Robust Extraction of Soil Characteristics Using Landsat 8 OLI/TIRS

Round 1

Reviewer 1 Report

The topic of the paper, extracting soil characteristics by using Landsat 8 OLI / TIRS, is an interesting one, and is of interest in research. This is because, as the authors point out, classical methods of mapping soil types and establishing their suitability for uses and crops are time-consuming.
However, the paper does not have a clear structure, the research is not presented in an academic way, with precision and accuracy.
The paper is written carelessly, the abbreviations used are not explained (including in the title and abstract), the units of measurement are not uniform, and the numbers are written sometimes with a dot and sometimes with a comma (this makes the paper difficult to understand).

My recommendations for major improvement are:
- rewrite the abstract so that it is a miniature of the paper; the abstract is not a summary of the introduction, it must also include the research methodology and especially the results.
- document the paper and specify what is the stage of using remote sensing images in the evaluation of the soil / soil parameters and what are the obstacles in this regard; then specify the research hypothesis in your paper and what the new methodology applied by you brings.
- specify the research methods supported by the related bibliography;
- improve the quality of the presentation of results in graphs, figures and tables;
- separate the discussion and radically improve it;
- summarize the conclusions of the paper.

Author Response

My recommendations for major improvement are:
Point 1 - rewrite the abstract so that it is a miniature of the paper; the abstract is not a summary of the introduction, it must also include the research methodology and especially the results.

Response to Point 1:

Revised:

Thank you for your valuable comment. We revised as followed

The research utilized various methods for extracting soil characteristics from the Landsat 8 OLI / TIRS imagery in the Thua Thien Hue province, Vietnam. In this study, the Object-based Oriented Classification (OBOC) method was used to extract information about the land cover (focus on rock outcrop) through indicators of TGSI, NDVI, and NDBI. The soil moisture information was resulted by examining the correlation between the Land Surface Temperature (LST) and the Normalized Difference Vegetation Index (NDVI). The findings point out that 40 study area locations are covered with rock outcrop, with a Kappa index of 85.10%. In addition, Soil moisture varied markedly from coastal sandy areas, urban areas, and hilly and mountainous areas on the study area's surface. The extracted soil information can be served as a foundation for local socio-economic development planning.

Point 2: - document the paper and specify what is the stage of using remote sensing images in the evaluation of the soil / soil parameters and what are the obstacles in this regard; then specify the research hypothesis in your paper and what the new methodology applied by you brings.

Response to Point 2:

We revised and improved in methodologies paragraph

Landsat-8 OLI/TIRS images are pre-processed before classification, including layer stacking bands, improving image quality, subsetting test site regions, geo-referencing, and projection transformation into the VN-2000 coordinate reference system. Finally, from the GISHue baseline data, the administrative unit of Thua Thien Hue province was cut to obtain an overview of the study area.

Figure 2. Flowchart of the research *The research hypothesis:

The research has extracted objects according to each level, such as level 1: land, water; level 2: vegetable and non-vegetable; level 3: sand land and non-sand land; level 4: build up and bare land; level 5: rock outcrop and non-rock outcrop with different segmentations parameters of shape, scale, parameter (table 2).

Table 2: Segementation objects

They are presented in figure 3. However, extracted rock outcrops very difficult, which are small than other objects and in tropical area, the classification objects can not defind by some basic ratio image, but TGSI can support extract small object. For example in study, rock outcrop extracted by TGSI.

Point 3: - specify the research methods supported by the related bibliography;

Response to Point 3:

2.3. Methodologies

1. Land Use/ Land Cover
2. Extracting moisture information from remote sensing images Landsat 8 OLI / TIRS
3. Assessment Accuracy

Point 4: - improve the quality of the presentation of results in graphs, figures and tables;

Response to Point 4:

Point 5- separate the discussion and radically improve it;

Response to Point 5:

Discussion:

The object-oriented classification approach has resulted in high accuracy; the in-terpretation results are very informative and precise for each object when automati-cally retrieved.

The analysis's results of the land cover objects appear in the correct positions as the distribution of which was according to the interpretation results. It appears in the right positions as the law of its formation, and the interpretation results of the cover objects according to the above method give results with the accuracy 87%.

It is clear from this study that the interpretation of rock outcrop produces diffi-cult-to-extract material that has been studied and tested. The kappa index 0.85, the overlap of 15/18 profiles measured using the conventional process, and the individual survey points confirmed the effects of interpreting the distribution of exposed rock re-gions.

Table point 5a. Assessment Accuracy 

The researchers analyzed 47 points containing exposed rock around Thua Thien Hue province to achieve the results, which were checked using a kappa index of 88.87 %. The field verified 40/47 points, and the precise percentage is 85.10%.

Table point 5b. Result of field survey

Information on soil surface moisture is calculated using a -1 - 1 scale; the nearer the highest values to one, the higher the humidity. Besides, vacant lands are low humidity in Hue city, Phu Loc district, Huong Tra city, and coastal sandy areas

Point 6: - summarize the conclusions of the paper.

Response to Point 6:

Conclusions

The use of  Landsat-8 imagery to extract rock outcrops using the OBOC method and index images has been examined. The analysis found that accessible remote sensing data sources make it easier to conduct a preliminary study to analyse surface objects.

The overall accuracy of rock outcrop objects from Landsat-8 images was >87%. The information extraction of built-up lands, bare ground, and specifically exposed rocks differed substantially from the image bands and index image values.

One of the essential aspects in retrieving outcrop information from remote sensing data in this paper is the TGSI index. TGSI is recommended for bare land identification, particularly for which rock outcrops are tiny objects that are difficult to detect with other ratio pictures in mountainous areas.

Furthemore, Mean Brightness boosts the spectral value's luminosity, making it ideal for re-moving sandy topsoil, particularly in coastal areas like Thua Thien Hue province.Using only NDBI and Mean Blue, the spectral reflectance ratios of floating sedi-ment and water surface are evident. It is more visible in building works and empty land areas. Luminosity The Mean Brightness and Mean Blue channels to distinguish building ground and bare land excludes exposed rocks due to ongoing construction.

Since Hue city has a high density of green trees combined with urban facilities, the NDBI index distinguished between building sites and barren ground. The data obtained above can be applied to a variety of other helpful measure-ment issues, such as soil moisture data that has been studied and presented

Author Response File: Author Response.pdf

Reviewer 2 Report

Dear Authors

In this study, the Object-based Oriented Classification (Object-based Oriented Classification) method was used to extract information about the land cover (using indicators TGSI, NDVI, and NDBI) and rock outcrop (in combination with other methods).

Line 35: Add the following sentence: “Soil properties and management influence crop yield and quality [1,2]”.

[1] Bünemann, EK, Bongiorno, G., Bai, Z., Creamer, RE, De Deyn, G., de Goede, R., ... & Brussaard, L. (2018). Qualità del suolo: una revisione critica. Biologia e biochimica del suolo , 120 , 105-125.

[2] Cataldo, E., Fucile, M., & Mattii, G. B. (2021). A Review: Soil
Management, Sustainable Strategies and Approaches to Improve the Quality
of Modern Viticulture. Agronomy, 11(11), 2359.

[3] Furey, G. N., & Tilman, D. (2021). Plant biodiversity and the regeneration of soil fertility. Proceedings of the National Academy of Sciences, 118(49).

Line 43: …?

Lines 36-50: Add references

Line 58: [2, 8, 13, 19]

Line 63: …?

Lines 67-69: Please rephrasing. “Yuemin and Kai - Lung Wang (2012), extracting photo synthetic vegetation information and a photosynthetic vegetation layer through the use of NDVI index and plant index analysis modeling Spectral Mixture Analysis NDVI-SMA (Spectral Mixture Analysis)”

Line 87: km²

Line 95: More information please. “8 OLI/TIRS”

Line 101: [9,15,17]

Line 105-110: The authors could add a figure that represents the outline of the work. “In this study, the object-oriented approach was used in this analysis in the following way: Fragmenting the test specimens in order to extract the rock outcrop; At the same time, NDVI and LST are extracted from the remote sensing image data. Remote sensing images were mixed, image resolution was improved, geographical borders were sliced through, and the coordinate reference system was rectified to the VN-2000 coordinate reference system before classification”.

Line 150: Table 2 Enter the authors in the table by numbering and not by name. [?] [?] [?]

Line 171: (Weng et al., 2004) [?]

Line 224: Table 3

Line 292: Table 4

Line 299: Table 5

Authors should implement the following sessions: introduction, discussion, and conclusions. Explaining better the significance of the work and giving more information about methods

Author Response

Dear Authors

In this study, the Object-based Oriented Classification (Object-based Oriented Classification) method was used to extract information about the land cover (using indicators TGSI, NDVI, and NDBI) and rock outcrop (in combination with other methods).

Point 1: Line 35: Add the following sentence: “Soil properties and management influence crop yield and quality [1,2]”.

[1] Bünemann, EK, Bongiorno, G., Bai, Z., Creamer, RE, De Deyn, G., de Goede, R., ... & Brussaard, L. (2018). Qualità del suolo: una revisione critica. Biologia e biochimica del suolo , 120 , 105-125.

[2] Cataldo, E., Fucile, M., & Mattii, G. B. (2021). A Review: Soil
Management, Sustainable Strategies and Approaches to Improve the Quality
of Modern Viticulture. Agronomy, 11(11), 2359.

[3] Furey, G. N., & Tilman, D. (2021). Plant biodiversity and the regeneration of soil fertility. Proceedings of the National Academy of Sciences, 118(49).

Response to Point 1: Dear Reviewer: We have added that sentence and 2 citations [2,3], the first citation ([1] Bünemann, EK, Bongiorno, G., Bai, Z., Creamer, RE, De Deyn, G., de Goede, R., ... & Brussaard, L. (2018). Qualità del suolo: una revisione critica. Biologia e biochimica del suolo , 120 , 105-125.), we don’t understand because it is not in english language it, so we did not cite it, so sorry, thanks so much for your support!

Point 2: Line 43: …?

Response to Point 2: Dear Reviewer: Yes, We have deleted those 3 dots.

Point 3: Lines 36-50: Add references

Response to Point 3: Dear Reviewer: Yes, we added the citation number [3]

Point 4: Line 58: [2, 8, 13, 19]

Response to Point 4: Dear Reviewer, we have edited it. Thank you.

Point 5: Line 63: …?

Response to Point 5: We deleted those dots. Thank you.

Point 6: Lines 67-69: Please rephrase. “Yuemin and Kai - Lung Wang (2012), extracting photosynthetic vegetation information and a photosynthetic vegetation layer through the use of NDVI index and plant index analysis modeling Spectral Mixture Analysis NDVI-SMA (Spectral Mixture Analysis)”

Response to Point 6: Dear Reviewer, we have rephrased it as follows: Yuemin and Kai-Lung Wang (2012), extracted photosynthetic vegetation information and a photosynthetic vegetation layer through the use of NDVI index and plant index analysis modeling Spectral Mixture Analysis NDVI-SMA (Spectral Mixture Analysis)

Point 7: Line 87: km²

Response to Point 7: Dear Reviewer: yes, we have edited it. Thank you.

Point 8: Line 95: More information please. “8 OLI/TIRS”

Response to Point 8: Dear Reviewer: we have added more information about it to the paper: Landsat-8 was released from Earth Explorer on April 25, 2019, and provided 30 m spatial resolution optical imagery on eight spectral bands via the Operational Land Imager sensor and two bands via the Thermal Infrared Sensor.

Point 9: Line 101: [9,15,17]:

Response to Point 9: Dear Reviewer: We delete that phrase. Thank you.

Point 10: Line 105-110: The authors could add a figure that represents the outline of the work. “In this study, the object-oriented approach was used in this analysis in the following way: Fragmenting the test specimens in order to extract the rock outcrop; At the same time, NDVI and LST are extracted from the remote sensing image data. Remote sensing images were mixed, image resolution was improved, geographical borders were sliced through, and the coordinate reference system was rectified to the VN-2000 coordinate reference system before classification”.

 Response to Point 10: Dear Reviewer, yes, we have added more to the figure 2: the flowchart of the research.

Point 11: Line 150: Table 2 Enter the authors in the table by numbering and not by name. [?] [?] [?]

Response to Point 11: Dear Reviewer: We changed that to Table 3, and we did enter by  numbering [15], [7], [12]. Thank you.

Point 12: Line 171: (Weng et al., 2004) [?]

Response to Point 12: Dear Reviewer: Yes, we change it by numbering [17]

Point 13: Line 224: Table 3

Response to Point 13: Dear Reviewer: We have renamed it. Thank you.

Point 14: Line 292: Table 4

Response to Point 14: Dear Reviewer: We have renamed it. Thank you.

Point 15: Line 299: Table 5

Response to Point 15: Dear Reviewer: We have renamed it. Thank you.

Authors should implement the following sessions: introduction, discussion, and conclusions. Explaining better the significance of the work and giving more information about methods.

Dear Reviewer: we have rewritten and separated the introduction, discussion, and conclusion in the paper. Thank you very much for your time and your valuable suggestions! We appreciate you so much!

Wish all the best!

Best regards,

Van

Author Response File: Author Response.pdf

Reviewer 3 Report

No discussion of results with the literature .

Author Response

Dear Reviewer: Firstly, We would like to thank you so much for your kind suggestions. Your comments help our paper improve alots. We have rewritten and separated the discussion as bellow:

Discussion:

The object-oriented classification approach has resulted in high accuracy; the in-terpretation results are very informative and precise for each object when automati-cally retrieved.

The results of the analysis of the land cover objects appear in the cor-rect positions as the distribution of which was according to the interpretation results appear in the correct positions as the law of its formation, the results of the interpreta-tion of the cover objects according to the above method give results with the accuracy 87%.

It is clear from this study that the interpretation of rock outcrop produces diffi-cult-to-extract material that has been studied and tested. The kappa index 0.85, the overlap of 15/18 profiles measured using the conventional process, and the individual survey points confirmed the effects of interpreting the distribution of exposed rock re-gions.

Table 7. Assessment Accuracy 

The researchers analyzed 47 points containing exposed rock around Thua Thien Hue province to achieve the results, which were checked using a kappa index of 88.87 %. The field verified 40/47 points, and the precise percentage is 85.10%.

Table 8. Result of field survey

Information on soil surface moisture is calculated using a -1 - 1 scale; the nearer the highest values to one, the higher the humidity. Besides, vacant lands are low humidity in Hue city, Phu Loc district, Huong Tra city, and coastal sandy areas.

Through the research process, the authors have drawn some of the following conclusions:

The object-oriented classification approach has high accuracy; the interpretation results are very informative and precise for each object when automatically retrieved. The analysis's results of the land cover objects appear in the correct positions as the distribution of which was according to the interpretation results. It appears in the right positions as the law of its formation, and the interpretation results of the cover objects according to the above method give results with the accuracy 87%.

It is clear from this study that the interpretation of rock outcrop produces difficult-to-extract material that has been studied and tested. The kappa index of 0.85, the overlap of 15/18 profiles measured using the conventional process, and the individual survey points confirmed the effects of interpreting the distribution of exposed rock regions. The researchers analyzed 47 points containing exposed rock around Thua Thien Hue province to achieve the results, which were checked using a kappa index of 88.87 %. The field verified 40/47 points, and the precise percentage is 85.10%.

Information on soil surface moisture is calculated using a 0-1 scale; the nearer the highest values to one, the higher the humidity. Besides, vacant lands are low humidity in Hue city, Phu Loc district, Huong Tra city, and coastal sandy areas.

In terms of analysis methods, data retrieval must be paired with other metrics to distinguish the subjects covered, such as:

+  Mean Brightness boosts the spectral value's luminosity, suitable for eliminating sandy topsoil, particularly in coastal areas like Thua Thien Hue province.

+  Mean Brightness boosts the spectral value's luminosity, making it ideal for removing sandy topsoil, particularly in coastal areas like Thua Thien Hue province.

+ Using only NDBI and Mean Blue, the spectral reflectance ratios of floating sediment and water surface are evident. It is more visible in building works and empty land areas. Luminosity The Mean Brightness and Mean Blue channels to distinguish building ground and bare land excludes exposed rocks due to ongoing construction.

+ Since Hue city has a high density of green trees combined with urban facilities, the NDBI index distinguished between building sites and barren ground.

+ The data obtained above can be applied to a variety of other helpful measurement issues, such as soil moisture data that has been studied and presente

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

I believe that the authors responded to all my requests and major improved the paper.
I recommend accepting the paper for publication.

Author Response

Dear Reviewer,

We would like to thank the Reviewer for your valuable comments on this paper. We highly appreciate your suggestion and your time to help our research improve a lot. 

Wish you all the best!

Kind regards,

Van

Reviewer 2 Report

Author Response

Dear Reviewer,

We would like to thank the Reviewer for your valuable comments on this paper. We highly appreciate your suggestions and your time to help our research improve a lot. 

Wish you all the best!

Kind regards,

Van