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 plot-based 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 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.

植被生物量是全球重要的与气候相关的陆地碳库，并且还通过蒸散作用驱动当地的水文系统。已经根据主动和被动遥感数据成功地估算了各种植被类型的植被生物量。但是，对于许多任务，跨几种植被类型的景观水平生物量图比单个植被类型的生物量图更合适。例如，生态水文模型的验证和碳预算通常需要独立于植被类型的空间连续的生物量估计。仍缺乏将多种植被或土地覆盖类型的生物量估算值合并为一个景观级生物量图的研究，必须开发相应的工作流程。这里，我们提出了一个工作流程，以得出智利中部一个大流域景观水平的生物量估计。我们的工作流程包括三个步骤：首先，我们将基于现场图的生物量估算值与从Sentinel-2，TanDEM-X和机载LiDAR数据收集的光谱和结构信息结合起来，使用随机森林回归图绘制草地，灌木丛，原生林和松树人工林的生物量具有自动功能选择。其次，我们将所有模型预测为整个格局。第三，我们得出了包括四种被考虑的植被类型的土地覆盖图。然后，我们使用此土地覆盖图，根据四种考虑的植被类型之一，为每个像素分配正确的植被类型特定的生物量估计值。使用一个可重复的工作流程，我们获得的生物量预测与早期研究相当，仅侧重于四种植被类型中的一种（Spearman相关系数在0.80和0.84之间；所有植被类型的归一化RMSE低于16％）。对于所有木本植被类型，高度度量均在所选的预测因子中，而对于草原，仅选择了Sentinel-2带。土地覆被的测绘精度也很高（OA = 83.1％）。最终的景观水平生物量图在空间上与流域中已知的生物量分布模式非常吻合。从植被类型特定的地图向景观水平的生物量地图发展，是将基于遥感的生物量估计值集成到水和碳管理模型中的重要步骤。所有植被类型的均方根均方根误差（RMSE）均低于16％）。对于所有木本植被类型，高度度量均在所选的预测因子中，而对于草原，仅选择了Sentinel-2带。土地覆被的测绘精度也很高（OA = 83.1％）。最终的景观水平生物量图在空间上与流域中已知的生物量分布模式非常吻合。从植被类型特定的地图向景观水平的生物量地图发展，是将基于遥感的生物量估计值集成到水和碳管理模型中的重要步骤。所有植被类型的均方根均方根误差（RMSE）均低于16％）。对于所有木本植被类型，高度度量均在所选的预测因子中，而对于草原，仅选择了Sentinel-2带。土地覆被的测绘精度也很高（OA = 83.1％）。最终的景观水平生物量图在空间上与流域中已知的生物量分布模式非常吻合。从植被类型特定的地图向景观水平的生物量地图发展，是将基于遥感的生物量估计值集成到水和碳管理模型中的重要步骤。最终的景观水平生物量图在空间上与流域中已知的生物量分布模式非常吻合。从植被类型特定的地图向景观水平的生物量地图发展，是将基于遥感的生物量估计值集成到水和碳管理模型中的重要步骤。最终的景观水平生物量图在空间上与流域中已知的生物量分布模式非常吻合。从植被类型特定的地图向景观水平的生物量地图发展，是将基于遥感的生物量估计值集成到水和碳管理模型中的重要步骤。