Evolution Patterns and Dominant Factors of Soil Salinization in the Yellow River Delta Based on Long-Time-Series and Similar Phenological-Fusion Images
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsComments to the Author
Dear authors,
Thank you for sharing your work.
This study applied the ESTARFM algorithm to obtain the similar phenological images in April during the past 20 years. Based on the random forest algorithm, the surface parameters of the salinization were optimized and the feature space index models were constructed. Combined with the ground measured data, the optimal monitoring index model of salinization was determined and then the spatio-temporal evolution patterns of salinization and its driving mechanisms in the Yellow River Delta were revealed.
The article has novelty and overall high quality.
I have some concerns that the authors should address before it is considered proper for acceptance.
1、 The last key word “Dominant factors soil” should be checked.
2、 More recent relative studies should be added in the Introduction and Discussions sections.
3、 Check the code of equations over the whole paper.
4、 It is advisable to discuss the limitations in the discussion section and add some relative references.
5、 The paper contains several grammatical errors. The authors should consider reviewing similar instances and rectifying the grammar issues.
6、 The authors may want to emphasize the rationale for selecting the ESTARFM method to construct time series images.
7、 The “km2” should be “km2”, please revise these problems all over the whole paper.
Comments on the Quality of English Language
Moderate editing of English language required.
Author Response
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Author Response File: 
Author Response.doc
Reviewer 2 Report
Comments and Suggestions for AuthorsGeneral comments:
Previous studies were mostly conducted based on sparse time series and different phenological images, which often ignored the dramatic changes of salinization evolution inner the year. Based on the Landsat and MODIS images from 2000 to 2020, this manuscript applied the ESTARFM algorithm to obtain the similar phenological images in April during the past 20 years. Based on the random forest algorithm, the surface parameters of the salinization were optimized and the feature space index models were constructed. Combined with the ground measured data, the optimal monitoring index model of salinization was determined and then the spatiotemporal evolution patterns of salinization and its driving mechanisms in the Yellow River Delta were revealed. It is seems like to be a good idea with clarify contents. Furthermore, the following questions require further investigation and should be addressed and revised before the manuscript is considered for publication.
Problem:
1. Some more recent references about the Yellow River delta should be cited in the Introduction.
2. The objective and novelty should be addressed in the Introduction.
3. The basis for the study area detail should be added in the section of “Study area”, and the map of the location of the study area needs to be further optimized.
4. Some content and its references should be added in the discussion.
5. The full text needs to address grammar paragraph structure, and language enhancement skills.
Specific comments:
• Some sentences are overly long - splitting into two sentences could improve readability in some cases.
• Carefully proofread to fix any typos, grammar issues or awkward phrasing.
• Consider reducing redundancy between sections - some background details are repeated.
Author Response
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Author Response File: Author Response.doc
Reviewer 3 Report
Comments and Suggestions for AuthorsGeneral comments:
In this study, the authors build the feature spaces models (NDSI-TGDVI) based on image fusion via ESTARFM algorithm. Although this topic is meaningful, the expression of this manuscript is poor. Even more important, I have read some similar papers authored by you (10.1080/19475705.2022.2156820 10.1016/j.catena.2023.107301 10.1007/s12665-019-8319-8). In my eyes, the authors only employed different soil salinity index, so called Geographic Detector and image fusion methods. I can hardly catch your academic innovation and contribution. In addition, this manuscript encompassing many irrelevant content of remote sensing technology.
Specific comments:
1. The language needs to be edited by native speakers. The use of language should be carefully checked. There are many unnecessary descriptions. There are more deficiencies in language use. English polishing is must.
2. In general, the logics among all sections are poor. Therefore, I can hardly get your objectives in this review.
3. The introduction needs to be expanded.
4. Please provide a bit more big-picture motivation of how your analyses benefit society and how they have evolved over the past decade. However, from my point of view, the article does not provide a sufficiently thorough review of the issue under study. There are good references for the study techniques, but the paper is missing a "big-picture" introduction with some references in my opinion. I suggest that the authors should do a better analysis of the literature. It seems that the bulk of the text is a sort of compilation of statements in the individual articles cited. It would be better, I think, to extract ideas from individual articles and tie them together into a more fluid and conceptually homogeneous text. As it is, the text looks rather clumsy.
5. Research gaps, objectives of the proposed work should be clearly justified before the problem formulation section. This paper includes some little useful information and the main objectives of the study is not well defined. Problem statement is not clear and the objectives are obscure. Furthermore, the paper lacks a very clear and good justification for what is new and innovative about this case or this approach.
6. The uncertainty is another key issue in soil mapping, and the simulation with a low uncertainty is needed. The uncertainty is always evaluated by repeatedly conducting the simulations. In this study, the authors adopted machine-learning method, which may generate high uncertainty, and thus, the uncertainty evaluation is necessary. However, the authors even did not evaluate the uncertainty. The result generated by the simulation only at one time was adopted as the final result, which is inappropriate and considerably influences the reliability of the results.
7. Considering the outstanding advantages of ESTARFM algorithm, I am wondering why the authors simulate the April data rather than monthly images?
8. km2 please check
9. The legend (RGB) in Fig.1 is unclear.
10. The content in Table 1 should be explained in figure, if possible.
11. The biggest problem is the filed data. The authors only describe this section in a very concise style. Without solid and credible filed data, the final result can hardly be recognized.
Comments on the Quality of English LanguageExtensive
Author Response
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Author Response File: 
Author Response.pdf
Reviewer 4 Report
Comments and Suggestions for AuthorsDear authors,
First of all, congratulations on your work and your efforts to produce a complete report of your research. Overall, the manuscript is well edited and complete. The innovative nature of the objectives needs to be clarified; please check the reference and the introduction in particular. Salinisation is a global issue and you could describe the problem firstly in a global framework and secondly from a Chinese perspective. Probably a flow chart could help to explain the process: in this kind of paper the organisation of the steps is crucial. Here are some specific comments:
- When introducing acronyms for the first time, please describe them;
- Figure 1: please improve. the large scale map has an illegible red label. The legend is redundant and the title is a bit cryptic". Check the caption too: it might be useful to cite the source of the natural colour stack raster used to describe the study area.
- Table 1 could be included as an appendix.
- Please review and revise section 2.2.2 thoroughly. It is not clear how the field data collection is carried out (where? what does "4-5 replicates" mean?). Could you indicate the location of the sampling points? It is not clear how you carried out the measurement of "soil salinity". It might also be useful to report a small table with the descriptive statistics of the measurement (mean, maximum, minimum, ...).
- Section 2.2.3: natural factor? Or "environmental"?
- Section 3.4: The salinisation scale used for mapping is not well explained. Please describe better the mapping approaches (how do you define the range?).
Author Response
Please see the attachment.
Author Response File: Author Response.doc
Round 2
Reviewer 3 Report
Comments and Suggestions for AuthorsI have read some similar papers authored by you (10.1080/19475705.2022.2156820 10.1016/j.catena.2023.107301 10.1007/s12665-019-8319-8). In my eyes, the authors only employed different soil salinity index, so called Geographic Detector and image fusion methods. I can hardly catch your academic innovation and contribution. In addition, this manuscript encompassing many irrelevant content of remote sensing technology.
Comments on the Quality of English LanguageI have read some similar papers authored by you (10.1080/19475705.2022.2156820 10.1016/j.catena.2023.107301 10.1007/s12665-019-8319-8). In my eyes, the authors only employed different soil salinity index, so called Geographic Detector and image fusion methods. I can hardly catch your academic innovation and contribution. In addition, this manuscript encompassing many irrelevant content of remote sensing technology.
Author Response
Please see the attachment.
Author Response File: Author Response.doc
