The manuscript presents a significant and novel contribution by producing the first sub-meter resolution map of Spartina alterniflora for mainland China. The proposed OSPPF method effectively integrates multi-source data and object-based analysis to address critical limitations of existing products. The study is well-structured, the methodology is sound, and the dataset is highly valuable for the community. However, several major and minor points require clarification and improvement before the manuscript can be considered for publication.

1. The use of Google Earth imagery from 2019 and 2021 alongside 2020 Sentinel-2 data introduces a potential source of error. S. alterniflora dynamics can be rapid. Please quantify the extent (e.g., percentage of area) where non-2020 imagery was used and discuss the potential impact of this temporal mismatch on classification accuracy and the final area estimate. A sensitivity analysis in these areas would significantly strengthen the manuscript.

2. The final manual refinement, while understandable for a first-of-its-kind map, introduces subjectivity. Please describe the protocol followed for this manual correction (e.g., number of interpreters, ruleset used, process for resolving disagreements) to ensure consistency. Discussing the potential magnitude of error or bias introduced by this step is crucial for assessing the dataset's reliability.

3. The method relies on existing products (CMSA) for defining the study area and segmentation buffers. This limits its application to regions or time periods where such prior maps are unavailable or inaccurate. Please discuss the transferability of the OSPPF method to other regions without relying on existing S. alterniflora products. Could the method be adapted to be more automated and independent?

4. The authors rightly identify DL as a promising future direction and even note that CM-SSM could serve as training data. Given that DL models (e.g., U-Net, Transformers) are now state-of-the-art for many fine-scale land cover mapping tasks, a discussion on why an object-based RF was chosen over a DL approach is necessary. A direct comparison, even on a subset, would greatly strengthen the methodological justification, or the limitations of not using DL should be explicitly acknowledged.

5. The reported improvements in OA and F1-score are substantial. However, please support these claims with a statistical test (e.g., McNemar's test) to confirm that the difference in accuracy between CMSA and CM-SSM is statistically significant and not due to chance.

6. The SOC comparison is a compelling application but is briefly described. Please provide more detail on the methodology: How was the "unified provincial-level SOC unit storage coefficient" derived? Was it based on field measurements? A table of these coefficients and a reference to the method (Zhang et al., 2024) should be included in the main text or supplement. This is critical for readers to assess the validity of the 706.69 Gg difference.

7. The Random Forest classifier provides the valuable ability to rank feature importance. An analysis showing which features (e.g., Sentinel-2 phenological bands, GE texture features, RGB indices) were most important for the classification would provide deeper insight into the ecology of S. alterniflora and validate the design choices of the OSPPF method.

8. The manuscript mentions masking water pixels (SCL=6) to reduce tidal effects. However, tidal state can significantly influence the appearance and detectability of S. alterniflora. Please clarify if the Sentinel-2 compositing process considered tidal height information to select images from a consistent low-tide period, or discuss the potential residual impact of tidal variability on the phenological feature compositing.

9. The text describing the workflow (Figure 2) could be more precise. Please explicitly state the final number of bands in the PPF, SPPF, and OSPPF feature sets. A clear listing of all input features for the RF model would improve reproducibility.

10. Figures 8, 9, and 10 are critical but lack clarity. The y-axis labels in Fig. 8 are cut off. Fig. 9's Venn diagram is simple but effective; ensure the values are clearly visible.

11. The captions for Figures 5, 6, and 7 should explicitly state that the OSPPF result is the final, manually refined CM-SSM product for clarity.

12. The term "sub-meter" is used throughout, but the actual resolution of the final CM-SSM product should be explicitly stated early in the abstract and method (it is 0.9m, as mentioned later). Briefly justify why this specific resolution from GE was chosen.