Downscaling census data into population grids can address the limitations of census data with irregular units. This paper proposed a new population downscaling method using ensemble learning and geospatial big data. The method is adopted to generate a 100-m gridded population dataset of China's seventh census. The accuracy assessment on the generated population dataset shows that it has higher accuracy than the two existing datasets of WorldPop and LandScan. In general, the paper is well-written, clear, concise and complete in structure. I believe the generated dataset is important to a wide range of geoscience applications. However, the following comments need to be addressed:

1. Line 100, what resample method was used for the varibale?

2. How to get the number of POIs and the road length for each grid in Section 2.2?

3. Although the authors provided the downscaling procedure (Fig. 4), it's still unclear why do the 'population density'  used instead of the direct population count. 

4. In Section 5.2, how do you get the feature importance for stacking? How about the three base models?

5. Section 5.3 presents the parameter selection, what's the search interval within the search space?

6. Please add the caption of Fig. 1c.

7. Please enlarge the font size of Figs. 1-3.

The authors develop a novel population downscaling approach that apply stacking ensemble learning and geospatial big data to produce 100-m population grids data. The RF, XGBoost, and LightGBM are combined to stack ensemble learning. Results show high accuracy and the proposed downscaling approach indeed provide a valuable option for producing gridded population datasets. However, there are still some problems to solve.

1.    The Introduction is well written. However, the author still need to add some research progress about the population estimation. The author should also add more about the machine learning method and ensemble learning method, and their comparison.

2.    In line 110, the authors used the NTL data, and resample it into 100m. The authors should clarify the method for resampling.

3.    There are some expressions that should be more rigorous, such as “100 grid” in line 105 should be “100m grid”

4.    The authors used the DEM and Slope features from ALOS data for the population downscaling. However, did DEM and Slope features have strong correlation with population density. In west of China, such as Chongqing, population also distributed in areas with large elevation and slope.

5.    The authors applied the inhabited area data to enhance the accuracy of gridded population estimates. The authors should clarify the data and method to extract the inhabited area. There are also some errors for this step, and how to minimize this problem. 

6.    The training data and test data are essential for the population estimation. The authors should introduce more about this.

7.    The stacking ensemble learning is the important part of this work. What is the technical innovation of proposed method when compared with other method. What is the technical reference for similar studies.

8.    The discussion part should be deeper about the methods, result comparison, advantages and disadvantages, model transferability, validation, future research prospect and etc.

9.    The figure should be improved. For figure4, the text is too large. Figure 5 should clarify more about the ensemble learning. Figure 7 should be more standard and beautiful. Figure 8 should show the comparison between existing products and estimated results, not only the existing products. The unit of y-axis in Figure 10 should be clarified.

10.    There are lots of good information and findings to highlight for the paper. What is the main contribution of this work from the perspective of technical problem. Please add accordingly in the abstract and conclusion parts. 

11.    The manuscript still exhibits minor issues in language expression, including spelling and grammatical errors. For example, some sentences in the introduction are overly complex, and the transitions between different viewpoints could be made smoother.