Explicitly Identifying the Desertification Change in CMREC Area Based on Multisource Remote Data
Round 1
Reviewer 1 Report
The study focused on identifying desertification change in the CMREC area based on multisource remote data using various algorithms. This study is significant as it showcases the ability of remote sensing to detect desertification over time and relate these findings to both climatic and human induced activities. Although the manuscript requires minor English editing, the methodology undertaken is scientifically sound and logically presented. Please see attached PDF for minor corrections.
Abstract:
This section effectively summarizes the research undertaken.
Introduction:
This section sets the scene of the study, providing background literature on the subject. However, the section doesn’t explicitly state the gaps in literature that the study will fulfill. Also, I would suggest having one aim and a few objectives that will fulfill the aim.
Results:
This section is scientifically sound and presented logically. However, there are some areas where the results in text do not correspond with what appears in the tables.
Discussion:
This section is well-written and relates findings to existing literature.
Conclusion:
This section concludes the findings of the study.
Comments for author File: 
Comments.pdf
Author Response
Dear Reviewer
Thank you for your great comments for improving the quality of our manuscript. In order to let you clearly see a new revised manuscript, we did not keep the revision trace in the revised manuscript, but saved it as another document to upload as an attachment “Remotesensing-918443-the version of keeping revised trace in the original manuscript”. Because we almost rewrote the article, and fully recreated the figures in the resubmitted manuscript. The responses to each of your comments are as follows.
Comment 1: Introduction doesn’t explicitly state the gaps in literature that the study will fulfill. Also, I would suggest having one aim and a few objectives that will fulfill the aim.
Response: Thank you for great comments. In order to highlight the aim and principal objective of the paper, we added the related content in the introduction, methods and materials, discussion, and conclusions, respectively, in the revised manuscript.
Comment 2: Result is scientifically sound and presented logically. However, there are some areas where the results in text do not correspond with what appears in the tables.
Response: Thank you for your comment. We have carefully checked and modified the results part in the revised manuscript.
Thank you very much again for your consideration.
Sincerely yours,
Zemeng Fan
Author Response File: 
Author Response.docx
Reviewer 2 Report
The manuscript with the title " Explicitly identifying the desertification change in CMREC area based on multisource remote data" presents a study that uses four classification methods to identify the change trends of desertification in CMRC area combined with massive multi-source spatial data sets based on the GEE cloud computing platform Google Earth Engine (GEE).
The paper takes into a worthy topic; however, the manuscript needs to go to carefully edition and must be revised by a native English speaker or a professional edition service. Therefore, it is hard to read and follow and I found it difficult to understand the principal objective of the paper since is not clear from the beginning. Besides, I do not see the unique contribution of the manuscript compare to was already written in literature. It is necessary to highlight the impact of the research.
This reviewer found several major issues as follows:
First, I believe the authors need to clarify the aim of the study because is it not clear what was the primary objective. For instance, Is it a comparison of classification methods/sensors/indices for mapping out desertification? An evaluation of the temporal trend of desertification in CMRC area?
Second, from all remote sensing data used how did the authors deal with all the different spatial, spectral resolutions of each sensor?
Third, from Table 3, how the criteria were established? From the Desertification definition provided by the UNCCD, desertification has resulted from human impact. I believe the desertification classes based on cover types is a little bit bias.
Fourth, the indicators used (section 2.3.2) presumably came from the same source[Landsat data}] (am I correct? By the way where is the Sentinel data mentioned before?). If they are coming from the same source, how the authors deal with multicollinearity problems?
Fifth, section 2.3.4 seems to be part of a different study. How did the authors link this section to previous developments if resolutions are different?
Finally, From lines 222-223 “Then, we used the temporal trend of PNPP and HANPP from 2000 to 2015 respectively as the relative contributions of climate change and human activities” how the authors support/justify this statement?
Particular comments:
Line 17. classification methods of classification???
Lines 56-57. Please revise this sentence “Since the 1970s, multispectral satellite imagery such as Landsat and MODIS have been used to provide useful information over the land surface for monitoring environmental change”. Landsat programs started in early 70’s but MODIS program started to produce data until 2000’s (Terra satellite was Launched in 1999 and Aqua satellite was launched in 2002).
Line 64. Please revise the reference style [21-24] [25]. They must be all together.
Lines 72-75. Please revise the sentences and edit accordingly. It seems to me that the connection and flow of the introduction are lost. Is the purpose to justify the study?
Figure 1. I believe that it would be better to provide vegetation types from the study area. Also, please add regional/global context since remote sensing has a global audience.
Author Response
Dear Reviewer,
Thank you for your great comments for improving the quality of our manuscript. In order to let you clearly see a new revised manuscript, we did not keep the revision trace in the revised manuscript, but saved it as another document to upload as an attachment “Remotesensing-918443-the version of keeping revised trace in the original manuscript”. Because we almost rewrote the article, and fully recreated the figures in the resubmitted manuscript. The responses to each of your comments are as follows.
Comment 1: The paper takes into a worthy topic; however, the manuscript needs to go to carefully edition and must be revised by a native English speaker or a professional edition service. Therefore, it is hard to read and follow and I found it difficult to understand the principal objective of the paper since is not clear from the beginning. Besides, I do not see the unique contribution of the manuscript compare to was already written in literature. It is necessary to highlight the impact of the research.
Response: We have carefully checked and revised the original manuscript from the abstract to conclusions. In order to highlight the principal objective of the paper, we added the related content in the introduction, methods and materials, discussion, and conclusions, respectively, in the revised manuscript, and the language was polished by a professional edition service.
Comment 2: First, I believe the authors need to clarify the aim of the study because is it not clear what was the primary objective. For instance, Is it a comparison of classification methods/sensors/indices for mapping out desertification? An evaluation of the temporal trend of desertification in CMRC area?
Response: We rewrote the introduction to describe the comparison of classification methods/sensors/indices for mapping out desertification and the evaluation of the temporal trend of desertification, especially in the finial part of the introduction, we added the aim of the study and discussed it in the discussion part.
Comment 3: Second, from all remote sensing data used how did the authors deal with all the different spatial, spectral resolutions of each sensor?
Response: we rewrote the materials part and added the explanations how to select and deal with the multisource data from the different spatial, spectral resolutions of each sensor.
Comment 4: Third, from Table 3, how the criteria were established? From the Desertification definition provided by the UNCCD, desertification has resulted from human impact. I believe the desertification classes based on cover types is a little bit bias.
Response: Thank you for your comments. We rewrote the materials part, added what is the reference for establishing the criteria of desertification degree types and how to build it (Table 2). E.g. “….In addition, the identifying criteria of desertification status was defined with the multisource data of Google history imageries, Google photos, European Space Agency global land cover products (ESA LC), MODIS land cover products (MCD12Q1), the Landsat Vegetation Continuous Fields tree cover layers (VCF), OpenLandMap sand content product, global flux observation tower sites data, and Sentinel-2 Level-1C satellite imagery (Table 2), which referred to the desertification classification criteria of China and Mongolia and the desertification assessment standards of FAO and UNEP [22,47,48]. The desertification types in the CMREC area were divided into the five degrees of non-desertification, slight, moderate, high and severe desertification (Table 2).”
Comment 5: Fourth, the indicators used (section 2.3.2) presumably came from the same source [Landsat data}] (am I correct? By the way where is the Sentinel data mentioned before?). If they are coming from the same source, how the authors deal with multicollinearity problems?
Response: Thank you for your comments, and we rewrote the indicators part (section 2.3.2). The indicators come from Landsat data, and the vegetation and soil indicators of 2000 and 2015 were extracted using Landsat 5/7 and Landsat 8, respectively. The Sentinel data mainly was used to produce the sample database, but not for calculating indicators. Moreover, the part of “2.2. Materials” has added the description about mapping index products and producing sample database. Thank you for your issue about the multicollinearity problems, the study utilized machine learning algorithms, such as the classification and regression tree (CART), support vector machine (SVM), random forest (RF), and remote sensing method, such as Albedo-NDVI model. As to machine learning algorithms, they, being more of black-box model, solely focused on prediction and forecasting, then multicollinearity is less of an issue, and the main issue with multicollinearity is that it messes up the coefficients (betas) of independent variables for studying the relationships between variables, establishing causality etc in traditional statistics. Furthermore, Random selection and node split are used to build individual trees in RF model, and SVM uses kernel functions to project and construct an optimal hyperplane that separates classes by creating the maximum distance between different classes; thus, they usually show superior predictive performance due to their effectiveness in handling correlations and multicollinearity among predictor variable. The part of “2.3.3 Classification and Accuracy Assessment Methods” has added the description about the multicollinearity issue according to your comment. As to the Albedo-NDVI model, we randomly selected 1000 points in the study area and measured that there was a negative correlation between Albedo and NDVI (R=-0.59), as shown in Figure 1.
Figure 1. Correlation between Albedo and NDVI
Comment 6: Fifth, section 2.3.4 seems to be part of a different study. How did the authors link this section to previous developments if resolutions are different?
Response: Thanks for your careful reminder. In the paper, this study aims for selecting and operating an optimal method from the CART, SVM, RF, and Albedo-NDVI models to identify the change patterns of desertification in the CMREC area between 2000 and 2015, in terms of the multisource data on the GEE cloud platform, explicate the differences between climate change and human activities on the desertification change, and discussed the driving mechanism of desertification expansion and reversion. In order to better link the section 2.3.4 to previous parts, we added and rewrote the section 2.
Comment 7: Finally, From lines 222-223 “Then, we used the temporal trend of PNPP and HANPP from 2000 to 2015 respectively as the relative contributions of climate change and human activities” how the authors support/justify this statement?
Response: Thank you for your comment. We added and rewrote the related content. “The study mainly used NPP to calculate the relative contributions of climate change and human activities. In the study, NPP can be formed by potential net primary productivity (PNPP), actual NPP (ANPP), and the human appropriation NPP (HANPP) which was defined as the difference between ANPP and PNPP. Among them, ANPP came from MODIS NPP data set, and means that the value of the ANPP has been impacted by climate change and human activities. In addition to, we can also measure PNPP only affected by the climate change, based on the Thornthwaite memorial model. Finally, the difference between ANPP and PNPP can be treated as HANPP only affected by human activities. The part of “2.2 Materials” has added the description about PNPP and HANPP.”
Comment 8: Line 17. classification methods of classification???
Response: Thank you for your careful comment. We have revised it and checked the whole manuscript carefully.
Comment 9: Lines 56-57. Please revise this sentence “Since the 1970s, multispectral satellite imagery such as Landsat and MODIS have been used to provide useful information over the land surface for monitoring environmental change”. Landsat programs started in early 70’s but MODIS program started to produce data until 2000’s (Terra satellite was Launched in 1999 and Aqua satellite was launched in 2002).
Response: Thank you for your comment, we have carefully checked and modified the related errors in the lines 56-57.
Comment 10: Line 64. Please revise the reference style [21-24] [25]. They must be all together.
Response: Thank you for your comment, we revised “the reference style [21-24] [25]”, and carefully checked all other references’ style in the revised manuscript, based on the Author Guidelines of Remote Sensing.
Comment 11: Lines 72-75. Please revise the sentences and edit accordingly. It seems to me that the connection and flow of the introduction are lost. Is the purpose to justify the study?
Response: Thank you for your comment. We have checked and revised the sentence about lines 72-75 in the original manuscript. E.g.”Multi-parameter integrated researches [17] show that the desertification dynamic change can be assessed and monitored through operating the methods of classification regression tree (CART) [24], support vector machine (SVM) [25], and random forest (RF) [26], and Albedo-NDVI model [25,26], and combing the parameters of NDVI, normalized water body index (NDWI), iron oxides index and surface temperature [27,28]. However, the current researches of desertification mainly based on the traditional mode rather than cloud platform, which are difficult to select the optimal model and need lots time to collect the available multi-resources data [29]”.
Comment 12: Figure 1. I believe that it would be better to provide vegetation types from the study area. Also, please add regional/global context since remote sensing has a global audience.
Response: Thank you for your comment. We have revised Figure 1, and added vegetation types and regional context in the revised manuscript. In addition, we have remapped other maps for improving the quality of revised manuscript.
Thank you very much again for your consideration.
Sincerely yours,
Zemeng Fan
Author Response File: Author Response.docx
Reviewer 3 Report
Dear Authors,
I have reviewed the paper "Explicitly identifying the desertification change in CMREC area based on multisource remote data". The aims of the paper are germane with Remote sensing applications topic, in this form of article fits with the international scientific standards. The paper is written with a moderate English level. The contribution of this paper to the scientific knowledge is good. Although, in my opinion, there some important flaws and I suggest the corrections in the comments:
General comments: Good introduction section and well-defined aims. Also material and methods and results sections are built in a clear way. In my opinion Discussion and Conclusions sections should be improved clearly reporting the importance of the main findings under sustainability point of view.
I am not a native English speaker but I think that an English language editing could be useful.
I suggest we look at the formatting of the references.
Abstract: sentences are too long and difficult to read, also English language editing seems to be necessary.
“km2” not “km2”.
Line 16-17: delete the repetition of “classification”
Line 35: “CMRC area” is still present in the title, better not to use it as keyword.
Line 64: check the references number, it should be [21-25].
Lines 140-142: delete the empty lines.
Lines 333-335: delete the empty lines.
Figure 6: please give a more detailed explanation of the figure which actually is not so clear.
Line 401: delete the repetition of “reversion”
Author Response
Dear Reviewer
Thank you for your great comments for improving the quality of our manuscript. In order to let you clearly see a new revised manuscript, we did not keep the revision trace in the revised manuscript, but saved it as another document to upload as an attachment “Remotesensing-918443-the version of keeping revised trace in the original manuscript”. Because we almost rewrote the article, and fully recreated the figures in the resubmitted manuscript. The responses to each of your comments are as follows.
Comment 1: Good introduction section and well-defined aims. Also, material and methods and results sections are built in a clear way. In my opinion Discussion and Conclusions sections should be improved clearly reporting the importance of the main findings under sustainability point of view.
Response: Thank you for your affirmation of our manuscript. We rewrote the Discussion and Conclusions parts in the revised manuscript.
Comment 2: I am not a native English speaker but I think that an English language editing could be useful.
Response: Thank you for your comment. We have carefully checked and revised the original manuscript from the abstract to conclusions, and the language was polished by a professional edition service.
Comment 3: I suggest we look at the formatting of the references.
Response: We have revised the formatting of the references according to the latest article published by Remote Sensing and instructions for authors in the journal.
Comment 4: Abstract: sentences are too long and difficult to read, also English language editing seems to be necessary.
Response: Thank you for your comment. We rewrote the abstract in the revised manuscript.
Comment 5: “km2” not “km2”.
Response: We have modified the error, and carefully checked the whole manuscript and revised similar errors one by one.
Comment 6: Line 16-17: delete the repetition of “classification”.
Response: Thank you for your comment. We have carefully revised the description in lines 16-17 of the original manuscript. E.g.”In this paper, four methods of classification and regression tree (CART), support vector machine (SVM), random forest (RF), and Albedo-NDVI model were used to identify the change trends of desertification based on Google Earth Engine (GEE) with multisource remote sensing datasets in CMREC area”.
Comment 7: Line 35: “CMRC area” is still present in the title, better not to use it as keyword.
Response: Thank you for your comment. The keywords of this manuscript have been changed to “change pattern of desertification; spatial-temporal analysis; climate change; human activities; Google Earth Engine” in the revised manuscript.
Comment 8: Line 64: check the references number, it should be [21-25].
Response: We have reedited the reference style to all together them according to your comment. Moreover, we also carefully checked and revised all reference style in the revised manuscript, based on the latest article published by Remote Sensing and instructions for authors in the journal.
Comment 9: Lines 140-142: delete the empty lines.
Response: We have deleted the empty lines in the revised manuscript according to your comment.
Comment 10: Lines 333-335: delete the empty lines.
Response: We have deleted the empty lines according to your comment, and have also carefully checked and modified the empty line errors in the revised manuscript.
Comment 11: Figure 6: please give a more detailed explanation of the figure which actually is not so clear.
Response: We have added the detailed explanation of Figure 6 in the caption to understand the figure. E.g. “Figure 6. Sankey plot showing conversions from one desertification status to another between 2000 and 2015. The left and right axes of this plot is are the area of the desertification status in 2000 and 2015, respectively. The band width represents the size of the converted area (Unit: 104 km2) from 2000 to 2015.”
Comment 12: Line 401: delete the repetition of “reversion”.
Response: Thank you for your comment. Reversion and significant reversion express the two change patterns of desertification. In order to let the description more clearly, we rewrote the related content: “The change patterns of desertification between 2000 and 2015 were divided into significant expansion, expansion, no conversion, reversion and significant reversion. The significant expansion presents the desertification degree in a certain pixel intensified two or above levels and the slight desertification was transformed into the high or severe desertification. Expansion presents the desertification degree in a certain pixel intensified one level. No conversion presents the desertification degree in a certain pixel had no change. Reversion presents the desertification degree in a certain pixel decreased by a level. Significant reversion presents the desertification degree in a certain pixel decreased by two or above levels.”
Thank you very much again for your consideration.
Sincerely yours,
Zemeng Fan
Author Response File: Author Response.docx
Reviewer 4 Report
The research article Explicitly identifying the desertification change in CMREC area based on multisource remote data are an interesting article. Please find some suggestions to improve the article further.
Abstract:
In the abstract sentences are too long. Please make it simple and split the sentence (page 1, line 16-21). Also, check the method name for all places of CRT, it should be CART.
1. Introduction
Please add some references to the application of NDWI and BSI application, for the analysis of the desertification process. Because you considered these indices as well for the vegetation and soil criteria in your research. How many specific objectives you have, it's not clear from the write-up?
2.2 Materials
For impact assessment of human activities and climate change datasets are poorly resolute. All tabulated data were not explained in the explanation section. Besides, some data sets like Sentinel-2 level-1C data is not found in the table. Moreover, it is not clear about the GDP data. Please explain with the year of the date chosen for the current research.
Please make a table for data acquisition dates for both periods. I think it’s a very important factor in the vegetation condition and soil status for change detection analysis. Please add it.
Table-2
What is the source of this desertification classifier? Please provide an appropriate reference. Also, need a brief explanation considering your research hypothesis.
How about other criteria like soil, terrain, and climate for the representation of the severity of desertification? Is there any classification level please justify it?
2.3.2 Indicators
Please rewrite this section with reference for your used multispectral indices (Page 6, line 168-174) and add the reference for page 7, line 181-183.
3.1 Quality Assessment
Before doing the accuracy assessment you should put all the criteria maps in a sub-heading. It would be helpful for the readers and make your research more meaningful and easier to understand. You are also requested to make an explanation part for your data testing and validation section. Then you can present the performance analysis of 4 remote sensing image classification methods.
3.2 Spatio-temporal dynamics of the desertification process
How did you prepare the maps for the spatial-temporal dynamics of the desertification process? Which indices you used to develop these maps please explain. Otherwise, general readers can't understand and will be lost the research interest. Did you use a single index or composite indices for your four criteria that you have taken for the desertification assessment? You have to clarify this section based on your research framework.
Table-7
Please make another row for each point and prepare the change by using the current condition (2015) minus previous (2000). Then you can explain how much change occurred during these 15 years. It would be easier for the readers as well. I will recommend explaining based on a country basis irrespective of desertification classifications.
Also requested to check table number 7 (page no. 12 and line no. 331). It should be at 8. In addition, requested to refer the table number in the explanation section. It would be a great help for the reader to check your explanation with tabulated value. You should do this in the entire explanation section of your manuscript. Please also check the page no. 13 and line no. 346 and page no. 14 and line no. 376.
4. Discussion
The discussion part is liked with your results section of factors influencing the desertification process (3.3 Relative roles of human activities and climate change). Why you separated this part as a discussion section? You should merge this section with your results section of 3.3. Then prepare an overall discussion section based on your entire research findings. You should also justify and validate your key findings with previous research done by researchers with similar methods and spectral indices and indicators. You must consider this section as the major corrections part of your manuscript.
Conclusion
Please rewrite this section based on your research objectives and key findings. Also, add some recommendations based on your findings to support policymakers for taking appropriate actions. Also, check the page no. 16 and line no. 429.
Finally, I would like to say that the research topic is very good, and the time demanded. Research findings are also good, but you should give more attention to the reviewer's comments.
Author Response
Dear Reviewer
Thank you for your great comments for improving the quality of our manuscript. In order to let you clearly see a new revised manuscript, we did not keep the revision trace in the revised manuscript, but saved it as another document to upload as an attachment “Remotesensing-918443-the version of keeping revised trace in the original manuscript”. Because we almost rewrote the article, and fully recreated the figures in the resubmitted manuscript. The responses to each of your comments are as follows.
Comment 1: In the abstract sentences are too long. Please make it simple and split the sentence (page 1, line 16-21). Also, check the method name for all places of CRT, it should be CART.
Response: Thank you for your comment. We have carefully checked and rewritten the abstract in the revised manuscription.
Comment 2: Please add some references to the application of NDWI and BSI application, for the analysis of the desertification process. Because you considered these indices as well for the vegetation and soil criteria in your research. How many specific objectives you have, it's not clear from the write-up?
Response: Thank you for your comment. We added the references ([22,23,27,28,50-52,]) about NDWI and BSI, and modified the related content in the revised manuscript.
Comment 3: For impact assessment of human activities and climate change datasets are poorly resolute. All tabulated data were not explained in the explanation section. Besides, some data sets like Sentinel-2 level-1C data is not found in the table. Moreover, it is not clear about the GDP data. Please explain with the year of the date chosen for the current research.
Response: Thank you for your comments. We have added the descriptions and explanations about the driving datasets of NPP data set and GDP data, and also added the Sentinel-2 data source and data acquisition dates of various datasets in the Table 1 in the revised manuscript.
Comment 4: Please make a table for data acquisition dates for both periods. I think it’s a very important factor in the vegetation condition and soil status for change detection analysis. Please add it.
Response: Thank you for your comments. In order to compare the data set status, we added the data acquisition dates for both periods as a column in Table 1 in the revised manusript.
Comment 5: What is the source of this desertification classifier in Table 2? Please provide an appropriate reference. Also, need a brief explanation considering your research hypothesis. How about other criteria like soil, terrain, and climate for the representation of the severity of desertification? Is there any classification level please justify it?
Response: Thank you for your comments. We rewrote the materials part, added what is the reference for establishing the criteria of desertification degree types and how to build it (Table 2). E.g. “….In addition, the identifying criteria of desertification status was defined with the multisource data of Google history imageries, Google photos, European Space Agency global land cover products (ESA LC), MODIS land cover products (MCD12Q1), the Landsat Vegetation Continuous Fields tree cover layers (VCF), OpenLandMap sand content product, global flux observation tower sites data, and Sentinel-2 Level-1C satellite imagery (Table 2), which referred to the desertification classification criteria of China and Mongolia and the desertification assessment standards of FAO and UNEP [22,47,48]. The desertification types in the CMREC area were divided into the five degrees of non-desertification, slight, moderate, high and severe desertification (Table 2).”
Comment 6: Please rewrite this section with reference for your used multispectral indices (Page 6, line 168-174) and add the reference for page 7, line 181-183.
Response: Thank you for your comment. We rewrote the section and added the related references in the revised manuscript. E.g.”The NDVI, FVC, MSAVI, and NDWI are normally used to represent the biophysical characteristics and coverage condition of the surface vegetation [50]. MSAVI is useful to increase the dynamic range of the vegetation signal and reduce the soil background influences [51]. NDWI can be used to evaluate the canopy relative water content by combining the leaf relative water content with measures of canopy structure [52]. The soil context index of TGSI, land Albedo, and BSI can be used to describe physical properties of soil [22,23]”. Moreover, we have added relative reference about the NDVI noise.
Comment 7: Before doing the accuracy assessment you should put all the criteria maps in a sub-heading. It would be helpful for the readers and make your research more meaningful and easier to understand. You are also requested to make an explanation part for your data testing and validation section. Then you can present the performance analysis of 4 remote sensing image classification methods.
Response: Thank you for your comment. We added the related content in the revised manuscript as follows: “Based on the Landsat 8 data, and the products obtained by CART, RF, SVM, and Albedo-NDVI method in this study, we selected severe desertification, high desertification, moderate desertification, slight desertification, and non-desertification to conduct an intuitive comparison (Figure 3). In general, the desertification pattern products obtained by the CART model efficiently reflected the true desertification type in the Landsat 8 satellite image. The desertification classification quality was better than those of the RF and SVM product. Moreover, the curacy of CART product was roughly equivalent to the Albedo-NDVI product in the severe desertification type, and superiors to the accuracy of the Albedo-NDVI product for classifying the high desertification, moderate desertification, slight desertification, and non-desertification areas.”
“Figure 3. Comparison of different desertification pattern products in 2015. Landsat 8 is false color composition in 2015 (RGB: B5, B4, B3). CART, RF, SVM and Albedo-NDVI are products of four kinds of methods, respectively.”
Comment 8: How did you prepare the maps for the spatial-temporal dynamics of the desertification process? Which indices you used to develop these maps please explain. Otherwise, general readers can't understand and will be lost the research interest. Did you use a single index or composite indices for your four criteria that you have taken for the desertification assessment? You have to clarify this section based on your research framework.
Response: Thank you for your comments. We almost rewrote the Materials and Methods, and Change Patterns of Desertification sections for more clearly explicating the indices used in this study and the change patterns of desertification. Moreover, we modified the Flowchart of desertification assessment and mechanism analysis (Figure 2)。
Comment 9: Please make another row for each point and prepare the change by using the current condition (2015) minus previous (2000). Then you can explain how much change occurred during these 15 years. It would be easier for the readers as well. I will recommend explaining based on a country basis irrespective of desertification classifications.
Response: In terms of your comment, we added another row in Table 7 for changes of desertification degrees from 2000 to 2015, and carefully checked and modified the related content in the revised manuscript.
Comment 10: Also requested to check table number 7 (page no. 12 and line no. 331). It should be at 8. In addition, requested to refer the table number in the explanation section. It would be a great help for the reader to check your explanation with tabulated value. You should do this in the entire explanation section of your manuscript. Please also check the page no. 13 and line no. 346 and page no. 14 and line no. 376.
Response: Thank you for your comment, we checked and revised the table number, and modified and added the related explanation on the basis of your comment.
Comment 11: The discussion part is liked with your results section of factors influencing the desertification process (3.3 Relative roles of human activities and climate change). Why you separated this part as a discussion section? You should merge this section with your results section of 3.3.
Response: Thank you for your comment, we merged the original discussion section with the results section of “3.3. Impacts of human activities and climate change on desertification”, and rewrote a new discussion section.
Comment 12: Please rewrite this section based on your research objectives and key findings. Also, add some recommendations based on your findings to support policymakers for taking appropriate actions. Also, check the page no. 16 and line no. 429.
Response: Thank you for your comment, we rewrote a new discussion section and finished a major revision of the conclusions section.
Thank you very much again for your consideration.
Sincerely yours,
Zemeng Fan
Author Response File: Author Response.docx
Round 2
Reviewer 2 Report
Many thanks to authors for resubmitting the manuscript. It has been substantially improved since its last version. However, I would suggested a carefully revision because I still found some some typographical mistakes over the document.
Also, please revise the statements about the driving forces for desertification. Althouhg is well documented that climate change is acceleration this process, the discussion needs more support about how climate change and desertification are produced by antropogenic causes.
Author Response
Dear Reviewer,
Thank you for your valuable comments and affirmation of the previous revised manuscript. The responses to each of your comments are as follows.
Comment 1: Many thanks to authors for resubmitting the manuscript. It has been substantially improved since its last version. However, I would suggested a carefully revision because I still found some some typographical mistakes over the document.
Response: We have carefully checked and modified the typographical mistakes from the abstract to conclusions in this revised manuscript.
Comment 2: Also, please revise the statements about the driving forces for desertification. Although is well documented that climate change is acceleration this process, the discussion needs more support about how climate change and desertification are produced by antropogenic causes.
Response: We revised the relative statements about the driving forces for desertification, added the discussion for explicating climate change on desertification and the related references in this revised manuscript.
Thank you very much again for your consideration.
Sincerely yours,
Zemeng Fan
Reviewer 3 Report
Dear Authors,
I have reviewed the paper "Explicitly identifying the desertification change in CMREC area based on multisource remote data" after corrections. The aims of the paper are germane with Remote sensing applications topic, in this form of article fits with the international scientific standards. The paper is written with a moderate English level. The contribution of this paper to the scientific knowledge is good.
Author Response
Dear Reviewer,
Thank you for your valuable comments and affirmation of the previous revised manuscript. The responses to your comments are as follows.
Comment 1: I have reviewed the paper "Explicitly identifying the desertification change in CMREC area based on multisource remote data" after corrections. The aims of the paper are germane with Remote sensing applications topic, in this form of article fits with the international scientific standards. The paper is written with a moderate English level. The contribution of this paper to the scientific knowledge is good.
Response: We have carefully checked the typographical mistakes, and polished the language from the abstract to conclusions in this revised manuscript.
Thank you very much again for your consideration.
Sincerely yours,
Zemeng Fan
