Europe-wide maps of biomass density based on satellite remote sensing data for 2017, 2020, 2021 and 2023

Spatially explicit information on forest structure and biomass is needed to meet the monitoring and reporting requirements of several European policies. Satellite images enable mapping and monitoring of the Europe's forest resources through operational observations from the Sentinel-1 Synthetic Aperture Radar (SAR) and the Advanced Land Observing Satellite 2 (ALOS-2) Phased Array l-band SAR 2 (PALSAR-2) instruments. Data acquired in 2017, 2020, 2021 and 2023 were used to generate annual maps of forest biomass variables, namely Growing Stock Volume (GSV), Aboveground Biomass (AGB) and Belowground Biomass (BGB), with a pixel size of 20 <i>m</i> × 20 m. All products are in the geometry of the Sentinel-2 tiling system. A spatially averaged map with a pixel size of 100 <i>m</i> × 100 m (1 hectare) in geographic projection is also supplied, for users who do not require the highest spatial resolution. The maps were generated with a fully documented processing chain that includes (i) pre-processing of the SAR data to create stacks of co-registered terrain geocoded images of the backscattered intensity and (ii) inversion of a physically-based model to estimate GSV. AGB and BGB were subsequently estimated using allometric relationships. Per-pixel standard deviations were computed for each biomass variable by propagating uncertainties from both the SAR observations and the model parameters. The maps clearly reproduce the expected spatial patterns of forest biomass across Europe and provide sufficient spatial detail to identify biomass dynamics related to, e.g., logging and regrowth. Validation against measurements collected by National Forest Inventories (NFIs) indicates poor agreement with map values at the pixel scale, with errors larger than 50% of the reference biomass. The correspondence substantially improved for spatial aggregates, such as administrative units, for which the bias was mostly negligible and the mean square error was below 30% of the reference value. The number of ALOS-2 PALSAR-2 images affected the inter-annual consistency of the maps, which was lower in regions with only one or two observations per year.

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Publication Details

Journal
Data in Brief
Published
2026-02-03
DOI
https://doi.org/10.1016/j.dib.2026.112536
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
0.00

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article

Europe-wide maps of biomass density based on satellite remote sensing data for 2017, 2020, 2021 and 2023

Jukka Miettinen, Oliver Cartus, Åke Rosenqvist, Santoro Maurizio et al.
Data in Brief
Remote Sensing in Agriculture
article

Europe-wide maps of biomass density based on satellite remote sensing data for 2017, 2020, 2021 and 2023

Jukka Miettinen, Oliver Cartus, Åke Rosenqvist, Santoro Maurizio, Arnan Araza, F. Seifert, Takeo Tadono, Kazufumi Kobayashi, Martin Herold
article en

Abstract

Spatially explicit information on forest structure and biomass is needed to meet the monitoring and reporting requirements of several European policies. Satellite images enable mapping and monitoring of the Europe's forest resources through operational observations from the Sentinel-1 Synthetic Aperture Radar (SAR) and the Advanced Land Observing Satellite 2 (ALOS-2) Phased Array l-band SAR 2 (PALSAR-2) instruments. Data acquired in 2017, 2020, 2021 and 2023 were used to generate annual maps of forest biomass variables, namely Growing Stock Volume (GSV), Aboveground Biomass (AGB) and Belowground Biomass (BGB), with a pixel size of 20 <i>m</i> × 20 m. All products are in the geometry of the Sentinel-2 tiling system. A spatially averaged map with a pixel size of 100 <i>m</i> × 100 m (1 hectare) in geographic projection is also supplied, for users who do not require the highest spatial resolution. The maps were generated with a fully documented processing chain that includes (i) pre-processing of the SAR data to create stacks of co-registered terrain geocoded images of the backscattered intensity and (ii) inversion of a physically-based model to estimate GSV. AGB and BGB were subsequently estimated using allometric relationships. Per-pixel standard deviations were computed for each biomass variable by propagating uncertainties from both the SAR observations and the model parameters. The maps clearly reproduce the expected spatial patterns of forest biomass across Europe and provide sufficient spatial detail to identify biomass dynamics related to, e.g., logging and regrowth. Validation against measurements collected by National Forest Inventories (NFIs) indicates poor agreement with map values at the pixel scale, with errors larger than 50% of the reference biomass. The correspondence substantially improved for spatial aggregates, such as administrative units, for which the bias was mostly negligible and the mean square error was below 30% of the reference value. The number of ALOS-2 PALSAR-2 images affected the inter-annual consistency of the maps, which was lower in regions with only one or two observations per year.

Data in BriefVol. 65
Japan Aerospace Exploration Agency (JP), GFZ Helmholtz Centre for Geosciences (DE), Gamma Remote Sensing (Switzerland) (CH), Remote Sensing Technology Center of Japan (JP), European Space Research Institute (IT), Toranomon Hospital (JP), VTT Technical Research Centre of Finland (FI), Wageningen University & Research (NL), Chuo University (JP)
European Space Agency
Life in Land
Openalex Percentile: Top 72%
Remote Sensing in Agriculture
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