Assessing forest properties with data-driven vegetation indices: insights from 900,000 forest stands

Fischer, Samuel Matthias; Fischer, Rico; Huth, Andreas

doi:10.5194/egusphere-2025-5198

Preprints

https://doi.org/10.5194/egusphere-2025-5198

Preprints

10 Nov 2025

| 10 Nov 2025

Assessing forest properties with data-driven vegetation indices: insights from 900,000 forest stands

Samuel Matthias Fischer, Rico Fischer, and Andreas Huth

Abstract. Vegetation indices (VIs) are widely used to assess forest properties, but deriving VIs for attributes not mechanistically linked to forests’ solar reflectance is challenging. Here, data-driven VIs could help, which yield information based on correlations identified in large datasets of forest and reflectance data. However, data-driven VIs are prone to bias and overfitting if data is limited and the functional form and wavelengths used for the VIs are not sensibly constrained. In this study, we facilitate the development of data-driven VIs by systematically analyzing VIs with two wavelengths (400 nm–2400 nm) and evaluating their correlations to biomass, leaf area index (LAI), gross primary production (GPP), and net primary production (NPP) subject to different sources of environmental and physiological uncertainty. Considering 900,000 forest stands simulated via a forest and radiative transfer modelling approach, we introduced a new class of VIs and found that data-driven VIs can provide highly accurate estimates. Particularly VIs combining near and shortwave infrared light yielded promising results, with biomass, LAI, and GPP often being well estimable from the same wavelength combinations; visible light gained importance in less dense and structurally heterogeneous forests. Both the functional form of the VIs and the considered uncertainty factors did not primarily reduce the achievable accuracy, but instead constrained the range of wavelengths from which good indices could be constructed. This suggests that data-driven vegetation indices can yield valuable results if the wavelength choice is optimized. This opens new pathways for utilizing recent hyperspectral satellite missions such as EnMAP.

Received: 20 Oct 2025 – Discussion started: 10 Nov 2025

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.

Download & links

Preprint (PDF, 4248 KB)

Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
Preprint (4248 KB)

Supplement (1810 KB)

Download & links

The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

Journal article(s) based on this preprint

20 Jul 2026

Assessing forest biomass and productivity with data-driven vegetation indices: insights from 900 000 simulated forest stands

Samuel M. Fischer, Rico Fischer, and Andreas Huth

Biogeosciences, 23, 4927–4942, https://doi.org/10.5194/bg-23-4927-2026,https://doi.org/10.5194/bg-23-4927-2026, 2026

Short summary

Samuel Matthias Fischer, Rico Fischer, and Andreas Huth

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2025-5198', Colin Bloom, 01 Dec 2025

Review: Assessing forest properties with data-driven vegetation indices: insights from 900,000 forest stands
This manuscript examines the effectiveness of existing vegetation indices and novel hyperspectral indices for estimating forest properties: biomass, leaf area index (LAI), gross primary production (GPP) and net primary production (NPP). The methods combine individual-based forest and radiative transfer models to simulate many forest stands, their estimated biomass, LAI, GPP, NPP and 250 canopy wavelengths. Results suggest that unique wavelength pairs often offer stronger estimates of NPP, GPP and biomass than common vegetation indices. A data driven approach also offers strong correlations.
In general, I found the applied methodology interesting and compelling. The manuscript offers several valuable insights, for example, Table 2 could serve as a nice reference when considering existing vegetation indices for estimating forest properties. This said, in my opinion, the manuscript also offers significant room for improvement, particularly in its focus and clarity. I apologize in advance for my many comments below, but I hope they will aid the authors in their revisions. Overarching comments are followed by line-by-line comments.
Introduction: A systematic and data driven identification of important reflectance wavelengths for forest properties is absolutely warranted. Few conventional vegetation indices have been systematically developed to target important forest parameters like NPP or GPP. This is a compelling argument for conducting this analysis. Hyperspectral data and data driven indices derived from synthetic samples are only one way to investigate the problem. Much of the introduction, however, is taken up by a methodological background (Lines 33 to 74), which attempts to set the stage for methodological novelty. I find this argument less compelling. As mentioned, Henniger et al. (2023) has already applied the novel combined methodology used in the analysis. Tweaking the approach is just an application of the methods. In my opinion, the methodological background could be substantially condensed and placed at the start of the methods. In its place, a clearer introduction to the overarching problem and research question would greatly improve the clarity of the work. Some relevant questions which I think need to be addressed therein: What are NPP, GPP, and biomass and why are they important? Why do we need to monitor these parameters? How have vegetation indices traditionally been developed? Have vegetation indices been produced for NPP or GPP specifically? What platforms exist (or will exist soon) to monitor changes? I believe more complete answers to these questions will make it abundantly clear why a new systematic approach to vegetation indices is needed.
Methods and Results: Understanding the manuscript in its entirety took several reads and more effort than I think is necessary. While I appreciate and understand the sensitivity testing of the radiative transfer model, this is, in my opinion, secondary to the overall intent of the main manuscript. Moving this portion of the analysis into the Supplement would make it much easier to read the manuscript through and to understand the methods and results.
Methods and Discussion: A lot of effort was made to evaluate different forest structures and sensitivity test the influence of different model parameters. However, it seems like the models are all based on the environmental conditions at one site in Germany (e.g., line 114). Does this also warrant a sensitivity test? At a minimum, it requires justification. Is this site representative of a broad range of sites? Is the model going to be overfit to the conditions at Hohes Holz? If not, why not?
Methods and Discussion: It seems that all wavelengths are an average of the plot, but I cannot find this mentioned. This needs to be discussed. Higher and higher spatial resolution multi- and hyperspectral data are commonplace. Are the model results still relevant on different scales? EnMAP, for example, has 30 m resolution. What is the influence of variability across the plot?
Methods and Discussion: Could a simple correlation analysis between parameters and single wavelengths provide some additional insight into appropriate wavelengths before looking at pairs?
Discussion: I believe this analysis would benefit immensely from (even a very small) application to real-world data. I realize that it is an additional effort which the authors may consider beyond the scope of the analysis, but it is relevant to the level of trust that can be placed in the primarily theoretical findings. Could several derived indices simply be applied to data from Hohes Holz to estimate biomass and compare with survey data? At a bare minimum, some concrete recommendations for next steps are required in the discussion. What models should we try with real-world data? For example, a short table of high performing wavelength combinations which match common satellite data could be very helpful. A huge amount of detailed effort has gone into the analysis, so it seems a bit of a shame for the discussion to end on high-level findings about general regions of the light spectrum that are good for GPP and NPP.
Line-by-line Comments:
Line 5: “with two wavelengths (400 nm- 2400 nm)” suggests only two wavelengths are evaluated. I think this should read something like: “using wavelengths between 400 nm and 2400 nm”
Line 9: “provide highly accurate estimates” of what?
Line 9-11: This sentence is generally unclear and ‘estimable’ (which means ‘worthy of great respect’) was unlikely the intended word choice.
Line 12: “did not primarily reduce the achievable accuracy” I am not sure what this means. The accuracy of what?
Line 21: “Here” implies that remote sensing is part of the analysis.
Line 24: Just because hyperspectral data captures more data does not make it inherently more useful than traditional multi-spectral data. See my comment on the direction of the introduction above.
Line 55: 2500 nm does not match the rest of the text.
Line 133: This seems somewhat circular but also makes a good case for applying new vegetation indices to the actual site to see how it performs.
Line 134 to 149: Moving this (and associated results) to the Supplement would help smooth out the main text.
Line 151 to 152: How much was removed by this filtering?
Line 161: sp. value
Table 1: Different sensors capture different wavelengths, why were these chosen for classical indices? I think it deserves an explanation in the text.
Line 182: “in the absence of noise” Is this the uncertainty analysis? It is not clear.
Line 195: Can this include a percent difference?
Line 202: “estimable” again.
Line 280: Yes, but it was not the original intent of most VIs to determine NPP or GPP. This should be clarified here and in the introduction.
Thank you for the opportunity to evaluate this work and I wish the authors the best of luck in their revisions.
Colin Bloom

Citation: https://doi.org/10.5194/egusphere-2025-5198-RC1
- AC2: 'Reply on RC1', Samuel Fischer, 23 Jan 2026
  
  Dear Colin Bloom,
  Thank you for taking the time to review our manuscript and for your many constructive comments and suggestions. For clarity, we have addressed each point in a separate response document, where we also propose corresponding revisions to improve the manuscript.
  Best regards,
  
  Samuel Fischer
  
  Citation: https://doi.org/10.5194/egusphere-2025-5198-AC2
RC2:
'Comment on egusphere-2025-5198', Anonymous Referee #2, 16 Dec 2025
Peer review report on “Assessing forest properties with data-driven vegetation indices: insights from 900,000 forest stands”
Comments to Authors:
Overview and Major Comments:
This manuscript presents a comprehensive and methodologically innovative study that systematically explores the potential of data-driven vegetation indices (VIs) to estimate key forest properties (biomass, LAI, GPP, and NPP). By coupling an individual-based forest dynamics model (FORMIND) with a multilayer radiative transfer model (mSCOPE) and applying a Monte Carlo sampling strategy, the authors generate an exceptionally large and well-structured synthetic dataset. The analysis of all possible two-wavelength combinations across the 400–2400 nm range, combined with an explicit treatment of multiple uncertainty sources, represents a substantial advance over existing studies.
Overall, the manuscript is scientifically sound, clearly written, and highly relevant to the remote sensing and forest ecology communities, particularly in the context of emerging hyperspectral satellite missions such as EnMAP. The study offers valuable conceptual insights into wavelength selection, index design, and uncertainty robustness. I think the manuscript suitable for publication after some revisions, mainly aimed at clarifying applicability and ensuring reproducibility.
Major comments
The introduction could be strengthened by improving accessibility for a broad audience. A greater emphasis on the ecological motivation would be beneficial. The authors could clarify why forest parameters such as forest biomass, LAI, GPP, and NPP are critical variables and why their large-scale estimation remains challenging.

While the comprehensive coupling of forest model and radiative transfer model is impressive, the manuscript would be more convincing if the authors could demonstrate that FORMIND reasonably represents real forest conditions. For example, how do the observation-based estimates of forest properties at sites such as “Hohes Holze” (i.e., biomass, LAI, GPP, NPP) compare with the simulated ranges generated by FORMIND? Are all observed values captured within the model’s simulated distributions?

Minor comments：
Line 5: “two wavelengths (400 nm-2400 nm)”à”two wavelength (within 400 nm -2400 nm), to avoid the confusion.
Line 116: You may specify the meaning of the ODM and the correct unit for different variables.
Lines 117-118: Consider briefly explaining the interpretation of DBH entropy values—for example, does a more negative value indicate lower heterogeneity?
For Table 1: It would be helpful to add references or explanations regarding the rationale for selecting the wavelengths used in classical indices.
Lines 179-180: what are the criteria to select the “separating thresholds” for biomass and DBH entropy?
Lines 341-342: The study would be further strengthened if the developed hybrid model could be validated against sites with hyperspectral observations and observation-based estimates of forest properties in future work.
Citation: https://doi.org/10.5194/egusphere-2025-5198-RC2
- AC1: 'Reply on RC2', Samuel Fischer, 23 Jan 2026
  
  Dear Reviewer,
  Thank you for taking the time to review our manuscript and for your constructive comments and suggestions. For clarity, we have addressed each point in a separate response document, where we also propose corresponding revisions to improve the manuscript.
  Best regards,
  
  Samuel Fischer
  
  Citation: https://doi.org/10.5194/egusphere-2025-5198-AC1

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2025-5198', Colin Bloom, 01 Dec 2025

Review: Assessing forest properties with data-driven vegetation indices: insights from 900,000 forest stands
This manuscript examines the effectiveness of existing vegetation indices and novel hyperspectral indices for estimating forest properties: biomass, leaf area index (LAI), gross primary production (GPP) and net primary production (NPP). The methods combine individual-based forest and radiative transfer models to simulate many forest stands, their estimated biomass, LAI, GPP, NPP and 250 canopy wavelengths. Results suggest that unique wavelength pairs often offer stronger estimates of NPP, GPP and biomass than common vegetation indices. A data driven approach also offers strong correlations.
In general, I found the applied methodology interesting and compelling. The manuscript offers several valuable insights, for example, Table 2 could serve as a nice reference when considering existing vegetation indices for estimating forest properties. This said, in my opinion, the manuscript also offers significant room for improvement, particularly in its focus and clarity. I apologize in advance for my many comments below, but I hope they will aid the authors in their revisions. Overarching comments are followed by line-by-line comments.
Introduction: A systematic and data driven identification of important reflectance wavelengths for forest properties is absolutely warranted. Few conventional vegetation indices have been systematically developed to target important forest parameters like NPP or GPP. This is a compelling argument for conducting this analysis. Hyperspectral data and data driven indices derived from synthetic samples are only one way to investigate the problem. Much of the introduction, however, is taken up by a methodological background (Lines 33 to 74), which attempts to set the stage for methodological novelty. I find this argument less compelling. As mentioned, Henniger et al. (2023) has already applied the novel combined methodology used in the analysis. Tweaking the approach is just an application of the methods. In my opinion, the methodological background could be substantially condensed and placed at the start of the methods. In its place, a clearer introduction to the overarching problem and research question would greatly improve the clarity of the work. Some relevant questions which I think need to be addressed therein: What are NPP, GPP, and biomass and why are they important? Why do we need to monitor these parameters? How have vegetation indices traditionally been developed? Have vegetation indices been produced for NPP or GPP specifically? What platforms exist (or will exist soon) to monitor changes? I believe more complete answers to these questions will make it abundantly clear why a new systematic approach to vegetation indices is needed.
Methods and Results: Understanding the manuscript in its entirety took several reads and more effort than I think is necessary. While I appreciate and understand the sensitivity testing of the radiative transfer model, this is, in my opinion, secondary to the overall intent of the main manuscript. Moving this portion of the analysis into the Supplement would make it much easier to read the manuscript through and to understand the methods and results.
Methods and Discussion: A lot of effort was made to evaluate different forest structures and sensitivity test the influence of different model parameters. However, it seems like the models are all based on the environmental conditions at one site in Germany (e.g., line 114). Does this also warrant a sensitivity test? At a minimum, it requires justification. Is this site representative of a broad range of sites? Is the model going to be overfit to the conditions at Hohes Holz? If not, why not?
Methods and Discussion: It seems that all wavelengths are an average of the plot, but I cannot find this mentioned. This needs to be discussed. Higher and higher spatial resolution multi- and hyperspectral data are commonplace. Are the model results still relevant on different scales? EnMAP, for example, has 30 m resolution. What is the influence of variability across the plot?
Methods and Discussion: Could a simple correlation analysis between parameters and single wavelengths provide some additional insight into appropriate wavelengths before looking at pairs?
Discussion: I believe this analysis would benefit immensely from (even a very small) application to real-world data. I realize that it is an additional effort which the authors may consider beyond the scope of the analysis, but it is relevant to the level of trust that can be placed in the primarily theoretical findings. Could several derived indices simply be applied to data from Hohes Holz to estimate biomass and compare with survey data? At a bare minimum, some concrete recommendations for next steps are required in the discussion. What models should we try with real-world data? For example, a short table of high performing wavelength combinations which match common satellite data could be very helpful. A huge amount of detailed effort has gone into the analysis, so it seems a bit of a shame for the discussion to end on high-level findings about general regions of the light spectrum that are good for GPP and NPP.
Line-by-line Comments:
Line 5: “with two wavelengths (400 nm- 2400 nm)” suggests only two wavelengths are evaluated. I think this should read something like: “using wavelengths between 400 nm and 2400 nm”
Line 9: “provide highly accurate estimates” of what?
Line 9-11: This sentence is generally unclear and ‘estimable’ (which means ‘worthy of great respect’) was unlikely the intended word choice.
Line 12: “did not primarily reduce the achievable accuracy” I am not sure what this means. The accuracy of what?
Line 21: “Here” implies that remote sensing is part of the analysis.
Line 24: Just because hyperspectral data captures more data does not make it inherently more useful than traditional multi-spectral data. See my comment on the direction of the introduction above.
Line 55: 2500 nm does not match the rest of the text.
Line 133: This seems somewhat circular but also makes a good case for applying new vegetation indices to the actual site to see how it performs.
Line 134 to 149: Moving this (and associated results) to the Supplement would help smooth out the main text.
Line 151 to 152: How much was removed by this filtering?
Line 161: sp. value
Table 1: Different sensors capture different wavelengths, why were these chosen for classical indices? I think it deserves an explanation in the text.
Line 182: “in the absence of noise” Is this the uncertainty analysis? It is not clear.
Line 195: Can this include a percent difference?
Line 202: “estimable” again.
Line 280: Yes, but it was not the original intent of most VIs to determine NPP or GPP. This should be clarified here and in the introduction.
Thank you for the opportunity to evaluate this work and I wish the authors the best of luck in their revisions.
Colin Bloom

Citation: https://doi.org/10.5194/egusphere-2025-5198-RC1
- AC2: 'Reply on RC1', Samuel Fischer, 23 Jan 2026
  
  Dear Colin Bloom,
  Thank you for taking the time to review our manuscript and for your many constructive comments and suggestions. For clarity, we have addressed each point in a separate response document, where we also propose corresponding revisions to improve the manuscript.
  Best regards,
  
  Samuel Fischer
  
  Citation: https://doi.org/10.5194/egusphere-2025-5198-AC2
RC2:
'Comment on egusphere-2025-5198', Anonymous Referee #2, 16 Dec 2025
Peer review report on “Assessing forest properties with data-driven vegetation indices: insights from 900,000 forest stands”
Comments to Authors:
Overview and Major Comments:
This manuscript presents a comprehensive and methodologically innovative study that systematically explores the potential of data-driven vegetation indices (VIs) to estimate key forest properties (biomass, LAI, GPP, and NPP). By coupling an individual-based forest dynamics model (FORMIND) with a multilayer radiative transfer model (mSCOPE) and applying a Monte Carlo sampling strategy, the authors generate an exceptionally large and well-structured synthetic dataset. The analysis of all possible two-wavelength combinations across the 400–2400 nm range, combined with an explicit treatment of multiple uncertainty sources, represents a substantial advance over existing studies.
Overall, the manuscript is scientifically sound, clearly written, and highly relevant to the remote sensing and forest ecology communities, particularly in the context of emerging hyperspectral satellite missions such as EnMAP. The study offers valuable conceptual insights into wavelength selection, index design, and uncertainty robustness. I think the manuscript suitable for publication after some revisions, mainly aimed at clarifying applicability and ensuring reproducibility.
Major comments
The introduction could be strengthened by improving accessibility for a broad audience. A greater emphasis on the ecological motivation would be beneficial. The authors could clarify why forest parameters such as forest biomass, LAI, GPP, and NPP are critical variables and why their large-scale estimation remains challenging.

While the comprehensive coupling of forest model and radiative transfer model is impressive, the manuscript would be more convincing if the authors could demonstrate that FORMIND reasonably represents real forest conditions. For example, how do the observation-based estimates of forest properties at sites such as “Hohes Holze” (i.e., biomass, LAI, GPP, NPP) compare with the simulated ranges generated by FORMIND? Are all observed values captured within the model’s simulated distributions?

Minor comments：
Line 5: “two wavelengths (400 nm-2400 nm)”à”two wavelength (within 400 nm -2400 nm), to avoid the confusion.
Line 116: You may specify the meaning of the ODM and the correct unit for different variables.
Lines 117-118: Consider briefly explaining the interpretation of DBH entropy values—for example, does a more negative value indicate lower heterogeneity?
For Table 1: It would be helpful to add references or explanations regarding the rationale for selecting the wavelengths used in classical indices.
Lines 179-180: what are the criteria to select the “separating thresholds” for biomass and DBH entropy?
Lines 341-342: The study would be further strengthened if the developed hybrid model could be validated against sites with hyperspectral observations and observation-based estimates of forest properties in future work.
Citation: https://doi.org/10.5194/egusphere-2025-5198-RC2
- AC1: 'Reply on RC2', Samuel Fischer, 23 Jan 2026
  
  Dear Reviewer,
  Thank you for taking the time to review our manuscript and for your constructive comments and suggestions. For clarity, we have addressed each point in a separate response document, where we also propose corresponding revisions to improve the manuscript.
  Best regards,
  
  Samuel Fischer
  
  Citation: https://doi.org/10.5194/egusphere-2025-5198-AC1

Peer review completion

AR – Author's response | RR – Referee report | ED – Editor decision | EF – Editorial file upload

ED: Reconsider after major revisions (03 Feb 2026) by Benjamin Stocker

Dear Dr. Fischer,

Thank you very much for submitting your manuscript "Assessing forest properties with data-driven vegetation indices: insights from 900,000 forest stands" to Biogeosciences. The reviewers and I appreciate the effort you have invested in this study and recognize its potential contribution.

Both reviewers, especially reviewer 1, however, have raised important concerns that require substantial revision before we can move forward. At this stage, the decision is to invite Major Revisions.

In particular, two points raised by reviewer 1 seem to be critical and I advise you to address the concerns raised more fully than proposed in your reply.

1. The discussion should address the aspect that all investigations were made *in silico*. In this model world, the link between LAI and GPP and NPP may be different from how these quantities relate in real forests. It should be discussed to what extent the findings presented here are subject to this caveat. This point was made also by Reviewer 2 (”Discussion: I believe this analysis would benefit immensely from (even a very small) application to real-world data.”).
2. The transferability of results to (perhaps structurally the same) forests growing under different environments (not under conditions found at Hohes Holz) is not clear. The paper deals with this point in terms of investigating “uncertainty” in the environment. However, this is also about *variations* in the environment, in view of typical applications of remote sensing-based assessments which typically aim at mapping a quantity of interest across space, that is, across environments. This point made also by Reviewer 1 (”Methods and Discussion: A lot of effort …”)

Now wearing a reviewer's hat, I found three more points critical and ask you to address them in your revisions:
3. I found this a somewhat misleading statement: "we analysed 900000 forest stands". Please clarify in title, abstract, discussion, and conclusion that your study analysed synthetic data.
4. An explicit view to time scales and the potential influence of short-term variations in the environment should be made in the description and interpretation of the target variables. LAI and biomass can be safely assumed that they do not change much over the course of a growing season, but NPP and GPP may vary strongly. Are results valid under short term variations in light, temperature, water availability, atmospheric humidity or are they representative for growing-season aggregates?
5. Are perturbed traits in the radiative transfer model affecting C dynamics in the forest model (FORMIND)?

We ask that you carefully address each of the reviewers’ points in your revision and include a clear, point-by-point response letter. If you disagree with a comment, please explain your reasoning clearly. Highlighting the changes in the revised manuscript will also be very helpful for the review process.

Please aim to resubmit your revised manuscript.

We value your contribution and look forward to receiving your revised work.

With best regards,

Beni Stocker

Associate Editor
Biogeosciences

Hide

AR by Samuel Fischer on behalf of the Authors (07 Apr 2026) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (08 Apr 2026) by Benjamin Stocker

RR by Colin Bloom (16 Apr 2026)

Suggestions for revision or reasons for rejection

Re-Review of Assessing forest biomass and productivity with data-driven vegetation indices: insights from 900,000 simulated forest stands
I have now re-examined the manuscript of Fischer et al. The focus of the work and the underlying analysis remain largely unchanged from the previous iteration, but the revised text offers a much stronger motivation in the introduction and a clearer methodology. These changes, along with revisions to the discussion, largely address my major concerns from the original manuscript. I also appreciate the updated title which is more indicative of the actual analysis.
One point I would still stress regards the large-scale applicability of the Hohes Holz site to different environmental conditions across Central Europe. I agree that Hohes Holz seems representative of many sites and the (now much clearer) variability scenarios address several of the most important factors affecting forests in Central Europe. However, factors like forest structure, climate and soil wetness are only a few of many which control forest condition and productivity. For instance the supplement suggests that rooting depth and soil type were taken directly from Hohes Holz but this will vary strongly across sites. Sites with different slope, aspect, tree species composition, understory, etc. may act differently. Of course, it is not necessary to evaluate all factors here and Lines 357 to 363 partially clarify limitations, but I think a more general clearly stated point of limitation is still missing. Something to the effect of: The findings here may only apply to sites with similar environmental conditions to Hohes Holz and additional studies are required to further assess the impacts of a wider range of environmental site characteristics on vegetation index performance.
Additionally, there are a number of small points (mostly grammar and language choice) that I would recommend addressing for clarity prior to publication. I will do my best to identify them line-by-line below:
Lines 2 to 3: Here, data-driven VIs could help, which yield information based on correlations identified in large datasets of forest and reflectance data. --> Data-driven VIs, which yield information based on correlations identified in large datasets of forest and reflectance data, could help.
Lines 6 to 9: “Particularly VIs combining near infrared with shortwave infrared light yielded good estimates, with biomass, LAI, and GPP often being well inferable reflectances of similar wavelength combinations.” The meaning is not completely clear to me. NIR and SWIR wavelengths yield good estimates of biomass, LAI, and GPP?
Lines 12 to 13: I do not follow the logic here. Linear models constrained the effective wavelength range not the data driven indices, right? I think the wording just needs to be clarified.
Line 17: However, they are also... --> However, temperate forests are also... (to clarify that forests not ecosystem services are affected by climate change)
Line 33: Although being used as inputs in these complex analysis pipelines, vegetation indices were... --> Although they were used as inputs in these complex analysis pipelines, vegetation indices were...
Line 42: Especially NPP is difficult to estimate... --> NPP is especially difficult to estimate...
Lines 69 to 70: As such datasets are often not available for NPP and other forest characteristics of interest, we bridged the data gap using forest models... --> Because such datasets are often not available for NPP and other forest characteristics of interest, we bridged the data gap using forest models...
Line 216: “NPP was best estimable based on wavelengths in the SWIR range combined with any other range.” I learned a new word, but I still think this causes unneeded confusion for the non-statistically inclined audience. I would recommend ‘best estimated’
Line 392: That way, it might be possible... --> For example, it might be possible
Line 401: ‘estimable’ same comment as above.
Together all changes should amount to no more than a very minor revision. Thank you for the opportunity to re-review this manuscript and I look forward to seeing it published.
Colin Bloom

Hide

RR by Anonymous Referee #2 (20 Apr 2026)

ED: Publish subject to minor revisions (review by editor) (21 May 2026) by Benjamin Stocker

AR by Samuel Fischer on behalf of the Authors (15 Jun 2026) Author's response Author's tracked changes Manuscript

ED: Publish as is (22 Jun 2026) by Benjamin Stocker

AR by Samuel Fischer on behalf of the Authors (06 Jul 2026)

Journal article(s) based on this preprint

20 Jul 2026

Assessing forest biomass and productivity with data-driven vegetation indices: insights from 900 000 simulated forest stands

Samuel M. Fischer, Rico Fischer, and Andreas Huth

Biogeosciences, 23, 4927–4942, https://doi.org/10.5194/bg-23-4927-2026,https://doi.org/10.5194/bg-23-4927-2026, 2026

Short summary

Samuel Matthias Fischer, Rico Fischer, and Andreas Huth

Supplement

https://doi.org/10.5194/egusphere-2025-5198-supplement

Data sets

Forest characteristics and reflectance spectra of simulated temperate forests under different uncertainty regimes Samuel M. Fischer, Rico Fischer, Andreas Huth https://zenodo.org/doi/10.5281/zenodo.16748241

Samuel Matthias Fischer, Rico Fischer, and Andreas Huth

Viewed

Total article views: 4,003 (including HTML, PDF, and XML)

HTML	PDF	XML	Total	Supplement	BibTeX	EndNote
2,645	1,156	202	4,003	402	172	172

HTML: 2,645
PDF: 1,156
XML: 202
Total: 4,003
Supplement: 402
BibTeX: 172
EndNote: 172

Views and downloads (calculated since 10 Nov 2025)

Month	HTML	PDF	XML	Total
Nov 2025	1,085	260	76	1,421
Dec 2025	348	355	60	763
Jan 2026	527	200	30	757
Feb 2026	186	105	10	301
Mar 2026	368	162	16	546
Apr 2026	81	24	2	107
May 2026	27	19	6	52
Jun 2026	10	3	0	13
Jul 2026	12	28	1	41
Aug 2026	1	1	2

Cumulative views and downloads (calculated since 10 Nov 2025)

Month	HTML	PDF	XML	Total
Nov 2025	1,085	260	76	1,421
Dec 2025	348	355	60	763
Jan 2026	527	200	30	757
Feb 2026	186	105	10	301
Mar 2026	368	162	16	546
Apr 2026	81	24	2	107
May 2026	27	19	6	52
Jun 2026	10	3	0	13
Jul 2026	12	28	1	41
Aug 2026	1	1	2

Viewed (geographical distribution)

Total article views: 3,976 (including HTML, PDF, and XML) Thereof 3,976 with geography defined and 0 with unknown origin.

Country	#	Views	%

Latest update: 07 Aug 2026

Download

The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

Preprint (4248 KB)
Metadata XML

Short summary

We explored how satellite light measurements can reveal forest health and growth more accurately. Using computer models of many forest types, we discovered new ways to combine light wavelengths that strongly relate to forest size and productivity. Our results show that with the right wavelength choices, satellite data could give precise insights into forests, supporting better monitoring and management of these vital ecosystems worldwide.


Total:	0
HTML:	0
PDF:	0
XML:	0