Advancements and challenges in estimating terrestrial vegetation biomass using satellite data

The above ground biomass (AGB) of woody vegetation is proportional to the amount of carbon stored primarily in the trunks and branches, with changes over time indicating sources or sinks of carbon. Accurate quantification of AGB is indispensable for climate studies and policy development, yet significant gaps persist due to limitations in current observational and modeling approaches. Satellite-based Earth Observation (EO) provides a promising avenue for global biomass estimation, particularly when a diversity of  data sources and advanced algorithms are used.

Recent initiatives, such as the European Space Agency’s (ESA) Climate Change Initiative (CCI) Biomass and BiomAP projects, have pioneered methodologies for generating time series of global maps of woody AGB at varying spatial resolutions. These efforts utilize multiple predictors derived from active and passive microwave data sources, including Sentinel-1, ALOS-2, SMOS, SMAP and ASCAT as well as LiDAR-based vegetation structural metrics. However, the absence of globally and evenly distributed AGB measurements acting as reference constrains retrievals to use fully physical models. These models are then calibrated using spatially explicit datasets from other satellite data (e.g., optical imagery) and AGB statistics. Evaluations of these maps with independent reference measurements not used in the retrieval process highlight the critical balance between data precision and algorithm design. The complexity of accurately mapping biomass at global scales is compounded by uncertainties in LiDAR sampling, satellite data uncertainty, and the dependence on high-quality reference data. Additionally, biases arise from the simplistic assumptions often required for model fitting, which can affect the reliability of AGB estimates. Temporal assessments of biomass change face additional hurdles, including uncertainties in AGB trends and a scarcity of reference data for validation.

Despite these challenges, EO-driven biomass mapping continues to advance, supported by improvements in sensor technologies and retrieval algorithms. Long-term maintenance of satellite missions suitable for AGB mapping is however essential as is the promotion of space-based LiDAR observations. Enhanced understanding of satellite signal characteristics will enable more accurate AGB retrievals, fostering the development of sophisticated retrieval models that may identify complex interactions not described by the physical models currently in use. Crucially, this progress must be complemented by spatially dense and continuous AGB measurements from local ground-based or airborne surveys.

The scope of this presentation is to emphasize the transformative potential of satellite EO in quantifying and monitoring AGB and detail efforts at quantifying and reducing uncertainties in retrieval. By reviewing existing data products and illustrating strategies to address data gaps and methodological challenges, this work aims to inform and guide future global biomass estimation efforts from existing, recently launched (e.g., ALOS-4 PALSAR, Sentinel-1C), and forthcoming (NASA/ISRO NISAR and ESA BIOMASS) missions.