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Dataset

 

ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the year 2017, v1

Update Frequency: Not Planned
Latest Data Update: 2020-03-10
Status: Completed
Online Status: ONLINE
Publication State: Citable
Publication Date: 2019-12-02
DOI Publication Date: 2019-12-02
Download Stats: last 12 months
Dataset Size: 60 Files | 54GB
  Citations: 30
This dataset has been superseded. See Latest Version here
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.

Citable as:  Santoro, M.; Cartus, O. (2019): ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the year 2017, v1. Centre for Environmental Data Analysis, 02 December 2019. doi:10.5285/bedc59f37c9545c981a839eb552e4084. https://dx.doi.org/10.5285/bedc59f37c9545c981a839eb552e4084
Cite this dataset

Abbreviation: Not defined
Keywords: ESA, CCI, Biomass

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

Previously used record identifiers:
No related previous identifiers.
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.
The data was derived from input data from the PALSAR-2 instrument on the ALOS-2 satellite, and data from the Sentinel-1 satellite. The ALOS-2 PALSAR-2 dataset was obtained from the Japanese Aerospace Exploration Agency (JAXA) in the form of a mosaic of images acquired in 2017. The Sentinel-1 dataset consisted of individual data takes by the 1A and 1B units during 2017 and provided by the European Space Agency.
Data Quality:
For information on the quality of the data see the documentation and website for the Biomass CCI
File Format:
Data are provided in both NetCDF and GeoTiff format

Related Documents

 Product User Guide
 ESA CCI Biomass project website
 Algorithm Theoretical Basis Document

Citations: 30

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.

Abbas, S., Wong, M.S., Wu, J., Shahzad, N. & Muhammad Irteza, S. (2020) Approaches of Satellite Remote Sensing for the Assessment of Above-Ground Biomass across Tropical Forests: Pan-tropical to National Scales. Remote Sensing 12, 3351. https://doi.org/10.3390/rs12203351 https://doi.org/10.3390/rs12203351
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
Araza, A., de Bruin, S., Herold, M., et al. (2022) A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps. Remote Sensing of Environment 272, 112917. https://doi.org/10.1016/j.rse.2022.112917 https://doi.org/10.1016/j.rse.2022.112917
Chang, Z., Fan, L., Wigneron, J.-P., et al. (2023) Estimating Aboveground Carbon Dynamic of China Using Optical and Microwave Remote-Sensing Datasets from 2013 to 2019. Journal of Remote Sensing 3. https://doi.org/10.34133/remotesensing.0005 https://doi.org/10.34133/remotesensing.0005
error occurred https://doi.org/10.5194/essd-2022-286-supplement
error occurred https://doi.org/10.3390/rs12111891
error occurred https://doi.org/10.3390/land10080817
error occurred https://doi.org/10.20527/jht.v10i3.14975
error occurred https://doi.org/10.5194/bg-2020-416
error occurred https://doi.org/10.1038/s41586-021-03728-4
error occurred https://doi.org/10.5194/bg-2022-85
Harper, K.L., Lamarche, C., Hartley, A., et al. (2022) A 29-year time series of annual 300-metre resolution plant functional type maps for climate models. https://doi.org/10.5194/essd-2022-296 https://doi.org/10.5194/essd-2022-296
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
Heinrich, V.H.A., Dalagnol, R., Cassol, H.L.G., et al. (2021) Large carbon sink potential of secondary forests in the Brazilian Amazon to mitigate climate change. Nature Communications 12. https://doi.org/10.1038/s41467-021-22050-1 https://doi.org/10.1038/s41467-021-22050-1
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
Liu, X., Wigneron, J.-P., Fan, L., et al. (2021) ASCAT IB: A radar-based vegetation optical depth retrieved from the ASCAT scatterometer satellite. Remote Sensing of Environment 264, 112587. https://doi.org/10.1016/j.rse.2021.112587 https://doi.org/10.1016/j.rse.2021.112587
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
Michael, Y., Kozokaro, G., Brenner, S. & Lensky, I.M. (2022) Improving WRF-Fire Wildfire Simulation Accuracy Using SAR and Time Series of Satellite-Based Vegetation Indices. Remote Sensing 14, 2941. https://doi.org/10.3390/rs14122941 https://doi.org/10.3390/rs14122941
Mukunga, T., Forkel, M., Forrest, M., Zotta, R.-M., Pande, N., Schlaffer, S. & Dorigo, W. (2023) Effect of Socioeconomic Variables in Predicting Global Fire Ignition Occurrence. Fire 6, 197. https://doi.org/10.3390/fire6050197 https://doi.org/10.3390/fire6050197
not a doi https://doi.org/10138/325415
Power, D. (2021) Reply on RC1. https://doi.org/10.5194/gmd-2021-77-ac1 https://doi.org/10.5194/gmd-2021-77-ac1
Power, D. (2021) Reply on RC2. https://doi.org/10.5194/gmd-2021-77-ac2 https://doi.org/10.5194/gmd-2021-77-ac2
Power, D. (2021) Reply on RC3. https://doi.org/10.5194/gmd-2021-77-ac3 https://doi.org/10.5194/gmd-2021-77-ac3
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
Wang, M., Fan, L., Frappart, F., Ciais, P., Sun, R., Liu, Y., Li, X., Liu, X., Moisy, C. & Wigneron, J.-P. (2021) An alternative AMSR2 vegetation optical depth for monitoring vegetation at large scales. Remote Sensing of Environment 263, 112556. https://doi.org/10.1016/j.rse.2021.112556 https://doi.org/10.1016/j.rse.2021.112556
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

This dataset was generated by a combination of instruments deployed on platforms and computations as detailed below.

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
Coverage
Temporal Range
Start time:
2017-01-01T00:00:00
End time:
2017-12-31T23:59:59
Geographic Extent

 
90.0000°
  
-180.0000°
  
180.0000°
 
-90.0000°
  
Related parties
Authors (2)
Publishers (1)