A Study on the Drivers of Remote Sensing Ecological Index of Aksu Oasis from the Perspective of Spatial Differentiation

Round 1

Reviewer 1 Report

The article proves to be relevant when exploring the relationship between the remote sensing ecological index (RSEI) in the spatial and temporal assessment of environmental quality in the Basin, to diagnose the factors that influence the change in the ecological environment. The article is of interest to the readers of this journal. As a suggestion the following should be considered to improve this article:

a) In the abstract, the basic information of the research problem was dealt with, but it lacked a brief history of what previous research showed regarding the application of the RSEI. Is there a gap in previous research?

b) b) in the methodology, elaborate a flowchart referring to the estimation of the ecological index of remote sensing. Present the equations of the RSEI indicators (greenness, humidity, dryness and heat)

c) In conclusion, it will be necessary to define more clearly the implications of your findings for science.

Author Response

Revise comments reply

Dear Evaluation Experts：

Thank you for your reviewers' insightful remarks. Your professional insights have been really beneficial to me and have also helped me recognize the limits of straight body research. The justification for the revision of the basis is provided below：

1. In the introduction, the short application history of RSEI is enhanced. Early researchers concentrated on metropolitan regions, soil erosion zones, and ecological protection zones on a small regional scale. Following the official introduction of the open-source GEE platform by Google, the calculation speed of the remote ecological sensing index (RSEI) was significantly increased due to the platform's real-time and large data cloud processing. Additionally, large-scale, long-term study on ecological environment quality has been conducted. Accordingly, the abstract (L18,19) and the introduction (L77-94) are updated.
2.  in the third chapter's section on research methodologies, included RSEI calculation flowchart. There were stated formulas for estimating the humidity, greenness, dryness, and temperature (L172-203).
3.  In the discussion section, the study's shortcomings are examined, and it is proposed that future research should reinforce the ground observation data in order to improve the RSEI evaluation ( L394-396 ).
4. Thank you for your insightful advise. Initially, we believe that the temperature and precipitation exhibit some fluctuation, but the association with RSEI is not evident. Therefore, temperature and precipitation are not the most influential elements on RSEI. The findings indicate that land use is the dominant factor influencing the spatial variation of the basin's biological environment, and that its influence on RSEI is much greater than that of precipitation and temperature. Additionally, groundwater storage and RSEI exhibited a steady pattern.( L448-459 ).
5.  We've incorporated the NDBSI idea and accompanying calculation formula (L179-186) as a result of your suggestions.
6.  As would be expected, red dashed line in Figure 11 ( Original Figure 10) is the result of 5 data points fitted by a nonlinear model.

Kind regards,

Mr. Ling

Author Response File: Author Response.docx

Reviewer 2 Report

Ling et al. presented a useful index of ecosystem condition (RSEI). I find the paper mostly comprehensive and well-organised. My only major concern is with the evaluation of RSEI.

Normally a new remote sensing product would be evaluated against real data—often ground-based observations. The evaluation of RSEI however, used only correlation against other satellite-derived products. Just having a better correlation with other products doesn’t mean the index is a better product. If the authors can get hold of ground-based observations, I would strongly suggest evaluating RSEI against those data. If not, please address this limitation in the discussion—in fact, this could be a good future direction. The authors also only looked at correlation but not response to environmental fluctuation (e.g., rainfall and temperature). So alternatively the authors could also look at annual anomaly of RSEI, other indices, and climate.

Detailed comments

L237: I don’t think NDBSI has ever been defined in the paper.

L392: Figure 10, not sure what the red dash line is about. Of course you can fit a nonlinear model to 5 data points and get r2=1.

Author Response

修改意见回复

尊敬的评测专家：

感谢您的审稿人富有洞察力的评论。您的专业见解对我非常有益，也帮助我认识到直体研究的局限性。基础修订的理由如下：

1.在讨论部分，检查了研究的不足，并建议未来的研究应加强地面观测数据以改进RSEI评估（L394-396）。

2 .感谢您的中肯建议。最初，我们认为温度和降水存在一定的波动，但与 RSEI 的关联并不明显。因此，温度和降水不是对RSEI影响最大的因素。研究结果表明，土地利用是影响流域生物环境空间变化的主导因素，其对RSEI的影响远大于降水和温度。此外，地下水储量和 RSEI 呈现稳定模式。( L448-459 )。

3.根据您的建议，我们采用了 NDBSI 思想和随附的计算公式 (L179-186)。

4. 正如所料，图 11（原图 10）中的红色虚线是非线性模型拟合 5 个数据点的结果。

其他修改：

1.  在介绍中，增强了RSEI的短暂应用历史。早期的研究主要集中在小区域尺度的都市圈、水土流失带和生态保护区。继谷歌正式推出开源GEE平台后，由于平台实时大数据云端处理，遥感生态指数（RSEI）计算速度大幅提升。此外，还开展了大规模、长期的生态环境质量研究。因此，更新了摘要 (L18,19) 和介绍 (L77-94)。
2. 在第三章的研究方法部分，包含了RSEI计算流程图。有用于估算湿度、绿度、干燥度和温度的规定公式 (L172-203)。

Author Response File: Author Response.docx

Round 2

Reviewer 2 Report

The authors addressed my comments well. I would love to see the paper published. 

A minor point is the terms GEE, MODIS, RSEI are used without definitation in the abstrtact.