SwissPhenoCam: A country-scale dataset of tree-level phenocam greenness captures species-specific phenological variation along elevation gradients in Switzerland
Abstract. Vegetation phenology, the seasonal timing of recurring plant life-cycle events, is a key regulator of ecosystem functioning, carbon and water cycling, and species interactions, while also serving as a sensitive indicator of climate variability and climate change. However, existing phenological observations often face trade-offs between species-level detail, temporal frequency, and spatial coverage. Networks of near-surface digital repeat cameras (phenocams) deliver robust, high-frequency phenological signals, but their spatial coverage remains a limitation. In addition to expanding such phenocam networks, this limitation can be tackled by repurposing the outdoor cameras already deployed for other purposes. Here, we present SwissPhenoCam, a country-scale dataset of tree-level phenological greenness observations derived from a network of outdoor digital cameras distributed across Switzerland. By repurposing cameras originally installed for meteorological monitoring, tourism, and other non-ecological applications, SwissPhenoCam demonstrates the potential of existing imaging infrastructure for large-scale phenological observation.
The dataset comprises imagery from 34 sites spanning broad elevational, climatic, and biogeographic gradients, with archives extending up to 15 years. Individual trees were delineated and identified to species level where possible, enabling the extraction of species-specific greenness time series and phenological transition dates. The curated dataset contains 5,855 tree-years of observations representing more than 20 tree species and captures substantial environmental variability across Switzerland.
Validation against independent observations from the Swiss Phenology Network demonstrates strong agreement between camera-derived and ground-based phenological transition dates, confirming that non-specialized webcams can provide reliable phenological information. Analyses of the dataset reveal pronounced species-specific responses to environmental gradients. Spring green-up is consistently delayed with increasing elevation across deciduous species, whereas autumn senescence shows comparatively weak elevational trends. As a result, growing-season length decreases primarily because of later spring onset rather than earlier autumn decline.
The SwissPhenoCam dataset is openly available through Zenodo (https://doi.org/10.5281/zenodo.20451225) (Garnot et al., 2026) and is intended as a living resource that will expand as observations accumulate. By providing species-resolved, individual-tree phenological observations across a topographically complex landscape, the dataset offers a valuable benchmark for evaluating remote-sensing products, improving phenological models, and assessing climate-change impacts on forest ecosystems. Future work can use this resource to investigate long-term phenological trends, species-specific climate sensitivities, and ecosystem responses to ongoing environmental change.
Dear authors,
First of all, I sincerely appreciate your hard work and fantastic study. The authors collected long-term phenology images at multiple sites in Switzerland and then analyzed the characteristics of spring and autumn phenology along the altitude gradient.
While this study is not novel, its uniqueness lies in combining various types of camera networks in Switzerland, including cameras not intended for phenological research. They also sincerely validated the data quality and uncertainty for analyses of RGB time-series data.
From the viewpoint of transparency and reproducibility, their study is unquestionable. I believe this study will support our deeper understanding of the interaction among ecosystem functions and services, and biodiversity under rapid climate changes. However, I have some comments to highlight the significance of this study. First, the volume of this manuscript is too large. I guess readers will struggle to find the significance of this study. Therefore, I strongly recommend arranging the structure of the main text. In other words, moving some parts to the appendix would be beneficial. Second, you should add more suitable references to strengthen your logic. In addition, some of your knowledge is outdated. Please see in my minor comments. Finally, you show many figures. Regarding my first concern, I recommend moving some figures to the appendix. Despite my three concerns, the authors will quickly improve their manuscript.
1: line 34 How about the ecosystem services (cultural) and biodiversity?
2: lines 53-58 Satellite remote sensing has already observed individual tree-level phenology. Please see the following papers.
Chen, B., Y., Jin, and P., Brown. 2019. “An enhanced bloom index for quantifying floral phenology using multi-scale remote sensing observations.” ISPRS Journal of Photogrammetry and Remote Sensing 156: 108–120. https://doi.org/10.1016/j.isprsjprs.2019.08.006.
Miura, T., S., Nagai, M., Takeuchi, K., Ichii, and H., Yoshioka. 2019. “Improved characterisation of vegetation and land surface seasonal dynamics in Central Japan with Himawari-8 hypertemporal data.” Scientific Reports 9: 15692. https://doi.org/10.1038/s41598-019-52076-x.
Pan, B., X., Xiao, S., Luo, et al. 2025. “Identify and track white flower and leaf phenology of deciduous broadleaf trees in spring with time series PlanetScope images.” ISPRS Journal of Photogrammetry and Remote Sensing 226: 127–145. https://doi.org/10.1016/j.isprsjprs.2025.05.013.
Wang, J., Y., Li, M. M., Rahman, et al. 2024. “Unraveling the drivers and impacts of leaf phenological diversity in a subtropical forest: A fine-scale analysis using PlanetScope CubeSats.” New Phytologist 243: 607–619. https://doi.org/10.1111/nph.19850.
Zhao, Y., C. K. F., Lee, Z., Wang, et al. 2022. “Evaluating fine-scale phenology from PlanetScope satellites with ground observations across temperate forests in eastern North America.” Remote Sensing of Environment 283: 113310. https://doi.org/10.1016/j.rse.2022.113310.
3: line 75 We also have a phenology network in Brazil.
https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2023.1223219/full
4: line 78 We have this kind of camera network in Japan.
https://www.sizenken.biodic.go.jp/index_en.php
https://www.sciencedirect.com/science/article/abs/pii/S1574954110000762?via%3Dihub
5: Figure 1 It's quite informative. It's not so easy to understand quickly.
6: Figure 2 I recommend moving this to the appendix.
7: Equations. From a mathematical viewpoint, your descriptions are perfect. However, I guess many readers won't understand them. Please add some explanations. For example, "arg max(g)"
8: Line 223 Please add suitable references.
9: Figure 8 I recommend moving this to the appendix.
10: Table 3 R2 >- R2
11: Figure 12 Please write the scientific name in italics.
12: Table 4 Please write the scientific name in italics.
13: Figure 13 Please write the scientific name in italics.
14: Line 516 Please briefly explain "SNR".
15: Table 6 Please write the scientific name in italics.
16: Figure 15 I recommend moving this to the appendix.
17: Line 557 Please add suitable references.
18: Line 580 How about the ecosystem services (cultural) and biodiversity?
19: Line 587 Regarding my second comment, please revise it. I guess you may find the latest study in Switzerland using Sentinel-2 satellites. Please survey it.
20: Line 589 Please add suitable references.
21: Line 597 Please add suitable references.
22: Line 600 Please add suitable references.
23: Line 602 Please add suitable references.
24: Line 623 Please add suitable references.
25: Conclusion The following papers may support your conclusions.
https://onlinelibrary.wiley.com/doi/10.1111/j.1466-8238.2008.00398.x
https://www.sciencedirect.com/science/article/abs/pii/S1470160X19304674
https://www.nature.com/articles/s41598-025-14547-2
https://link.springer.com/article/10.1007/s00484-020-01953-6
https://www.frontiersin.org/journals/forests-and-global-change/articles/10.3389/ffgc.2021.659910/full
26: Fig A2, A3, A8, A11, A15, and A17. You lost the colour bar in the figures. In addition, please explain the numbers of x and y axes.
27: Line 724 iif -> if
28: Table B1 Please explain such as BEB, BEA, and BLB.
Best wishes,
Shin