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34 results for “ICESat-2”
Retrieved snow depth in Mainland Norway (2018.10-2022.10) based on ICESat-2 ATL08 and DEMs
<h3><strong>Introduction</strong></h3> <p>This dataset's snow depth data was derived using elevation differencing, which is simply the snow surface elevation (ICESat-2 ATL08) minus the reference surface elevation (obtained from Digital Elevation Models):</p> <ol> <li><strong>DEM Co-registration</strong>: DEMs are co-registered to ICESat-2 ATL08 snow-off reference without vertical bias adjustment.</li> <li><strong>Elevation Bias Correction</strong>: The elevation bias between the DEMs and ICESat-2 is corrected using ICESat-2 ATL08 snow-off segments.</li> <li><strong>Snow Depth Calculation</strong>: Determining snow depth by subtracting <strong>the bias-free reference ground elevation(from Step 2)</strong> from ICESat-2 ATL08 snow-on segments.</li> </ol> <p>This dataset is presented in a tabular format, which simplifies the preprocess for machine learning models. While co-registration has been done (1), users have the flexibility to train a bias correction model again (2) and retrieve snow depth measurements anew (3). Alternatively, the snow depth can be directly used for various analytical purposes. Detailed methodologies for the co-registration, bias correction, and snow depth determination are thoroughly documented in the paper (under submission) to support users in leveraging this dataset for their research needs.<br> </p> <h3><strong>Meta Information</strong></h3> <ul> <li><strong>Study Area</strong>: Mainland Norway</li> <li><strong>Acquisition Period (ICESat-2)</strong>: October 2018 to October 2020</li> <li><strong>ICESat-2 data source</strong>: ATL08 (level3, version 5)</li> <li><strong>Reference DEMs</strong>: Norway DTM1, Norway DTM10, Copernicus GLO30, FABDEM. (see reference links)</li> <li><strong>Reference snow depth: </strong>ERA5 Land (hourly), ERA5 Land (monthly).</li> <li><strong>Snow condition</strong>: The dataset contains snow depth retrieved (snow_on_alt08_segments_and_snow_depth.csv) and snow-free observations (snow_free_alt08_segments_and_dems.csv).</li> <li><strong>Data Cleaning</strong>: No, this is a raw dataset that may contain outliers.</li> <li><strong>Mask</strong>: Excluded water surface and permanent ice at a spatial resolution of 100 m. </li> </ul> <h3><strong>Description</strong></h3> <p>This dataset encapsulates a wide array of attributes derived from ICESat-2 observations, alongside measurements pertinent to snow depth, terrain, and environmental conditions across Mainland Norway. For detailed attribute descriptions, refer to the <a href="https://nsidc.org/data/atl08/versions/5#anchor-2">ICESat-2 ATL08 documentation</a>. The dataset is structured into several columns, each representing a specific attribute:</p> <ol> <li>'latitude': Latitude coordinates of the data points in WGS 84.</li> <li>'longitude': Longitude coordinates of the data points in WGS 84.</li> <li>'segment_landcover': Land cover classification for each segment.</li> <li>'segment_snowcover': Snow cover classification for each segment.</li> <li>'h_te_best_fit': Best-fit elevation of the terrain.</li> <li>'h_te_std': Standard deviation of terrain elevation.</li> <li>'n_te_photons': Number of photons used for terrain elevation estimation.</li> <li>'subset_te_flag': Quality flag (5 = all geosegments available, 4 = four geosegments...).</li> <li>'segment_cover': Woody vegetation fractional cover derived from the 2019 Copernicus 100m shrub and forest fractional cover data product.</li> <li>'h_canopy': Canopy height above terrain from ICESat-2 (only for snow-off segments).</li> <li>'h_mean_canopy': Mean canopy height ICESat-2 (only for snow-off segments).</li> <li>'canopy_openness': Canopy openness from ICESat-2 (only for snow-off segments).</li> <li>'h_canopy_winter': Canopy height above terrain from ICESat-2 (only for snow-on segments).</li> <li>'h_mean_canopy_winter':Canopy mean height from ICESat-2 (only for snow-on segments).</li> <li>'canopy_openness_winter':Canopy openness from ICESat-2 (only for snow-on segments).</li> <li>'tree_presence': the presence of trees in the segment (1 = tree, 0 = no tree, binary of h_canopy).</li> <li>'pair': Pair flag for ICESat-2.</li> <li>'beam': Beam flag for ICESat-2.</li> <li>'p_b': Pair and beam flag for ICESat-2.</li> <li>'region': Region identifier for ICESat-2.</li> <li>'cloud_flag_atm': Atmospheric cloud flag for ICESat-2.</li> <li>'urban_flag': Urban area flag for ICESat-2.</li> <li>'h_te_skew': Skewness of terrain elevation of segments.</li> <li>'snr': Signal-to-noise ratio for ICESat-2.</li> <li>'terrain_slope': Slope of the terrain from ICESat-2.</li> <li>'h_te_uncertainty': Uncertainty in terrain elevation estimation.</li> <li>'night_flag': Flag indicating nighttime data.</li> <li>'brightness_flag': Brightness flag for ICESat-2.</li> <li>'h_te_interp': Interpolated terrain elevation.</li> <li>'E': Easting coordinate in EPSG 32633.</li> <li>'N': Northing coordinate in EPSG 32633.</li> <li>'slope': Terrain slope computed from DTM10.</li> <li>'aspect': Terrain aspect computed from DTM10.</li> <li>'planc': Plan curvature computed from DTM10.</li> <li>'profc': Profile curvature computed from DTM10.</li> <li>'curvature': Overall terrain curvature computed from DTM10.</li> <li>'tpi': Terrain Position Index computed from DTM10.</li> <li>'tpi_9': TPI with a 90-meter radius.</li> <li>'tpi_27': TPI with a 270-meter radius.</li> <li>'wf_positive': Positive wind aspect index.</li> <li>'wf_negative': Negative wind aspect index.</li> <li>'smlt_acc': Snowmelt accumulation calculated from ERA5 Land monthly snow melting (currently not in use).</li> <li>'sf_acc': Snowfall accumulation calculated from ERA5 Land monthly snowfall (currently not in use).</li> <li>'sd_era': Snow depth from ERA5 Land reanalysis, coupled with ICESat-2 measurements at daily resolution,</li> <li>'sde_era': Snow depth linear interpolated from ERA5 Land reanalysis.</li> <li>'date': Date of data acquisition.</li> <li>'date_': Date in Pandas Datatime data dype.</li> <li>'month': Month of data acquisition.</li> <li>'difference': The elevation difference between segment and subsegment at the midpoint ( 'h_te_best_fit_20m_2' minus 'h_te_best_fit'). If you want to use h_te_best_fit_20m_2 instead of h_te_best_fit as elevation from ICESat-2, you can do it by df_after_dtm1 - difference, snowdepth_dtm1 - difference.</li> </ol> <p>Columns on elevation difference and snow depth (in meters):</p> <ol> <li>'<strong>dh_after_dtm1</strong>': The elevation difference between the snow-free segment and DTM1 (ICESat-2 minus DTM1). This serves as an independent variable y in the bias correction model for DTM1. Here, 'after' means after co-registration.</li> <li>'<strong>snowdepth_dtm1</strong>': The elevation difference between the snow-on segment and DTM1 (ICESat-2 minus DTM1), representing the raw snow depth as measured against DTM1.</li> <li>'<strong>sd_correct_dtm1</strong>': Corrected snow depth using DTM1, adjusted by bias correction model.</li> <li>'<strong>df_dtm1_era5</strong>': Difference betwen 'sd_correct_dtm1' and 'sde_era'. (sd_correct_dtm1 minus sde_era), providing a comparison between corrected snow depth from DTM1 and snow depth from ERA5 Land reanalysis</li> <li><strong>'dh_after_dtm10'</strong>: The elevation difference between the snow-free segment and DTM10 (ICESat-2 minus DTM10), used in bias correction for DTM10.</li> <li><strong>'snowdepth_dtm10'</strong>: The elevation difference between the snow-on segment and DTM10 (ICESat-2 minus DTM10).</li> <li><strong>'sd_correct_dtm10'</strong>: Corrected snow depth using DTM10, adjusted by bias correction model.</li> <li><strong>'df_dtm10_era5'</strong>: Difference between 'sd_correct_dtm10' and 'sde_era'.</li> <li><strong>'dh_after_cop30'</strong>: The elevation difference between the snow-free segment and Copernicus GLO30 (ICESat-2 minus Copernicus GLO30).</li> <li><strong>'snowdepth_cop30'</strong>: The elevation difference between the snow-on segment and Copernicus GLO30.</li> <li><strong>'sd_correct_cop30'</strong>: The adjusted snow depth using Copernicus GLO30, adjusted by bias correction model.</li> <li><strong>'df_cop30_era5'</strong>: The discrepancy between 'sd_correct_cop30' and 'sde_era'.</li> <li><strong>'dh_after_fab'</strong>: The elevation difference between the snow-free segment and FABDEM (ICESat-2 minus FABDEM), used in bias correction for FABDEM.</li> <li><strong>'snowdepth_fab'</strong>: The elevation difference between the snow-on segment and FABDEM, representing the uncorrected snow depth.</li> <li><strong>'sd_correct_fab'</strong>: The corrected snow depth using FABDEM, adjusted by bias correction model.</li> <li><strong>'df_fab_era5'</strong>: The difference between 'sd_correct_fab' and 'sde_era'.</li> </ol> <p>More explanation (especially on how the parameters are calculated, such as wind aspect index) is available in related works and blog posts on<a href="https://zhihaol.eu.org/blog/2023/subgrid/"> snow depth</a>, and <a href="https://zhihaol.eu.org/blog/2023/dataset/">DEM bias correction</a>.</p> <p>This dataset includes a comprehensive collection of snow depth data and correlated environmental variables for Mainland Norway. Researchers can use this dataset to investigate the following:</p> <ul> <li>The difference between ICESat-2 and DEMs. For example, how 'df_after_dtm1'<strong> </strong>relates to terrain parameters.</li> <li>The residual bias of ICESat-2 derived snow depth, for example, snowdepth_dtm1 and bias-corrected sd_correct_dtm1. You can train a better bias correction to retrieve snow depth again. You can compare your model with my model by 'dh_reg_dtm1', 'dh_reg_dtm10', 'dh_reg_cop30', and 'dh_reg_fab', which are the elevation differences after bias correction for each DEM.</li> <li>The difference between ICESat-2-derived snow depth and snow depth from ERA5 Land, for example, 'df_dtm1_era5'.</li> <li>The spatial distribution of snow depth or subgrid variability.</li> </ul>
ICESat-2 monthly gridded winter Arctic sea ice thickness
<p>Monthly gridded (winter only) Arctic sea ice thickness estimates from ICESat-2 derived using ATL10 freeboards (https://nsidc.org/data/atl10) together with snow depth and density estimates from the NASA Eulerian Snow on Sea Ice Model (NESOSIM, https://github.com/akpetty/NESOSIM). Along-track data (from the three strong beams) are binned to the 25 km x 25 km NSIDC polar stereographic projection (EPSG:3411). The full processing chain is described in Petty et al., (2020) (code available at https://github.com/akpetty/ICESat-2-sea-ice-thickness) including several updates as detailed below.</p> <p>Temporal range: November 2018 - April 2019, October 2019 to April 2020.</p> <p>Data: A single netCDF file is included for each month. Variables include:</p> <ul> <li>Sea ice freeboard (from ATL10)</li> <li>Snow depth (redistributed NESOSIM)</li> <li>Snow density (redistributed NESOSIM)</li> <li>Bulk sea ice density</li> <li>Sea ice type (from OSI SAF)</li> <li>Sea ice thickness uncertainty</li> <li>Mean day of month in a given grid cell</li> <li>Number of freeboard segments in a given grid cell.</li> </ul> <p>A summary of the differences between the version 1 and version 2 winter Arctic sea ice thickness estimates are being presented at AGU 2020 and prepared for publication.</p> <p>Key changes from version 1 (Petty et al., 2020) to version 2 include:</p> <ul> <li>Use of release 003 ATL10 freeboards. A detailed assessment of the freeboard changes from release 002 to release 003 is provided in Kwok et al., (2020).</li> <li>Upgrade to NESOSIM v1.1: CloudSat scaling of ERA5 snowfall, a new atmospheric wind loss term, calibration against recent OIB snow depths, an extended Arctic Ocean domain and various bug fixes (<a href="https://github.com/akpetty/NESOSIM">https://github.com/akpetty/NESOSIM</a>).</li> <li>Use of all three strong beams (instead of just strong beam #1).</li> </ul> <p>The data have also been made available on a Google Cloud bucket to enable rapid data analysis from any cloud-based analytics platform: <em>gs://sea-ice-thickness-data/v2/</em></p>
IRIS: ICESat-2 River Surface Slope
<p><strong>ICESat-2 River Surface Slope (IRIS)</strong></p> <p>When using this data please cite<strong> </strong><em>Scherer D., Schwatke C., Dettmering D., Seitz F.</em>: <strong>ICESat-2 river surface slope (IRIS): A global reach-scale water surface slope dataset</strong>. Scientific Data, 10(1), 359, <a href="https://doi.org/10.1038/s41597-023-02215-x">10.1038/s41597-023-02215-x</a>, 2023.</p> <p>A detailed description of the methodology and validation is published in <em>Scherer D., Schwatke C., Dettmering D., Seitz F. </em>: <strong>ICESat-2 Based River Surface Slope and Its Impact on Water Level Time Series From Satellite Altimetry</strong>. Water Resources Research, <a href="http://doi.org/10.1029/2022WR032842">10.1029/2022WR032842</a>, 2022.</p> <p><strong>1. Summary</strong><br>The unique multibeam lidar altimeter of ICESat-2 is used to measure reach-scale water surface slope (WSS) every time the spacecraft’s orbit crosses a reach. The method of deriving WSS from simultaneous ICESat-2 ATL13 (<em>Jasinski et al., 2021</em>) observations is described in detail and validated in <em>Scherer et al. </em>(2022). In this ICESat-2 River Surface Slope (IRIS) dataset, we provide the minimum, average, and maximum slope derived with three different approaches (across, along, and combined) per reach. Additionally, we give the standard deviation and epochs of the derived WSS data. The reaches are defined by the SWOT River Database (SWORD, <em>Altenau et al., 2021</em>).</p> <p>An interactive map is available at <a href="https://dahiti.dgfi.tum.de/en/products/water-surface-slope/.">DAHITI</a>.</p> <p><strong>2. Version History</strong></p> <p>IRIS <strong>v0</strong>: Only includes the reaches studied in Scherer et al. (2022).<br>Based on ICESat-2 ATL13 v5, Cycle 1-13 (October 2018 to October 2021) and SWORD Version v1.</p> <p>IRIS <strong>v1</strong>: Global coverage (limited by ICESat-2 data availability and cloud cover).<br>Based on ICESat-2 ATL13 v5, Cycle 1-16 (October 2018 to August 2022) and SWORD Version v2.</p> <p>IRIS <strong>v2</strong>: Global coverage with 6,083 additional reaches and 92,347 more observations compared to v1.<br>Based on ICESat-2 ATL13 <strong>v6</strong>, Cycle 1-19 (October 2018 to April 2023) and SWORD Version v15.</p> <p>IRIS <strong>v2.1</strong>: Based on ICESat-2 ATL13 v6, Cycle 1-19 (October 2018 to April 2023) and <strong>SWORD Version v16</strong>.</p> <p>IRIS <strong>v2.2</strong>: 3,251 additional reaches and 58,862 new observations compared to v2.1.<br>Based on ICESat-2 ATL13 v6, Cycle 1-<strong>20</strong> (October 2018 to <strong>August</strong> 2023) and SWORD Version v16.</p> <p>IRIS <strong>v2.3</strong>: 1,595 additional reaches and 32,590 new observations compared to v2.2.<br>Based on ICESat-2 ATL13 v6, Cycle 1-<strong>21</strong> (October 2018 to <strong>October </strong>2023) and SWORD Version v16.</p> <p>IRIS <strong>v2.6</strong>: 2,755 additional reaches and 362,136 new observations compared to v2.3.<br>Based on ICESat-2 ATL13 v6, Cycle 1-<strong>23</strong> (October 2018 to <strong>May 2024</strong>) and SWORD Version v16.</p> <p>IRIS <strong>v2.9</strong>: 2,485 additional reaches and 184,549 new observations compared to v2.6.<br>Based on ICESat-2 ATL13 v6, Cycle 1-<strong>24</strong> (October 2018 to <strong>August 2024</strong>) and SWORD Version v16.<br>Fixed some broken geometries in the gpkg data.</p> <p>IRIS <strong>v3.0</strong>: Based on ICESat-2 ATL13 v6, Cycle 1-24 (October 2018 to August 2024) and <strong>SWORD Version</strong> <strong>v17</strong>.</p> <p>IRIS <strong>v3.2</strong>: 1,370 additional reaches and 210,951 new observations compared to v3.0.<br>Based on ICESat-2 ATL13 v6, Cycle 1-<strong>26</strong> (October 2018 to <strong>December 2024</strong>) and SWORD Version v17.</p> <p><strong>3. Data Format and Variable Description</strong></p> <p>From Version 2.6, <strong>IRIS is also available as GeoPackage</strong>.<br>The IRIS data is stored in a single NetCDF4 file which is structured in a single group containing the following variables:<br><strong><em>reach_id</em></strong>:<br>The SWORD reach identifier [-]<br><strong><em>lon</em></strong>:<br>Approx. centroid longitude of the SWORD reach [degrees east]<br><strong><em>lat</em></strong>:<br>Approx. centroid latitude of the SWORD reach [degrees north]<br><strong><em>across_flag, along_flag, combined_flag:</em></strong><br>Flags indicating whether ICESat-2 [across/along/combined] slope is available (1) for the reach or not (0) [-]<br><strong><em>avg_across_slope, avg_along_slope, avg_combined_slope:</em></strong><br>Average (median) ICESat-2 [across/along/combined] slope for the reach [mm/km]<br><strong><em>min_across_slope, min_along_slope, min_combined_slope:</em></strong><br>Minimum ICESat-2 [across/along/combined] slope for the reach [mm/km]<br><strong><em>max_across_slope, max_along_slope, max_combined_slope:</em></strong><br>Maximum ICESat-2 [across/along/combined] slope for the reach [mm/km]<br><strong><em>std_across_slope, std_along_slope, std_combined_slope:</em></strong><br>ICESat-2 [across/along/combined] slope standard deviation for the reach [mm/km]<br><strong><em>n_across_slope, n_along_slope, n_combined_slope:</em></strong><br>Number of days with ICESat-2 [across/along/combined] slope observations for the reach [-]<br><strong><em>min_date_across_slope, min_date_along_slope:, min_date_combined_slope:</em></strong><br>First date of ICESat-2 [across/along/combined] slope observations for the reach [days since 2000-01-01]<br><strong><em>max_date_across_slope, max_date_along_slope:, max_date_combined_slope:</em></strong><br>Latest date of ICESat-2 [across/along/combined] slope observations for the reach [days since 2000-01-01]</p> <p><strong>4. References</strong></p> <p><em>Scherer D., Schwatke C., Dettmering D., Seitz F.</em>: <strong>ICESat-2 river surface slope (IRIS): A global reach-scale water surface slope dataset</strong>. Scientific Data, 10(1), 359, <a href="https://doi.org/10.1038/s41597-023-02215-x">10.1038/s41597-023-02215-x</a>, 2023<br><em>Scherer D., Schwatke C., Dettmering D., Seitz F. (2022): <strong>ICESat-2 Based River Surface Slope and Its Impact on Water Level Time Series From Satellite Altimetry</strong>, Water Resources Research, https://doi.org/10.1029/2022WR032842</em><br><em>Jasinski M., Stoll J., Hancock D., Robbins J., Nattala J., Morison J., Jones B., Ondrusek M., Pavelsky T.M., Parrish C. and the ICESat-2-Science-Team (2021). <strong>ATLAS/ICESat-2 L3A Inland Water Surface Height</strong>, Version 5. [Dataset]</em><br><em>Altenau E.H., Pavelsky T.M., Durand, M.T., Yang X., Frasson, R.P.d.M., Bendezu, L. (2021): <strong>SWOT River Database (SWORD)</strong> [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3898569</em></p>
Dataset for assessing amazon rainforest regrowth with GEDI and ICESat-2 data
<p>This dataset includes GEDI data, ICESat-2 data, auxiliary data, and intermediate results necessary to reproduce results in <a href="https://doi.org/10.1016/j.srs.2022.100051">Milenkovic et al. 2022</a>. The code required to process the data is on: <a href="https://github.com/MilutinMM/SecFor-Regrowth.git">https://github.com/MilutinMM/SecFor-Regrowth.git</a>.</p> <p>Short descriptions of files:</p> <ul> <li><strong>ATL08_gdf.json</strong> - ICESat-2 ATL08 segments in Rondonia </li> <li><strong>ATL08_gdf_Para_MG.json</strong> - ICESat-2 ATL08 segments intersecting the two calibration sites</li> <li><strong>ATL08_h5_fileNames_Rondonia.txt</strong> - A list of ICESat-2 orbits (ATL08 h5 files) intersecting Rondonia (primary input)</li> <li><strong>calibartionModels.zip</strong> - GEDI and ICESat-2 calibration models and statistics (xlsx files)</li> <li><strong>deforested_poligons_2018_2019.zip</strong> - SPH file of a deforested polygon in the calibration site</li> <li><strong>gedi_L2A_allTime_gdf_Para_MG.json</strong> - GEDI shots intersecting the two calibration sites</li> <li><strong>gedi_L2A_allTime_MG_all.csv</strong> - GEDI shots within the FN calibration site</li> <li><strong>gedi_L2A_allTime_Para_all.csv</strong> - GEDI shots within the TNF calibration site</li> <li><strong>GEDI_L2A_fileNames_Rondonia.txt </strong> - A list of GEDI orbits (L2A h5 files) intersecting Rondonia (primary input)</li> <li><strong>gedi_L2A_gdf_Para_MG_sens_a2.json</strong> - GEDI shots intersecting the two calibration sites with sensitivities derived from the algorithm setting group 2</li> <li><strong>gedi_L2A_gdf_sens_a2.json</strong> - GEDI shots in Rondonia</li> <li><strong>gedi_L2A_MG_all_sens_a2.csv </strong>- GEDI shots within the FN calibration site (sensitivity from the alg. set. group 2)</li> <li><strong>gedi_L2A_Para_all_sens_a2.csv</strong> - GEDI shots within the TFN calibration site (sensitivity from the alg. set. group 2)</li> <li><strong>gedi_L2A_Rondonia_all_sens_a2.csv</strong> - GEDI shots in Rondonia </li> <li><strong>MG_ATL08_h5_fileNames.txt</strong> - A list of ICESat-2 orbits (ATL08 h5 files) intersecting the FN calibration site</li> <li><strong>Para_ATL08_h5_fileNames.txt</strong> - A list of ICESat-2 orbits (ATL08 h5 files) intersecting the TFN calibration site</li> <li><strong>svbr-rondonia-2018.tif</strong> - Forest age map for Rondonia (Silva Junior et al. 2020)</li> <li><strong>svbr-rondonia-2018_bw_eroded.tif</strong> - a secondary forest extent mask with removed border pixels</li> </ul> <p>References:</p> <p>Milenković, M., Reiche, J., Armston, J., Neuenschwander, A., De Keersmaecker, W., Herold, M., Verbesselt, J., Assessing amazon rainforest regrowth with GEDI and ICESat-2 data, <em>Science of Remote Sensing</em>, 2022, 100051, ISSN 2666-0172, <a href="https://doi.org/10.1016/j.srs.2022.100051">https://doi.org/10.1016/j.srs.2022.100051</a>.</p> <p>Silva Junior, C.H.L., Heinrich, V.H.A., Freire, A.T.G. <em>et al.</em> Benchmark maps of 33 years of secondary forest age for Brazil. <em>Sci Data</em> <strong>7, </strong>269 (2020). <a href="https://doi.org/10.1038/s41597-020-00600-4">https://doi.org/10.1038/s41597-020-00600-4</a></p>
ICESat-2 Arctic Sea Ice Surface Topography from the University of Maryland-Ridge Detection Algorithm: April 2019, 2020, and 2021
<p>This dataset is derived from the ICESat-2 (IS-2) Global Geolocated Photon Height Product (ATL03) using the University of Maryland-Ridge Detection Algorithm (UMD-RDA). The UMD-RDA is applied to ATL03 on a per-shot basis, nominally resulting in elevation measurements at IS-2's maximum along-track resolution of ~0.7 m. From these elevation measurements, the UMD-RDA can measure various sea ice parameters including, but not limited to, individual ridge crests and their respective sail heights, the distance between ridges, and sea ice surface roughness.</p> <p><strong>********Changes in Version 2********</strong></p> <p><em>Version 2 includes a column for time (seconds since 2018-01-01) in all parameter files in addition to longitude, latitude, and parameter value.</em></p> <p><em>The full resolution UMD-RDA derived elevation data was too large to host here, but is available upon request. If you need a particular track or segment for your research please contact me with your request by email: kd</em><em>uncan at umd dot edu</em></p>
ICESat-2 Water Depth Retrieval Comparisons for Four Supraglacial on Amery Ice Shelf, East Antarctica
<p>This archive contains the code used for analysis and producing figures for the following paper:</p> <p>Fricker, H.A., Arndt, P.S., Brunt, K.M., Datta, R.T., Fair, Z., Jasinski, M.F., Kingslake, J., Magruder, L.A., Moussavi, M., Pope, A. and Spergel, J.J., 2021. “ICESat-2 meltwater depth estimates: application to surface melt on Amery Ice Shelf, East Antarctica.” Geophysical Research Letters, 48(8), DOI: 10.1029/2020GL090550. URL: <a href="https://doi.org/10.1029/2020GL090550">https://doi.org/10.1029/2020GL090550</a><br><br>These materials are also on GitHub:<br><a href="github.com/fliphilipp/ameryMeltLakesICESat2">https://github.com/fliphilipp/ameryMeltLakesICESat2</a> </p> <p> </p> <p>The code for generating manually annotated baseline depth estimates from ICESat-2 ATL03 photon data is available here:<br><a href="https://github.com/fliphilipp/pondpicking">https://github.com/fliphilipp/pondpicking</a></p>
ICESat-2 sea ice ancillary data - Mean Sea Surface Height Grids
<p>File format: NetCDF</p> <p>Mean Sea Surface (MSS) Height data grids used for the production of ICESat-2 sea ice data products (ATL07, ATL10, ATL20, ATL21). Blended data from CryoSat-2 and DTU13.</p>
University of Tromso Arctic Ocean freeboard and snow depth product from CryoSat-2, AltiKa and ICESat-2
<p>Dual-frequency snow depth estimates for the Arctic Ocean in Oct-Apr 2018-2023 derived from gridded 25-km resolution CryoSat-2 and SARAL AltiKa radar freeboards and ICESat-2 laser freeboards. Waveform modelling approach applied to radar altimeters, ICESat-2 laser altimetry freeboards from ATL20 r004. See acompanying publication in The Cryosphere for further details.</p>
Global Water Body Levels Derived from ICESat-2
<p>This dataset contains water level records derived from ICESat-2 for 227,386 lakes spanning Oct 14, 2018 to July 16, 2020. These records have been updated to reflect an error in the calculation of lake area in the previous version which led to an overestimation of lake area at high latitudes. For details on how this correction was performed, please see the 2023 Addenda to Cooley et al (2021).</p> <p>A complete description of the method used to derive water level from ICESat-2 can be found in Cooley et al (2021), but is briefly summarized below:</p> <ol> <li>We create a conservative water mask modified from the Global Surface Water Occurrence (GSWO) product (Pekel et al., 2016);</li> <li>We intersect ATL08 mean terrain height returns with this water mask, requiring water bodies to receive at least three ICESat-2 point observations on the same day to be included in the analysis;</li> <li>We filter observations based on the mean standard deviation of returns, among other factors;</li> <li>We aggregate observations to monthly timesteps;</li> <li>We calculate seasonal variability in water level as the maximum minus the minimum monthly water level over the 22-month period.</li> </ol> <p>This dataset contains:</p> <ol> <li><strong>ICESat2_lake_variability_v2_updated.shp</strong>: A shapefile containing lake points and summary statistics (i.e. height variability, storage variability, etc) <em>*updated to include corrected lake area and storage values</em></li> <li><strong>ICESat2_lake_height_time_series_v2_updated.csv</strong>: A csv file of the monthly water height time series used to calculate the global lake level variability <em>*updated to include corrected lake area values</em></li> <li><strong>265 water mask GeoTiffs</strong>: Water masks created from GSWO which we intersect with ICESat-2 data to produce the water level time series <em>*unchanged from previous version</em></li> <li><strong>ICESat2_mask_reference_v2_updated.csv</strong> – A csv file which lists the corresponding water mask for each water body in the dataset <em>*updated to include corrected lake area values</em></li> <li><strong>USGS_height_validation_v2_updated.csv </strong>– A csv file containing the height comparison between ICESat-2 and USGS gauges used for validation and uncertainty analyses <em>*updated to include corrected lake area values</em></li> <li><strong>USGS_range_validation_ v2_updated</strong>.<strong>csv </strong>– A csv file containing the range comparison between ICESat-2 and USGS gauges used for validation and uncertainty analyses <em>*updated to include corrected lake area values</em></li> <li><strong>California_storage_validation_v2_updated.csv </strong>– A csv file containing the storage comparison between ICESat-2 and California Department of Water Resource gauges used for validation and uncertainty analyses <em>*updated to include corrected lake area and storage values</em></li> </ol> <p>See the README file for a more detailed description of this dataset. Anyone wishing to use this dataset should cite Cooley et al. 2021) and contact Sarah Cooley at <a href="mailto:scooley2@uoregon.edu">scooley2@uoregon.edu</a> with a description of the work and any questions so that we may offer guidance in regards to the best usage of our dataset. </p> <p>Cooley, S.W., Ryan, J.C., and Smith, L.C., (2021), Human alteration of global surface water storage variability, <em>Nature, </em>https://doi.org/10.1038/s41586-021-03262-3 </p>
Data for Directional Surface Wave Spectra And Sea Ice Structure from ICEsat-2 Altimetry
<p>This is data used for <em>Directional Surface Wave Spectra And Sea Ice Structure from ICEsat-2 Altimetry</em> in the Cryosphere.</p> <p>The code that reproduces this data can be found at</p> <pre>10.5281/zenodo.6908645</pre> <p>See README.md for further instructions.</p> <p> </p>
Inputs (forcing, observations and config file) for the experiments included in "Spatio-temporal snow data assimilation with the ICESat-2 laser altimeter".
<p>Inputs or the experiments included in the manuscript <a href="https://doi.org/10.5194/egusphere-2024-1404">Spatio-temporal snow data assimilation with the ICESat-2 laser altimeter</a>. </p> <p>Three experiment's inputs (forcing, observations and config file) for the Multiple Snow data Assimilation system (<a href="https://doi.org/10.5281/zenodo.11147258">MuSA</a>, v2.1) for the experimental catchment of Izas in the Spanish Pyrenees. All the experiments use ERA5 data downscaled to 20 m spatial resolution with the statistical downscaling tool <a href="https://doi.org/10.21105/joss.05059">TopoPySCALE</a>. The experiments assimilate different variables. </p> <p> C) assimilation of fSCA retrieved from Sentinel-2;</p> <p> D) assimilation of snow depth profiles retrieved with ICESat-2;</p> <p> J) joint assimilation of variables in C) and D).</p> <p> </p> <p>All the experiments assimilate the observations with the deterministic ensemble smoother with multiple data assimilation (DES-MDA) scheme.</p>
Data For: A Framework for Automated Supraglacial Lake Detection and Depth Retrieval in ICESat-2 Photon Data Across the Greenland and Antarctic Ice Sheets
<p>HDF5 data files for 1249 supraglacial lakes detected in ICESat-2 ATL03 data over Central West Greenland (melt seasons 2019 and 2020) and the Amery Ice Shelf Catchment (melt seasons 2018-19 and 2020-21). Each HDF5 data file is associated with a .jpg "quicklook" file of the same name, showing ATL03 photon elevations with the estimated along-track fits to the lake surface and lakebed and the resulting maximum lake depth, along with the corresponding ICESat-2 ground track over cloud-free concurrent satellite imagery.</p> <p>The data files are structured as following: </p> <div> <div> <div> <div> <div> <pre>group: depth_data/ - dataset: bathymetry_confidence - dataset: lakebed_fit_elevation_meters - dataset: lat - dataset: lon - dataset: surface_fit_elevation_meters - dataset: water_depth_meters - dataset: x_along_track_meters group: fluid_bathymetry_peaks/ - dataset: elevation_meters - dataset: peak_prominence - dataset: x_along_track_meters group: mframe_data/ - dataset: delta_time - dataset: density_ratio_1 - dataset: density_ratio_2 - dataset: density_ratio_3 - dataset: density_ratio_4 - dataset: major_frame_id - dataset: passes_bathymetry_check - dataset: passes_flatness_check - dataset: photon_density_peak_elevation - dataset: q_1_number_peaks - dataset: q_2_prominece - dataset: q_3_elev_spread - dataset: q_4_alignment - dataset: q_s - dataset: x_along_track_meters_end - dataset: x_along_track_meters_start group: photon_data/ - dataset: afterpulse_probability - dataset: fluid_signal_confidence - dataset: geoid_elevation_meters - dataset: lat - dataset: lon - dataset: photon_elevation_above_geoid_meters - dataset: pulse_saturation_level - dataset: x_along_track_meters group: properties/ - dataset: beam_number - dataset: beam_strength - dataset: cycle_number - dataset: granule_id - dataset: gtx - dataset: ice_sheet - dataset: lake_quality - dataset: lat - dataset: lon - dataset: melt_season - dataset: rgt - dataset: sc_orient - dataset: surface_elevation - dataset: time_utc </pre> </div> </div> </div> </div> </div>
water level change rate over 2003-2021 derived from ICESat and ICESat-2 data
<p>This dataset is the water level change rate information over 2003-2021 derived from ICESat and ICESat-2 data for 22008 lakes and reservoirs across the globe.</p>
ICESat-2 Sea Ice Surface Topography from the University of Maryland-Ridge Detection Algorithm: Coastal Alaska
<p>This dataset is derived from the ICESat-2 Global Geolocated Photon Height Product (ATL03, <a href="https://nsidc.org/data/atl03/versions/6" target="_blank" rel="noopener">Neumann et al., 2023</a>), release 006, using the University of Maryland-Ridge Detection Algorithm (UMD-RDA, <a href="https://doi.org/10.1029/2022GL100272" target="_blank" rel="noopener">Duncan & Farrell, 2022</a>). The UMD-RDA is applied to ATL03 granules on a per-shot basis, nominally resulting in elevation measurements at ICESat-2's along-track sampling of ~0.7 m.</p> <p><strong>Data Bounds (lon,lat): </strong></p> <p><em>LL corner: (-169.71, 68.32), UR corner: (-137.1, 72.18)</em></p> <p> </p> <p><strong>File Format</strong></p> <p>Coastal Alaska data are provided in .xz compressed format and decompress to ascii text. The columns are as follows:</p> <ol> <li>Longitude (degrees)</li> <li>Latitude (degrees)</li> <li>Time (seconds since 2018-01-01)</li> <li>Elevation (meters, relative to DTU18 Mean Sea Surface)</li> </ol> <p>Data are provided monthly for the period December 2021 (202112) to May 2022 (202205).</p> <p> </p> <p><strong>Users of this dataset are asked to cite the following publication:</strong></p> <p><em>Duncan, K. and Farrell, S. L. (2022). Determining Variability in Arctic Sea Ice Pressure Ridge Topography with ICESat-2. Geophys. Res. Lett., 49, e2022GL100272. https://doi.org/10.1029/2022GL100272</em></p> <p> </p> <p><em>This dataset is supported by NASA Cryosphere Grant 80NSSC20K0966</em></p> <p> </p> <p>For any questions regarding this dataset you can contact Kyle Duncan by email: kduncan at umd . edu</p>
Evaluation of reconstructed multi-decadal lake levels using ICESat, ICESat-2, and Landsat satellites
<p>Overview: This dataset contains lake hypsometry and time-varying lake levels (1992-2018) for 342 lakes worldwide with sizes ranging from 1 to 81,844 km<sup>2</sup>. The method is described in detail in Yao et al., <em>Water Resources Research</em> (in review), but a quick overview is provided here.</p> <p>1. We developed a proxy-based approach to reconstruct global-scale, multi-decadal lake levels using water areas and hypsometric curves.</p> <p>2. We depended on elevation measurements from laser altimeters ICESat and ICESat-2 and water areas from Landsat imagery to construct lake hypsometry.</p> <p>3. We used dynamic water masks to reduce the uncertainties in water levels derived from ICESat and ICESat-2.</p> <p>4. We refined water areas mapped from Landsat based on topographic constraints on lake growth and shrinkage and levels from ICESat and ICESat-2.</p> <p>5. We validated the method on 342 lakes against observed long-term water levels from in-situ gauges or satellite radar altimeters.</p> <p> </p> <p> </p> <p>This dataset includes:</p> <p>1. Lake hypsometry from Landsat, ICESat, and ICESat-2 satellites</p> <p>2. Reconstructed water levels using Landsat-derived water areas and hypsometric curves</p> <p>3. Reference level data</p>
The Results of Amery Ice Shelf Supraglacial Lake Dection and Surveying Using ICESat-2 and Sentinel-2
<p>This is the first release of the results and verification used for the submitted paper:</p> <p><strong>Zhang et al., 2024: </strong>Automatically Detection and Surveying Supraglacial Lakes Using Machine Learning from ICESat-2 and Sentinel-2 Data</p>
Coastal slope of sandy beach in texas from icesat-2 data
<p>Coastal slopes of sandy beach in Texas, USA from ICESat-2 data</p>
Global Forest Aboveground Carbon Stocks and Fluxes from GEDI and ICESat-2, 2018-2021
This dataset provides global gridded estimates of forest aboveground carbon stocks and potential fluxes at a 0.01-degree resolution. It was derived by initializing a newly developed global Ecosystem Demography model (ED v3.0) with novel remote sensing observations of tree canopy height collected by GEDI and ICESat-2, two NASA spaceborne lidar missions. A total of 3.77 billion lidar samples were used to generate gridded canopy height histograms that were then linked to ED simulations of canopy height and carbon dynamics during ecosystem succession. This process constrained representation of contemporary forest conditions and associated carbon stocks and fluxes in the model. Inputs that drove these simulations included meteorology, carbon dioxide levels, and soil properties. The data are provided in cloud-optimized GeoTIFF format.
Aboveground Biomass Density for High Latitude Forests from ICESat-2, 2020
This dataset provides estimates of Aboveground dry woody Biomass Density (AGBD) for high northern latitude forests at a 30-m spatial resolution. It is designed both for boreal-wide mapping and filling the northern spatial data gap from NASA's Global Ecosystem Dynamics Investigation (GEDI) project. Mapping forest aboveground biomass is essential for understanding, monitoring, and managing forest carbon stocks toward climate change mitigation. The AGBD estimates cover the extent of high latitude boreal forests and extend southward to 50 degrees latitude outside the boreal zone. AGBD was predicted using two modeling steps: 1) Ordinary Least Squares (OLS) regression related field plot measurements of AGBD to NASA's ICESat-2 30-m lidar samples, and 2) random forest models were used to extend estimates beyond the field plots by relating ICESat-2 AGBD predictions to wall-to-wall covariate stacks from Harmonized Landsat Sentinel-2 (HLS) and the Copernicus DEM. Per-pixel uncertainties are estimated from bootstrapping both models. Non-vegetated areas (e.g. built up, water, rock, ice) were masked out. HLS composites and ICESat-2 data were from 2019-2021; three years of conditions were aggregated into the circa 2020 map. ICESat-2 data were filtered to include only strong beams, growing seasons (June through September), solar elevations less than 5 degrees, snow free land (snow flag set to 1), and "msw_flag" equal to 0 (clear skies and no observed atmospheric scattering). ICESat-2's ATL08 product was resampled to a 30-m spatial resolution to better match both the field plots and mapped pixels. HLS data (L30HLS) were used to create a greenest pixel composite of growing season multispectral data, which was then used to compute a suite of vegetation indices: NDVI, NDWI, NBR, NBR2, TCW, TCG. These were then used, in combination with the slope and elevation data from the Copernicus DEM product, to predict 30-m AGBD per 90-km tile. Estimates of mean AGBD with standard deviation are provided in cloud-optimized GeoTIFF (CoG) format. Training data are in comma-separated values (CSV) format. A polygon map of data tiles is included as a GeoPackage file and a Shapefile.
ICESat-2 Pre-Launch Simulated Multiple Altimeter Beam Experimental Lidar Advanced Topographic Laser Altimeter System (MATLAS) Data, Version 2
Multiple Altimeter Beam Experimental Lidar (MABEL) Advanced Topographic Laser Altimeter System (MATLAS) simulates the expected performance of the ICESat-2 ATLAS instrument. The data are produced from MABEL data by adjusting the detected number of signal and noise photons to that predicted using ATLAS instrument model design cases. Each data file consists of approximately 1 minute of HDF5-derived MABEL data.
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Allen Brain Atlas
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OpenNeuro
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