General comments. According to the World Meteorological Organization, 2023 is the hottest year on record. Therefore, it is of great significance to develop a long-term cropland dataset to explore the climatic effects of human land use. This study reconstructs millennial cropland for Northeast China. Topics fits the aims and scope of the ESSD. The following comments and suggestions should be considered for revisions.

--First, why only area estimation, and no spatial reconstruction? A 5' × 5' cropland dataset is developed for Northeast China from AD 1000 to 1200 by these authors (Gridded reconstruction of cropland cover changes in Northeast China from AD 1000 to 1200. https://link.springer.com/article/10.1007/s10113-023-02118-y ). But in this study, only provincial-level or county-level cropland area is available. Why? Obviously, the datasets reconstructed in this study cannot be used by climate modelers. In addition, in terms of data from 1000 to 1200 years, is there any improvement in this paper compared to the paper mentioned above (Gridded reconstruction of cropland cover changes in Northeast China from AD 1000 to 1200)?

--Second, the applicability of the reconstruction method of estimating the cropland area for a small area by population. Generally speaking, estimating cropland by population is mostly applicable at continental to global scales. In the case of a small region, more other factors will affect the relationship between population and cropland.

--Third, failure to evaluate the reliability or accuracy or uncertainties of the reconstructed dataset will affect the user's use of the dataset. The comparison with the global dataset does not indicate the reliability of the dataset developed in this paper, because the global dataset itself has a large degree of uncertainty. The fact that the reconstruction results in this paper are very different from the global dataset does not mean that the dataset developed in this paper is reliable.

-Fourth, writing is not done from the perspective of data development (Data description paper), it is more like a research paper. For example, the core content of the results should not be the analysis of the spatio-temporal characteristics of cropland changes, but the rationality, reliability, accuracy, and potential uses of the data products developed in this paper. More specific comments are as follows.

--Title and Introduction. Why reconstruction for 1000 to 2020 in Northeast China? More explanations are necessary. Based on Figure 5, From 1000-1700, there was only a small area of cropland in Northeast China. Line 393, In 1200, cropland fraction of 1.17%; In 1400, line 395, cropland fraction of only 0.19%. The environmental impact of such a small area of cropland is completely negligible. Based on figure 5, the topic for past 300 years (Ye, Y., Fang, X., Ren, Y., Zhang, X., and Chen, L.: Cropland cover change in northeast china during the past 300 years, Science China Earth Sciences, 52, 1172-1182, https://doi.org/10.1007/s11430-009-0118-8, 2009.) is good, but for 1000 to 2020 may be not a good research topic.

--Data and Methods. Not clear enough. For example, Line 91-115, It only introduces population data, per household population data, and interpolates the population according to the population growth rate, and does not involve how to estimate the cropland area at all. Line 107-110 mentions how to estimate the area of cropland, but it is very simple and there is no specific method. As far as Northeast China is concerned, why such an estimate is reasonable is not explained at all. From the perspective of historical land use reconstruction, estimating cropland area based on population as a proxy is only applicable to large-scale scales such as global and continental. For example, HYDE uses population to estimate the world's historical cropland. It also makes sense to reconstruct China's historical cropland in this way. But the Northeast is only a small region of China, so there is a lot of uncertainty in the results of this estimate.

--In addition, for 1000, 1100, and 1200, what’s the difference between this study and the paper mentioned above (Gridded reconstruction of cropland cover changes in Northeast China from AD 1000 to 1200).

--Line 170-197；Line 257-278. Introduce too much about the estimation methods in published papers (Ye et al, 2009; Tian et al., 2005). It needs to be drastically cut, and readers can read these papers at all. In short, the writing of the method section is too lengthy and will scare off the vast majority of readers.

-Line 232. Correct negative or zero values of cropland. If the estimated results have a negative value, then there must be a problem with the previous interpolation and fitting methods, and we have reason to suspect that all the results obtained by the interpolation are problematic. Just correct negative or zero values of cropland isn't enough, what about the other results? From this point, it can be seen that this paper needs to have an uncertainty assessment of the estimation results, otherwise readers will not dare to use this data product to carry out downstream research.

-Results. ESSD readers are more concerned about the reliability, availability, and accuracy of data products. However, the spatiotemporal variation characteristics of cropland area are not the most important.

--4.1 comparison. The comparison with the global dataset does not indicate the reliability of the dataset developed in this paper, because the global dataset itself has a large degree of uncertainty. The fact that the reconstruction results in this paper are very different from the global dataset does not mean that the dataset developed in this paper is reliable. Line 516-517, the following statement is not acceptable “Comparative analysis with global historical LUCC datasets indicates that the results of this study are relatively credible and more rational.”

-- technical corrections. Figure 5, no titles for x and y axes.

The authors developed a dataset to document cropland area over the past 1000 years in the North China. By using historical records and recent datasets, the manuscript particularly looked at the spatial changes and possible improvement to the accuracy of the regional dataset. I have a few concerns and suggestions for the authors to consider if they decide to revise the manuscript.

The novelty of this manuscript is not clearly presented. The authors have already published a few similar papers in the past few years, and even one for the Northeast China region. The only difference is the time period covered here. Land use change, especially for such long history with spatial coverage, is deemed important in understanding carbon budget, land emissions, and many other studies. This is what the authors also emphasized in the Introduction. However, this particular study presented only a few snapshots (i.e., 28), and just one relatively small area in China (not the ones with rich ancient history like capitals or the areas along the rivers/Yellow River that nurtured Chinese agriculture). Why is this study so unique and important? This can be made clearer in the Introduction.

Also, please note that the current Introduction is quite similar to what’s included in the Jia 2023 paper published at Regional Environmental Change, both the structure and argument of novelty. Quite a few sentences from the 2023 paper are used here again. This is not acceptable.

Next, in terms of the methods used here compared with others published by the same group of authors including the 2023 one, any significant difference besides data/records used? Any improvement to the methods? Could we expect any improvement of methods from an additional paper? HYDE have already developed global scale LUC data, with even longer history and higher resolution, and this study has always compared their results with HYDE. From what angle can we justify that this dataset has “higher reliability” or can “improve the accuracy and reliability”? Comparing a regional study with global work, or filling a few missing data (aim 1) do not make this a better paper. The authors need to better clarify the intention, methods, and even the comparison in the discussion.

Other points for consideration:

L24: again, the Introduction is quite similar to Jia 2023, this has to be revised to be acceptable anywhere?

L52: aims not aim.

L55-57: how many aims do you have exactly? Two or four? These do not seem to be complete sentences.

L113: this seem to be quite large for per person, can this value be used for the whole region?

L384: The method is done by now, but how did you compute the spatial distribution of cropland across time? The previous methods mainly focused on total area numbers, but should the spatial pattern change with time, as the factors influencing cropland distribution change? For the area records, would the administrative region boundary change over time, which affect the statistics? Fig. 5 is an example that may be impacted by boundary changes.

L455: there are several comparisons here, how do you justify that your estimates are better than others? Or do you suggest that as long as you have more data records then it should be more accurate?

L514: this is NOT “uncertainty analysis”, there is no “analysis” at all. Just some random discussions.

L533: don’t you think the conclusion is a bit too long?