DOI: 10.5281/zenodo.10048875
title: Retrieved snow depth in Mainland Norway (2018.10-2022.10) based on ICESat-2 ATL08 and DEMs

description: Introduction

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):



DEM Co-registration: DEMs are co-registered to ICESat-2 ATL08 snow-off reference without vertical bias adjustment.

Elevation Bias Correction: The elevation bias between the DEMs and ICESat-2 is corrected using ICESat-2 ATL08 snow-off segments.

Snow Depth Calculation: Determining snow depth by subtracting the bias-free reference ground elevation(from Step 2) from ICESat-2 ATL08 snow-on segments.


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. 

Meta Information



Study Area: Mainland Norway

Acquisition Period (ICESat-2): October 2018 to October 2020

ICESat-2 data source: ATL08 (level3, version 5)

Reference DEMs: Norway DTM1, Norway DTM10, Copernicus GLO30, FABDEM. (see reference links)

Reference snow depth: ERA5 Land (hourly), ERA5 Land (monthly).

Snow condition: 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).

Data Cleaning: No, this is a raw dataset that may contain outliers.

Mask: Excluded water surface and permanent ice at a spatial resolution of 100 m. 


Description

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 ICESat-2 ATL08 documentation. The dataset is structured into several columns, each representing a specific attribute:



'latitude': Latitude coordinates of the data points in WGS 84.

'longitude': Longitude coordinates of the data points in WGS 84.

'segment_landcover': Land cover classification for each segment.

'segment_snowcover': Snow cover classification for each segment.

'h_te_best_fit': Best-fit elevation of the terrain.

'h_te_std': Standard deviation of terrain elevation.

'n_te_photons': Number of photons used for terrain elevation estimation.

'subset_te_flag':  Quality flag (5 = all geosegments available, 4 = four geosegments...).

'segment_cover': Woody vegetation fractional cover derived from the 2019 Copernicus 100m shrub and forest fractional cover data product.

'h_canopy': Canopy height above terrain from ICESat-2 (only for snow-off segments).

'h_mean_canopy': Mean canopy height ICESat-2 (only for snow-off segments).

'canopy_openness': Canopy openness from ICESat-2 (only for snow-off segments).

'h_canopy_winter': Canopy height above terrain from ICESat-2 (only for snow-on segments).

'h_mean_canopy_winter':Canopy mean height from ICESat-2 (only for snow-on segments).

'canopy_openness_winter':Canopy openness from ICESat-2 (only for snow-on segments).

'tree_presence': the presence of trees in the segment (1 = tree, 0 = no tree, binary of h_canopy).

'pair': Pair flag for ICESat-2.

'beam': Beam flag for ICESat-2.

'p_b': Pair and beam flag for ICESat-2.

'region': Region identifier for ICESat-2.

'cloud_flag_atm': Atmospheric cloud flag for ICESat-2.

'urban_flag': Urban area flag for ICESat-2.

'h_te_skew': Skewness of terrain elevation of segments.

'snr': Signal-to-noise ratio for ICESat-2.

'terrain_slope': Slope of the terrain from ICESat-2.

'h_te_uncertainty': Uncertainty in terrain elevation estimation.

'night_flag': Flag indicating nighttime data.

'brightness_flag': Brightness flag for ICESat-2.

'h_te_interp': Interpolated terrain elevation.

'E': Easting coordinate in EPSG 32633.

'N': Northing coordinate in EPSG 32633.

'slope': Terrain slope computed from DTM10.

'aspect': Terrain aspect computed from DTM10.

'planc': Plan curvature computed from DTM10.

'profc': Profile curvature computed from DTM10.

'curvature': Overall terrain curvature computed from DTM10.

'tpi': Terrain Position Index computed from DTM10.

'tpi_9': TPI with a 90-meter radius.

'tpi_27': TPI with a 270-meter radius.

'wf_positive': Positive wind aspect index.

'wf_negative': Negative wind aspect index.

'smlt_acc': Snowmelt accumulation calculated from ERA5 Land monthly snow melting (currently not in use).

'sf_acc': Snowfall accumulation calculated from ERA5 Land monthly snowfall (currently not in use).

'sd_era': Snow depth from ERA5 Land reanalysis, coupled with ICESat-2 measurements at daily resolution,

'sde_era': Snow depth linear interpolated from ERA5 Land reanalysis.

'date': Date of data acquisition.

'date_': Date in Pandas Datatime data dype.

'month': Month of data acquisition.

'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.


Columns on elevation difference and snow depth (in meters):



'dh_after_dtm1': 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.

'snowdepth_dtm1': The elevation difference between the snow-on segment and DTM1 (ICESat-2 minus DTM1), representing the raw snow depth as measured against DTM1.

'sd_correct_dtm1': Corrected snow depth using DTM1, adjusted by bias correction model.

'df_dtm1_era5': 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

'dh_after_dtm10': The elevation difference between the snow-free segment and DTM10 (ICESat-2 minus DTM10), used in bias correction for DTM10.

'snowdepth_dtm10': The elevation difference between the snow-on segment and DTM10 (ICESat-2 minus DTM10).

'sd_correct_dtm10': Corrected snow depth using DTM10, adjusted by bias correction model.

'df_dtm10_era5': Difference between 'sd_correct_dtm10' and 'sde_era'.

'dh_after_cop30': The elevation difference between the snow-free segment and Copernicus GLO30 (ICESat-2 minus Copernicus GLO30).

'snowdepth_cop30': The elevation difference between the snow-on segment and Copernicus GLO30.

'sd_correct_cop30': The adjusted snow depth using Copernicus GLO30, adjusted by bias correction model.

'df_cop30_era5': The discrepancy between 'sd_correct_cop30' and 'sde_era'.

'dh_after_fab': The elevation difference between the snow-free segment and FABDEM (ICESat-2 minus FABDEM), used in bias correction for FABDEM.

'snowdepth_fab': The elevation difference between the snow-on segment and FABDEM, representing the uncorrected snow depth.

'sd_correct_fab': The corrected snow depth using FABDEM, adjusted by bias correction model.

'df_fab_era5': The difference between 'sd_correct_fab' and 'sde_era'.


More explanation (especially on how the parameters are calculated, such as wind aspect index) is available in related works and blog posts on snow depth, and DEM bias correction.

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:



The difference between ICESat-2 and DEMs. For example, how 'df_after_dtm1' relates to terrain parameters.

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.

The difference between ICESat-2-derived snow depth and snow depth from ERA5 Land, for example, 'df_dtm1_era5'.

The spatial distribution of snow depth or subgrid variability.