In Situ Hyperspectral Reflectance Sensing for Mixed Water Quality Monitoring: Insights from the RUT Agricultural Irrigation District

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This manuscript reports on the use of hyperspectral remote sensing technology to monitor wastewater quality at a select agricultural area. It is strongly recommended to shorten the manuscript. It is 10 – 20 pages longer than a typical Water journal article I’ve read. It is way too long for me to enjoy reading.

 

• Analysis on each parameter is conducted for all three sample campaigns. The way the results are presented is similar among the three campaigns. With the contents in the “discussion” section, removal of most individual presentations does not seem to compromise the overall conclusions.
• There are huge overlaps in the results and the discussion. A quick example is the overlap between L773-825 and sections 3.1.4 and 3.1.5 at L401-470. The authors should reorganize the text and maybe combine directly the results and the discussion.

 

Here are a few minor specific comments.

1. It is better to briefly introduce/define what “hyperspectral technology” is in this manuscript. The “hyperspectral technology” is only one specific sector of remote sensing. From the listed examples, is it used interchangeably with “remote sensing” in this manuscript? Do the cited papers all use the same actual principle of “hyperspectral technology”?
2. L163-174. The specific focus on the agricultural irrigation canal should be indicated in the objectives.
3. What are those spectrometers, e.g., models?
4. Tables 2-4.

Author Response

This manuscript reports on the use of hyperspectral remote sensing technology to monitor wastewater quality at a select agricultural area. It is strongly recommended to shorten the manuscript. It is 10 – 20 pages longer than a typical Water journal article I’ve read. It is way too long for me to enjoy reading. The manuscript is now 26 pages long instead of 35 pages long.

Analysis on each parameter is conducted for all three sample campaigns. The way the results are presented is similar among the three campaigns. With the contents in the “discussion” section, removal of most individual presentations does not seem to compromise the overall conclusions. Suggestion followed.

There are huge overlaps in the results and the discussion. A quick example is the overlap between L773-825 and sections 3.1.4 and 3.1.5 at L401-470. The authors should reorganize the text and maybe combine directly the results and the discussion. Suggestion followed.

Here are a few minor specific comments.

It is better to briefly introduce/define what “hyperspectral technology” is in this manuscript. The “hyperspectral technology” is only one specific sector of remote sensing. From the listed examples, is it used interchangeably with “remote sensing” in this manuscript? Do the cited papers all use the same actual principle of “hyperspectral technology”? All the manuscript use the term “Hyperspectral Reflectance Sensing”.

L163-174. The specific focus on the agricultural irrigation canal should be indicated in the objectives. Solved

What are those spectrometers, e.g., models? Solved

Tables 2-4. Removed

Reviewer 2 Report

Comments and Suggestions for Authors

I congratulate the authors on their well-researched and well-conducted study and on their findings, which bode well for the future.

I have a few comments that the authors could briefly address in the text.

1. The research was not carried out over the full annual cycle. Was this due to limited organisational or financial capacity or other reasons? Please indicate this in the methodology.

2. Please consider whether it is necessary to present the results of water quality analyses in tables and box plots. Wouldn't graphs be enough? They allow statistical data to be read and differences between sampling series to be assessed. I ask the authors to consider and possibly explain why the same results need to be presented in two different forms.

3. Similarly, Figures 11, 12 and 13 and Tables 5, 6 and 7 - is it necessary to duplicate the results in the tables? In my opinion it is not necessary. Please provide a brief explanation or change in the text.

Please consider changes in the presentation of the results (points 2 and 3). Please also provide a possible explanation why the changes are not being introduced.

Author Response

1. The research was not carried out over the full annual cycle. Was this due to limited organisational or financial capacity or other reasons? Please indicate this in the methodology. Solved (limited financial capacity L195-196)
2. 2. Please consider whether it is necessary to present the results of water quality analyses in tables and box plots. Wouldn't graphs be enough? They allow statistical data to be read and differences between sampling series to be assessed. I ask the authors to consider and possibly explain why the same results need to be presented in two different forms.
3. 3. Similarly, Figures 11, 12 and 13 and Tables 5, 6 and 7 - is it necessary to duplicate the results in the tables? In my opinion it is not necessary. Please provide a brief explanation or change in the text.

Please consider changes in the presentation of the results (points 2 and 3). Please also provide a possible explanation why the changes are not being introduced. All changes were followed.

Reviewer 3 Report

Comments and Suggestions for Authors

The authors presented a study in which they used spectrometer data to predict values of pH, turbidity, nitrates, and chlorophyll-a. Their results indicate that it has been possible to use the optical data obtained to model the parameters' variations. Nevertheless, some information is necessary for properly evaluating the performance of the obtained models, such as R2 values or other metrics. Some aspects of the paper need to be improved, the most important one being the length of the paper. Following this, a series of comments aimed at enhancing the quality of the paper has been added.

1. The introduction of the analyzed problem in the abstract is too long. Please reduce the provided information to just one or two sentences.
2. In the abstract, the authors have to highlight their results, including numerical values of the performance of their proposal. For example, the authors can include the correlation coefficients between bands and named parameters such as pH or turbidity, among others.
3. Avoid using the terms already used in the title as a keyword. The authors should delete keywords included in the title and provide new keywords.
4. The introduction is excessively long. I strongly recommend dividing the introduction into two sections: Introduction and Related Work.
5. The efforts to predict water quality based on optical parameters combined with machine learning are an updated topic that the authors did not mention. Thus, the authors have to add some references to provide a broader context for their paper. Following, I include a couple of recent references that deal with this topic:

Combination of machine learning and RGB sensors to quantify and classify water turbidity. Chemosensors, 12(3), 34.

(2024). Tracking changes in chlorophyll-a concentration and turbidity in Nansi Lake using Sentinel-2 imagery: A novel machine learning approach. Ecological Informatics, 81, 102597.

6. In Subsecction 2.3.1 a table outlining all the sampling days would be valuable.
7. The model of both used spectrometers must be provided in Subsection 2.3.2.
8. For the training of regression models, the authors have to describe how the dataset was split. This information must be provided in Section 2. The dataset must be also defined in more detail, including the total number of lectures.
9. The results are too long. I strongly recommend that the authors focus on the main findings. They have to add some data which helps readers to have a general view of their result. For example, a Table in which they include the R2 values of obtained models for different parameters across campaings are needed.
10. The discussion section is excessively long and has no structure. I suggest the authors add a subsection in the discussion to facilitate the lecture of the paper. In the discussion, they have to strongly defend and justify why their results indicate that pH can be predicted using IR data. Is it possible that the pH in this river is linked to the presence of certain suspended materials, which is impacting IR light patterns?

Author Response

The authors presented a study in which they used spectrometer data to predict values of pH, turbidity, nitrates, and chlorophyll-a. Their results indicate that it has been possible to use the optical data obtained to model the parameters' variations. Nevertheless, some information is necessary for properly evaluating the performance of the obtained models, such as R2 values or other metrics. Some aspects of the paper need to be improved, the most important one being the length of the paper. Following this, a series of comments aimed at enhancing the quality of the paper has been added.

Solved, figures 11, 12 and 13 now present the R2, RMSE and RPD performance metrics.

 

1. The introduction of the analyzed problem in the abstract is too long. Please reduce the provided information to just one or two sentences.

Solved. The introduction of the problem has been explicitly presented in one sentence in the abstract (3 lines at the beginning of the abstract).

2. In the abstract, the authors have to highlight their results, including numerical values of the performance of their proposal. For example, the authors can include the correlation coefficients between bands and named parameters such as pH or turbidity, among others.

Solved. Performance metrics were explicitly added.

3. Avoid using the terms already used in the title as a keyword. The authors should delete keywords included in the title and provide new keywords.

Solved. New keywords were added, the old keywords were deleted.

4. The introduction is excessively long. I strongly recommend dividing the introduction into two sections: Introduction and Related Work.

Solved. Two sections have been developed: Introduction and Related Work. The sections have been summarized.

5. The efforts to predict water quality based on optical parameters combined with machine learning are an updated topic that the authors did not mention. Thus, the authors have to add some references to provide a broader context for their paper. Following, I include a couple of recent references that deal with this topic:

Combination of machine learning and RGB sensors to quantify and classify water turbidity. Chemosensors, 12(3), 34. (2024).

Tracking changes in chlorophyll-a concentration and turbidity in Nansi Lake using Sentinel-2 imagery: A novel machine learning approach. Ecological Informatics, 81, 102597.

Solved. A summary of the main findings of the recommendations has been included in the related work section.

6. In Subsection 2.3.1 a table outlining all the sampling days would be valuable.

Solved. Table 2 has been added with the sampling dates and environmental conditions. The table was added in subsection 3.2 instead of 2.3.1 to ensure a better structure and flow.

7. The model of both used spectrometers must be provided in Subsection 2.3.2.

Solved. The model and the manufacturer has been added to subsection 3.3.2.

8. For the training of regression models, the authors have to describe how the dataset was split. This information must be provided in Section 2. The dataset must be also defined in more detail, including the total number of lectures.

Solved. The dataset split has been explained in section 3.4.2. The number of lectures has been included in section 3.3.2.

9. The results are too long. I strongly recommend that the authors focus on the main findings. They have to add some data which helps readers to have a general view of their result. For example, a Table in which they include the R2 values of obtained models for different parameters across campaigns are needed.

The results section has been reduced. The R2 values have been added to figures 11, 12 and 13.

10. The discussion section is excessively long and has no structure. I suggest the authors add a subsection in the discussion to facilitate the lecture of the paper. In the discussion, they have to strongly defend and justify why their results indicate that pH can be predicted using IR data. Is it possible that the pH in this river is linked to the presence of certain suspended materials, which is impacting IR light patterns?

Solved part 1: the discussion section has been improved. Solved part 2: Subsections have been added to the discussion section. Solved part 3: a paragraph has been added to the discussion addressing the question of the reviewer.

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The authors have addressed my comments. 

Reviewer 3 Report

Comments and Suggestions for Authors

All the previous questions and comments have been correctly addressed and I have no additional concerns on this paper.