Assessing Trends and Interactions of Essential Climate Variables in the Historic Urban Landscape of Sfax (Tunisia) from 1985 to 2021 Using the Digital Earth Africa Data Cube

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 in the re-submitted file.

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

Comments 1: Section 2.2.1 on the USI threshold employs the ‘1st percentile to 100th percentile’ range as an automatic threshold. However, the paper fails to adequately explain why this particular range was selected or how robust it proves to be across different indices and years. It is recommended that the authors supplement their justification or cite relevant references. Additionally, using the 100th percentile in remote sensing is highly risky, as it frequently represents outliers (e.g., sensor saturation or noise) rather than valid urban pixels. Please provide a robust justification for the rationality of selecting the 0/100 percentile method over more standard approaches.

Response 1: Thank you for pointing this out. We agree with this comment. Therefore, we have added a justification of the percentile threshold selection and clarified the potential limitations of using the 100th percentile. We explained that the 1st–100th percentile range allows automated, reproducible extraction of USI values for large time-series studies. To avoid bias, a water mask was applied prior to index computation.

This clarification has been added to section 2.2.1. “This choice was motivated by the need for a fully automated and temporally consistent thresholding method applicable to long-term time series and multiple indices, avoiding manual tuning for individual years. Percentile-based thresholds have been widely used in urban remote sensing to accommodate inter-annual spectral variability and sensor differences. However, the use of the 100th percentile may include extreme values potentially associated with sensor noise or mixed pixels. To mitigate this limitation, water bodies were masked prior to index computation.”

And in the discussion: “Accuracy assessment using the GHSL dataset indicates that the tested USIs achieve comparable performance levels in delineating built-up areas. While overall accuracy values are relatively high, these results should be interpreted with caution. The GHSL product has above 90% overall accuracy from the adopted validation process (Pesaresi et al., 2013). Melchiorri et al. (2019) have tested the GHSL and identified it as an essential variable for Sustainable Development Goal 11: sustainable cities and communities. However, it is important to mention that the automatic thresholding method using the 1st and the 100th percentile is operationally efficient for large-scale and long-term analyses but does not necessarily represent an optimal urban extraction strategy for individual years or highly heterogeneous urban fabrics.”

Comments 2: The regression analysis presented in Section 3.2.3 merely compares two linear trends increasing over time (global warming vs. urban expansion). This does not demonstrate a causal relationship where "USIs explain air temperature." The observed high correlation may be spurious, as both variables naturally exhibited growth trends over the 37-year period independent of one another.

Response 2: Agree. We have avoided implying causality. We now emphasize that the analysis identifies temporal covariance rather than causal effects. This idea is highlighted in the discussion: “Furthermore, temporal variations in the USIs are more strongly associated with Ta than with LST, which reflects microclimatic conditions, as indicated by the multivariate linear regression results. While the classical state of the art has largely focused on relationships between USIs and LST, recent large-scale studies have highlighted the complexity and spatial instability of urban–climate relationships when air temperature is considered. For example, Li et al. (2020) demonstrated that correlations between surface urban heat island (SUHI) intensity and Ta vary substantially across climatic regions and are strongly influenced by urban–rural vegetation differences. Such findings suggest that the stronger associations observed in our study between USIs and Ta reflect context-dependent microclimatic co-variations rather than direct causal effects. This reinforces the need for further research on the UHI and SUHI effects and their predictors to better understand the implications of USIs and Ta for urban climate analysis”.

Comments 3: Section 2.2, Table 1 of the article presents ambiguities in the index formulae listed: firstly, the band abbreviations used in the table (e.g., PIR, swir1, Blue, Red) lack explicit mapping to specific Landsat band numbers or wavelength ranges in either the table notes or main text, with no clarification provided regarding adaptation for Landsat 5/7/8 or Collection 2; Secondly, index names appear inconsistently throughout the text. While Table 1 omits BAIE, other sections (e.g., the Discussion) refer to it as BAEI. It is recommended that the authors supplement Table 1 or its caption with: (1) the corresponding Landsat band number/wavelength range for each band abbreviation; and (2) standardised spelling for all index names.

Response 3: Agree. We have added table 1 : Landsat 5, 7 and 8 spectral bands properties used in USIs calculations. And standardised spelling for all index names in the text.

3. Additional clarifications

The manuscript has been revised to address the reviewer’s major conceptual comments. Additional comments will be considered in the final revised version.

Response to Reviewer 3 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 in the re-submitted file.

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

Comments 1: For the abbreviations in Figure 1, it is recommended to write out the full names in the figure caption to facilitate readers' understanding.

Response 1: We have accordingly revised the figure caption: Location of the study area in Africa and Tunisia with the Historic Urban Landscape boundaries

Comments 2: It is suggested to revise the conclusion section, elaborating on the scientific significance of this study and condensing the main conclusions

Response 2: Agree. The conclusion has been fully reformulated “This study applied a satellite-based ECV framework to analyse long-term urbanisation and climate trends within the historic urban landscape of Sfax using the Digital Earth Africa data cube. Results highlight consistent warming trends and decreasing precipitation, reflecting global phenomena of climate change and urbanisation as well as heating and drought in the Mediterranean. The comparative assessment of six urban spectral indices demonstrates that their performance and sensitivity vary according to spectral band combinations, with SWIR–NIR-based indices showing stronger statistical associations with temperature variables. Importantly, the observed relationships between urban spectral indices and temperature reflect temporal co-variation with Ta. The proposed cloud-based and reproducible workflow provides an operational tool for long-term urban climate monitoring in historic cities, while acknowledging limitations related to thresholding methods and reference datasets. Future work should integrate vegetation indices, land-use information, and comparisons between different urban landscapes to further validate and generalise the findings.”

3. Additional clarifications

The manuscript has been revised to address the reviewer’s major conceptual comments. Additional comments will be considered in the final revised version.

Response to Reviewer 1 Comments

1. Summary

Thank you very much for taking the time to review this manuscript and for your positive evaluation of the revised version.

Response to Reviewer 2 Comments

1. Summary

Thank you very much for taking the time to review this manuscript and for your positive evaluation of the revised version..

Response to Reviewer 3 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 in the re-submitted file.

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

Comments 1: it is still recommended to further expand the study area to verify the universality of the paper's important conclusions.

Response 1: Thank you for this valuable comment. We agree that extending the study to additional urban areas would further strengthen the generalizability of the conclusions. Accordingly, we have explicitly addressed this limitation in the revised manuscript and highlighted the extension of the proposed methodology to multiple urban contexts as an important direction for future research. This clarification has been added to the Discussion section, where we emphasize that applying the workflow to other historic and non-historic cities under different climatic conditions would help verify the robustness and universality of the observed relationships.

Added text in the manuscript:

“Finally, although this study focuses on the Historic Urban Landscape of Sfax, future research should extend the proposed methodology to additional cities and climatic contexts to assess the robustness and generalizability of the observed relationships. Applying the workflow to multiple historic and non-historic urban environments would allow verification of the universality of the conclusions and further strengthen the applicability of the proposed ECV-based framework.”

I also revised the English language and the quality of the figure, I hope it meets your expectations