Spatial Differentiation and Environmental Controls of Land Consolidation Effectiveness: A Remote Sensing-Based Study in Sichuan, China

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Dear Editor, the study “Spatial Differentiation and Environmental Controls of Land 2 Consolidation Effectiveness: A Remote Sensing-based Study in 3 Sichuan, China”, is interesting. This article investigates the effectiveness of 14 Land Consolidation in Sichuan Province, 2020, to enhance farmland productivity. The article is well presented, but some editing revision is necessary. The results presentation is the Strong point of the article. References need to be improved as Jurnals required, there is no reference from 2023 and 2024. The citations need to be confirmed once, some authors are cited in the text and not in the references.

Comments on the Quality of English Language

English should be improved.

Author Response

Comments 1：References need to be improved as Jurnals required, there is no reference from 2023 and 2024.

Response 1：Thanks to the reviewer for the reminder. We have revised the references according to the journal's requirements. It is possible that due to problems with the formatting of our previous references, the reviewer did not notice that our references included 2023 and 2024.

 

Comments 2：The citations need to be confirmed once, some authors are cited in the text and not in the references.

Response 2：We apologize for our error and thank the reviewer for alerting us. We have corrected the relevant errors.

 

Comments 3：English should be improved.

Response 3：Thanks to the reviewer for the valuable suggestions. We have revised and enhanced the English presentation of the article.

 

 

Reviewer 2 Report

Comments and Suggestions for Authors

This article investigates the effectiveness of LC in Sichuan Province, 2020, to enhance farmland productivity. It employs time-series remote sensing data to analyze LC’s impact on farmland capacity. The study integrates Sentinel and Landsat satellite data to calculate CumVI and assesses the LC project’s spatiotemporal evolution. It constructs indices for yield level and stability to evaluate LC’s effectiveness and uses Getis-Ord Gi* to identify spatial differentiation in LC’s impact. GeoDetector and GWR are used to analyze how natural factors like elevation, slope, soil organic carbon, and rainfall affect LC effectiveness. The methodology used in the article is innovative, the structure of the article is complete, and the researchable factors considered by the authors are more comprehensive, and the results can provide a certain reference for the agricultural strategy in Sichuan Province. However, there are still some deficiencies in the article, which are presented here for the author's reference.

1. In section 2.3, “Propose a feature parameter method for identifying productivity changes to recognize changes in yield levels and yield stability before and after LC”, the authors should discuss the advantages of this method over conventional feature parameter techniques that can be used to identify productivity changes, such as machine learning and deep learning;

2. For the calculation of CumVI, it is recommended that traditional agricultural statistics or field survey data be taken into account to verify the accuracy of the results of remote sensing calculations, and that seasonal adjustments of CumVI may be required to more accurately reflect crop growth and yield, taking into account the effects of different seasons on the calculation of NDVI due to crop growth characteristics;

3. In section 3.4, it is mentioned that GWR was used to regress LC yield level and yield stability respectively, while Table 4 compares the model parameters of OLS and GWR, which is rather abrupt here, and it is suggested that the authors explain the concept of the OLS model before comparing the model parameters, and then by comparison, it is considered that the model fit is better by using GWR;

4. For the discussion part of this article, the authors describe simple reasons or speculate on the research results, and the discussion of the manuscript lacks depth and does not provide complete evidence. It is suggested to add survey data as evidence. It can also be fully discussed with different regional climate, DEM, topography and other factors in Sichuan Province;

In conclusion, the study analyzes the impact of elevation, slope, SOC, and rainfall on the effectiveness of LC. Through remote sensing technology and spatial measurement analysis, the study reveals the influence of land consolidation on farmland productivity, which holds significant implications for the design, implementation, and evaluation of land consolidation projects. The proposed method can be widely applied in related research fields. In addressing the identified shortcomings, it is hoped that the authors will make corresponding modifications to enhance the persuasiveness of the research findings.

Author Response

Comments 1：In section 2.3, “Propose a feature parameter method for identifying productivity changes to recognize changes in yield levels and yield stability before and after LC”, the authors should discuss the advantages of this method over conventional feature parameter techniques that can be used to identify productivity changes, such as machine learning and deep learning;

Response 1：Thanks to the reviewer for the valuable suggestions. We have added the following to section “4.1 Advantage and advancement”: “Machine learning and deep learning algorithms have developed rapidly in recent years and have been widely used in recognizing cropland information and predicting crop yields. However, this method mainly relies on establishing the correlation between yield and characteristic variables, which greatly depends on field sampling or actual measured yield data. This study establishes a relative yield relationship, which does not depend on actual measured yield data. Therefore, it greatly reduces the cost of data acquisition and is an efficient acquisition method. However, it should also be seen that our study does not utilize the actual measured yield data, so the measured yield is not the absolute capacity of arable land. However, it has a good advantage in the field of analyzing the effect of land consolidation on the capacity of cropland.”（lines 474—484）

 

Comments 2：For the calculation of CumVI, it is recommended that traditional agricultural statistics or field survey data be taken into account to verify the accuracy of the results of remote sensing calculations, and that seasonal adjustments of CumVI may be required to more accurately reflect crop growth and yield, taking into account the effects of different seasons on the calculation of NDVI due to crop growth characteristics;

Response 2：

（1）We thank the reviewer for suggestions, indeed, we did not validate the accuracy of the remote sensing calculations by combining agricultural statistics and field survey data, which is due to the lack of statistical data and the limitation of the difficulty of large-scale field surveys. The specificity of the method proposed in this study for measuring the effectiveness of land consolidation also determines that there is no need to verify the accuracy with the help of statistical data and field survey data:

Firstly, this study is based on accurate cropland plot data from the Sichuan Land Reclamation Database, and the reasonableness of using NDVI to characterize crop yields has been confirmed by a number of studies (e.g., David M et al. evaluated corn and soybean yields in the U.S. using MODIS NDVI, and the results showed a positive correlation between the yields of corn and soybeans and NDVI). Secondly, unlike machine learning and deep learning which require a large amount of field sampling or measured yield data, this study establishes a relative yield relationship that does not depend on actual measured yield data, which greatly reduces the cost of data acquisition and is more suitable for a larger scope of research. In addition, this study aims to represent the change in arable land production capacity by calculating the change in CumVI before and after land consolidation, and thus evaluating the effectiveness of land consolidation, and therefore focuses more on the rate of change in the time dimension rather than focusing on the accuracy of yield data for each year.

（2）The impact of seasonal changes on crop yield is huge, so seasonal adjustment of CumVI is meaningful and will further improve the accuracy of our results, which is also discussed in the 4.3 Research Limitations section of the article. However, the determination of such modification coefficients is still a large and complex task, and the purpose of this study is mainly to propose a method for evaluating the effectiveness of land consolidation based on remote sensing and spatial analysis techniques, so the model can be continuously upgraded and improved in the subsequent studies, and your suggestions will be further valued in our subsequent studies.

 

Comments 3：In section 3.4, it is mentioned that GWR was used to regress LC yield level and yield stability respectively, while Table 4 compares the model parameters of OLS and GWR, which is rather abrupt here, and it is suggested that the authors explain the concept of the OLS model before comparing the model parameters, and then by comparison, it is considered that the model fit is better by using GWR;

Response 3：Thanks to the reviewer for the valuable suggestions. We add to the concepts and limitations of OLS in subparagraph (2) of the “2.3.4. Quantitative attribution of natural factors for LC effectiveness” section, and lead to a later comparison of the parameters of the two：“Traditional linear regression models (OLS models) only estimate all samples and parameters globally and do not incorporate the consideration of elements such as spatial patterns[38]. GWR is solved through a local weighted regression analysis model about the position and uses parameter estimation results that change with different spatial positions to quantitatively reflect the heterogeneity or non-stationary characteristics in the spatial data relationship [39]. Therefore, we used the GWR model to perform the analysis. We compared the parameters of OLS and GWR in the follow-up results to verify the suitability of the GWR model.” （lines 298—305）

 

Comments 4：For the discussion part of this article, the authors describe simple reasons or speculate on the research results, and the discussion of the manuscript lacks depth and does not provide complete evidence. It is suggested to add survey data as evidence. It can also be fully discussed with different regional climate, DEM, topography and other factors in Sichuan Province;

Response 4：Thanks to the reviewer for the valuable suggestions. In this study, 227 land consolidation projects in Sichuan Province were measured using remote sensing technology and characteristic parameter model, and the results showed that most of the project areas had a relatively significant increase in arable land production capacity after the implementation of land consolidation projects, which is consistent with our cognitive experience and the objectives of land consolidation. In addition, an interesting result found in this study is the decline in arable land production capacity in certain areas after the implementation of land consolidation projects, so we speculate based on the existing research literature and related speculations, which may lack substantial evidence to validate our speculations. However, since these data and evidence are related to the political, social, economic and natural factors of the project area, which are obviously diverse and different, and very difficult to obtain and collect, we can only analyze the reasons based on the results of the existing relevant studies and personal experience. As for the interesting results we found above, we expect to analyze the causes in more detail and reveal the specificities and differences of different regions in our subsequent studies, taking into account the field survey data.

We have added the following to the section “4.2 Impacts and Implications of Legislation on Farmland Productivity”:

（1）For example, in the northern part of Guangyuan City and the northwestern part of Mianyang City, which are located on the northwestern edge of the Sichuan Basin, the elevations and slopes are larger, and the yield level of arable land is less elevated. On the contrary, in the central part of Nanchong City, where the elevation and slope are smaller, the yield level of arable land is higher. （lines 543—548）

（2）The annual precipitation in the Sichuan Basin decreases from the periphery to the center; for example, Nanchong City and the southwestern part of Bazhong City are located in the central hilly area of the Sichuan Basin, and the annual rainfall is less than 900mm, and the rate of improvement of the level of land consolidation yield is higher; however, the western part of Mianyang City and the eastern part of Dazhou City are located in the western and eastern rims of the Sichuan Basin, respectively, and the rainfall is generally more than 1,200mm, and the rate of improvement of the level of land consolidation yield is generally lower. （lines 551—558）

Reviewer 3 Report

Comments and Suggestions for Authors

On the general level it could be stated that paper comprehensively discuss the topic and it is structured according to the issue.

However, some details could be a issue for further discussion and (I hope) the potentially improve the paper.

Notice 1: It seems that references are not concordant with the template for authors.

Notice 2: The sentence “The principle is that when the explanatory and explanatory variables have similar spatial distributions, then such explanatory variables have a significant effect on the explanatory variables …” seems to require a better formulation (lines: 273-275).

Notice 3: formula (5) needs description of parameters included (line 277).

Notice 4: After the explanation the model given by formula (6), in my opinion, it is necessary to explain the method of LC effectiveness measurement in comprehensive manner. The reader, in that case, might better understand the point of research.

Notice 5: in the final part of conclusion, it is expected summarization of whole work from the aspect of paper’s title: “Spatial Differentiation and Environmental Controls of Land Consolidation Effectiveness: A Remote Sensing-based Study in Sichuan, China”.

I expected the general conclusion about the effectiveness and general conclusion about the results obtained in research area. Also, I expected discussion about further development of the model regarding its weaknesses and potential sensitivity of proposed model in case of condition changes.

Because of time shortage and complexity of the topic there was no possibility to investigate the proposed method and results in details.

Author Response

Comments 1: It seems that references are not concordant with the template for authors.

Response 1: Thanks to the reviewer for the reminder. We have revised the references according to the journal's requirements.

 

Comments 2: The sentence “The principle is that when the explanatory and explanatory variables have similar spatial distributions, then such explanatory variables have a significant effect on the explanatory variables …” seems to require a better formulation (lines: 273-275).

Response 2: Thanks to the reviewers' valuable suggestions, we have revised the formulation of this sentence: "The rationale is that when the spatial distributions of the explanatory and explanatory variables are more similar, the effect of the explanatory variable on the explanatory variable is more significant." (lines 274—276）

 

Comments 3: Formula (5) needs description of parameters included (line 277).

Response 3: I apologize for not drawing the reviewer's attention to the parameter descriptions, probably due to the structure of our writing. The parameters of equation (5) have already been explained in this paper, in lines 279—284: "In the formula, the value of “q” represents the index of the spatial differentiation of the influence of the four types of natural condition factors on the effect of LC, and the more significant the value, the greater the impact; “h = 1,2...L” is the classification of the four types of natural condition factors; “N_h” and “N” are the number of units in the h-level region and the entire region respectively; “” and “” are the variance of the LC effectiveness for the whole area and the area at level “h” respectively."

 

Comments 4: After the explanation the model given by formula (6), in my opinion, it is necessary to explain the method of LC effectiveness measurement in comprehensive manner. The reader, in that case, might better understand the point of research.

Response 4: Thanks to the reviewer's valuable suggestion. In “2.3 Research methods”, we begin with a systematic introduction to the process of analyzing “LC effectiveness measurement, spatial analysis, and influencing factors”. We believe that this layout is more in line with the structure of the article and serves as a good overview. Therefore, in order to avoid repetition, we will not add anything at the end of this section. We sincerely hope the reviewer will understand.

 

Comments 5: In the final part of conclusion, it is expected summarization of whole work from the aspect of paper’s title: “Spatial Differentiation and Environmental Controls of Land Consolidation Effectiveness: A Remote Sensing-based Study in Sichuan, China”. I expected the general conclusion about the effectiveness and general conclusion about the results obtained in research area. Also, I expected discussion about further development of the model regarding its weaknesses and potential sensitivity of proposed model in case of condition changes.

Response 5: Thanking the reviewers for their valuable suggestions, we have made the following additions to the conclusion section:

(1) In this study, we proposed an NDVI-based method for monitoring LC effectiveness's spatial and temporal processes and characteristics in Sichuan Province. Based on the data of 227 LC project areas, we integrated Sentinel and Landsat satellites to extract NDVI data. We utilized an integration algorithm to calculate the annual yield of the project areas. We used "YL_change" and "YS_change" indexes to evaluate the effectiveness of LC. Finally, we analyzed the impacts of elevation, slope, SOC, and rainfall on the LC effectiveness using the GeoDetector and GWR model. (lines 591—597）

（2）The method proposed in this study for evaluating the effectiveness of land consolidation combines remote sensing and spatial analysis techniques to systematically reveal the regularity of the impact of land consolidation on the productivity of arable land. Our method is significant for designing, implementing, and evaluating land consolidation projects, so it can be popularized and applied to research in related fields. However, the model establishes a relative yield relationship, which does not depend on the actual measured yield data, and the measured results are not the absolute capacity. Although it has a better application in this scenario, it ultimately does not reflect the actual yield change of arable land, so this direction should be improved in the future. （lines 611—620）

 

Reviewer 4 Report

Comments and Suggestions for Authors

The paper provides a well-defined research purpose: to assess the effectiveness of land consolidation (LC) in Sichuan Province, China. This is evaluated through its impact on farmland productivity using time-series remote sensing data and spatial analysis methods.

 “4.1. Advantages and advancement”
I suggest you use a specific subtitle. Additionally, this subsection needs to be improved.

 

Please compare the research design and methodologies used in other papers studying similar topics. Look into their use of remote sensing data, satellite sources, filtering algorithms, and how they handle complex topography. For the Natural Condition Factors, please try to understand how other studies account for natural conditions like elevation, slope, soil organic carbon, and rainfall. Do they integrate these factors into their analysis? If yes, how does their approach compare to yours?  Furthermore, compare the results of your study to the findings of others. Are there commonalities or differences? Divergent results might be due to different methodologies, geographic regions, or timescales.

 

The number order of the formulas is wrong.

Comments on the Quality of English Language

The paper provides a well-defined research purpose, which is to assess the effectiveness of land consolidation (LC) in Sichuan Province, China. This is evaluated through its impact on farmland productivity using time-series remote sensing data and spatial analysis methods.

 

“4.1. Advantages and advancement”
I suggest you use a specific subtitle. Additionally, this subsection needs to be improved.

 

Please compare the research design and methodologies used in other papers studying similar topics. Look into their use of remote sensing data, satellite sources, filtering algorithms, and how they handle complex topography. For the Natural Condition Factors, please try to understand how other studies account for natural conditions like elevation, slope, soil organic carbon, and rainfall. Do they integrate these factors into their analysis? If yes, how does their approach compare to yours?  Furthermore, compare the results of your study to the findings of others. Are there commonalities or differences? Divergent results might be due to different methodologies, geographic regions, or timescales.

 

The number order of the formulas is wrong.

Author Response

Comments 1:  “4.1. Advantages and advancement”

I suggest you use a specific subtitle. Additionally, this subsection needs to be improved.

Please compare the research design and methodologies used in other papers studying similar topics. Look into their use of remote sensing data, satellite sources, filtering algorithms, and how they handle complex topography. For the Natural Condition Factors, please try to understand how other studies account for natural conditions like elevation, slope, soil organic carbon, and rainfall. Do they integrate these factors into their analysis? If yes, how does their approach compare to yours?

Response 1: Thank you for your valuable suggestions. The data and methods used in other papers on similar topics have been discussed in the Introduction section (lines 101-110), and we have also discussed the advantages of the data and methods in this paper in section "4.1 Advantages and advancement". Since the limitations of the existing studies have been mentioned in the aboved section, and we believe that the layout is more in line with the structure of the article, we will not discuss further in "4.1 Advantages and advancement" in order to avoid repetition. We sincerely hope reviewer to understand. 

But we have made the following additions to the “4.1 Advantages and advancement section”: "Most of the studies on similar topics only drew on existing experience to speculate on the factors affecting land consolidation effectiveness [26], without using real data to analyse the influencing factors of land consolidation effectiveness from a quantitative perspective. In contrast, this study utilised GeoDetector and GWR models to quantitatively analyse the impacts of the four natural factors, respectively, to reveal the regularity of the influence of natural conditions on the effectiveness of LC. Therefore, this study is of a certain degree of cutting-edge and reference significance." (lines 496—503）

 

Comments 2:  Furthermore, compare the results of your study to the findings of others. Are there commonalities or differences? Divergent results might be due to different methodologies, geographic regions, or timescales.

Response 2: Through an in-depth analysis of a large number of existing studies, we found that the vast majority of studies only analyzed the impact of land consolidation on changes in cropland production capacity and drew on existing experience to speculate on factors affecting the effectiveness of land consolidation (Fan,Y.; Jin, X.; Xiang, X.; Yang, X.; Huang, X.; Zhou, Y. Prediction and evaluation of characteristic of agricultural productivity change influenced by farmland consolidation: Method and case study. Geogr. Res. 2016, 35, 1935-1947.), without using real data to analyze the factors affecting the effectiveness of land consolidation from a quantitative perspective.In addition, there are almost no studies using quantitative analysis of natural factors on land consolidation effectiveness to explore the relationship between the two, so this study has a certain cutting-edge and reference significance. In this study, the degree of influence and trend of the four natural factors were quantitatively analyzed using geographic probes and geographic weighted regression models, respectively, and the results of the study can reveal the regularity of the influence of natural conditions on the effectiveness of land consolidation, which is of great significance for the design, implementation and evaluation of land remediation projects.

We have made the following additions to the “4.1 Advantages and advancement section”: "Our results show that land consolidation can effectively improve the productivity of arable land in general, but there are still some project areas where the productivity of arable land decreases after land consolidation. Du et al. measured China's land consolidation project areas in 2006 and 2007 using NDVI data [27]. Their results showed that in 2006 and 2007, 78.67% and 78.32% of the project areas experienced either improved or stabilised productivity following the LC. However, there were also project areas where productivity declined or fluctuated. This indicates that land consolidation can enhance the productivity of arable land significantly. Their study shares similarities with our findings, providing additional support for the validity of this study." (lines 503—512）

 

Comments 3: The number order of the formulas is wrong.

Response 3: We apologize for our error and thank the reviewer for alerting us. We have corrected the relevant errors.