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Dataset results
5 results for “RADARSAT-2”
5405 MHz SigMF baseband recording of RCM-2 (Radarsat Constellation) using PlutoPlus SDR and four log periodic array (LPA) antennas
<p>This dataset contains a recording of the <a href="https://www.asc-csa.gc.ca/eng/satellites/radarsat/technical-features/radarsat-comparison.asp">RCM-2</a> (<a href="https://en.wikipedia.org/wiki/RADARSAT_Constellation">Radarsat Constellation</a>) satellite as it passed over Berkeley, California. The recording was made on 2024-08-13 and is about 15 seconds long, containing acquisition of signal pulses and loss of signal at the tail end of the recording. The dataset is stored in SigMF format. The data files are compressed with xz to reduce their size. The IQ sample rate is 20.0 Msps and the center frequency is 5405 MHz.</p> <p>The linear antenna array used to record contained four HT5 antennas, labeled as: "HT5 antenna UWB log-periodic antenna 1300MHz-10GHz". These four antennas were spaced 21 cm apart. The input from these four antennas was combined with a SP-TX-4B splitter/combiner using equal lengths of LMR400 coax, then amplified using an LNA labeled as "RF AMP 04A: TQP3M9037 0.1-6GHz". The LNA was powered via a +5 volt bias-tee. A "Pluto+" or Pluto Plus SDR was used to sample, with SatDump software. </p> <p> </p>
Supplementary Material: Automated Extraction of Inundated Areas from Multi-Temporal Dual-Polarization RADARSAT-2 Images of the 2011 Central Thailand Flood
<p>Supplementary figures for Figure 4. Automated threshold values using the neighborhood valley method of water references in the red elliptical areas from the HH, HV and HH + HV sigma-naught values. The dashed lines represent unimodal distributions, and the solid lines represent bimodal distributions.</p>
TVC Experiment 2018/19: Radarsat-2 backscatter data
<p><span>This dataset contains the processed, backscatter data from Radarsat-2 (RSAT-2) satellite data, as part of Environment and Climate Change Canada's 2018-2019 Trail Valley Creek Snow Experiment (TVC Experiment 18/19). These RSAT-2 data were collected and processed evaluate against a network of Steven’s HydraProbe soil monitoring sensors, and coincident in situ snowpit measurements, airborne radar, and other satellite radar data to better understand soil-snow-radar interactions in a tundra environment. The RSAT-2 data was ordered to provide wintertime coverage from September 2018 to July 2019, over the Trail Valley Creek research station (https://www.trailvalleycreek.ca/) in Northwest Territories, Canada. Three periods of in situ snow measurement took place in November 2018, January 2019, and March 2019. RSAT-2 data was acquired in Wide Fine Quad mode HH+HV+VH+VV. The RSAT-2 products were processed using the European Space Agency’s (ESA), Sentinel Application Platform (SNAP) software which included image calibration to sigma nought and orthorectification. An average of the calibrated backscatter and incidence angles was then calculated for an area 100 x 100 meters surrounding the geographic coordinates of each snowpit.</span></p>
Using generalized regression neural network to retrieve bare surface soil moisture from Radarsat-2 backscatter observations, regard less of roughness effect
<p>Description of soil moisture, correlation length, and RMS height from ground measurements of 147 sampling sites, full-polarized backscattering coefficients extracted from Radarsat-2 scene corresponding to those ground measurement sites.</p>
Calibrated Radarsat-2 soil moisture retrievals at the Trail Valley Creek study site
<p>Calibrated soil moisture retrievals from Radarsat-2 along with in-situ soil moisture/soil temperature observations over 14 sites (in two different plots) in the Trail Valley Creek study area.<br> The data are compiled in two records: i) satellitetime (at the time of the satellite overpass: 2014 and 2016), and ii) timeseries (dense time series: 2014 only; 2016 already published at doi:10.5683/SP/NDTRLG).</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.