National-scale mapping of temperate forest above-ground biomass in the Czech Republic using Sentinel-1 and Sentinel-2 data

Rising atmospheric CO₂ concentrations increase demand for spatially explicit, repeatable estimates of forest carbon stocks. Yet operational Earth observation (EO)-based above-ground biomass (AGB) products for temperate forests remain limited. This study presents the first national-scale EO-based forest AGB dataset for the Czech Republic, produced through a reproducible workflow combining cloud-based data processing and external modelling. Sentinel-1 radar backscatter and Sentinel-2 multispectral reflectance were processed in Google Earth Engine, with texture metrics derived separately. Forest AGB was modelled in Python using 107 in-situ reference plots, including DendroNetwork forest plots and non-forest low-biomass sites used to constrain the lower end of the biomass gradient. Spatial transferability was assessed using nested spatial cross-validation with 1 km blocking (5 × 5 GroupKFold). Random Forest, eXtreme Gradient Boosting, and LightGBM were evaluated. Random Forest was selected for map generation because of its robust operational behaviour and competitive spatially independent accuracy (R² = 0.69 ± 0.12; RMSE = 79.28 ± 16.14 Mg·ha⁻¹; MAE = 48.03 ± 10.20 Mg·ha⁻¹). The resulting maps represent national forest AGB conditions during the 2023 growing season. AGB estimates were converted to carbon stocks for comparison with national forest inventory data.