite overpass paths centered over eastern Iowa during the Global Precipitation Measurement (GPM) mission Iowa Flood Studies (IFloodS) field campaign. The campaign aimed to collect detailed measurements of precipitation at the Earth’s surface while simultaneously collecting data from satellites passing overhead. This dataset consists of paths for Earth observation satellites operating during the campaign: NASA’s AQUA, TERRA, and CloudSat satellites; NOAA’s NOAA-15, NOAA-16, NOAA-17, NOAA-18, and Suomi NPP satellites; Europe’s MetOp-A and MetOp-B satellites, and DMSP’s F-15, F-16, F-17, and F-18 satellites. The satellite overpasses are provided as PNG plot images and as KML files with which the paths can be imported and viewed in Google Earth.

GPM Ground Validation TRMM Multi-satellite Precipitation Analysis (TMPA) IFloodS V7

The GPM Ground Validation TRMM Multi-satellite Precipitation Analysis (TMPA) IFloodS dataset is a subset of the TMPA 3B42RT gridded precipitation real-time product selected for the time period of the GPM Ground Validation Iowa Flood Studies (IFloodS) held in Iowa during April 1, 2013 to June 30, 2013. The goals of IFloodS were to collect detailed measurements of precipitation at the Earth’s surface using ground instruments and advanced weather radars and to simultaneously collect data from satellites passing overhead. TMPA is a calibration-based sequential scheme for combining microwave (MW) and infrared (IR) precipitation estimates from multiple satellites, as well as surface precipitation gauge analyses where feasible, to produce precipitation estimates at fine scales: 3-hourly, 0.25 degree maps. The TMPA IFloodS product is available in netCDF-4 and binary formats, as well as 3-hour rainfall browse images in JPG format.

GPM GROUND VALIDATION SATELLITE SIMULATED ORBITS LPVEX V1

The GPM Ground Validation Satellite Simulated Orbits LPVEx dataset is available in the Orbital database, which takes account for the atmospheric profiles, the cloud/rain profiles, and the detailed surface/terrain information from the Cloud-Resolving Model (CRM) database. Unique geometry and antenna gain patterns of each sensor (GMI imager, GMI sounder, DPR Ku, DPR Ka_MA, DPR Ka_HS) are considered. The Orbital database consists of satellite orbit parameters, geolocation of Field of View and satellite location, and simulated Level 1B/Level 2-like parameters in satellite orbital grid. Orbital data covers a portion of sampling right over the Cloud-resolving model (CRM) domain. All orbital data format is NetCDF3, and it contains dimensions, parameter descriptions, and parameter units. Each project's data is distributed as a separate dataset. MC3E occurred in Oklahoma, USA in 2011; LPVEX took place in Finland in 2010; C3VP experiment was held in Canada in 2007 and TWP-ICE took place in Australia in 2006.

GPM GROUND VALIDATION COMPOSITE SATELLITE OVERPASSES GCPEX V1

The GPM Ground Validation Composite Satellite Overpasses GCPEx dataset provides satellite overpasses from the Special Sensor Microwave Imager/Sounder (SSMIS) satellites (F-16, 17, 18) during the GPM Cold-season Precipitation Experiment (GCPEx) which took place in Ontario, Canada, January 17, 2012 through February 29, 2012. The radiometric data was matched up with other datasets necessary to carry out land surface emissivity studies. These other datasets include the NEXRAD National Mosaic and Multi-Sensor QPE (NMQ) radar mosaic for knowledge of rain structure and intensity at the time of the overpass, as well as the previous accumulated precipitation prior to the satellite overpass time, the NOAA Interactive Multisensor Snow and Ice Mapping System (IMS) snow mapping system (to identify surface snow or ice cover), and the NASA/GMAO Modern-Era Retrospective Analysis for Research (MERRA) land and atmospheric reanalysis (for background land and atmospheric state needed for microwave radiative transfer calculations). The identified SSMIS satellite overpasses passed within 700-km of the central field site.

GPM Ground Validation Global Satellite Mapping of Precipitation (GSMaP) IFloodS V1

The GPM Ground Validation Global Satellite Mapping of Precipitation (GSMaP) IFloodS dataset consists of rainfall rate estimates from the GSMaP project. The GSMaP global rain rate maps are derived by a collection of algorithms that utilize microwave (MW) radiometer data and geostationary Infrared (IR) data. The GSMaP Precipitation data product is provided on a 0.1 degree spatial resolution every hour and was made available for use during the Global Precipitation Measurement (GPM) Ground Validation Iowa Flood Studies (IFloodS) field campaign. These data are available in netCDF-4 and binary formats from April 22, 2013 through June 30, 2013. The near real-time GSMaP data can be obtained from the JAXA GSMaP web page.

GPM GROUND VALIDATION COMPOSITE SATELLITE OVERPASSES MC3E V1

The GPM Ground Validation Composite Satellite Overpasses MC3E dataset provides satellite overpasses from the AQUA satellite during the Midlatitude Continental Convective Clouds Experiment (MC3E), which took place in central Oklahoma April 22 - June 5, 2011. The radiometric data was matched up with other datasets necessary to carry out land surface emissivity studies. These other datasets include the NEXRAD National Mosaic and Multi-Sensor QPE (NMQ) radar mosaic for knowledge of rain structure and intensity at the time of the overpass, as well as the previous accumulated precipitation prior to the satellite overpass time, the NOAA Interactive Multisensor Snow and Ice Mapping System (IMS) snow mapping system (to identify surface snow or ice cover), and the NASA/GMAO Modern-Era Retrospective Analysis for Research (MERRA) land and atmospheric reanalysis (for background land and atmospheric state needed for microwave radiative transfer calculations). The AQUA satellite overpasses included in this dataset passed within 700-km of the central field site.

GPM GROUND VALIDATION CARE SATELLITE OVERPASS IMAGES GCPEX V1

The GPM Ground Validation CARE Satellite Overpass GCPEx Images are the satellite overpass images for the GPM Cold-season Precipitation Experiment (GCPEx), which occurred in Ontario, Canada, January 15, 2012 through February 28, 2012. GCPEx addressed limitations in the GPM snowfall retrieval algorithm through the collection of microphysical properties, associated remote sensing observations, and coordinated model simulations of precipitating snow. The satellite tracks include the DMSP satellite numbers 15, 16, 17, 18, and 19. A list of starting overpass times per satellite and day is included.

Global Navigation Satellite System (GNSS) IGS Weekly Ionosphere Vertical Total Electron Content (VTEC) Grid Validation Product from NASA CDDIS

This derived product set consists of Global Navigation Satellite System Ionosphere Vertical Total Electron Content (VTEC) comparison product (daily files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe’s Galileo, China’s Beidou, Japan’s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. GNSS observations from a global network can be utilized for atmospheric measurements. Analysis Centers (ACs) of the International GNSS Service (IGS) retrieve GNSS data on regular schedules to produce independently computed VTEC maps. The IGS Ionosphere Analysis Center Coordinator (ACC) uses these individual AC solutions to generate the official IGS VTEC maps. The validation products are used to compare the IGS and AC solutions of generated VTEC maps. There are three types of ionosphere product evaluation/validation products: 1) the upcwWWWW.YYv.Z files provide an evaluation of the final weekly combination solution of VTEC maps with the individual analysis center contributions; 2) the gpsgDDD0.YYi.Z files are provided by the Center for Orbit Determinate (CODE) at the Astronomical Institute at the University of Bern (AIUB) Switzerland; these files contain GPS broadcast ionosphere model for day YYDDD; and 3) the ckmgDDD0.YYi.Z products are computed by CODE using their Klobuchar model, best fitting CODE’s final ionosphere solution, also available from the CDDIS.

GPM GROUND VALIDATION SATELLITE SIMULATED ORBITS TWP-ICE V1

The GPM Ground Validation Satellite Simulated Orbits TWP-ICE dataset is available in the Orbital database, which takes account for the atmospheric profiles, the cloud/rain profiles, and the detailed surface/terrain information from the Cloud-Resolving Model (CRM) database. Unique geometry and antenna gain patterns of each sensor (GMI imager, GMI sounder, DPR Ku, DPR Ka_MA, DPR Ka_HS) are considered. The Orbital database consists of satellite orbit parameters, geolocation of Field of View and satellite location, and simulated Level 1B/Level 2-like parameters in satellite orbital grid. Orbital data covers a portion of sampling right over the Cloud-resolving model (CRM) domain. All orbital data format is NetCDF3, and it contains dimensions, parameter descriptions, and parameter units. Each project's data is distributed as a separate dataset. MC3E occurred in Oklahoma, USA in 2011; LPVEX took place in Finland in 2010; C3VP experiment was held in Canada in 2007 and TWP-ICE took place in Australia in 2006.

GPM GROUND VALIDATION SATELLITE SIMULATED ORBITS C3VP V1

The GPM Ground Validation Satellite Simulated Orbits C3VP dataset is available in the Orbital database, which takes account for the atmospheric profiles, the cloud/rain profiles, and the detailed surface/terrain information from the Cloud-Resolving Model (CRM) database. Unique geometry and antenna gain patterns of each sensor (GMI imager, GMI sounder, DPR Ku, DPR Ka_MA, DPR Ka_HS) are considered. The Orbital database consists of satellite orbit parameters, geolocation of Field of View and satellite location, and simulated Level 1B/Level 2-like parameters in satellite orbital grid. Orbital data covers a portion of sampling right over the Cloud-resolving model (CRM) domain. All orbital data format is NetCDF3, and it contains dimensions, parameter descriptions, and parameter units. Each project's data is distributed as a separate dataset. MC3E occurred in Oklahoma, USA in 2011; LPVEX took place in Finland in 2010; C3VP experiment was held in Canada in 2007 and TWP-ICE took place in Australia in 2006.

GPM Ground Validation Multi-Radar/Multi-Sensor (MRMS) Precipitation Reanalysis for Satellite Validation Product V1

The GPM Ground Validation Multi-Radar/Multi-Sensor (MRMS) Precipitation Reanalysis for Satellite Validation Product dataset contains precipitation rate and type estimates, quality control products, and precipitation corrective factors products. These data products were created using the NOAA MRMS System which ingests Weather Surveillance Radar 88 Doppler (WSR-88D) radar data, Rapid Update Cycle (RAP) model analysis fields, and gauge data. It should be noted that these data products are not standard MRMS. Significant post-processing is applied to MRMS to generate products specifically adapted to satellite purposes and needs over North America. These data are available from March 2, 2014 through October 30, 2018 in ASCII format.

Multi-Radar/Multi-Sensor (MRMS) Precipitation Reanalysis for Satellite Validation Product IMPACTS V1

The Multi-Radar/Multi-Sensor (MRMS) Precipitation Reanalysis for Satellite Validation Product IMPACTS dataset contains reflectivity products using the MRMS system during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. Data are available from January 1, 2022, through March 2, 2023, in netCDF-4 format.

GPM GROUND VALIDATION SATELLITE SIMULATED ORBITS MC3E V1

The GPM Ground Validation Satellite Simulated Orbits MC3E dataset is available in the Orbital database , which takes account for the atmospheric profiles, the cloud/rain profiles, and the detailed surface/terrain information from the Cloud-Resolving Model (CRM) database. Unique geometry and antenna gain patterns of each sensor (GMI imager, GMI sounder, DPR Ku, DPR Ka_MA, DPR Ka_HS) are considered. The Orbital database consists of satellite orbit parameters, geolocation of Field of View and satellite location, and simulated Level 1B/Level 2-like parameters in satellite orbital grid. Orbital data covers a portion of sampling right over the Cloud-resolving model (CRM) domain. All orbital data format is NetCDF3, and it contains dimensions, parameter descriptions, and parameter units. Each project's data is distributed as a separate dataset. MC3E occurred in Oklahoma, USA in 2011; LPVEX took place in Finland in 2010; C3VP experiment was held in Canada in 2007 and TWP-ICE took place in Australia in 2006.

GPM Ground Validation Composite Satellite Overpasses OLYMPEX

The GPM Ground Validation Composite Satellite Overpasses OLYMPEX dataset provides brightness temperature, precipitation, and total column water vapor estimates from multiple satellite overpasses including DMSP F16-19, GCOM-W1, GPM, MetOp, NOAA, and NPP for the OLYMPEX field campaign. The OLYMPEX field campaign took place between November, 2015, and January, 2016, with additional ground sampling continuing through February, on the Olympic Peninsula in the Pacific Northwest of the United States. This field campaign provides ground-based validation support of the findings resulting from the Global Precipitation Measurement (GPM) Core Observatory satellite. Data files are available from November 1, 2015 thru May 1, 2016 in HDF-5 format.