Publisher’s note: the content of this comment was removed on 13 February 2026 since the comment was posted by mistake.

Publisher’s note: the content of this comment was removed on 13 February 2026 since the comment was posted by mistake.

Publisher’s note: the content of this comment was removed on 13 February 2026 since the comment was posted by mistake.

This study presents the CCAV-10m dataset, the first annual, species-level coastal wetland vegetation map of China at a 10-meter resolution, spanning from 2016 to 2023. By developing a novel phenology-guided classification network (P_SVCN) that integrates Sentinel-1 SAR data with Sentinel-2 optical imagery, the authors have generated a highly consistent dataset that captures the spatiotemporal dynamics of six key coastal wetland vegetation types. The manuscript is well-written and presents a technically sound methodology, effectively filling a critical data gap in coastal ecosystem monitoring.

However, I have some minor comments before this manuscript is published.

(1) Please ensure that all botanical taxonomic names follow standard nomenclature throughout the manuscript. Genus names should be capitalized, specific epithets should be lowercase, and the entire binomial must be italicized (e.g., Spartina alterniflora). After the first mention of the full name, the genus may be abbreviated (e.g., S. alterniflora)

(2) While Figure 7 provides a clear visual trend of provincial stacked areas, it is difficult for readers to extract precise annual values for specific provinces and species . To enhance the data’s utility and citability for future research, I recommend including a detailed statistical table in the Appendix/Supplementary Materials containing these specific values.

(3) The manuscript states that Sentinel-2 imagery was selected as bi-temporal data based on phenological information. However, the selection criteria or compositing strategy for Sentinel-1 imagery remains unclear. Please supplement the Methodology section with a description of how the Sentinel-1 data were selected.

(4) High-quality vegetation samples are critical for model training. In Section 3.3, it is mentioned that "samples underwent systematic quality control," but the specific procedures are not detailed. Please provide a clear explanation of the quality control criteria and methods used to ensure the reliability and representativeness of the training data.

(5) The public availability of the CCAV-10m dataset via a DOI is highly commendable, yet the long-term maintenance of the data remains a point of interest. Since the title emphasizes "Annual Spatiotemporal" monitoring while the series currently ends in 2023, could the authors clarify whether there are concrete plans to extend the dataset to include 2024, 2025, and subsequent years?

This study produces CCAV-10m, an annual 10 m species-level coastal wetland vegetation dataset from 2016 to 2023, using a phenology-guided deep learning framework. The topic is well suited to Earth System Science Data, and the dataset is valuable for long-term monitoring of coastal wetland vegetation, invasive species expansion, and ecosystem management. The manuscript is generally well organized, and the reported classification performance is impressive. My comments mainly concern clarity, reproducibility, and several points of interpretation and presentation.

1. The dataset is valuable and timely, but the methodological description could be more reproducible.

The manuscript provides a reasonably clear description of the P_SVCN framework, including the dual-branch design, the SAR–optical fusion strategy, and the attention-based architecture. However, the actual training procedure remains insufficiently described. I therefore suggest that the authors add a concise but explicit description of the model training procedure. This could include, where applicable, the loss function, optimizer, learning rate, stopping criterion, and software framework used. These details are important because the novelty of the manuscript partly lies in the deep learning framework itself.

1. The manuscript would benefit from clearer interpretation of dataset characteristics and limitations.

The manuscript reports good classification performance for the CCAV-10m dataset, and the proposed P_SVCN model shows higher accuracy than the previous SVCN model. I suggest that the authors add more discussion of the mechanisms or characteristics of the P_SVCN model that may explain its better performance. At the same time, since coastal wetland vegetation is strongly affected by tides, salinity gradients, and phenological variation, the discussion should more explicitly address how these factors may still influence uncertainty or local misclassification. In Figure 7, the results suggest large interannual variations in the area of some species, for example, P. australis and Suaeda spp. in Liaoning from 2022 to 2023, and Suaeda spp. in Shandong before and after 2020. Do these large interannual variations reflect real-world changes, or might they be influenced by uncertainties or local misclassifications? The manuscript already acknowledges these issues to some extent, but the discussion could be strengthened by linking them more directly to dataset quality and potential limitations in application.

Specific comments

1 Since the study area only covers eight coastal provinces and does not include Guangdong, Guangxi, or Hainan. I suggest revising the title to make the spatial coverage more precise, for example by using “eastern coastal China” rather than “China”.

2 Line 94:  The manuscript states that 320 Sentinel-2 scenes were selected, which is much smaller than the number of Sentinel-1 scenes. Please clarify the criteria used for scene selection, such as acquisition timing and cloud coverage thresholds.

3 Line 111-113: The description of the manually interpreted and field-based samples is useful, but I suggest adding a clearer visual summary of their spatial distribution in the supplementary material. A map would improve transparency.

4 Figure2: Does each line represent the NDVI of one representative plot, or the average NDVI of all wetlands within that region? Also, why is the NDVI time series of S. alterniflora in Figure 2(c) discontinuous?

5 It would be helpful to clarify whether the reported validation design is entirely sample-based or whether there was also any spatial separation to reduce possible spatial autocorrelation between training and validation samples.

6 Figure 4: The left panel appears to show mangroves in Hangzhou Harbor. Do mangroves actually exist there?

Overall, I find the dataset contribution promising and suitable for publication in Earth System Science Data after minor revision.