Nationwide estimation of boreal forest above-ground biomass using ICESat-2 data

Our objective was to train a nationwide ICESat-2 model for the estimation of above-ground biomass (AGB) and its uncertainty for the entire country of Finland. The model was trained using data from eight forest inventory areas from different parts of the country. The inventory areas had airborne laser scanning, Sentinel-2 data, and field plots publicly available, and these data were used to construct proxy models that were employed to predict AGB values for the ICESat-2 tracks overlapping with the inventory areas. The final ICESat-2 AGB model was based on n = 11676 track segments (90 x 15 m) from the eight training areas. Both day and night data were used in the construction of ICESat-2 model, but all data with snow or cloud cover were omitted.

The ICESat-2 model was applied to all forested ICESat-2 segments (n = 288391) obtained from Finland in year 2021. The total AGB for Finland and its uncertainty were estimated using a hierarchical hybrid approach that only used this sample of ICESat-2 tracks without wall-to-wall mapping. The uncertainty estimation considered tree biomass models, proxy models, the nationwide model, and sampling as  error components. The final biomass estimate was compared with the official statistic from the Finnish National Forest Inventory (NFI).

The total AGB estimated for Finland was 1063.0±114.9 million tons, while the reference value from NFI was 1308 million tons. Thus, our method resulted in clear underestimation of AGB. Probable reasons for the observed underestimation include averaging of large biomass values due to the long model chains, and misclassification of sparser canopies as noise. Nevertheless, our result shows that ICESat-2 is feasible for AGB estimation in large areas, but more research is needed to reduce the underestimation.