The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations

Abstract

Abstract. The terrestrial forest carbon pool is poorly quantified, in particular in regions with low forest inventory capacity. By combining multiple satellite observations of synthetic aperture radar (SAR) backscatter around the year 2010, we generated a global, spatially explicit dataset of above-ground live biomass (AGB; dry mass) stored in forests with a spatial resolution of 1 ha. Using an extensive database of 110 897 AGB measurements from field inventory plots, we show that the spatial patterns and magnitude of AGB are well captured in our map with the exception of regional uncertainties in high-carbon-stock forests with AGB >250 Mg ha−1, where the retrieval was effectively based on a single radar observation. With a total global AGB of 522 Pg, our estimate of the terrestrial biomass pool in forests is lower than most estimates published in the literature (426–571 Pg). Nonetheless, our dataset increases knowledge on the spatial distribution of AGB compared to the Global Forest Resources Assessment (FRA) by the Food and Agriculture Organization (FAO) and highlights the impact of a country's national inventory capacity on the accuracy of the biomass statistics reported to the FRA. We also reassessed previous remote sensing AGB maps and identified major biases compared to inventory data, up to 120 % of the inventory value in dry tropical forests, in the subtropics and temperate zone. Because of the high level of detail and the overall reliability of the AGB spatial patterns, our global dataset of AGB is likely to have significant impacts on climate, carbon, and socio-economic modelling schemes and provides a crucial baseline in future carbon stock change estimates. The dataset is available at https://doi.org/10.1594/PANGAEA.894711 (Santoro, 2018).

A global map of root biomass across the world's forests

“…Note that the scales of the y axes are different between (a) and (c) and between (b) and (d). Model training and prediction were conducted on filtered data with R : S falling between the 1st and 99th percentiles and shoot biomass matching the range derived from GlobBiomass AGB (Santoro et al, 2021;Santoro, 2018) to reduce impacts from outliers. means across grid cells at the biome level (Weighted R : S Ratio in the Supplement, Table S3, vs.…”

“…After comparing the results of all three machine learning techniques and the allometric upscaling, we chose the random forest (RF) model because it performed best on cross-validation samples (see section "Building predictive models" below and Supplement, Table S8), and we only used the RF for subsequent mapping and analysis. Using this RF model, we mapped the root biomass of an average tree over an area of ∼ 1 km × 1 km across the globe using as predictors gridded maps of shoot biomass (weight per area) (Santoro et al, 2021(Santoro et al, , 2018, tree height (Simard et al, 2011), soil nitrogen (Wang et al, 2014), pH (Wang et al, 2014), bulk density (Wang et al, 2014), clay content (Wang et al, 2014), sand content (Wang et al, 2014), base saturation (Wang et al, 2014), cation exchange capacity (Wang et al, 2014), water vapor pressure (Fick and Hijmans, 2017), mean annual precipitation (Fick and Hijmans, 2017), mean annual temperature (Fick and Hijmans, 2017), aridity (Trabucco and Zomer, 2019) and water table depth (Fan et al, 2013) (Supplement, Figs. S10-S12).…”

“…As in situ field measurements of aboveground biomass (AGB) do not offer a full global coverage, gridded shoot biomass data were derived from satellite AGB products to predict root biomass at the global scale with a 1 km by 1 km spatial resolution. The gridded global shoot biomass dataset used in our study has been extensively calibrated with in situ observations and is currently the most reliable source of information on shoot biomass offering global coverage (Santoro et al, 2021(Santoro et al, , 2018Spawn et al, 2020). To derive the shoot or AGB per tree (in units of weight per tree) to generate spatially explicit global root biomass, we combined the GlobBiomass AGB satellite data product (Santoro et al, 2021(Santoro et al, , 2018 (in units of weight per unit area) with a tree density map (number of trees per unit area) (Crowther et al, 2015b).…”

“…The gridded global shoot biomass dataset used in our study has been extensively calibrated with in situ observations and is currently the most reliable source of information on shoot biomass offering global coverage (Santoro et al, 2021(Santoro et al, , 2018Spawn et al, 2020). To derive the shoot or AGB per tree (in units of weight per tree) to generate spatially explicit global root biomass, we combined the GlobBiomass AGB satellite data product (Santoro et al, 2021(Santoro et al, , 2018 (in units of weight per unit area) with a tree density map (number of trees per unit area) (Crowther et al, 2015b). The GlobBiomass dataset was based on multiple remote sensing products (radar, optical, lidar) and a large pool of in situ observations of forest variables (San-toro et al, 2015(San-toro et al, , 2021Santoro, 2018).…”

“…1-3 and S2-S17 are also provided at Figshare. Santoro et al (2021) and Santoro (2018). See the Supplement, Table S7, for details.…”