| 16 Mar 2026
Linking individual-based forest modelling with a radar simulator for determining forest structure and biomass
Abstract. Mapping forest structure, critical for assessing carbon stocks and fluxes, remains challenging with remote sensing. We propose a novel framework linking an individual-based forest model (FORMIND), which generates explicit 3D forest structures and dynamics, with a radar simulator (here used for TanDEM-X). We investigate radar coherence from simulated forests to predict aboveground biomass (AGB) across varying spatial scales, measurement noise levels, and successional stages. The framework is applied to the Barro Colorado Island (BCI) tropical forest, where we evaluate simulated coherence against TanDEM-X observations and invert canopy height, comparing the results with airborne laser scanning (ALS) data.
Results indicate a positive link between forest structure and interferometric patterns, with AGB prediction showing a clear dependence on spatial resolution. This novel approach offers a pathway to map forest structure by combining broad radar data coverage with an ecologically explicit forest model.
This manuscript with reference ID egusphere-2026-613 presents a novel framework linking an individual-based forest model (FORMIND) with a radar simulator (TanDEM-X) with the goal to investigate the relationship between forest structure, interferometric coherence, and aboveground biomass (AGB). The approach is applied to the Barro Colorado Island (BCI) tropical forest, where simulated radar signals are evaluated against observations and canopy height estimates are compared with airborne laser scanning (ALS) data. The study demonstrates that forest structural properties influence radar coherence and that AGB retrieval is scale-dependent, highlighting the potential of combining ecological modelling with radar remote sensing.
General comments:
This study presents an innovative and interdisciplinary study bridging forest ecology, remote sensing, and modelling. However, the manuscript would benefit from clearer and more concise presentation of the methodological limitations and the transferability of the study finding. For instance, the authors indicate that the applied method "is transferable via ecological parameterization and directly relevant for missions such as ESA BIOMASS". However the manuscript (as it currently stands) is lacking actual examples of how to make us of the proposed approach for for early warning change detection or improved global carbon accounting. Most strikingly, the manuscript closes with a remark on improving robustness across successional stages of forests and the appendix (Figure A1) actually highlights some of the accuracy across successional stages and noise levels for tropical forest in Panama, BCI. Hence, to me this appears to be one of the most interesting results that should find their way into the main text, which currently just presents a rather technical description of the applied framework and thus could be improved by a more lively discussion of the study findings in light of the recent literature.
Specific comments:
Figure 4 depicts the results of biomass prediction from different radar coherence metrics but it remains unclear why the slopes change from negative (in panels a, b) to positive (in panels c, d).
Figure 5 shows forest height and radar coherence but what are the units of y-axis (in panel a, b)?
Figure A1 indicates the accuracy of biomass prediction across different successional stages, which to me is one of the most interesting results as this highlights a shift of biomass (and its accuracy) across early-, mid-, and late-, succession, which could be added to the main text.