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.
- Preprint
(2268 KB) - Metadata XML
-
Supplement
(424 KB) - BibTeX
- EndNote
Status: open (until 16 Aug 2026)
- RC1: 'Comment on essd-2026-329', Anonymous Referee #1, 07 Jul 2026 reply
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
Viewed
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 75 | 33 | 3 | 111 | 14 | 7 | 2 |
- HTML: 75
- PDF: 33
- XML: 3
- Total: 111
- Supplement: 14
- BibTeX: 7
- EndNote: 2
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 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.Â
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.
Â