Review Reports

Round 1

Reviewer 1 Report

Dear authors

The article is consistent with the theme of the journal. It also seems well documented and contains an acceptable literature review, but I would have some comments:
- The abstract and introduction suggest that the article should analyse the effects of urbanisation on landscape changes, but it only presented environmental factors for 20 years. I think it would be easier to reword these lines and perhaps cover them in detail in another article.
- Please explain why you decided to collect data only in the rainy season. Is this sufficiently relevant to the time analysis?
- The article could be improved by including some final diagrams on landscape changes.
- Also, please correct the alignment of images, typos, punctuation errors, or incomplete sentences as this makes the manuscript difficult to read.

Author Response

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Author Response File: Author Response.pdf

Reviewer 2 Report

The manuscript presented is well written. It is enough well structured and according to the present reviewer only few revisions have to be performed in order to improve the quality of the work. If it will be done I will certainly recommend the pubblication on Earth. 

The introduction section is fine, I advice you to better discuss the role of vegetation and more in general landscape taking into account the effect of climate change. In order to do this please consider this works:

• https://doi.org/10.3390/rs12213542
• https://doi.org/10.1016/j.jag.2005.05.003 
• https://doi.org/10.1117/12.2533110
• https://doi.org/10.1016/j.uclim.2016.04.002

I suggest you to divide the data section in two parts in one discussing the methods and in another the materials. It is worth to note that to permit reseach scalability it is well welcome to share as supplementary material the script you adopted to perform all the analysis mentioned in Google Earth Engine. I advice you to do this or consider to put a link to find it. 

Please better describe also all the software adopted including version number.

The discussion are a litte bit too short please add some something more considering application to other realities and limits.

 

Author Response

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Author Response File: Author Response.pdf

Reviewer 3 Report

During the last decades the ever growing use of RS and EO data has been gradually increased. To that end, it is particular important to briefly present the advantages of the cloud-computing platforms, for instance Google Earth Engine (GEE). Google Earth Engine (GEE), like other cloud computing platforms, are very popular because they provide efficient methods for storing, accessing, and analyzing datasets on high-performance servers. GEE is launched by Google in 2010 and makes freely available remote sensing datasets via its internet-based Application Programming Interface (API) provided by Python and a JavaScript web-based Interactive Development Environment (IDE). Since worldwide geospatial data are often retrieved and analyzed, few studies managed to build the RUSLE model in GEE taking advantage of its stable and robust cloud- based environment. Add some comments highlighted GEE usage.

Make clear for the readers the novelty of this approach and the research gap answered by the current approach. The products of VCI are also freely accessible through Copernicus Land Service, why not use this one?

State the importance of the current issue and give some examples

Tariq, A., & Shu, H. (2020). CA-Markov Chain Analysis of Seasonal Land Surface Temperature and Land Use Land Cover Change Using Optical Multi-Temporal Satellite Data of Faisalabad, Pakistan. Remote Sensing, 12(20), 3402.

Add some more details about how the accurancy assessment derived? Uncertainties?

 

 

Author Response

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Author Response File: Author Response.pdf

Round 2

Reviewer 3 Report

The article improved and now can be accepted