Understanding the uncertainty of forest aboveground biomass maps derived from satellite observations

The role of remote sensing observations in quantifying the biomass of forests is frequently debated because of both their strengths and limitations. Satellite remote sensing is nowadays standard in research activities thanks to missions designed to last over decades. Nonetheless, satellites cannot measure the organic mass stored in trees. As such, indirect approaches are developed that combine multiple observations and mathematical models together with ground-based observations to provide a set of estimates presented in the form of a map.

While small-scale studies profit from a strategy that collects observations best suited to estimate biomass, continental and global mapping efforts need to restrict to datasets that have been collected following observation plans and are free of charge. In turn, this increases the demand on the performance of the models selected to link the predictor metrics derived from remote sensing and the response variable biomass. A map of biomass is eventually the result of an interplay between sensitivity of the remote sensing data to response forest variables, the spatial resolution of the sensors, the number of remote sensing observations and the capability of the models to reproduce the relationship between predictors and response variables. A consequence of such interplay is the level of accuracy affecting the biomass estimate, which ultimately is a key parameter to inform user communities on the reliability and efficiency of biomass maps. A comparison of biomass estimates obtained with different predictors and models for the same region provides additional measures to increase our understanding of the uncertainty affecting current biomass maps derived from satellite data.

In this presentation, we explore such uncertainties by comparing four maps of forest aboveground biomass (AGB) based on satellite images acquired in 2020 and covering Europe. The maps were based on different predictors (Sentinel-1 and ALOS-2 PALSAR-2, ASCAT, SMOS as well as spaceborne LiDAR metrics) but share the same modelling framework for biomass retrieval. Depending on the spatial resolution of the satellite data, spatial scales ranging between 100 m and 25 km were covered.

Validation of each of the datasets indicates that the overall spatial distribution of AGB is well captured even in regions with dense mature forests. However, the maps show substantial discrepancies at the level of individual pixels, regardless of the set of predictors. In addition, the precision of individual AGB estimates is rather low, between 30 and 50% of the estimated value. AGB biases were identified in specific regions and were mostly explained as imperfect modelling of the relationship between predictors and forest variables. The maps’ precision increases with spatial averaging; nonetheless, the spatial correlation of errors implies that the resulting estimates can still be affected by non-negligible uncertainty. These results in turn explain why AGB values from the different maps are highly correlated although the magnitudes can be substantially different. In conclusion, the reliability of biomass maps from satellite data is questionable at the scale of the spatial resolution; their use is instead advised at the landscape scale and for understanding broad spatial patterns.