Temporal and Spatial Evolution, Prediction, and Driving-Factor Analysis of Net Primary Productivity of Vegetation at City Scale: A Case Study from Yangzhou City, China

Response to Reviewer 1 Comments

1. Summary

Thank you very much for taking the time to review this manuscript. Please find the detailed responses below and the corresponding revisions/corrections highlighted/in track changes in the re-submitted files.

2. Questions for General Evaluation

Reviewer’s Evaluation

Response and Revisions

Is the content succinctly described and contextualized with respect to previous and present theoretical background and empirical research (if applicable) on the topic?

Must be improved

The authors have carefully improved.

Are all the cited references relevant to the research?

Can be improved

The authors have carefully improved

Are the research design, questions, hypotheses and methods clearly stated?

Must be improved

The authors have carefully improved

Are the arguments and discussion of findings coherent, balanced and compelling?

Must be improved

The authors have carefully improved

For empirical research, are the results clearly presented?

Must be improved

The authors have carefully improved

Is the article adequately referenced?

Can be improved

The authors have carefully improved

Are the conclusions thoroughly supported by the results presented in the article or referenced in secondary literature?

Can be improved

The authors have carefully improved

3. Point-by-point response to Comments and Suggestions for Authors

Comments 1:The manuscript aims to model net primary production (NPP) using the CASA model over Yangzhou region in China. Further, the driving factors of NPP are discussed. The manuscript needs significant revision in terms of novelty and methodology. The authors must highlight what is the novelty in this study? At present this appears like a routine work. Further, how this work will fit into the scope of the journal sustainability?

Response 1: Thank you for pointing this out. We agree with this comment. Therefore, we have added relevant content as follows:

1. The innovation of this article includes: (1) Existing research is limited by satellite remote sensing technology, mostly focusing on large-scale research scope and past time series NPP changes. There are relatively few high-resolution predictions for the future, and accurate predictions for the future are crucial for formulating green and low-carbon sustainable development strategies. This article is based on 30m high-definition satellite imagery and multi-source data such as climate data, social data, and ecological data in Yangzhou City, China. An improved CASA model is used to estimate the NPP value of Yangzhou City from 2000 to 2020. Then, various methods such as Theil-Sen Median trend analysis and Hurst index method are used to analyze its future trends and predict the future development direction of NPP, providing theoretical exploration and practical basis for low-carbon and sustainable development in Yangzhou City.

(2) In existing studies on the driving factors of vegetation NPP changes, most of them focus on climate and land use changes. This article draws inspiration from the sociological PPM (push pull mooring) theory and innovatively adds the analysis of ecological and social factors such as NDVI, NDSBI, LUCC, etc. Using geographical detectors to analyze from multiple aspects such as climate, society, ecology, etc., it explores the positive factors (push), negative factors (pull), and dominant factors (mooring) that affect vegetation NPP changes in Yangzhou City, expanding the research scope of driving factors for vegetation NPP changes, Provide theoretical exploration and practical basis for regional low-carbon sustainable development. (line 117-146)

2. The methodology section of the CASA model has supplemented the details of the model and explained each variable in the equation. The supplementary parts have been marked in blue.(line209-233;)

3. Studying vegetation NPP is of great significance for the implementation of sustainable development strategies.

With the acceleration of urbanization, carbon dioxide emissions from urban development activities such as deforestation, land reclamation, and large-scale extraction of fossil fuels have disrupted the original carbon balance of ecosystems, had a significant impact on regional sustainability, and triggered a series of environmental issues. China promises to reach carbon peak by 2030 and achieve carbon neutrality by 2060. To achieve this goal, it is necessary to clarify the trends and driving factors of net primary productivity in different regions of China, in order to achieve sustainable low-carbon development. The estimation of vegetation NPP and the analysis of long-term spatiotemporal changes and driving factors have become important research topics for sustainable development.

The research objective of this article is to take Yangzhou, an important city in China, as the research object. Based on the improved CASA model, climate data, social data, remote sensing ecological data and other multi-source data types, Tyson medium trend analysis, Hurst index and other methods are used to analyze its spatiotemporal evolution characteristics, predict future change trends, and use geographic detectors to analyze the impact of climate, social, ecological and other factors on NPP changes in the study area, Intended to provide theoretical exploration and practical basis for achieving the "low-carbon" sustainable development goal in the region.(line 43-55)

Comments 2: CASA model is en empirical model and have been applied extensively in previous studies. However, the necessary model details relevant to understand this work must be given here. How each variable in equations 1, 2 and 3 were estimated and how the maximum light use efficiency has been assigned to different land cover types must be explained.

Response 2: Agree. We have supplemented the details of the CASA model and explained each variable in equations 1, 2, and 3, as well as how the maximum solar energy utilization rate is allocated to different land cover types. The modified parts have been highlighted in blue. (line 209-233)

Comments 3: What is the spatial and temporal scale of the NPP that was estimated?

Response 3: The spatial scale of NPP estimated by the model in this article is at the city level, with a time scale of 5 years and a resolution of 30m; The MODIS17A3 NPP product dataset released by NASA has a spatial resolution of 500 meters and a temporal resolution of 1 year. (line 481-484)

Comments 4: Explain all the variables in equation 15.

Response 4: The authors have provided additional explanations for all variables in the paper and highlighted them in blue. (line 209-233)

Comments 5: From figure 7, it is observed that there is a bias in the NPP modelled in this study when compared with MODIS NPP. Quantify that. Provide RMSE and bias values. Only R-Squared is not enough.

Response 5: Agree. We have supplemented the analysis of RMSE and bias values. This part has been marked in blue in the revision text. (line 493-494)

Comments 6: In addition, MODIS data cannot be treated as a independent validation method. Any validation using field observations must be produced.

Response 6: Agree. Thanks for your comments. The improved CASA model is not a method innovation in this article. The reason for comparing it with MODIS data is mainly to verify the accuracy of the estimation data used in this article. Although this study cannot meet the conditions for obtaining validation data through on-site observation, this article has supplemented the validation results in the literature (Zhu et al., 2005), and the author has supplemented RMSE and bias values as required.

Comments 7: Line 422: MODIS product is not released by China National Space Administration I guess. It is from NASA which is US space agency.

Response 7: You are right. This section is the author's error and has been corrected. (line 483)

Comments 8: Section 4.4: Discuss the uncertainty in NPP estimation with respect to the CASA model itself. the discussions are written only surrounding the driving variables but not the underlying model itself.

Response 8: In section 4.4, the uncertainty of the improved CASA model itself has been supplemented. The supplementary content has been highlighted in blue font. (line 488-489)

4. Response to Comments on the Quality of English Language

Point 1: English language needs significant revision. It was difficult to understand the text at many places. Euclidean distance is mentioned as European distance at lines 139 and at one more place.

Response 1: Thanks for your comments. Due to the fact that English is not the author's native language and there are certain limitations to their proficiency, we will entrust a professional department to assist in polishing. The errors pointed out by the teacher have been corrected and highlighted in blue in the article.(line 187,286,419)

5. Additional clarifications

no