Using Sentinel-2 and canopy height models to derive a landscape-level biomass map covering multiple vegetation types

Vegetation biomass is a globally important climate-relevant terrestrial carbon pool and also drives local hydrological
systems via evapotranspiration. Vegetation biomass of individual vegetation types has been successfully
estimated from active and passive remote sensing data. However, for many tasks, landscape-level biomass
maps across several vegetation types are more suitable than biomass maps of individual vegetation types. For
example, the validation of ecohydrological models and carbon budgeting typically requires spatially continuous
biomass estimates, independent from vegetation type. Studies that derive biomass estimates across multiple
vegetation or land-cover types to merge them into a single landscape-level biomass map are still scarce, and
corresponding workflows must be developed. Here, we present a workflow to derive biomass estimates on
landscape-level for a large watershed in central Chile. Our workflow has three steps: First, we combine field plotbased
biomass estimates with spectral and structural information collected from Sentinel-2, TanDEM-X and
airborne LiDAR data to map grassland, shrubland, native forests and pine plantation biomass using random forest
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regressions with an automatic feature selection. Second, we predict all models to the entire landscape. Third, we
derive a land-cover map including the four considered vegetation types. We then use this land-cover map to
assign the correct vegetation type-specific biomass estimate to each pixel according to one of the four considered
vegetation types. Using a single repeatable workflow, we obtained biomass predictions comparable to earlier
studies focusing on only one of the four vegetation types (Spearman correlation between 0.80 and 0.84;
normalized-RMSE below 16 % for all vegetation types). For all woody vegetation types, height metrics were
amongst the selected predictors, while for grasslands, only Sentinel-2 bands were selected. The land-cover was
also mapped with high accuracy (OA = 83.1 %). The final landscape-level biomass map spatially agrees well with
the known biomass distribution patterns in the watershed. Progressing from vegetation-type specific maps towards
landscape-level biomass maps is an essential step towards integrating remote-sensing based biomass estimates
into models for water and carbon management.