I am very appreciated to give me the chance to review such an interesting article, the longer period, higher resolution, and covering the whole China. Such a valuable dataset will hugely improve the ability to estimate greenhouse gas fluxes, climate change impacts and crop production. The authors developed a machine learning model after taking appropriate data preprocessing strategies (so-called addressing the challenges of cloud contamination and missing data in optical remote sensing observations) to produce CCD-Rice (China Crop Dataset-Rice), from 1990 to 2016, with the overall accuracy of 89.89 % at a provincial administrative region, and 85% and 78% for single- and double-season rice at a county administrative scale. Their study is fallen closely within ESSD, and will potentially attract wider readerships. I am highly concerning on the issue of your samples for training and evaluating. However, I am several concerns listed bellows:

1. Training samples: how many training samples were collected? Where are their locations?Is there any time-features (day, month, year etc.) labelled for these training samples. Are they isolated from your validation samples.
2. Accuracy: remote sensing products are generally validated by two ways: sampling points by a confusion matrix, and identified areas by statistics books (both province and county, it is more popular at a smaller administrative scale). Please clearly showcase your results related with two validation methods, and clearly involve them into key-contents sections, such as ABSTRACT.
3. Comparison: As conducting your comparison with other open products, it will be more scientific and objective to downscale your resolution into coarser one to align your product with other coarser product; or upscale your resolution into a higher resolution for comparing yours with other refined product.
4. It will be better to emphasize your improvements comparing with the recent publications (Shen et al. 2023a, 2023b; Pan et al. 2021a, 2021b). If no any significant improvement has been made, I think it will be more reasonable to update the related datasets, rather than blindly pursuing the number of publications.
5. Other: As you said in ABSTRACT “This study developed a new optical satellite-based rice mapping method using a machine learning model and appropriate data preprocessing strategies to address the challenges of cloud contamination and missing data in optical remote sensing observations. It will be better to remove “address the challenges of cloud contamination and missing data in optical remote sensing observations” because you have NOT input any substantive efforts to improve the image quality or solve the issue of cloud contamination and missing data. Please remove such statement throughout your paper to avoid readers’ discontent and protect yourselves.
6. Carefully check the manuscript to eliminate the spelling mistakes，e.g. resent in Fig.3