This data set (ATL08) contains along-track heights above the WGS84 ellipsoid (ITRF2014 reference frame) for the ground and canopy surfaces. The canopy and ground surfaces are processed in fixed 100 m data segments, which typically contain more than 100 signal photons. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.

ATL08 contains along-track estimates of terrain height, canopy height, and canopy cover, as well as beam and reference parameters. Data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the ICESat-2 observatory.

ATL08QL is the quick look version of ATL08. Once final ATL08 files are available the corresponding ATL08QL files will be removed. ATL08 contains along-track heights above the WGS84 ellipsoid (ITRF2014 reference frame) for the ground and canopy surfaces. The canopy and ground surfaces are processed in fixed 100 m data segments, which typically contain more than 100 signal photons. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.

This data set (ATL08) contains along-track heights above the WGS84 ellipsoid (ITRF2014 reference frame) for the ground and canopy surfaces. The canopy and ground surfaces are processed in fixed 100 m data segments, which typically contain more than 100 signal photons. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.

ATL08QL is the quick look version of ATL08. Once final ATL08 files are available the corresponding ATL08QL files will be removed. ATL08 contains along-track heights above the WGS84 ellipsoid (ITRF2014 reference frame) for the ground and canopy surfaces. The canopy and ground surfaces are processed in fixed 100 m data segments, which typically contain more than 100 signal photons. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.

The ICESat-2 ATL08 footprint point AGB and canopy metrics dataset provides aboveground biomass (AGB) estimates and canopy structural parameters at the footprint level (~17 m diameter) derived from the ICESat-2 ATL08 product. This dataset includes key variables such as latitude and longitude, AGB (Mg/ha), canopy height (h_canopy), canopy cover, canopy density, and statistical height metrics (h_max, h_95, h_50). Additional attributes include photon counts and beam type (strong/weak) to assess data quality. AGB estimates are obtained from forest inventory data and linked to ICESat-2 footprints for analysis. The dataset is valuable for biomass estimation, forest structure analysis, and carbon stock assessments. To enhance modeling accuracy, it can be integrated with optical or radar remote sensing data. Data quality is influenced by factors such as laser signal strength, terrain conditions, and atmospheric interference, requiring appropriate filtering techniques.

This data set (ATL08) contains along-track heights above the WGS84 ellipsoid (ITRF2014 reference frame) for the ground and canopy surfaces. The canopy and ground surfaces are processed in fixed 100 m data segments, which typically contain more than 100 signal photons. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.

This data set (ATL08) contains along-track heights above the WGS84 ellipsoid (ITRF2014 reference frame) for the ground and canopy surfaces. The canopy and ground surfaces are processed in fixed 100 m data segments, which typically contain more than 100 signal photons. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.

This data set provides a quality-filtered set of ATLAS/ICESat-2 L3A Land and Vegetation Height, Version 5 (ATL08) observations of relative canopy heights and aboveground biomass density model results for circumpolar boreal forests. The data were collected at 30 m along-track segment lengths for strong beams only during the 2019–2021 high northern latitude growing seasons. The ATL08 point observations were clipped to the extent of the boreal forest spatial domain.

This dataset provides global rasters of relative height metrics for vegetation from Global Ecosystem Dynamics Investigation (GEDI) L2A data and Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) L3A ATL08 data at 100-, 200-, 500-, and 1000-m spatial resolutions. The metrics include the relative heights RH98, RH90, RH75, and RH50, corresponding to the height at which the respective 98th, 90th, 75th, and 50th percentile of returned energy is reached relative to the ground. These metrics provide measures of vegetation canopy height and structure. The different relative height metrics were intercalibrated over the overlap area (50 - 52 degrees N). GEDI data were collected from 2019-2022, and ICESat2 data were from 2019-2021. The data are provided in cloud optimized GeoTIFF format.

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):

1. DEM Co-registration: DEMs are co-registered to ICESat-2 ATL08 snow-off reference without vertical bias adjustment.
2. Elevation Bias Correction: The elevation bias between the DEMs and ICESat-2 is corrected using ICESat-2 ATL08 snow-off segments.
3. 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:

1. 'latitude': Latitude coordinates of the data points in WGS 84.
2. 'longitude': Longitude coordinates of the data points in WGS 84.
3. 'segment_landcover': Land cover classification for each segment.
4. 'segment_snowcover': Snow cover classification for each segment.
5. 'h_te_best_fit': Best-fit elevation of the terrain.
6. 'h_te_std': Standard deviation of terrain elevation.
7. 'n_te_photons': Number of photons used for terrain elevation estimation.
8. 'subset_te_flag': Quality flag (5 = all geosegments available, 4 = four geosegments...).
9. 'segment_cover': Woody vegetation fractional cover derived from the 2019 Copernicus 100m shrub and forest fractional cover data product.
10. 'h_canopy': Canopy height above terrain from ICESat-2 (only for snow-off segments).
11. 'h_mean_canopy': Mean canopy height ICESat-2 (only for snow-off segments).
12. 'canopy_openness': Canopy openness from ICESat-2 (only for snow-off segments).
13. 'h_canopy_winter': Canopy height above terrain from ICESat-2 (only for snow-on segments).
14. 'h_mean_canopy_winter':Canopy mean height from ICESat-2 (only for snow-on segments).
15. 'canopy_openness_winter':Canopy openness from ICESat-2 (only for snow-on segments).
16. 'tree_presence': the presence of trees in the segment (1 = tree, 0 = no tree, binary of h_canopy).
17. 'pair': Pair flag for ICESat-2.
18. 'beam': Beam flag for ICESat-2.
19. 'p_b': Pair and beam flag for ICESat-2.
20. 'region': Region identifier for ICESat-2.
21. 'cloud_flag_atm': Atmospheric cloud flag for ICESat-2.
22. 'urban_flag': Urban area flag for ICESat-2.
23. 'h_te_skew': Skewness of terrain elevation of segments.
24. 'snr': Signal-to-noise ratio for ICESat-2.
25. 'terrain_slope': Slope of the terrain from ICESat-2.
26. 'h_te_uncertainty': Uncertainty in terrain elevation estimation.
27. 'night_flag': Flag indicating nighttime data.
28. 'brightness_flag': Brightness flag for ICESat-2.
29. 'h_te_interp': Interpolated terrain elevation.
30. 'E': Easting coordinate in EPSG 32633.
31. 'N': Northing coordinate in EPSG 32633.
32. 'slope': Terrain slope computed from DTM10.
33. 'aspect': Terrain aspect computed from DTM10.
34. 'planc': Plan curvature computed from DTM10.
35. 'profc': Profile curvature computed from DTM10.
36. 'curvature': Overall terrain curvature computed from DTM10.
37. 'tpi': Terrain Position Index computed from DTM10.
38. 'tpi_9': TPI with a 90-meter radius.
39. 'tpi_27': TPI with a 270-meter radius.
40. 'wf_positive': Positive wind aspect index.
41. 'wf_negative': Negative wind aspect index.
42. 'smlt_acc': Snowmelt accumulation calculated from ERA5 Land monthly snow melting (currently not in use).
43. 'sf_acc': Snowfall accumulation calculated from ERA5 Land monthly snowfall (currently not in use).
44. 'sd_era': Snow depth from ERA5 Land reanalysis, coupled with ICESat-2 measurements at daily resolution,
45. 'sde_era': Snow depth linear interpolated from ERA5 Land reanalysis.
46. 'date': Date of data acquisition.
47. 'date_': Date in Pandas Datatime data dype.
48. 'month': Month of data acquisition.
49. '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):

1. '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.
2. '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.
3. 'sd_correct_dtm1': Corrected snow depth using DTM1, adjusted by bias correction model.
4. '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
5. 'dh_after_dtm10': The elevation difference between the snow-free segment and DTM10 (ICESat-2 minus DTM10), used in bias correction for DTM10.
6. 'snowdepth_dtm10': The elevation difference between the snow-on segment and DTM10 (ICESat-2 minus DTM10).
7. 'sd_correct_dtm10': Corrected snow depth using DTM10, adjusted by bias correction model.
8. 'df_dtm10_era5': Difference between 'sd_correct_dtm10' and 'sde_era'.
9. 'dh_after_cop30': The elevation difference between the snow-free segment and Copernicus GLO30 (ICESat-2 minus Copernicus GLO30).
10. 'snowdepth_cop30': The elevation difference between the snow-on segment and Copernicus GLO30.
11. 'sd_correct_cop30': The adjusted snow depth using Copernicus GLO30, adjusted by bias correction model.
12. 'df_cop30_era5': The discrepancy between 'sd_correct_cop30' and 'sde_era'.
13. 'dh_after_fab': The elevation difference between the snow-free segment and FABDEM (ICESat-2 minus FABDEM), used in bias correction for FABDEM.
14. 'snowdepth_fab': The elevation difference between the snow-on segment and FABDEM, representing the uncorrected snow depth.
15. 'sd_correct_fab': The corrected snow depth using FABDEM, adjusted by bias correction model.
16. '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.

Full data set for comparison of ICESat-2 100m and 20m segments within areas consistently identified as pasture from 2017-2022. ATL08 v5 was used to sample 40 location in August 2023 of the 2947 measurements taken within 10 orbital passes that occurred between September 2022 and September 2023. An overview of all sampled field plots, planned and obtained, together with the sampling design and schematic of field sampling procedure are included in RioVermelho_AllPlots.pdf.

Field work was conducted between 10/01 and 10/05/2023 including field measurements and drone imagery. The canopy height models produced from RGB drone imagery flown over each plot coincident with field height measurements are provided in Drone_CHM.zip. CHMs that are not part of this dataset are not available due to file corruption and were not used in any subsequent analysis or publications. Photographs were taken at 5 locations along each of the 4 transects at nadir and panoramic. Some photographs are geolocated, but not all (typically, lines A and D are better geolocated). Photographs are divided by transect (in order of West to East) with Transect A (Fotos_Nathalia.zip), Transect B (Fotos_Maria.zip), Transect C (Fotos_WIlton.zip) and Transect D (Fotos_Lucas.zip) included. All field measurements are included in "Alturas_raw.csv" and annotated with which segments are best aligned with ICESat-2 in "Alturas_alinhadas.csv". Plants are separated by growth form into Grasses (P), Shrubs (S) and Trees (T), with their heights measured in meters. Heights less than 2 m were measured with a measuring tape, whereas taller plants' heights are estimated to the nearest meter.

ICESat-2 data for ATL08 version 6 was processed and separated for field sampled areas (atl08_fieldsegs.gpkg). Raw data from ICESat-2 ATL03 v6 was downloaded and filtered to remove potential noise photons (signal_conf_ph > 2) and those that are flagged as having potential reflection within the instrument (ph_quality > 0). The photons coincident with each field location are separated by their field id and provided in BHRV_ATL03.zip. Polygons were drawn for each segment based on photon locations and corrected to 20 x 11 m and 100 x 11 m to facilitate comparison between all datasets (20m_polygons.gpkg and 100m_polygons.gpkg).

Metrics including mean, median, and maximum height of vegetation as well as % tree cover and % vegetation cover were calculated for each of the four datasets as possible. Summarise_all.R runs summary calculations for 20m and 100m segments and outputs summary statistics by data source. Summary data files are also provided including: Field_*m.csv, Drone_*m.csv, ATL08_*m.csv, ATL03_*m.csv.

This ancillary ICESat-2 data set contains four static surface masks (land ice, sea ice, land, and ocean) provided by ATL03 to reduce the volume of data that each surface-specific along-track data product is required to process. For example, the land ice surface mask directs the ATL06 land ice algorithm to consider data from only those areas of interest to the land ice community. Similarly, the sea ice, land, and ocean masks direct ATL07, ATL08, and ATL12 algorithms, respectively. A detailed description of all four masks can be found in section 4 of the Algorithm Theoretical Basis Document (ATBD) for ATL03 linked under technical references.

SpatioTemporal Asset Catalog (STAC) Item - lon=-60_lat=60_year=2023_icesat-2_atl08 in ICESat-2_ATL08v6

This dataset includes GEDI data, ICESat-2 data, auxiliary data, and intermediate results necessary to reproduce results in Milenkovic et al. 2022. The code required to process the data is on: https://github.com/MilutinMM/SecFor-Regrowth.git.

Short descriptions of files:

• ATL08_gdf.json - ICESat-2 ATL08 segments in Rondonia
• ATL08_gdf_Para_MG.json - ICESat-2 ATL08 segments intersecting the two calibration sites
• ATL08_h5_fileNames_Rondonia.txt - A list of ICESat-2 orbits (ATL08 h5 files) intersecting Rondonia (primary input)
• calibartionModels.zip - GEDI and ICESat-2 calibration models and statistics (xlsx files)
• deforested_poligons_2018_2019.zip - SPH file of a deforested polygon in the calibration site
• gedi_L2A_allTime_gdf_Para_MG.json - GEDI shots intersecting the two calibration sites
• gedi_L2A_allTime_MG_all.csv - GEDI shots within the FN calibration site
• gedi_L2A_allTime_Para_all.csv - GEDI shots within the TNF calibration site
• GEDI_L2A_fileNames_Rondonia.txt - A list of GEDI orbits (L2A h5 files) intersecting Rondonia (primary input)
• gedi_L2A_gdf_Para_MG_sens_a2.json - GEDI shots intersecting the two calibration sites with sensitivities derived from the algorithm setting group 2
• gedi_L2A_gdf_sens_a2.json - GEDI shots in Rondonia
• gedi_L2A_MG_all_sens_a2.csv - GEDI shots within the FN calibration site (sensitivity from the alg. set. group 2)
• gedi_L2A_Para_all_sens_a2.csv - GEDI shots within the TFN calibration site (sensitivity from the alg. set. group 2)
• gedi_L2A_Rondonia_all_sens_a2.csv - GEDI shots in Rondonia
• MG_ATL08_h5_fileNames.txt - A list of ICESat-2 orbits (ATL08 h5 files) intersecting the FN calibration site
• Para_ATL08_h5_fileNames.txt - A list of ICESat-2 orbits (ATL08 h5 files) intersecting the TFN calibration site
• svbr-rondonia-2018.tif - Forest age map for Rondonia (Silva Junior et al. 2020)
• svbr-rondonia-2018_bw_eroded.tif - a secondary forest extent mask with removed border pixels

References:

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, Science of Remote Sensing, 2022, 100051, ISSN 2666-0172, https://doi.org/10.1016/j.srs.2022.100051.

Silva Junior, C.H.L., Heinrich, V.H.A., Freire, A.T.G. et al. Benchmark maps of 33 years of secondary forest age for Brazil. Sci Data 7, 269 (2020). https://doi.org/10.1038/s41597-020-00600-4

This data set provides a regression-based canopy height model of the contiguous United States (CONUS) using data from ATLAS/ICESat-2 L3A Land and Vegetation Height (ATL08), as well as data from Landsat, LANDFIRE, and NASADEM.

Overview This dataset provides a high-precision, wall-to-wall Digital Elevation Model (DEM) for the vast subtropical and tropical regions of southern China. It was developed to address the severe terrain underestimation and systematical bias commonly found in existing global DEM products under dense forest canopies and steep topographic conditions.

Methodology The DEM was generated through a novel, multi-stage framework that integrates spaceborne LiDAR and machine learning:

1. Optimized Ground Photon Extraction: We proposed an advanced pipeline integrating ICESat-2 ATL08 and ATL03 data. We successfully eliminated pseudo-ground returns and accurately recovered true ground photons beneath complex, highly occluded forest structures.

2. Machine Learning Error Modeling: The highly accurate ground elevations extracted from ICESat-2 were used as target variables. Using advanced machine learning algorithms, combined with spatial covariates including NASADEM and high-dimensional environmental features from Google Embedding Datasets, we modeled and corrected the systematic errors across the entire study area to produce this spatially continuous bare-earth terrain model.

Dataset Characteristics

• Study Area: Subtropical and tropical regions of southern China (covering provinces such as Fujian, Zhejiang, Anhui, Guangxi, Yunnan, Hainan, etc.).

• Spatial Resolution: 1 arc-second (~30 meters).

• Data Format: GeoTIFF (.tif).

• Coordinate Reference System (CRS): WGS 84 (EPSG:4326).

SpatioTemporal Asset Catalog (STAC) Item - lon=-5_lat=50_year=2021_icesat-2_atl08 in ICESat-2_ATL08v6

ATL08QL is the quick look version of ATL08 and is based on the same algorithms that generate the ATL08 final data products. Once final ATL08 files are available, the corresponding ATL08QL files are removed. ATL08QL contains along-track estimates of terrain height, canopy height, and canopy cover, as well as beam and reference parameters. Data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the ICESat-2 observatory.

The ATL18 and ATL28 data products comprise several gridded terrain and canopy elevation parameters derived from the 20 m geosegment height measurements of ATLAS/ICESat-2 L3A Land and Vegetation Height (ATL08). ATL18 is a composite of data over the entire mission, while ATL28 provides monthly data.