the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 04 Jun 2026
Mapping forest canopy height over Europe by integrating Sentinel-1, Sentinel-2, GEDI, and ICESat-2 data
Abstract. Timely, accurate, and spatial explicit information on forest structure, such as canopy height, is important to understand and respond to ongoing changes in forests and to support the mapping of habitat structure. The availability of spaceborne LiDAR data, such as those from GEDI, has stimulated the development of continental to global canopy height maps. Yet, while GEDI data are often used to train canopy height models, these data are lacking in northern areas. In this study, we mapped canopy height over Europe at 10 m resolution by combining Sentinel-1 and Sentinel-2 data and integrating training data from GEDI and ICESat-2. The integration of ICESat-2 and GEDI data mostly enhanced the model performance in the north of Europe, where GEDI data are lacking. The model reached a RMSE of 5.77 m and a MAE of 4.09 m based on an independent validation with ALS data over about 3,700 patches across Europe. The resulting canopy height map and validation dataset have been made publicly available at https://doi.org/10.5281/zenodo.13324731 and https://doi.org/10.5281/zenodo.18471620, respectively.
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Status: final response (author comments only)
- RC1: 'Comment on essd-2026-329', Anonymous Referee #1, 07 Jul 2026
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RC2: 'Comment on essd-2026-329', Anonymous Referee #2, 21 Aug 2026
The manuscript presents a new 10 m canopy height dataset for Europe by combining GEDI and ICESat-2 observations with Sentinel-1 and Sentinel-2 data. Considerable effort has been devoted to collecting and processing the training data as well as compiling an extensive independent ALS validation dataset across Europe. The resulting dataset is likely to be valuable for forest monitoring and ecosystem studies. However, I have several concerns regarding the methodological description, validation strategy, and interpretation of the results that should be addressed before publication.
Major comments
1. The manuscript states that the objective is to map canopy height of woody vegetation (trees and shrubs), while the title and abstract mainly refer to forests. It would be helpful to clarify this distinction throughout the manuscript, as the mapped product appears to include woody vegetation beyond forests. In addition, the training samples are selected based on the intersection of multiple land-cover products (e.g., ESA WorldCover 2020/2021 and the Copernicus High Resolution Forest Type layer from 2018). These products differ in acquisition year and classification methodology, which may introduce temporal inconsistencies and classification uncertainties.
2. The description of the GEDI and ICESat-2 training samples is insufficient. Please report the final number of training, validation, and testing samples for each dataset, together with their spatial distribution. A map similar to Figure 1 showing the spatial density or number of GEDI and ICESat-2 samples would help readers better understand the representativeness of the training data.
3. The manuscript mainly reports the independent validation results using the ALS dataset. Since the canopy height model was trained using GEDI and ICESat-2 observations, the evaluation on the held-out GEDI/ICESat-2 test dataset should also be presented more explicitly.
4. The independent validation is based on ALS RH98, whereas the compared canopy height products are not necessarily based on the same canopy height metric (e.g., RH95 in Potapov et al.). It would be helpful to summarize these differences in the Methods or Results to facilitate a fair interpretation of the comparisons.
5. The manuscript frequently refers to the canopy height maps from Pauls et al. in the Introduction. However, their height maps are not included in the quantitative comparison.
6. Figure 4 only presents the comparison using all validation samples. Since the manuscript emphasizes the improved performance in northern Europe, it would be helpful to include equivalent comparisons for northern and southern Europe for all reference products. In addition, please clarify whether exactly the same validation samples and filtering criteria (land cover) were applied to all products to ensure a fair comparison.
7. More methodological details are needed regarding the combined training using GEDI and ICESat-2 observations. Since the two datasets differ in footprint size, sampling characteristics, and measurement uncertainty, please clarify how the training samples from the two sensors were combined and balanced during model training. In addition, the key CatBoost hyperparameters should be reported to improve reproducibility.
8. According to Figure 3, the model trained solely with ICESat-2 achieves a very similar accuracy to the model trained with both GEDI and ICESat-2. Therefore, the additional contribution of GEDI is not entirely clear.
9. Figure 5 shows a clear saturation effect, with increasing underestimation for canopy heights above approximately 25 m. In this height range, the proposed model performs worse than Lang et al. (2023).
10. Figure 6 appears to exhibit noticeable block-like spatial patterns across large parts of Europe. It is unclear whether these patterns originate from the visualization (e.g., map resampling) or from the underlying prediction product. In addition, it would be useful to compare the canopy height distributions of different maps, which may help readers better understand where the differences originate.
11. It is not clear which dataset was used to generate the results shown in Figure 7. The reported accuracy differs from the results presented in Figures 3 and 4.
12. Overall, I believe the main strength of this dataset lies in the improved canopy height mapping over northern Europe through the integration of ICESat-2 observations. However, additional clarification of the methodology, more comprehensive validation, and improved comparisons with existing products are needed to fully demonstrate the advantages of the proposed dataset. Providing sufficient methodological details would also substantially improve the reproducibility and long-term value of this dataset.Citation: https://doi.org/10.5194/essd-2026-329-RC2 -
RC3: 'Comment on essd-2026-329', Anonymous Referee #3, 23 Sep 2026
This manuscript presents a 10 m canopy-height product for Europe derived from Sentinel-1 and Sentinel-2 data and trained using GEDI and ICESat-2 observations. The modelling framework itself is largely based on established approaches. However, I do not consider the limited methodological novelty to be a fundamental problem for ESSD. The main community value lies in the Europe-wide data product and, particularly, in the effort devoted to compiling and processing a geographically extensive independent ALS validation dataset. The reported performance is competitive, and the annual temporal compositing and filtering appear to produce a useful product.
I therefore consider the datasets potentially suitable for publication in ESSD. However, the present version does not yet meet the journal’s expectations for data completeness, reproducibility, uncertainty characterization, and transparent quality assessment. I recommend major revision.
Review of “Mapping forest canopy height over Europe by integrating Sentinel-1, Sentinel-2, GEDI, and ICESat-2 data”
Major comments
1. Consistency of the released ALS dataset
The public validation dataset does not appear to match the dataset described in the manuscript. Supplementary Table S2 reports 3,695 patches across 18 countries, including 385 patches from Poland, whereas the Zenodo record contains 3,310 patches across 17 countries and does not include Poland. The difference is exactly 385 patches. Please reconcile this discrepancy and systematically verify that the manuscript, repository description, spatial index, and released files are mutually consistent.2. Reproducibility of the modelling workflow
The present description is insufficient to reproduce the model-derived product. Although the quantiles and general weighting approach are described, essential information such as the CatBoost version, complete hyperparameters, stopping procedure, random seeds, exact weighting implementation, and GEDI–ICESat-2 sample balance is missing. The final numbers of training, validation, and test samples for each sensor should also be reported.At minimum, the manuscript and repository should provide all parameters, software versions, spatial split information, and processing details required to reproduce the results. Archiving version-controlled code and an environment file would be the clearest way to achieve this. CatBoost should also be properly cited.
3. ALS validation design and geographic representativeness
The ALS dataset is extensive but strongly concentrated in a few countries. Spain, Norway, Finland, and Sweden account for approximately 57% of the reported patches, whereas Italy contributes only 10 patches and several regions are not represented. Therefore, the pooled accuracy should not be interpreted as geographically uniform European performance. Country- or ecological-region-level results and an appropriate macro-average would provide a more balanced assessment.In addition, the ten pixels sampled within each 640 m patch are spatially correlated and share the same ALS acquisition and processing characteristics. Validation should therefore also be summarized at the patch level, or uncertainty should be estimated using patches as the resampling unit. Confidence intervals are particularly important before interpreting the very small performance differences among the models as meaningful.
4. Temporal and phenological mismatch in the ALS validation
The map represents 2020, whereas the ALS observations span 2011–2023 and include both leaf-on and leaf-off acquisitions. The current filtering only reduces mixtures of flight dates within a patch; it does not address differences between the ALS acquisition and the target year. Consequently, the reported error includes forest growth, disturbance, and seasonal effects in addition to retrieval error.The authors should quantify how validation performance changes with temporal distance from 2020 and with acquisition season, and should more clearly state that the reported RMSE is not a purely contemporaneous mapping error.
5. Different spatial supports of GEDI and ICESat-2
GEDI samples are linked to predictors averaged over a 3 × 3 window, whereas ICESat-2 samples use a single 10 m pixel. Thus, the two training sources have different spatial supports when they are combined in the same model. It is also unclear whether wall-to-wall prediction uses the raw 10 m predictors or the same spatial averaging applied to the GEDI samples.This choice requires clearer justification and at least a simple sensitivity analysis comparing the current approach with a common spatial-support treatment. The manuscript should also distinguish between the clear benefit of adding ICESat-2 to the GEDI-only model in northern Europe and evidence for actual synergy between the two sensors. Relative to the ICESat-2-only model, the combined model improves the European RMSE only from 5.78 to 5.77 m and performs slightly worse in southern Europe.
6. Predictive uncertainty
The model predicts q0.05, q0.50, and q0.95, but the uncertainty estimates are not validated. The authors should demonstrate whether the nominal 90% interval achieves approximately 90% empirical coverage, including across different height ranges and geographic regions.Moreover, the public map repository appears to contain only q0.50 rasters. The q0.05 and q0.95 predictions, or the derived PI90/PIR layers, should be released if uncertainty is presented as part of the product. Otherwise, the related claims should be weakened. Equation 1 also defines prediction-interval width rather than the interval itself, and the treatment of PIR where q0.50 is zero or close to zero should be clarified.
7. Additional methodological information
Several processing steps need concise but more complete documentation. For Sentinel-1, the authors should specify whether γ⁰ was expressed in linear or dB scale, the applied radiometric and terrain corrections, the multitemporal speckle filter, and whether ascending and descending observations were combined or treated separately. These choices directly affect the definitions of VH–VV and RVI.The 100 × 100 km grid should be defined by its CRS and origin, and the cell-allocation procedure and random seed should be reported. The rationale and implementation of the ±1° coordinate perturbation also require clarification. Finally, training excludes slopes above 10%, whereas validation excludes slopes above 20% and the final map apparently covers all terrain. The authors should quantify the resulting excluded area and discuss the reliability of predictions over mountainous regions.
Minor comments
1. Clarify whether the ALS metrics were calculated for all valid pixels or only for pixels classified as woody vegetation.2. Clearly distinguish performance on the held-out GEDI/ICESat-2 test set from performance against the independent ALS data, particularly in Figure 7.
3. Correct the inconsistency between “S1S2-9B” in Table 1 and “S2S2-B9” in Figure 7.
4. Use consistent terminology for forest canopy height and woody-vegetation height, as the training and prediction domains also include shrubs and non-woody zero-height samples.
Citation: https://doi.org/10.5194/essd-2026-329-RC3
Data sets
European canopy height map W. De Keersmaecker et al. https://doi.org/10.5281/zenodo.13324731
ALS-based canopy height across Europe L. Bertels et al. https://doi.org/10.5281/zenodo.18471620
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- 1
The authors present a study aimed at producing a 10 m forest canopy height map for Europe by integrating Sentinel-1, Sentinel-2, GEDI and ICESat-2 observations. Overall, this is a good study that complements the growing landscape of continental and global canopy height products. Whilst the overall modelling framework is largely based on established approaches, the work provides an important contribution by extending training to northern Europe through the integration of ICESat-2 data and by delivering an openly available European canopy height dataset.
In my opinion, one of the strongest aspects of the manuscript is the validation strategy. The authors assembled an extensive independent validation dataset from heterogeneous airborne LiDAR datasets collected across multiple European countries. Such a large-scale independent validation effort is relatively uncommon and substantially increases confidence in both the reported accuracy and the resulting data product.
The manuscript is generally well written, the methodology is clearly described, and the data product will likely be valuable for a wide range of ecological and forestry applications. My comments below are intended to further strengthen the manuscript.
Comments:
1. The independent ALS validation dataset is one of the principal strengths of this study. However, the ALS acquisitions span multiple years, while the canopy height map represents conditions in 2020. Although the manuscript acknowledges this limitation, I encourage the authors to discuss more explicitly how temporal mismatches may influence the reported validation statistics.
2. Clarify the added value of this dataset. The manuscript compares the proposed product with several existing canopy height datasets and demonstrates modest improvements in validation statistics. While these improvements are encouraging, I suggest that the Discussion better emphasise the broader advantages of the dataset. In particular, the integration of ICESat-2 substantially improves training coverage in northern Europe, the extensive independent validation dataset increases confidence in the results, and the provision of uncertainty estimates enhances the usefulness of the product. These aspects arguably represent the primary contribution of the study and deserve greater emphasis than the relatively small differences in RMSE.
Minor comments:
In the Abstract and the Introduction, "spatial explicit" should be corrected to "spatially explicit"
Please clarify whether the reported RMSE and MAE are computed over all validation pixels or only over pixels classified as woody vegetation.
Table 1 refers to the combined Sentinel-1/Sentinel-2 model as "S1S2-9B", whereas Figure 7 uses "S2S2-B9". This appears to be a typographical inconsistency and should be corrected.
The manuscript occasionally uses the terms height estimate, height prediction, and canopy height map interchangeably. Using consistent terminology throughout would improve readability.
The choice of adding random noise of ±1° to the latitude and longitude variables, together with the decision to saturate the sample weights above 35 m canopy height, would benefit from a brief justification.
A concise table summarising the main characteristics of this product and the existing canopy height datasets used for comparison (e.g. training data, spatial resolution, spatial extent, independent validation, and uncertainty estimates) would provide useful context for readers.