Dataset
ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2010, 2017, 2018, 2019 and 2020, v4
Abstract
This dataset comprises estimates of forest above-ground biomass for the years 2010, 2017, 2018, 2019 and 2020. They are derived from a combination of Earth observation data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat’s ASAR instrument and JAXA’s Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources. The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team.
This release of the data is version 4. Compared to version 3, version 4 consists of an update of the three maps of AGB for the years 2010, 2017 and 2018 and new AGB maps for 2019 and 2020. New AGB change maps have been created for consecutive years (2018-2017, 2019-2018 and 2020-2019) and for a decadal interval (2020-2010). The pool of remote sensing data now includes multi-temporal observations at L-band for all biomes and for all years. The AGB maps rely on revised allometries which are now based on a longer record of spaceborne LiDAR data from the GEDI and ICESat-2 missions. Temporal information is now implemented in the retrieval algorithm to preserve biomass dynamics as expressed in the remote sensing data. Biases between 2010 and more recent years have been reduced.
The data products consist of two (2) global layers that include estimates of:
1) above ground biomass (AGB, unit: tons/ha i.e., Mg/ha) (raster dataset). This is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots
2) per-pixel estimates of above-ground biomass uncertainty expressed as the standard deviation in Mg/ha (raster dataset)
In addition, files describing the AGB change between two consecutive years (i.e., 2018-2017, 2019-2018 and 2020-2010) and over a decade (2020-2010) are provided (labelled as 2018_2017, 2019_2018, 2020_2019 and 2020_2010). Each AGB change product consists of two sets of maps: the standard deviation of the AGB change and a quality flag of the AGB change. Note that the change itself can be simply computed as the difference between two AGB maps, so is not provided directly.
Data are provided in both netcdf and geotiff format.
Details
Previous Info: |
No news update for this record
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Previously used record identifiers: |
No related previous identifiers.
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Access rules: |
Public data: access to these data is available to both registered and non-registered users.
Use of these data is covered by the following licence(s): https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_biomass_terms_and_conditions_v2.pdf When using these data you must cite them correctly using the citation given on the CEDA Data Catalogue record. |
Data lineage: |
Data was created by the Biomass CCI team. |
Data Quality: |
For information on the quality of the data see the documentation and website for the Biomass CCI
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File Format: |
Data are netCDF and geotiff formatted.
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Related Documents
Product Fact Sheet for Biomass CCI |
ESA CCI Biomass project website |
Project documentation for Biomass CCI |
Algorithm Theoretical Basis Document |
Product User Guide for Biomass CCI |
Citations: 5
The following citations have been automatically harvested from external sources associated with this resource where DOI tracking is possible. As such some citations may be missing from this list whilst others may not be accurate. Please contact the helpdesk to raise any issues to help refine these citation trackings.
Feng, Y., Ciais, P., Wigneron, J.-P., et al. (2024) Global patterns and drivers of tropical aboveground carbon changes. Nature Climate Change. https://doi.org/10.1038/s41558-024-02115-x https://doi.org/10.1038/s41558-024-02115-x |
Johnson, B.A., Umemiya, C., Magcale-Macandog, D.B., Estoque, R.C., Hayashi, M. & Tadono, T. (2023) Better monitoring of forests according to FAO’s definitions through map integration: Significance and limitations in the context of global environmental goals. International Journal of Applied Earth Observation and Geoinformation 122, 103452. https://doi.org/10.1016/j.jag.2023.103452 https://doi.org/10.1016/j.jag.2023.103452 |
Qiu, L., He, J., Yue, C., Ciais, P. & Zheng, C. (2024) Substantial terrestrial carbon emissions from global expansion of impervious surface area. Nature Communications 15. https://doi.org/10.1038/s41467-024-50840-w https://doi.org/10.1038/s41467-024-50840-w |
Yu, L., Fan, L., Ciais, P., et al. (2024) Forest degradation contributes more to carbon loss than forest cover loss in North American boreal forests. International Journal of Applied Earth Observation and Geoinformation 128, 103729. https://doi.org/10.1016/j.jag.2024.103729 https://doi.org/10.1016/j.jag.2024.103729 |
Zang, J., Qiu, F. & Zhang, Y. (2024) A global dataset of forest regrowth following wildfires. Scientific Data 11. https://doi.org/10.1038/s41597-024-03896-8 https://doi.org/10.1038/s41597-024-03896-8 |
Process overview
Instrument/Platform pairings
Sentinel 1 Synthetic Aperture Radar (SAR) | Deployed on: Sentinel 1A |
PALSAR-2 | Deployed on: ALOS-2 |
ENVISAT ASAR | Deployed on: Envisat |
Phased Array type-L band Synthetic Aperture Radar (PALSAR) | Deployed on: Advanced Land Observing Satellite (ALOS) |
Computation Element: 1
Title | The ESA Biomass Climate Change Initiative above ground biomass retrieval algorithm, v3.0 |
Abstract | For information on the derivation of the Biomass CCI data, please see the ATBD (Algorithm Theoretical Baseline Document). |
Input Description | None |
Output Description | None |
Software Reference | None |
Output Description | None |
- units: Mg/ha
- long_name: Above-ground biomass
- var_id: agb
- units: Mg/ha
- long_name: Above-ground biomass change quality flag
- var_id: diff_qf
- units: Mg/ha
- long_name: Above-ground biomass change standard_deviation
- var_id: diff_sd
- long_name: Above-ground biomass standard_error
- units: Mg/ha
- var_id: agb_se
- var_id: crs
- long_name: CRS definition
- var_id: lat_bnds
- var_id: lon_bnds
- var_id: time_bnds
Co-ordinate Variables
- units: degrees_north
- standard_name: latitude
- var_id: lat
- long_name: WGS84 latitude coordinate
- units: degrees_east
- standard_name: longitude
- var_id: lon
- long_name: WGS84 longitude coordinate
- standard_name: time
- var_id: time
- units: days
- long_name: single-year period
Temporal Range
2010-01-01T00:00:00
2020-12-31T23:59:59
Geographic Extent
90.0000° |
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-180.0000° |
180.0000° |
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-90.0000° |