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14 results for “Envisat”
Arctic sea ice radar freeboard from ERS-1, ERS-2, Envisat and CryoSat-2
<p>This dataset presents a radar freeboard time series from 1993 to 2021 for Arctic sea ice. Envisat, ERS-2 and ERS-1 radar freeboards have been estimated using CryoSat-2 as a reference, they are "SAR-like" estimations as they have been calibrated on CS-2 SAR TFMRA50 radar freeboard. </p>
InSAR stack of the 2008 Wells, Nevada earthquake from Envisat descending track 399 processed with Gamma
<p>A stack of unwrapped interferograms on Wells, Nevada for the <a href="https://earthquake.usgs.gov/earthquakes/eventpage/nn00234425/executive">2008 Wells earthquake</a>.</p> <p>Sensor: Envisat ASAR descending track 399 frame 2871</p> <p>Time: 2007.07.09 - 2008.09.01, 11 acquisitions, 51 interferograms</p> <p>Processor: GAMMA</p> <p>A minimum coherence of 0.3 is chosen during phase unwrapping, thus the low coherent pixels are assigned the zero phase value (green in the unwrapPhase_wrap.png).</p> <p>Tropospheric delay estimated from ERA-5 using PyAPS is attached.</p> <p>This is an input dataset for the time series analysis with <a href="https://github.com/insarlab/MintPy">MintPy</a>.</p>
ICESat, ERS1, ERS2, Envisat Laser and Radar Altimetry Datasets for the Cryosphere model Comparison Tool (CmCt) Input for Greenland and Antarctica
<div> <p>These datasets contain the ICESat, ERS1, ERS2, Envisat Laser and Radar Altimetry Datasets for CmCt Input data for Greenland and Antarctica. These reference observational datasets are used in the CmCt to compare ice sheet models with.</p> <p>The <strong>ICESat/GLAS</strong> instrument was a lidar altimeter and the primary instrument on the NASA ICESat mission. It took point elevation measurements approximately every 170 meters along its track, and each shot had a footprint of approximately 70 meters in diameter.</p> <p>The GLAS instrument contained 3 lasers, but due to some instrumentation issues, it was decided to turn the lasers on and off during predetermined time periods. For more detailed information about GLAS and the ICESat mission, visit the <a href="http://icesat.gsfc.nasa.gov/icesat/" target="_blank" rel="nofollow noopener noreferrer noopener noreferrer noopener noreferrer">ICESat website</a>.</p> <p>For use with the CmCt project, the Greenland elevation data from ICESat/GLAS (Zwally et al, 2002) were preprocessed. The data were cleaned and limited to the ice sheets. At the time of creation the 634 release of the <a href="https://nsidc.org/data/GLA12/versions/34" target="_blank" rel="nofollow noopener noreferrer noopener noreferrer noopener noreferrer">GLAS12</a> product was used (<em>Zwally et al, 2014</em>). </p> <p>The processing was accomplished by:</p> <ol> <li>restricting the data to GLAS data points only on the ice surface</li> <li>applying two data quality filters we required the GLAS surface reflectivity to be > 0.0375 and we required the uncertainty associated with the GLAS fitting procedure to be < 0.0375 (the numerical coincidence is in fact a coincidence). These are the same quality criteria that were used for <a href="http://imbie.org/" target="_blank" rel="nofollow noopener noreferrer noopener noreferrer noopener noreferrer">IMBIE2</a> and thus are being implemented for the CmCt.</li> <li>checking the data against the reerence DEM (GIMP 90-m DEM for Greenland or Bamber 1-km DEM for Antarctica), requiring the elevation difference to be < 200m.</li> </ol> <p>Please find more details on the data preprocessing in the Supporting Docs tab.</p> <p>The<a href="https://www.esa.int/Applications/Observing_the_Earth/Envisat" target="_blank" rel="nofollow noopener noreferrer noopener noreferrer noopener noreferrer"> <strong>Envisat</strong></a> (Environmental Satellite), <strong><a href="https://eoportal.org/web/eoportal/satellite-missions/e/ers-1" target="_blank" rel="nofollow noopener noreferrer noopener noreferrer noopener noreferrer">ERS1</a></strong>, <strong><a href="https://eoportal.org/web/eoportal/satellite-missions/e/ers-2" target="_blank" rel="nofollow noopener noreferrer noopener noreferrer noopener noreferrer">ERS2</a></strong> (European Remote Sensing Satellites 1 and 2) radar altimeter datasets were also preprocessed to prepare the data to generate a comparison data set for the CmCt. Several filters were used to remove data that are not on the ice sheet or have questionable elevations. Please see the detailed processing descriptions in the Supporting Docs.</p> <p>Radar and laser altimeters measure similar parameters. They measure the time of flight of photons from the spacecraft to the reflection point and back to the spacecraft. The time of flight is then used to calculate an elevation. Accurate elevations require precise knowledge of the spacecraft orbit, corrections for atmospheric scattering, and other factors.</p> <p>There are several differences between the radar and laser altimetry data available here that should be noted:</p> <ul> <li>The accuracy of the elevations calculated from the radar data is generally lower than the accuracy of elevations based on laser data, primarily because <ul> <li>the radar beam is much broader (several km by the time it reaches the ground vs < 100 m for the laser beam).</li> <li>the radar photons penetrate snow and ice a significant amount (cm to m), whereas the laser photons from ICESat penetrate minimally if at all.</li> </ul> </li> <li>ERS and Envisat worked at a lower pulse rate than ICESat, and had a shorter repeat period, so the data are sparser on the ground (but repeat approximately monthly). On the other hand, the radar satellites worked continuously, whereas ICESat only operated for 2-3 months per year. </li> <li>The radar data collectively cover a longer period of time, starting more than a decade earlier and extending past the end of the ICESat data.</li> <li>ERS and Envisat were in orbits that left larger holes at the poles than ICESat (8.5 degrees for the radar satellites vs 4 degrees for ICESat).</li> <li>Radar beams penetrate clouds, whereas the ICESat laser beam was scattered by clouds, with returns becoming unusable if the optical depth was much greater than 1.</li> </ul> <h4> </h4> <h4>Laser and Radar Altimetry Available Data Time Range:</h4> <h4> </h4> <table> <tbody> <tr> <td>ERS1:</td> <td>1991-1995</td> </tr> <tr> <td>ERS2:</td> <td>1996-2002</td> </tr> <tr> <td>Envisat:</td> <td>2003-2012</td> </tr> <tr> <td>ICESat/GLAS:</td> <td>2003-2009</td> </tr> </tbody> </table> <h4> </h4> <h4>Downloading Data</h4> <p><a href="https://theghub.org/resources?id=4737"><strong>The data can be downloaded from the Globus GHub-CmCt endpoint. Please log in and click on the Download tab to receive the Download instructions.</strong></a></p> </div> <h4>References</h4> <div> <p>Howat, I. M., A. Negrete, and B. E. Smith, The Greenland Ice Mapping Project (GIMP) land classification and surface elevation data sets, The Cryosphere 8.4 (2014): 1509-1518.</p> <p> </p> <p>Howat, I. M., A. Negrete, and B. E. Smith. MEaSUREs Greenland Ice Sheet Mapping Project (GIMP) Digital Elevation Model, Boulder, Colorado USA: NASA National Snow and Ice Data Center Distributed Active Archive Center (2015).</p> <p> </p> <p>Zwally, H. J., et al. ICESat's laser measurements of polar ice, atmosphere, ocean, and land, Journal of Geodynamics 34.3 (2002):405-445.</p> <p> </p> <p>Zwally, H. J., et al. GLAS/ICESat L2 Antarctic and Greenland ice sheet altimetry data V034, National Snow and Ice Data Center, Boulder, Colorado (2014).</p> </div>
Arctic and Antarctic sea ice thickness climate data record from ERS-1, ERS-2, Envisat and CryoSat-2
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SMEX03 ENVISAT ASAR Data, Alabama, Version 1
This data set is comprised of browse images acquired over the regional study areas of Alabama, Georgia, and Oklahoma USA as part of the 2003 Soil Moisture Experiment (SMEX03).
SMEX03 ENVISAT ASAR Data, Georgia, Version 1
This data set is comprised of browse images acquired over the regional study areas of Alabama, Georgia, and Oklahoma USA as part of the 2003 Soil Moisture Experiment (SMEX03).
SMEX03 ENVISAT ASAR Data, Oklahoma, Version 1
This data set is comprised of browse images acquired over the regional study areas of Alabama, Georgia, and Oklahoma USA as part of the 2003 Soil Moisture Experiment (SMEX03).
SMEX04 ENVISAT ASAR Data: Arizona, Version 1
This data set is comprised of browse images acquired over the regional study area of Arizona, USA for the 2004 Soil Moisture Experiment (SMEX04).
ENVISAT MERIS Reduced Resolution (RR) Data, version 4
MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration.
ENVISAT MERIS Full Resolution, Full Swath (FRS) Data, version 4
MERIS (Medium Resolution Imaging Spectrometer) is a programmable, medium-spectral resolution, imaging spectrometer operating in the solar reflective spectral range. Fifteen spectral bands can be selected by ground command. The instrument scans the Earth's surface by the so called 'push-broom' method. Linear CCD arrays provide spatial sampling in the across-track direction, while the satellite's motion provides scanning in the along-track direction. MERIS is designed so that it can acquire data over the Earth whenever illumination conditions are suitable. The instrument's 68.5-degree field-of-view around nadir covers a swath width of 1150 km. This wide field of view is shared between five identical optical modules arranged in a fan shape configuration.
Antarctic sea ice radar freeboard from ERS-1, ERS-2, Envisat and CryoSat-2
<p>This dataset presents a radar freeboard time series from 1993 to 2021 for Antarctic sea ice. Envisat, ERS-2, ERS-1 radar freeboards have been estimated using CryoSat-2 as a reference, they are "SAR-like" estimations as they have been calibrated on CS-2 SAR TFMRA50 radar freeboard.</p>
ENVISAT MERIS Global Binned Cyanobacteria Index (CI) Data, version 5.0
Cyanobacteria Assessment Network (CyAN) is a multi-agency project among EPA, the National Aeronautics and Space Administration (NASA), the National Oceanic and Atmospheric Administration (NOAA), and the United States Geological Survey (USGS) to support the environmental management and public use of U.S. lakes and estuaries by providing a capability of detecting and quantifying cyanobacteria algal blooms. This effort has resulted in the production of satellite remote sensing products using the cyanobacteria index (CI) algorithm to estimate cyanobacteria concentrations (CI_cyano) in lakes across the contiguous United States (CONUS) and Alaska. The Merged S3 product combines Sentinel-3A and Sentinel-3B OLCI data.
ENVISAT MERIS Global Binned CyAN Project, True Color (TC) Data, version 5.0
Cyanobacteria Assessment Network (CyAN) is a multi-agency project among EPA, the National Aeronautics and Space Administration (NASA), the National Oceanic and Atmospheric Administration (NOAA), and the United States Geological Survey (USGS) to support the environmental management and public use of U.S. lakes and estuaries by providing a capability of detecting and quantifying cyanobacteria algal blooms. This effort has resulted in the production of satellite remote sensing products using the cyanobacteria index (CI) algorithm to estimate cyanobacteria concentrations (CI_cyano) in lakes across the contiguous United States (CONUS) and Alaska. The Merged S3 product combines Sentinel-3A and Sentinel-3B OLCI data.
ENVISAT MERIS Global Mapped CyAN Project, True Color (TC) Data, version 5.0
Cyanobacteria Assessment Network (CyAN) is a multi-agency project among EPA, the National Aeronautics and Space Administration (NASA), the National Oceanic and Atmospheric Administration (NOAA), and the United States Geological Survey (USGS) to support the environmental management and public use of U.S. lakes and estuaries by providing a capability of detecting and quantifying cyanobacteria algal blooms. This effort has resulted in the production of satellite remote sensing products using the cyanobacteria index (CI) algorithm to estimate cyanobacteria concentrations (CI_cyano) in lakes across the contiguous United States (CONUS) and Alaska. The Merged S3 product combines Sentinel-3A and Sentinel-3B OLCI data.
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OpenNeuro
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