Multispectral Remote Sensing Monitoring of Soil Particle-Size Distribution in Arid and Semi-Arid Mining Areas in the Middle and Upper Reaches of the Yellow River Basin: A Case Study of Wuhai City, Inner Mongolia Autonomous Region

Round 1

Reviewer 1 Report

Comments for author File: Comments.pdf

Author Response

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Reviewer 2 Report

Very interesting and well-structured work, The only thing missing is a commentary on how the field sampling was carried out and a figure detailing it.

Author Response

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Reviewer 3 Report

The paper is well structured and the methodology is complete and quite adequate.

I suggest the improvement of the Introduction (and state of the art sections) in order to:

• include some other recent work about soil characterization using machine learning methods  (I suggest some of them to study its relationship)

• the choice of satellite vs. other technologies such as Sentinel is not justified.

• Neither the real motivation about the importance of the study of the different types of soil is justified.  It talks generically about ecology and mining activity but without correlation with the object of this study. 

• Advantages and disadvantages of your methodology are not contrasted with other methodologies

Some additional doubts or improvements: 

• You use three layers of (0-20 cm, 20-40 cm, 40-132 60 cm). ¿What does it mean? I assume that it is the depth of the soil sample but it could be the diameter. Later you merge all this soil to obtain an homogenous sample of about 1kgs??. Is it independ of this size? If so, please give a clearer explanation 

• Samples C and H X 3 thickness does not correspond with the charts of Figure 5 (CH11, CH12, etc are not explained)

• It is shocking that all the values and processes used are explained, but the regression analysis lacks additional explanation (Table 3, 4). Even when the evaluation indexes are the usual ones for evaluating regression models, please indicate their function in this context. 

• How do you obtain maps of Figure 13?

• Line 330, missing period??

Others:

• review the images because they appear disconnected from the caption on different pages (Fig 6)

• tables in two different pages should maintain the header

 

Azizi K., Garosi Y., Ayoubi S., Tajik S.

Integration of Sentinel-1/2 and topographic attributes to predict the spatial distribution of soil texture fractions in some agricultural soils of western Iran

(2023) Soil and Tillage Research, 229, art. no. 105681, Cited 0 times.

DOI: 10.1016/j.still.2023.105681

 

Shirazi, M. P., Abtahi, S. A., Nejad, M. B., Moosavi, A. A., & Navidi, M. N. (2023). Improving soil texture digital mapping using landsat 8 satellite imageries in calcareous soils of southern iran. Journal of Agricultural Science and Technology, 25(2), 485-502. Retrieved from www.scopus.com

 

Tavakoli, H., Correa, J., Sabetizade, M., & Vogel, S. (2023). Predicting key soil properties from vis-NIR spectra by applying dual-wavelength indices transformations and stacking machine learning approaches. Soil and Tillage Research, 229 doi:10.1016/j.still.2023.105684

 

Chursin, V. V., Kuzhevskaya, I. V., Merzlyakov, O. E., Valevich, T. O., & Ruchkina, K. V. (2021). Design of satellite sensing data classification algorithm based on machine learning using the example of granulometric composition of soils in agricultural landscapes of western siberia. Sovremennye Problemy Distantsionnogo Zondirovaniya Zemli Iz Kosmosa, 18(2), 39-50. doi:10.21046/2070-7401-2021-18-2-39-50

Author Response

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Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

Thanks so much for authors to take my comments into account. I thinke the manuscript has been improved a lot. It can be accept for publication. I only have one comment left:

1. When MATLAB software was used to calculate the coefficients k_i and b of the MLR and PLSR regression equations. Do you apply all the measurements data? Or part of them? 

Author Response

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Author Response File: Author Response.docx