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>&nbsp;</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&nbsp;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.&nbsp;</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&nbsp;<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': &nbsp;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&nbsp;for snow-off segments).</li> <li>'canopy_openness': Canopy openness from ICESat-2 (only&nbsp;for snow-off segments).</li> <li>'h_canopy_winter': Canopy height above terrain from ICESat-2 (only&nbsp;for snow-on segments).</li> <li>'h_mean_canopy_winter':Canopy mean height from ICESat-2 (only&nbsp;for snow-on segments).</li> <li>'canopy_openness_winter':Canopy openness&nbsp;from ICESat-2 (only&nbsp;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&nbsp;monthly snow melting (currently not in use).</li> <li>'sf_acc': Snowfall accumulation calculated from ERA5 Land&nbsp;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&nbsp;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>