Dataset
ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the year 2017, v1
Abstract
This dataset comprises estimates of forest above-ground biomass for the year 2017. 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.
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) for the year 2017 (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 standard error in Mg/ha (raster dataset)
This release of the data is version 1, with data provided in both netcdf and geotiff format.
Details
| Previous Info: |
2020-03-10 Pleae note, 3 geotiff tiles were accidentally omitted from the original data delivery. These were added on the 10th March 202… Show More 2020-03-10 Pleae note, 3 geotiff tiles were accidentally omitted from the original data delivery. These were added on the 10th March 2020 and can be found in the geotiff/errata_additional_files_20200310 directory. Show Less |
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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 provided in both NetCDF and GeoTiff format
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Related Documents
| Product User Guide |
| ESA CCI Biomass project website |
| Algorithm Theoretical Basis Document |
Citations: 10
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.
| Araza, A., de Bruin, S., Hein, L. & Herold, M. (2023) Spatial predictions and uncertainties of forest carbon fluxes for carbon accounting. Scientific Reports 13. https://doi.org/10.1038/s41598-023-38935-8 https://doi.org/10.1038/s41598-023-38935-8 |
| Harper, K.L., Lamarche, C., Hartley, A., et al. (2023) A 29-year time series of annual 300 m resolution plant-functional-type maps for climate models. Earth System Science Data 15, 1465–1499. https://doi.org/10.5194/essd-15-1465-2023 https://doi.org/10.5194/essd-15-1465-2023 |
| Jung, M., Arnell, A., de Lamo, X., et al. (2020) Areas of global importance for terrestrial biodiversity, carbon, and water. https://doi.org/10.1101/2020.04.16.021444 https://doi.org/10.1101/2020.04.16.021444 |
| Jung, M., Arnell, A., de Lamo, X., et al. (2021) Areas of global importance for conserving terrestrial biodiversity, carbon and water. Nature Ecology & Evolution 5, 1499–1509. https://doi.org/10.1038/s41559-021-01528-7 https://doi.org/10.1038/s41559-021-01528-7 |
| Luo, K., Wei, Y., Du, J., et al. (2021) Machine learning-based estimates of aboveground biomass of subalpine forests using Landsat 8 OLI and Sentinel-2B images in the Jiuzhaigou National Nature Reserve, Eastern Tibet Plateau. Journal of Forestry Research 33, 1329–1340. https://doi.org/10.1007/s11676-021-01421-w https://doi.org/10.1007/s11676-021-01421-w |
| Power, D., Rico-Ramirez, M.A., Desilets, S., Desilets, D. & Rosolem, R. (2021) Cosmic-Ray neutron Sensor PYthon tool (crspy 1.2.1): an open-source tool for the processing of cosmic-ray neutron and soil moisture data. Geoscientific Model Development 14, 7287–7307. https://doi.org/10.5194/gmd-14-7287-2021 https://doi.org/10.5194/gmd-14-7287-2021 |
| Schepaschenko, D., Moltchanova, E., Fedorov, S., et al. (2021) Russian forest sequesters substantially more carbon than previously reported. Scientific Reports 11. https://doi.org/10.1038/s41598-021-92152-9 https://doi.org/10.1038/s41598-021-92152-9 |
| Schmidt, L., Forkel, M., Zotta, R.-M., Scherrer, S., Dorigo, W.A., Kuhn-Régnier, A., van der Schalie, R. & Yebra, M. (2023) Assessing the sensitivity of multi-frequency passive microwave vegetation optical depth to vegetation properties. Biogeosciences 20, 1027–1046. https://doi.org/10.5194/bg-20-1027-2023 https://doi.org/10.5194/bg-20-1027-2023 |
| Wang, J.A., Baccini, A., Farina, M., Randerson, J.T. & Friedl, M.A. (2021) Disturbance suppresses the aboveground carbon sink in North American boreal forests. Nature Climate Change 11, 435–441. https://doi.org/10.1038/s41558-021-01027-4 https://doi.org/10.1038/s41558-021-01027-4 |
| Xu, Y., Yu, L., Ciais, P., Li, W., Santoro, M., Yang, H. & Gong, P. (2022) Recent expansion of oil palm plantations into carbon-rich forests. Nature Sustainability 5, 574–577. https://doi.org/10.1038/s41893-022-00872-1 https://doi.org/10.1038/s41893-022-00872-1 |
Process overview
Instrument/Platform pairings
| Sentinel 1 Synthetic Aperture Radar (SAR) | Deployed on: Sentinel 1A |
| PALSAR-2 | Deployed on: ALOS-2 |
Computation Element: 1
| Title | GlobBiomass retrieval algorithm used in the production of the Biomass CCI global dataset of forest above ground biomass for the year 2017. |
| Abstract | The GlobBiomass retrieval algorithm is used to derive estimates of forest above-ground biomass (AGB) from satellite data for the year 2017 as part of the Biomass CCI project. First, per-pixel estimates of growing stock volume (GSV, unit: m3/ha) were obtained from spaceborne SAR images acquired in 2017 (ALOS-2 PALSAR-2, Sentinel-1) with the BIOMASAR algorithm, adapted specifically to ALOS-2 PALSAR-2 and Sentinel-1 data. Individual per-pixel GSV estimates were then combined with a set of merging rules to form the final per-pixel estimate of GSV. The retrieval was supported by LiDAR (ICESAT) data and auxiliary datasets . AGB was then obtained from GSV with a set of Biomass Expansion and Conversion Factors (BCEF) following approaches to extend on ground estimates of wood density and stem-to-total biomass expansion factors to obtain a global raster dataset. See the Algorithm Theoretical Basis Document for details on the EO datasets, the biomass retrieval algorithms and the estimation of the BCEF (see http://cci.esa.int/biomass) |
| Input Description | None |
| Output Description | None |
| Software Reference | None |
| Output Description | None |
- units: Mg/ha
- long_name: Above-ground biomass
- var_id: agb
- names: Above-ground biomass
- long_name: Above-ground biomass standard_error
- units: Mg/ha
- var_id: agb_se
- names: Above-ground biomass standard_error
- var_id: crs
- long_name: CRS definition
- names: 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
- names: latitude, WGS84 latitude coordinate
- units: degrees_east
- standard_name: longitude
- var_id: lon
- long_name: WGS84 longitude coordinate
- names: longitude, WGS84 longitude coordinate
- standard_name: time
- var_id: time
- long_name: single-year period
- names: time, single-year period
Temporal Range
2017-01-01T00:00:00
2017-12-31T23:59:59
Geographic Extent
90.0000° |
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-180.0000° |
180.0000° |
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-90.0000° |