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Maps of above-ground biomass (AGB) maps derived from Earth Observation (EO) have been increasing as a reflection of their importance in global and national applications such as global climate modelling and carbon accounting, respectively. Here we present Plot-To-Map, a tool to validate the accuracy of AGB maps using plot data from freely available global plot networks or own user data. The tool consists of five main steps starting from quality checking and harmonizing plot data to address the spatial (e.g., forest area) and temporal gaps between plots and maps. Uncertainties of plots are assessed before map comparison so plots with high uncertainty will have less impact on the comparisons. The comparison is initiated at a coarse spatial scale e.g., 10 km to eliminate random errors and enable systematic errors (bias) to be revealed and eventually be modelled. Bias predictions from decision tree-based regression model and other ecological EO products are key for producing bias-reduced maps and estimates of country AGB with associated uncertainties. The tool can be used locally and interactively via https://github.com/arnanaraza/PlotToMap. Its access and use are also possible through the Multi-Mission Algorithm Platform (MAAP), a joint project of ESA and NASA to revolutionize AGB research.

The backscattered power recorded by a spaceborne scatterometer operating at C-band is sensitive to land surface parameters and is operationally used by some global remote sensing services, e.g., to estimate soil moisture. The estimation of forest variables, in particular above-ground biomass (AGB), from scatterometer data instead was seldom explored. Given the availability of multi-decadal sets of scatterometer observations from space, it is of interest to address the contribution of C-band scatterometer data to the quantification of carbon stocks stored in forests even if the spatial resolution of spaceborne scatterometers is very coarse. In this paper, we investigated the prospects of AGB estimation using backscatter observations by the MetOp Advanced SCATterometer (ASCAT) with a spatial resolution of 0.25°. For this study, ASCAT observations acquired in 2010 were used to be contemporary with AGB datasets selected to benchmark the performance of the estimation. A Water Cloud Model that integrates two allometric equations derived from spaceborne LiDAR data reproduced the relationship between observations of radar backscatter as a function of AGB. Estimates of AGB from individual observations were then combined with a weighted average to reduce uncertainties. Finally, a correction was introduced to compensate for the offset introduced by sloped terrain and surfaces not covered by woody vegetation on the AGB estimate of a pixel. Uncertainties associated with the scatterometer observations, and the modelling framework were propagated to obtain per-pixel values of the standard deviation of an AGB estimate. The proposed method explains much of the variance in AGB estimates when compared to measurements from inventory data (R2 = 0.72) and generated unbiased estimates globally (bias: −3.3 Mg⋅ha−1). Nonetheless, the discrepancy between estimated and plot-based AGB values tended to increase for decreasing biomass level from 20% to 60% of the reference AGB level. A further assessment related to global stocks indicated that the value estimated from the scatterometer dataset (596 Pg, 95% of which 563 Pg stored in forest land) was in line with two published estimates based on forest inventory data only (571 Pg and 600 Pg, respectively). Despite the coarse spatial resolution, our results indicate that C-band scatterometer observations from space can contribute to the characterization of terrestrial biomass pools. The record of observations starting in the early 1990s may provide an unprecedented way to look at long-term forest dynamics as well as to constrain the strength of carbon-climate cycle feedback simulated by Earth System models.