Linking individual-based forest modelling with a radar simulator for determining forest structure and biomass

Grohmann, Leonard; Albrecht, Lea; Papathanassiou, Kostas; Huth, Andreas

doi:10.5194/egusphere-2026-613

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https://doi.org/10.5194/egusphere-2026-613

Preprints

16 Mar 2026

| 16 Mar 2026

Linking individual-based forest modelling with a radar simulator for determining forest structure and biomass

Leonard Grohmann, Lea Albrecht, Kostas Papathanassiou, and Andreas Huth

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.

Received: 02 Feb 2026 – Discussion started: 16 Mar 2026

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Leonard Grohmann, Lea Albrecht, Kostas Papathanassiou, and Andreas Huth

Status: final response (author comments only)

RC1:
'Comment on egusphere-2026-613', Anonymous Referee #1, 23 Mar 2026

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.

Citation: https://doi.org/10.5194/egusphere-2026-613-RC1
- AC1: 'Reply on RC1', Leonard Grohmann, 18 May 2026
  
  Reply on RC1
  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.
  AC
  
  Thank you for your comments and effort to help us improving the manuscript. We plan to revise the text on methodological limitations and to add text for the transferability of the study. Below we respond to your individual comments.
  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.
  AC
  
  Thanks for this comment. To better explain the transferability of our study results, we will include additional text on the success of employing ecosystem-specific parametrizations for different forest types (Fischer et al. 2016) and regarding the usage of LAI profiles (derived from Lidar) as proxy for scatterer profiles for X-band (Choi 2024, Albrecht et al. 2025, Qi et al. 2025).
  
  Additionally, we plan to add text about the possible usage of our proposed approach for the Biomass mission in the discussion.
  
  For P-band, we would need to estimate the scatterer distribution over height by estimating the distribution of branches (in certain size ranges) over height. This is possible with indiv. based models like FORMIND by adding modules for tree branch classes (West et al. 2009, Enquist et al. 2012).
  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.
  AC
  
  We agree that the main point of the paper can be helped by moving Fig. A1 to the main results text. This will allow us to discuss the role of successional stages and the recent literature in more detail.
  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).
  AC
  
  We will add text to clarify the application of the linear prediction model.
  Figure 5 shows forest height and radar coherence but what are the units of y-axis (in panel a, b)?
  AC
  
  We will add the units.
  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.
  AC
  
  Thanks for this comment. We agree Fig. A1 should be added to the main text. Including these findings will allow us to more discuss specifically the roles of forest successional states.
  Additional references
  Choi, C. (2024). Combining TanDEM-X interferometric SAR and GEDI lidar measurements for improving forest height, structure and biomass estimates (Doctoral dissertation, ETH Zurich; DLR-FB-2023-16). DLR.
  Albrecht, L. M., Basargin, N., Mansour, I., Guliaev, R., Romero Puig, N., Pardini, M., & Papathanassiou, K. (2024). Characterization of forest structure changes exploiting TanDEM-X and GEDI synergies. In IGARSS 2024 (pp. 1–4). IEEE.
  Qi, W., Lee, S.-K., Hancock, S., Armston, J., Tang, H., Dubayah, R., et al. (2025). Mapping large-scale pantropical forest canopy height by integrating GEDI lidar and TanDEM-X InSAR data. Remote Sensing of Environment.
  Qi, W., Lee, S. K., Hancock, S., Luthcke, S., Tang, H., Armston, J., & Dubayah, R. (2019). Improved forest height estimation by fusion of simulated GEDI Lidar data and TanDEM-X InSAR data. Remote Sensing of Environment, 221, 621-634.
  Köhler, P., & Huth, A. (2010). Towards ground-truthing of spaceborne estimates of above-ground life biomass and leaf area index in tropical rain forests. Biogeosciences, 7(8), 2531-2543.
  West, G. B., Enquist, B. J., & Brown, J. H. (2009). A general quantitative theory of forest structure and dynamics. Proceedings of the National Academy of Sciences, 106(17), 7040-7045.
  Enquist, B. J., & Bentley, L. P. (2012). Land plants: new theoretical directions and empirical prospects. Metabolic ecology: a scaling approach, 164-187.
  Cloude, S., Papathanasiou, K. P., & Pottier, E. (2001). Radar polarimetry and polarimetric interferometry. IEICE Transactions on Electronics, 84(12), 1814-1822.
  Lavalle, M., & Hensley, S. (2015). Extraction of structural and dynamic properties of forests from polarimetric-interferometric SAR data affected by temporal decorrelation. IEEE Transactions on Geoscience and Remote Sensing, 53(9), 4752-4767.
  
  Citation: https://doi.org/10.5194/egusphere-2026-613-AC1
RC2:
'Comment on egusphere-2026-613', Anonymous Referee #2, 21 Apr 2026

Dear authors,

the overall research topic addressed in this paper is certainly relevant, and especially the insights of a 4D forest structure model to better parameterize and analyse a decorrelation model from radar observations.

Nonetheless, the paper suffers from several drawbacks and its content is rather disappointing regarding the proposed research questions.

First of all, the so-called 'radar simulator' lies actually just on an elegant but simple analytical formulation of the volume decorrelation proposed 20 years ago, which adaptation using the Leaf Area Density is clearly not sufficiently justified. Indeed, we would expect much more from a 'radar simulator' (with for instance speckle modeling, non homogeneous 3D representation, etc), and detailed explanations of how the extinction term inside the integral is replaced by the LAD (although correct mathematically, the physical links would be significantly more complicated). In that respect, please note that none of the cited references ([Treuhaft,1996 or 2000]) give this shortcut (as the writing could let us understand).

Likewise, the paper also reduces forest structure to the LAD (vertical profile of LAI), which could be also disappointing for the reader.

Regarding then the methodology, I really do not see why forest AGB and height are not analyzed together, using the same criteria (it seems for instance that the noise model would not impact forest height retrieval, or that biomass retrieval would not be relevant or worth to show). This is particularly regrettable since the FORMIND model would give a very detailed (in terms of vegetation component) link between both.

Regarding the results, the sensitivity of forest AGB to coherence amplitude is clearly overstated, but it reveals the limitations of the analytical model for volume decorrelation. Height retrieval seems much better that what could be expected at X-band, but the details given in §2.6 are not sufficient to really understand what has been done, especially with the use of inventory data (l138, no more). I would therefore strongly recommend to refocus the paper on this latter part (on forest height inversion), with further details on the processing steps and comparison with standard methods that do not exploit the FORMIND LAD information (to better emphasize its importance through this formulation).

Citation: https://doi.org/10.5194/egusphere-2026-613-RC2
- AC2: 'Reply on RC2', Leonard Grohmann, 18 May 2026
  
  Reply on RC2
  RC
  
  Dear authors,
  the overall research topic addressed in this paper is certainly relevant, and especially the insights of a 4D forest structure model to better parameterize and analyse a decorrelation model from radar observations.
  Nonetheless, the paper suffers from several drawbacks and its content is rather disappointing regarding the proposed research questions.
  AC
  
  Thank you for your comments and efforts. Based on the comments, we will revise our research questions. Below you find the response to the individual comments.
  RC
  
  First of all, the so-called 'radar simulator' lies actually just on an elegant but simple analytical formulation of the volume decorrelation proposed 20 years ago, which adaptation using the Leaf Area Density is clearly not sufficiently justified. Indeed, we would expect much more from a 'radar simulator' (with for instance speckle modeling, non homogeneous 3D representation, etc), and detailed explanations of how the extinction term inside the integral is replaced by the LAD (although correct mathematically, the physical links would be significantly more complicated). In that respect, please note that none of the cited references ([Treuhaft,1996 or 2000]) give this shortcut (as the writing could let us understand).
  AC
  
  Thanks for this comment. We agree the justification for using LAI profile as proxy for scatterer profiles should be clarified. We assume that the LAI profiles (derived from Lidar, Knapp et al. 2018, Rödig et al. 2019) can be used as a proxy for the scatterer distribution for X-Band (leaves, small branches, Choi 2024, Albrecht et al. 2024, Qi et al. 2025) which is multiplied with an extinction term.
  
  We will also add to the discussion the role of branch size distributions over height and their relation to LAI density (West et al. 2009, Enquist et al. 2012). We agree that our simulator focuses only on coherence based on a volume over ground approach (Claude et al. 2001, Lavalle & Hensley 2015, Qi et al. 2019).
  
  We plan to rename the used approach into radar coherence simulator and will revise the references.
  RC
  
  Likewise, the paper also reduces forest structure to the LAD (vertical profile of LAI), which could be also disappointing for the reader.
  AC
  
  We will add text to clarify how we use LAI profiles as approximation for forest structure and the distribution of small branches (Knapp et al. 2018).
  RC
  
  Regarding then the methodology, I really do not see why forest AGB and height are not analyzed together, using the same criteria (it seems for instance that the noise model would not impact forest height retrieval, or that biomass retrieval would not be relevant or worth to show). This is particularly regrettable since the FORMIND model would give a very detailed (in terms of vegetation component) link between both.
  AC
  
  We agree that further clarification regarding the connection of the forest height and the biomass prediction would be useful.
  
  Additionally, we plan a restructuring of the results to work out the specific roles of biomass and forest height retrieval respectively.
  RC
  
  Regarding the results, the sensitivity of forest AGB to coherence amplitude is clearly overstated, but it reveals the limitations of the analytical model for volume decorrelation.
  AC
  
  We will add text to clarify the limitations of the volume over ground model, specifically regarding the estimation of the extinction factor. Additionally, including Fig. A1 in the main text would allow us to discuss the model limitations for specific successional stages.
  RC
  
  Height retrieval seems much better that what could be expected at X-band, but the details given in §2.6 are not sufficient to really understand what has been done, especially with the use of inventory data (l138, no more).
  AC
  
  We will add additional text on the height retrieval method. This includes re-emphasizing the assumptions of uniform LAD profile instead of specific LAI profiles.
  RC
  
  I would therefore strongly recommend to refocus the paper on this latter part (on forest height inversion), with further details on the processing steps and comparison with standard methods that do not exploit the FORMIND LAD information (to better emphasize its importance through this formulation).
  AC
  
  We agree that our study would benefit from this suggestion. We plan to restructure the results into height retrieval at the beginning and the model-based biomass predictions as follow-up. We will also include further text on processing steps and comparisons. We will also revise the research questions. In the current version, height estimation is not based on LAD information.
  
  Additional references
  Choi, C. (2024). Combining TanDEM-X interferometric SAR and GEDI lidar measurements for improving forest height, structure and biomass estimates (Doctoral dissertation, ETH Zurich; DLR-FB-2023-16). DLR.
  Albrecht, L. M., Basargin, N., Mansour, I., Guliaev, R., Romero Puig, N., Pardini, M., & Papathanassiou, K. (2024). Characterization of forest structure changes exploiting TanDEM-X and GEDI synergies. In IGARSS 2024 (pp. 1–4). IEEE.
  Qi, W., Lee, S.-K., Hancock, S., Armston, J., Tang, H., Dubayah, R., et al. (2025). Mapping large-scale pantropical forest canopy height by integrating GEDI lidar and TanDEM-X InSAR data. Remote Sensing of Environment.
  Qi, W., Lee, S. K., Hancock, S., Luthcke, S., Tang, H., Armston, J., & Dubayah, R. (2019). Improved forest height estimation by fusion of simulated GEDI Lidar data and TanDEM-X InSAR data. Remote Sensing of Environment, 221, 621-634.
  Köhler, P., & Huth, A. (2010). Towards ground-truthing of spaceborne estimates of above-ground life biomass and leaf area index in tropical rain forests. Biogeosciences, 7(8), 2531-2543.
  West, G. B., Enquist, B. J., & Brown, J. H. (2009). A general quantitative theory of forest structure and dynamics. Proceedings of the National Academy of Sciences, 106(17), 7040-7045.
  Enquist, B. J., & Bentley, L. P. (2012). Land plants: new theoretical directions and empirical prospects. Metabolic ecology: a scaling approach, 164-187.
  Cloude, S., Papathanasiou, K. P., & Pottier, E. (2001). Radar polarimetry and polarimetric interferometry. IEICE Transactions on Electronics, 84(12), 1814-1822.
  Lavalle, M., & Hensley, S. (2015). Extraction of structural and dynamic properties of forests from polarimetric-interferometric SAR data affected by temporal decorrelation. IEEE Transactions on Geoscience and Remote Sensing, 53(9), 4752-4767.
  
  Citation: https://doi.org/10.5194/egusphere-2026-613-AC2

Leonard Grohmann, Lea Albrecht, Kostas Papathanassiou, and Andreas Huth

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Short summary

We developed a new way to better understand forest structure and biomass by linking a detailed forest growth model with a satellite-based radar simulation. By combining ecological processes with radar observations, we show how forest height and biomass can be estimated more reliably across different forest stages. This approach improves the interpretation of satellite data and supports more accurate large-scale forest monitoring and carbon assessment.


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