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5 results for “spaceborne radar”
Data for study: Obukhov length estimation from spaceborne radars
<p>Post-processed data used during study "Obukhov length estimation from spaceborne radars". To be used in example notebook available at <a href="http://github.com/owenodriscoll/supplement_paper/">http://github.com/owenodriscoll/supplement_paper/</a>. CSV's contain the processed buoy, ERA5 and SAR data with which to reproduce study's results and Jupyter Notebooks. Air/sea temperature, surface pressure, wind speeds and measurement heights of the buoy and ERA5 data are provided to the COARE-like algorithm available at <a href="https://cerform.readthedocs.io/en/latest/api/cerform.flux.html#module-cerform.flux.coare4">https://cerform.readthedocs.io/en/latest/api/cerform.flux.html#module-cerform.flux.coare4</a>, from which all the higher level atmospheric statistics originate.</p> <p>Contents:</p> <ol> <li>df_buoys.csv: co-location information between buoys and ERA5. Parameters without suffix "_era5" are from the buoy.</li> <li>df_cells.csv: SAR co-locations with ERA5 for scenes classified as cells. </li> <li>df_rolls.csv: SAR co-locations with ERA5 for scenes classified as rolls. </li> <li>df_rolls_buoy.csv: triple co-location between buoys, ERA5 and SAR scenes classified as rolls. Parameters without suffix "_era5" and "_buoy" are derived from the SAR observation.</li> <li>roll.nc: example radar scene containing a roll-like structure (with superimposed swell). To be used as an example of feature extraction from SAR scenes.</li> </ol>
Elevation bias due to penetration of spaceborne radar signal on Grosser Aletschgletscher, Switzerland - supplementary datasets
<h1><strong>Introduction</strong></h1> <p>We provide DEMs from Pléiades stereo images and TanDEM-X acquired in March 2021 and their difference DEM (dDEM) used to estimate the elevation bias due to radar penetration on Grosser Aletschgletscher, Switzerland. Additionally, we provide the reference DEM (i.e., swissALTI3D) used for the Pléiades DEM co-registration and TanDEM-X production, stable terrain masks used for the co-registration and the glacier outline</p> <p>These are supplementary datasets associated to the publication by Bannwart et al., (2024a): “Elevation bias due to penetration of spaceborne radar signal on Grosser Aletschgletscher, Switzerland”, Journal of Glaciology. DOI: <a href="https://doi.org/10.1017/jog.2024.37">https://doi.org/10.1017/jog.2024.37</a></p> <h1><strong>Datasets and Citation</strong></h1> <p>The following datasets have been used and/or produced as part of the study above. Below you find a description of the individual datasets and their generation, including a citation example for each dataset. In general, when one or more datasets are used, both the individual dataset as well as the study (Bannwart et al. 2024) need to be cited. If you refer to the entire dataset, cite the Zenodo entry as well as the study.</p> <ul> <li> <p>TanDEM-X_DEM_6m_EPSG_32632_non_coreg.tif</p> </li> <li> <p>TanDEM-X_DEM_6m_EPSG_32632_coreg.tif</p> </li> <li> <p>Pleiades_DEM_2m_EPSG_32632_non_coreg.tif</p> </li> <li> <p>Pleiades_DEM_6m_EPSG_32632_coreg.tif </p> </li> <li> <p>swissALTI3D_2m_EPSG_32632.tif</p> </li> <li> <p>dDEM_non_corrected_6m_EPSG_32632.tif</p> </li> <li> <p>dDEM_corrected_6m_EPSG_32632.tif</p> </li> <li> <p>stable_terrain_mask_coreg_Pléiades_2m_EPSG_32632.tif</p> </li> <li> <p>stable_terrain_mask_coreg_TanDEM-X_6m_EPSG_32632.tif</p> </li> <li> <p>stable_terrain_mask_coreg_TanDEM-X_modified_6m_EPSG_32632.tif</p> </li> <li> <p>glacier_outline_2021.shp</p> </li> </ul> <h1><strong>Data and Methods</strong></h1> <h2>TanDEM-X DEMs (non co-registered and co-registered):</h2> <p>We used an InSAR DEM generated from TanDEM-X Coregistered Single look Slant range Complex (CoSSC) data provided by the German Aerospace Center (DLR) through the project XTI_GLAC7746. The DEM originates from a single bistatic X-band (9.65 GHz) InSAR acquisition in the polarisations VV and HH from 30 March 2021 at 5:45 Central Europe Time. We used the operational processing system of the TanDEM-X mission to generate the DEM. The removal of artefacts resulted in data voids. The TanDEM-X DEM was then co-registered with the Pléiades DEM (see publication).</p> <p>Resolution: 6 m</p> <p>Coordinate reference system: EPSG:32632</p> <p><strong>Citation of individual dataset: </strong></p> <p>TanDEM-X © DLR 2024</p> <p>DLR-IMF: TanDEM-X Payload Ground Segment - CoSSC Generation and Interferometric Considerations, German Aerospace Center (DLR), Remote Sensing Technology Institute (IMF) Oberpfaffenhofen, Germany, Issue 1.0, available at: https://tandemx-science.dlr.de/ (last access: 3 May 2024), doc. TD-PGS-TN-3129.</p> <p>DLR-EOC: TanDEM-X Ground Segment – DEM Products Specification Document, German Aerospace Center (DLR) – Earth Observation Center (EOC), EOC, DLR, Oberpfaffenhofen, Germany, 3.2 Edn., available at: https://tandemx-science.dlr.de/ (last access: 3 May 2024), doc. TD-GS-PS-0021, 2018.</p> <p><strong>Citation example:</strong></p> <p>“The TanDEM-X DEM (DLR 2024; DLR-IMF 2024; DLR-EOC 2024) from the study of Bannwart et al. (2024a)....”</p> <p><strong>Licence:</strong></p> <p>This dataset is licensed under a Creative Commons CC BY-NC-SA 4.0 International License (Attribution-NonCommercial-ShareAlike).</p> <h2>Pléiades DEMs (non co-registered and co-registered):</h2> <p>The stereo pair was acquired on 31 March 2021 at 10:40 Central Europe Time. The images were processed with the NASA Ames Stereo Pipeline (ASP, Beyer and others, 2018) to generate the DEM with a ground-sampling distance of 2 m using the Semi-Global-Matching algorithm and the processing parameters from Deschamps-Berger and others (2020) without ground control points (GCPs). The Pléiades DEM was then co-registered with the reference DEM (see publication). To calculate the DEM differencing, the Pléiades DEM was resampled using bilinear interpolation to 6 m to match the resolution of the TanDEM-X DEM.</p> <p>Resolution: 2 m (non co-registered), 6 m (co-registered)</p> <p>Coordinate reference system: EPSG:32632</p> <p><strong>Citation of individual dataset: </strong></p> <p>Pléiades © CNES 2023, distribution AIRBUS DS</p> <p>Berthier, E., Lebreton, J., Fontannaz, D., Déprez, A., Michéa, D., Malet, J.-P., LEGOS-OMP / Data Terra (ForM@Ter-Theia) (2024). Pléiades Glacier Observatory Data Products. EOST. (Collection). doi: 10.25577/313a-a978</p> <p><strong>Citation example:</strong></p> <p>“The Pléiades DEM (CNES 2023; Berthier et al. 2024) from the study of Bannwart et al. (2024a)....”</p> <p><strong>Licence:</strong></p> <p>This dataset is licensed under a Creative Commons CC BY-NC 4.0 International License (Attribution-NonCommercial).</p> <h2>swissALTI3D: </h2> <p>We used the swissALTI3D DEM from swisstopo (Swiss Federal Office of Topography (2021a) as a reference DEM for (a) the co-registration of the Pléiades DEM and (b) the generation of the TanDEM-X DEM (i.e., phase unwrapping). This swissALTI3D DEM has a resolution of 2 m and was generated using aerial images acquired between July and September 2017. The downloaded version was provided in orthometric heights and swiss coordinate system CH1903+ / LV95 (EPSG:2056). Therefore, we converted to WGS84 UTM projection (EPSG:32632) using cubic interpolation and to ellipsoidal heights using the Swiss geoid model provided by swisstopo (Swiss Federal Office of Topography, 2021b). The geoid is provided in the geographic coordinate system (EPSG:4258), which was projected to UTM using the cubic method.</p> <p>Resolution: 2 m</p> <p>Coordinate reference system: EPSG:32632</p> <p><strong>Citation of individual dataset: </strong> </p> <p>Swiss Federal Office of Topography (2021). swissALTI3D. Das hochpräzise digitale Höhenmodell der Schweiz. Bundesamt für Landestopografie swisstopo, ed. Wabern. <a href="https://www.swisstopo.admin.ch/de/geodata/height/alti3d.html">https://www.swisstopo.admin.ch/de/geodata/height/alti3d.html</a>.</p> <p><strong>Citation example:</strong></p> <p>“The swissALTI3D (Swiss Federal Office of Topography 2021) as used in Bannwart et al. (2024a)....”</p> <p><strong>Licence:</strong></p> <p>This dataset is licensed under a Creative Commons CC BY 4.0 International License (Attribution).</p> <h2>DEM differencing (dDEM, non corrected and corrected):</h2> <p>We used DEM differencing to quantify the elevation difference (dh) between the TanDEM-X DEM and the Pléiades DEM. To calculate the dDEM we subtracted the Pléiades DEM from the TanDEM-X DEM (after co-registration). Values larger than ±50m in the dDEM are considered outliers and removed. Further, on the tongue of Grosser Aletschgletscher we encountered a bias in the dDEM originating from the phase unwrapping during the TanDEM-X DEM production. This resulted in unrealistic positive elevation differences at the tongue (up to 50 m). Therefore, on-glacier, we additionally removed positive differences larger than 5 m. For direct comparison of the elevation differences with the GPR, snow pit and snow core measurements one has to assume a signal propagation velocity within the snow and ice medium, which will lead to a smaller height of ambiguity in the snow volume and a reduced actual penetration depth. As a measure for the actual radar penetration depth the previously calculated dDEM due to signal penetration is scaled proportionally with (approx. 78%). The resulting file is called “dDEM_corrected”. For more information we refer to the publication.</p> <p>Resolution: 6 m</p> <p>Coordinate reference system: EPSG:32632</p> <p><strong>Citation of individual dataset: </strong> </p> <p>Bannwart, J., Piermattei, L., Dussaillant, I., Krieger, L., Floricioiu, D., Berthier, E., Roeoesli, C., Machguth, H. and Zemp, M. (2024). Elevation bias due to penetration of spaceborne radar signal on Grosser Aletschgletscher, Switzerland - supplementary datasets [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11071899</p> <p><strong>Citation example:</strong></p> <p>“The dDEM (Bannwart et al. 2024b) as produced in Bannwart et al. (2024a)....”</p> <p><strong>Licence:</strong></p> <p>This dataset is licensed under a Creative Commons CC BY-NC-SA 4.0 International License (Attribution-NonCommercial-ShareAlike).</p> <h2>Stable terrain masks:</h2> <p>The stable terrain masks have been used to co-register the Pléiades DEM to the swissALTI3D and the TanDEM-X DEM to the Pléiades DEM. The two masks were created using the Pléiades orthophoto and a supervised classification method following the approach by Deschamps-Berger C and others (2020). The masks consist of off-glacier areas where no changes are expected between the two DEMs, and only snow-free pixels were retained. The masks are a binary file with a resolution of 2m and 6 m, respectively, in UTM32N projection. To evaluate the sensitivity of the radar penetration bias to co-registration, we tested two co-registration algorithms on two stable terrain masks used to co-register the TanDEM-X DEM. In the modified stable mask, we manually removed pixels in the sparse forest and dark shadow areas based on a visual inspection of the Pléiades orthophoto.<strong> </strong></p> <p>Resolution: 2 m (mask used to co-register Pléiades DEM to the swissALTI3D), 6 m (masks used to co-register the TanDEM-X DEM to the Pléiased DEM)</p> <p>Coordinate reference system: EPSG:32632</p> <p><strong>Citation of individual dataset:</strong></p> <p>Bannwart, J., Piermattei, L., Dussaillant, I., Krieger, L., Floricioiu, D., Berthier, E., Roeoesli, C., Machguth, H. and Zemp, M. (2024). Elevation bias due to penetration of spaceborne radar signal on Grosser Aletschgletscher, Switzerland - supplementary datasets [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11071899<a href="https://doi.org/10.5281/zenodo.11071899"> </a></p> <p><strong>Citation example:</strong></p> <p>“The stable terrain mask to co-register the Pléiades DEM (Bannwart et al. 2024b) in Bannwart et al. (2024a)....”</p> <p><strong>Licence:</strong></p> <p>This dataset is licensed under a Creative Commons CC BY-NC 4.0 International License (Attribution-NonCommercial).</p> <h2>Glacier outline:</h2> <p>The outline as a shapefile of Grosser Aletschgletscher was manually adjusted to the glacier’s outline in 2021 based on the GLIMS outlines from 2015 (Paul and others, 2019) and the Pléiades orthophoto.</p> <p><strong>Citation of individual dataset:</strong></p> <p>Bannwart, J., Piermattei, L., Dussaillant, I., Krieger, L., Floricioiu, D., Berthier, E., Roeoesli, C., Machguth, H. and Zemp, M. (2024). Elevation bias due to penetration of spaceborne radar signal on Grosser Aletschgletscher, Switzerland - supplementary datasets [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11071899</p> <p>Paul, F. (submitter), Azzoni, R.S., Fugazza, D., Le Bris, R., Nemec, J., Paul, F., Rabatel, A., Ramusovic, M.; Rastner, P., Schaub, Y., Schwaizer (nee Bippus), G (analysts). (2019) GLIMS Glacier Database. Boulder, Co. National Snow and Ice Data Center. http://dx.doi.org/10.7265/N5V98602.</p> <p><strong>Citation example:</strong></p> <p>“The glacier outline of Grosser Aletschgletscher in 2021 (Bannwart et al 2024b; Paul et al. 2019) as produced in Bannwart et al. (2024a)....”</p> <p><strong>Licence:</strong></p> <p>This dataset is licensed under a Creative Commons CC BY-NC 4.0 International License (Attribution-NonCommercial).</p> <h1>Acknowledgements</h1> <p>We thank swisstopo for providing the swissALTI3D and CNES for their support.</p> <p> </p>
A 3-D Reconstruction Strategy for Small Solar Bodies with Single Flyby Spaceborne Radar
<p>Small solar body exploration is of great scientific significance in understanding the evolution of the solar system. However, most of the small bodies are far away from the earth, and the small size, weak gravity, irregular shape, and high speed rotation further increase the difficulty of exploring a small body. In order to characterize as many targets as possible in one mission, we propose a novel 3-D imaging strategy to reconstruct 3-D shape of the rapidly spinning small bodies in flyby trajectories. Firstly, we establish an observation model and analyze the imaging geometry and the characteristics of the signal. Then we describe in detail of the reconstruction procedure, and propose a modified algorithm to improve the imaging quality. Finally, we present the simulated results to validate our proposed method.</p> <p><a href="https://zenodo.org/api/files/b6d2b3fc-96ae-47af-9289-b369c031996a/ryugu.stl?versionId=1c758c95-a883-4e16-afae-b44461e0d6e8">ryugu.stl</a> is the scaling model for 3-D shape simulation. </p> <p><a href="https://zenodo.org/api/files/b6d2b3fc-96ae-47af-9289-b369c031996a/echodata.csv?versionId=102b451a-f208-4945-957a-04d1d9db908b">echodata.csv</a> are the echo data generated in forward simulation.</p> <p><a href="https://zenodo.org/api/files/b6d2b3fc-96ae-47af-9289-b369c031996a/recdata.zip?versionId=0fa554ad-b24b-48d7-b3df-f61083a07e1e">recdata.zip</a> are the original data of reconstructed results generated by the proposed algorithm.</p> <p> </p>
Spaceborne Imaging Radar-C Precision
Spaceborne Imaging Radar-C/X-band Synthetic Aperture Radar (SIR-C/X-SAR) is a joint project of the National Aeronautics and Space Administration (NASA), the German Space Agency, Deutsche Agentur fur Raumfahrtangfelegenheiten (DARA), and the Italian Space Agency, Agenzia Spaziale Italiana (ASI). An imaging radar system launched aboard the NASA Space Shuttle twice in 1994, SIR-C/X-SAR's unique contributions to Earth observation and monitoring are its capability to measure, from space, the radar signature of the surface at three different wavelengths and to make measurements for different polarizations at two of those wavelengths. The SIR-C image data help scientists understand the physics behind some of the phenomena seen in radar images at just one wavelength/polarization, such as those produced by SeaSAT. Investigators on the SIR-C/X-SAR Science team use the radar image data to make measurements of vegetation type, extent and deforestation, soil moisture content, ocean dynamics, wave and surface wind speeds and directions, volcanism and tectonic activity, and soil erosion and desertification. The SIR-C provides multi-frequency, multi-polarization radar data.The SIR-C instrument is composed of several subsystems: an antenna array, a transmitter, receivers, a data-handling subsystem, and a ground SAR processor. The data are processed into images with selectable resolution from 10 to 200 meters. The width of the area mapped by the radar varies from 15 to 90 kilometers, depending on how the radar is operated and on the direction in which the antenna beams are pointing. Data from SIR-C/X-SAR are used to develop automatic techniques for extracting information from radar image data.
Spaceborne Imaging Radar C-band (SIR-C)
Spaceborne Imaging Radar-C (SIR-C) is part of an imaging radar system that was flown on board two Space Shuttle flights (9 - 20 April, 1994 and 30 September - 11 October, 1994). The USGS distributes the C-band (5.8 cm) and L-band (23.5 cm) data. All X-band (3 cm) data is distributed by DLR. There are several types of products that are derived from the SIR-C data: Survey Data is intended as a "quick look" browse for viewing the areas that were imaged by the SIR-C system. The data consists of a strip image of an entire data swath. Resolution is approximately 100 meters, processed to a 50-meter pixel spacing. Files are distributed via File Transfer Protocol (FTP) download. Precision (Standard) Data consists of a frame image of a data segment, which represents a processed subset of the data swath. It contains high-resolution multifrequency and multipolarization data. All precision data is in CEOS format. The following types of precision data products are available: Single-Look Complex (SLC) consists of one single-look file for each scene, per frequency. Each data segment will cover 50 kilometers along the flight track, and is broken into four processing runs (two L band, two C-band). Resolution and polarization will depend on the mode in which the data was collected. Available as calibrated or uncalibrated data. Multi-Look Complex (MLC) is based on an averaging of multiple looks, and consists of one file for each scene per frequency. Each data segment will cover 100 km along the flight track, and is broken into two processing runs (one L band and one C band). Polarization will depend on the modes in which the looks were collected. The data is available in 12.5- or 25-meter pixel spacing. Reformatted Signal Data (RSD) consists of the raw radar signal data only. Each data segment will cover 100 km along the flight track, and the segment will be broken into two processing runs (L-band and C-band). Interferometry Data consists of experimental multitemporal data that covers the same area. Most data takes were collected during repeat passes within the second flight (days 7, 8, 9, and/or 10). In addition, nine data takes were collected during the second flight that were repeat passes of the first flight. Most data takes were also single polarization, although dual and quad polarization data was also collected on some passes. A Digital Elevation Model (DEM) is not included with any of the SIR-C interferometric data. The following types of interferometry products are available: Interferometric Single-Look Complex (iSLC) consists of two or more uncalibrated SLC images that have been processed with the same Doppler centroid to allow interferometric processing. Each frame image covers 50 kilometers along the flight track. The data is available in CEOS format. Raw Interferogram product (RIn) involves the combination of two data takes over the same area to produce an interferogram for each frequency (L-band and C-band). The data is available in TAR format. Reformatted Signal Data (RSD) consists of radar signal data that has been processed from two or more data takes over the same area, but the data has not been combined. Although this is not technically an interferometric product, the RSD can then be used to generate an interferogram. Each frame will cover 100 km along the flight track. The data is available in CEOS format.
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