The manuscript proposes a sugarcane mapping dataset that combines the GEDI and Sentinel-2 datasets to map sugarcane for the 13 largest sugarcane-producing countries based on sugarcane phenology and height for the period 2019 to 2022. Overall, the article is well organized and nicely written and falls within the scope of this journal. However, I have some questions about the data production process, mainly as follows:

Major Comments:

1) The criteria used to classify sugarcane from other crops might not distinguish effectively between bamboo and sugarcane, both of which are tall, perennial members of the grass family. Given the similarities in their growth habits and physical characteristics, there is a risk of misclassification in regions where bamboo is prevalent. It would be helpful to include a sensitivity analysis addressing this potential issue. Adding discriminative remote sensing indices or additional ground truth data to differentiate between bamboo and sugarcane could significantly enhance the classification accuracy.

2)  The manuscript used a uniform threshold ("Tall month") across different countries for sugarcane classification, which might not account for regional variations in sugarcane phenology influenced by local climatic conditions and sugarcane varieties. In Figure 3 we see that the shape of the curves of the kappa coefficients in response to the threshold varies considerably from country to country. It may be helpful to explore the diversity of sugarcane cultivated species within different countries and region-specific thresholds to improve classification accuracy and to account for uncertainty in some regions.

3)  The validation results show a significant discrepancy between the F1 (0.64) and R2 (0.97 with a slope of 1) in Guangxi. A more detailed investigation into these discrepancies is warranted. Otherwise, it is difficult to distinguish whether the match between the sugarcane planting area obtained and the government report is a coincidence or not.

4) Have the epidemic and climate change had a large impact on sugarcane planting? What specific year's sugarcane extent does map reflect? Or is it the combined acreage for the three years from 2019 to 2022?

Minor Comments:

1) The heading "Area" in Table 1 might change into "Country" to align with Table 2 & 3.

2) Line 52, the abbreviation GEDI should appear after the first occurrence of the full name.

3) Line 82, what is the threshold of the cloud probability you used for masking?

Dear authors,

How to map sugarcane is vital especially at global scale, this study makes full use of GEDI and Sentinel-2 imagery to generate the sugarcane maps for top 13 producing countries, and achieving >80% agreement.

However, there are several issues in the current manuscript as:

1. The novelty of the method is weak, it has been published in their previous works in 2023. The scope of ESSD aim to be innovative not only in terms of results, but also in terms of methodology.
2. The method cannot convince me in some key steps.

• How to generate accurate training samples? Authors mentioned that the GEDI can provide the canopy heights, however, the error of GEDI cannot be directly ignored. I think that the quality control in the GEDI data on GEE cannot solve the vertical error. Meanwhile, we also think only GEDI dataset cannot be used to derive high-confidence training samples, for example, the height of maize also reached tall height, so how to distinguish maize and sugarcane. How training samples for other land classes are obtained? How many training samples were used? The quality and size of training samples greatly affected the accuracy of mapping.
• Section 3.4, you reduce spatial artifacts during the mosaicking of adjacent cells by creating predictions for pixels in a 0.2^o, it doesn't convince me either. Actually, you trained the classification models in each 2^o×2^o tile, so the spatial artifacts were caused by the difference in trained classification models.
• How to use the crop mask in ESA, ESRI and GLAD data is also unclear.

Results

1. The classification maps are generated in each 2^o×2^o tile, and the relationships between tall months and kappa score in Figure 3 are analyzed at national scale. So how to determine the thresholds for tiles that span multiple countries.
2. More descriptions about the Section 4.3.2 should be greatly strengthen, for example, why China achieved the lower F1 score of 0.47?

This study introduced an innovative algorithm for sugarcane mapping using GEDI and Sentinel-2 data and published the resulting mapping dataset. The GEDI data was  used to derive the tall and short crops to train Sentinel-2 optical data to derive wall-to-wall monthly short and tall crop maps, which is then thresholded to derive sugarcane maps. The thresholds are defined from training samples collected from different formats and sources.  The results are reasonable over most countries except a few countries where the sugarcane is mixed with corn or cassava. This reviewer evaluated only the manuscript, not the maps.

I have a few comments on clarity on the paper.

Line 17, What is the full name of OECD?

Lines 26-28, the sentence does not make sense to me, please rephrase “sugar receives commodity-specific transfers of more than 20% of farm receipts globally, higher than any other food commodity”

Line 73, 1.51 rad? Can you use unit degrees?

It is good to see the authors use various sources of samples.

Line 155-156, rephrase the sentence

What is the definition of tall and short crops?

Section 4.2 needs more comments.

The authors in fact used a decision fusion method where monthly classified tall short crops are fused. An alternative way is to fuse the time series by using deep learning models. This is feasible since the Transformer can classify raw irregular time series data, as demonstrated by several studies. Can the authors discuss the possible other fusion methods? In particular consider that the authors are experts on deep learning applications.

The article presents an innovative approach to mapping global sugarcane cultivation at a high resolution using data from the Global Ecosystem Dynamics Investigation (GEDI) and Sentinel-2 (S2) satellites. Focusing on the top 13 sugar-producing countries from 2019 to 2022, the study generates detailed maps that identify sugarcane by leveraging the characteristic of sugarcane being the predominant tall crop over extended periods. The resulting maps were validated against field data, existing maps, and government statistics, showing high precision and recall, with pixel-level agreement exceeding 80% in most countries. These maps aim to aid in understanding the environmental and social impacts of sugarcane cultivation and related outcomes such as yield mapping.

While the article is well-written and presents a significant advancement in agricultural mapping, it does not clearly explain how sugarcane is differentiated from other tall crops globally. For instance, bamboo can visually resemble sugarcane, and it is not clear how the methodology distinguishes between such similar tall crops on a global scale. Clarification on the criteria and processes used to ensure accurate differentiation would enhance the robustness of the study's findings.

Aside from this concern, the article is comprehensive, methodologically sound, and should be accepted for publication.