Estimating vegetation carbon stock components by linking ground databases with Earth observations

Land ecosystems dampen the increase of atmospheric CO₂ by storing carbon in soils and vegetation. In order to estimate how long carbon stays in land ecosystems, a detailed knowledge about the distribution of carbon in different vegetation components is needed. Current Earth observation products provide estimates about total above-ground biomass but do not further separate between carbon stored in trees, understory vegetation, shrubs, grass, litter or woody debris. Here we present an approach in which we link several Earth observation products with a ground-based database to estimate biomass in various vegetation components. Therefore, we use information about the statistical distribution of biomass components provided by the North American Wildland Fuels Database (NAWFD), which are however not available as geocoded data. We use ESA CCI AGB version 3 data from 2010 as a proxy in order to link the NAWFD data to the spatial information from Earth observation products. The biomass and corresponding uncertainty from the ESA CCI AGB and a map of vegetation types are used to select the likely distribution of vegetation biomass components from the set of in-situ measurements of tree biomass. We then apply Isolation Forest outlier detection and bootstrapping for a robust comparison of both datasets and for uncertainty estimation. We use Random Forest and Gaussian Process regression to predict the biomass of trees, shrubs, snags, herbaceous vegetation, coarse and fine woody debris, duff and litter from ESA CCI AGB and land cover, GEDI canopy height, Sentinel-3 LAI and bioclimatic data. The regression models reach high predictive power and allow to also extrapolate to other regions. Our derived estimates of vegetation carbon stock components provide a more detailed view on the land carbon storage and contribute to an improved estimate of potential carbon emissions from respiration, disturbances and fires.