High Spatiotemporal Remote Sensing Images Reveal Spatial Heterogeneity Details of Soil Organic Matter
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsDear Authors,
The work "High spatiotemporal remote sensing images reveal spatial heterogeneity details of soil organic matter" fits thematically well into the framework of the Sustainability journal.
The authors attempted to identify the spatiotemporal organic matter based on remote sensing data from Landsat, Santinel and Gaofen. The work also examined differences in prediction for data from subsequent satellites. The analysis was performed on data from the 3-year research period 2020-22. The analysis of the research area along with a detailed description of the measurements performed, including the preparation of samples for determinations, is described in the methodology chapter, and the chapter itself is well prepared. The work is interesting and the content is supported by careful drawings. The obtained results are confirmed by the adopted assumptions. In particular, we receive important information regarding the accuracy of phase images processed simultaneously from two satellites. It should be clarified whether soil samples were also taken for analysis during the 3-year research period in the first one (2020). If not, why? In my opinion, the content of chapter 4.4 could be included in the summary (5). Some of them were basically repeated in the summary. This may be revised after consideration.
Author Response
Dear reviewer
We would like to express our sincere appreciation for your helpful comments. These comments were valuable and helpful for revising and improving our manuscript and provided the important guiding significance to our research. We addressed the points noted in PDF file.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for AuthorsThe manuscript should be improved before its publication as following:
1. A details map should be provided to state the location (the location in Heilongjiang Province?), and (b) and (c) may be not necessary because they provide no useful information.
2. The authors try to predict SOM. However, the "SOM Prediction Model" should be more detailed. the theory? how to calculate? .....
3. The text to explain Figure 3 in the " 2.6 Technology Framework" section is necessary.
4. The factors influencing the predicting SOM accuracy should be discussed more.
Just for reference!
Author Response
Dear reviewer
We would like to express our sincere appreciation for your helpful comments. These comments were valuable and helpful for revising and improving our manuscript and provided the important guiding significance to our research. We addressed the points noted in PDF file.
Author Response File: Author Response.pdf
Reviewer 3 Report
Comments and Suggestions for Authors1. In the introduction section, there should have been a lot of research on the methods of remote sensing inversion of SOM, but this paper does not carry out a systematic summary of the specific problems and shortcomings of these studies.
2. An area map of the study location is to be added to Figure 1, e.g. which part of Heilongjiang Province in China is to be labeled.
3. The modeling method in this paper only uses Random Forest, is there any other machine learning method for comparative study?
4. This paper does not present a reasonable explanation for the fact that the method of multi-phase synthetic image modeling is more accurate than the method of single-phase prediction.
5. Is there any data correction between the different sensors when synthesizing images in multiphase?
Comments on the Quality of English LanguageMinor editing of English language required.
Author Response
Dear reviewer
We would like to express our sincere appreciation for your helpful comments. These comments were valuable and helpful for revising and improving our manuscript and provided the important guiding significance to our research. We addressed the points noted in PDF file.
Author Response File: Author Response.pdf
Reviewer 4 Report
Comments and Suggestions for AuthorsThe main objective of this work is to utilise Landsat-8, Sentinel-2, and Gaofen-6 satellite data to establish a soil organic matter prediction model. It aimed to explore the variations in soil organic matter prediction capabilities among these satellites in typical black soil regions (Youyi Farm, Heilongjiang Province, China).
The outcomes are consistent, and the cartography is acceptable. The paper is well-written and well-organized, although it could benefit from improved English as some sentences need reworking. Minor improvements in grammar and language could enhance the manuscript's clarity and readability. The results presented in the paper are good. Some issues need improvement and are listed in the PDF file as sticky notes.
Comments for author File: Comments.pdf
The main objective of this work is to utilise Landsat-8, Sentinel-2, and Gaofen-6 satellite data to establish a soil organic matter prediction model. It aimed to explore the variations in soil organic matter prediction capabilities among these satellites in typical black soil regions (Youyi Farm, Heilongjiang Province, China).
The outcomes are consistent, and the cartography is acceptable. The paper is well-written and well-organized, although it could benefit from improved English as some sentences need reworking. Minor improvements in grammar and language could enhance the manuscript's clarity and readability. The results presented in the paper are good. Some issues need improvement and are listed in the PDF file as sticky notes.
Author Response
Dear reviewer
We would like to express our sincere appreciation for your helpful comments. These comments were valuable and helpful for revising and improving our manuscript and provided the important guiding significance to our research. We addressed the points noted in PDF file.
Author Response File: Author Response.pdf
Round 2
Reviewer 3 Report
Comments and Suggestions for Authors1. Figure 1 is not standardized, it would be clearer to add text labels in Figure 1a.
2. Are multi-phase synthetic images and single-phase high-temporal remote sensing images also compared in terms of accuracy using GBDT, CART, and RF machine learning modeling, respectively?
Comments on the Quality of English LanguageMinor editing of English language required.
Author Response
Dear reviewer
We would like to express our sincere appreciation for your helpful comments. These comments were valuable and helpful for revising and improving our manuscript and provided the important guiding significance to our research. We addressed the points noted in PDF file.
Author Response File: Author Response.pdf
