The authors propose a Canopy Structure-driven Multi-feature Fusion Network (CSMFNet) to produce a 30 m resolution aboveground biomass dataset for Southwest China by upscaling plot-level inventory using sub-meter GaoFen satellite data. Based on independent NFI plot data and UAV LiDAR derived CHM data, they show that the new regional dataset outperforms existing global and national-scale AGB products, which can provide critical support for regional studies.

While the proposed CSMFNet seems appealing by fusing the forest spectral information with its horizontal and vertical structural characteristics to achieve high-precision AGB estimation,  the major issue lies in the temporal gap between ABG label and remote sensing features. The NFI plot data were collected in 2014-2018 whereas the GaoFen imagery were collected in 2023-2024. I do not think that the filtering procedure based on a random forest model (lines 180-185) can mitigate this issue, because samples used to train and validate the random forest model were associated with uncertainties and lacking of the temporal information.

The authors evaluate the agreement using R² and r inconsistently. I suggest using the same measure consistently throughout the paper.

The tree crown segmentation dataset is an important input for the model. How accurately were the trees delineated? It is necessary to report accuracies measures such as F1, recall, and precision, as well as how the accuracy assessment was performed. What does R² (0.82) refer to? It is not a commonly used metric in individual tree segmentation.

Section 2.2.3: does the UAV LiDAR data cover the same area of canopy height dataset or just part of it? Please provide more details about the canopy height dataset, such as collection time, point density, location, spatial resolution of CHM, etc.

Section 2.3.2: consider adding a map to show the coverage of the UAV LiDAR. When were the UAV LiDAR data collected? What’s the spatial resolution of UAV LiDAR derived CHM?

The spatial resolution of ESA CCI product is different from that of the product in this study. How did the authors address this scale mismatch between the ESA CCI pixels and NFI plots?

According to Table 1, there is significant temporal gap between the AGB datasets and NFI plots. How reliably could the comparison below evaluate the performances of different products?

Fig 5: it is difficult to judge whether the difference between products should be attributed to model performance or temporal gap.

Fig. 6: the low performance of other AGB datasets could be caused by temporal mismatch with the NFI plots.

Line 263: how the uncertainty was predicted should be explained. I could not find the uncertainty map from the given link.

This study generated a dataset of aboveground biomass in southwest China using high-resolution satellite data. This study aims to use direct biophysical parameters, such as height and crown size to model biomass. It should be more reliable than other datasets using environmental variables, since this region has intense human activities which could affect the statistic relationship between AGB and environmental factors. I do agree with authors about this principle.

Major concerns:

1. study area: the four provinces are not typically defined southwest China. To make it aligned with commonly used definition. The area should include Sichuan, Chongqing, and Tibet.
2. The influence of plot selection: authors used remote sensing indices (ndvi, crown cover-age, and canopy height) and a RF model to exclude plots which are far away from the model predictions. The remaining plots were finally used for the formal model training and validation. This approach would generate a biased and overestimated accuracy since the sample selection already used the information for model construction. The more scientific approach is to find the real reason of problematic plots and use data source which is independent to the model input to conduct plot selection.
3. Uncertainty of crown segmentation: authors mentioned the minimal size of detected canopy is one pixel, but how reliable it is? How to solve overlapped crown problem in dense forests.
4. Figure 5c: this figure cannot well show the distribution of NFI AGB in each bin, so we do not know the agreement between predicted AGB and NFI AGB. It is better to use violin plot
5. The contribution of three inputs should be quantitively assessed.
6. The assessment of topographic effects should be done, since this region has many hills which affect the remote sensing observation. I would like to know how the accuracy changes with the slope.
7. Data problems after I checked the dataset, please see the attached pdf.

This study used the CSMF-Net to combine NFI, GF, tree crown segmentation, and canopy height to map 30m AGB in Southwest China. Then this model was validated and results were compared with the existing AGB datasets.  

The overall accuracy of the existing tree canopy segmentation dataset is 0.82. However, when applying the corresponding method to other regions for use, the accuracy situation still needs to be reflected. The article does not mention this. The overall accuracy of the existing tree canopy segmentation dataset is 0.82. However, when applying the corresponding method to other regions for use, the accuracy situation still needs to be reflected. The article does not mention this.

When describing the use of GF data in the text, it is necessary to clearly specify which series of data is being referred to.

Section 2.2.3, “the errors are substantially lower than those reported for previously published canopy height products (r ≤ 0.22)”. What exactly is the canopy height product? It seems there are more than one product. Please clearly state the accuracy values for comparing them.

Lines 179-181: How these spectral and structural variables (mean NDVI, crown coverage, ad canopy height) can be combined with AGB to reduce errors?

Fig. 3: The first part of the data, the canopy segmentation data, was copied from another algorithm. It is necessary to distinguish which parts are original and which ones are already available algorithms, and it is necessary to clearly define this.

Section 3.1: The last sentence has clearly demonstrated the advantages of the CSMF-Net model in AGB prediction. Therefore, there is no need to conduct any verification regarding the purpose of this study. Overall, the research approach is clear and there is a certain degree of innovation. However, many parts of the text are not clearly explained, which affects the reliability of the results.

How much NFI survey data and LIDAR data were used? There needs to be a space to display or mark the data. Some schematic diagrams are shown in Figure 3. But were only these covered location data actually used? The text needs to be clear about this and provide spatial display.

What is the specific situation of the GF data? The article mentions some numbers of the data, but is it true that the distribution is across all regions? How are the areas without data distribution handled?

The text mentions that the AGB data from this study needs to be compared with many existing biomass data. It is suggested to pay attention to whether the definitions of forests in different data sets are consistent. If the definitions differ greatly, then comparing the AGB values with them would be of little significance, as the differences in definitions and spatial distribution would lead to significant discrepancies in the results. It is necessary to check whether the definitions of each data for the forest are consistent.

Clearly state which year's AGB data were obtained in this study.

The discussion section is not focused enough. The first and second parts are all comparing with other AGB data, but no clear discussion points have been identified. For instance, the algorithms of these products are different, and the definitions of forests in different data are also different. It is necessary to identify the comparable points.

Section 5.3.3, This section appears to be a summary and outlook, and there is still a need for a closer connection with the verification data.