1. Page 3, Paragraph 2: Although authors have mentioned literature for remote sensing-based SSC estimation methods, please categorized the existing methods, viz., semi-analytical approach and empirical approach. Subsequently, describe the pros and cons of approaches to motivate the present study.
   
   We will categorize these approaches as mostly empirical and we will briefly describe the pros/cons of this approach (e.g. this method has been successfully implemented/validated by other studies however, the work is not directly physically-based). We are not sure if the reviewer has seen that in a later section (3.1), we have further categorized that various empirical equations (band values) used.
2. Page 3, Paragraph 2: For narrow-width rivers, there are several existing research studies are available using Landsat, Sentinel-2, Synthetic Landsat (derived from MODIS), and other commercial satellites (e.g., CubeSat, Planet lab satellites, etc.). Please highlight those studies in this section.
   Thank you, we will include references to these additional studies.
3. Figure 1: Please provide the elevation of the study site and demark the tributaries, main river, and catchment area distinctly.
   
   We will incorporate the suggested changes. 
4. Page 6, Paragraph 2, Section 2.2: While highlighting the methodology, the authors mentioned that they have first established the regression model in the section where in situ data is available and then, the models are used in the entire mainstream. I agree with this hypothesis is true for lakes and estuaries where the water is nearly stagnant or less affected by lateral flows; however, adopting the same for dynamic river systems is still questionable.
   Although it is true that lateral flows certainly affect the turbidity of the river in the lateral direction, the degree to which lateral flows affect the turbidity differs from reach to reach and riffle to riffle. Here we wanted to see the limits of applying a single regression to a dynamic system. We know that larger errors are introduced from the application of one general regression to the entire stem (as opposed to individual regressions for the reaches around the individual in-situ stations - further explained in section 3.1) and we quantified these errors and compared the R² of the regressions (overall vs. single stations) in section 3.1. 
5. Page 6, Paragraph 2, Section 2.2: What is the significance of the citation “Talluto (2023)” in this statement?
   We apologize. This seems to have been an oversight. The citation will be removed.
6. Page 6, Section 2.2.2: Why the ACOLITE correction was adopted in this study? Although there are several other atmospheric correction algorithms are available for water colour inversion studies in the literature, why its only ACOLITE? It may be SNAP (specially developed for Sentinel) or SeaDAS etc.
   Although there are many atmospheric correction algorithms for water, ACOLITE was specifically developed for atmospheric correction over inland waterbodies.
7. Page 6, Paragraph 2, Section 2.2: What is the average depth of water for deep water? Is there any threshold defined for deep water?
   The average depth of the water for deep water pixels is unknown. An explanation of how the threshold (in sr-1) was selected is explained in section 2.2.2, but it was mostly done through trial and error through visual inspection of the Sentinel images in addition to personal observations during the kayak sampling.
8. Page 6-7, Paragraph 1, Section 2.2: When the authors considered the rivers are narrow, especially the tributary, the water pixels are more pronounced to the land adjacency effect. How to address those effects while considering this framework?
   Since the entire width of the river, from headwaters to sea for the main stem and tributaries, is narrow, we assume that land adjacency has an effect everywhere (on all processed pixels in this study). To investigate the effect of land adjacency would go beyond the scope of this work, but adjacency effects are important and should be investigated on narrow (and large) rivers. 
9. Page 7, Paragraph 1, Section 2.2: Please mentioned the type of interpolation followed during the processing of the images.
   We simply split each pixel into four pixels (to fit into the 10x10m grid). We will clarify this.
10. Page 8, Section 3.1: From the results, it was found that the best correlation of the in situ turbidity was found in the blue band of electromagnetic radiation also represented by Eqn. 1. However, it is completely different from the findings of other studies as the majority of the literature tested and proved the sensitive bands are red and NIR bands (as elaborated in Page 9, Section 3.1) which is also obvious due to the peak occurred in that region only. So, on what basis, the blue band will be considered?
    Some of the outlined studies in section 3.1 also used the green band, not only red and NIR. However, since there are many factors that affect the optical properties of inland water bodies, and since these empirical equations are not physically-based, we decided to investigate all of the available bands and fit models to several combinations of bands. Using the Bayesian Information Criterion (BIC), we were able to select an empirical model using only those bands that act as the best predictors for turbidity (in our catchment).
11. Page 8-9, Section 3.1: What are the correlation values in the red and NIR region when the reflectances of ROI (region of influence) are compared against the in situ SSC or Turbidity? Please provide scattered plots for individual stations (like region-specific).
    We see the benefit of the referee's suggestion to investigate the effect that turbidity has on the red and/or NIR bands (since these bands were discussed already in section 3.1 and used in several past studies). We will provide some additional information on the relationship between red+turbidity and NIR+turbidity within each ROI.
12. Page 8-9, Section 3.1: Band ratio techniques are a very popular technique for both SSC and turbidity estimation along the river claimed in many literatures. Include observations while comparing the developed model with the existing models.
    We are unsure what the author means by "include observations." During our BIC analysis, we had also investigated empirical relationships using band ratios, however eqn. (1) was the best predictor of turbidity in our catchment.
13. Page 8-9, Section 3.1: As this study emphasized narrow-width rivers, please include the studies in this section that were exclusively conducted along rivers using the finer resolution satellite images (Sentinel, Landsat, or any derived satellite products).
    Here, we wanted to give an impression of the performance range (R²) in similar studies and there were very few studies conducted on narrow rivers. For this reason, we included studies from wide rivers and other inland water bodies.
14. Page 8-10, Section 3.1: If the particular methodology will shift to another river system worldwide, how to implement this? Please add a comprehensive discussion regarding this.
    Thank you for this comment. This would certainly add to our paper, we will engage with this question.
15. Page 10, Section 3.2: How the land adjacency effect was addressed while mapping the turbidity along the narrow-width river section?
    Unfortunately, addressing the effects of adjacency goes beyond the scope of this work (see reply to comment #8).
16. Page 14, Section 3.3: With reference to #Comment 15, while averaging the 106 number of image time series river pixels, the uncertainty of Turbidity estimation is still existing and due to the land adjacency effect, the estimation may be quite high near the bank. In this aspect, it will be very much difficult to judge the concept provided in this section. Please provide substantial evidence regarding the claim.
    We assume that land adjacency affects all of the river pixels. It would be interesting to see a future study that investigates at which point (e.g. at which river width) does land adjacency not affect the pixels in the middle of the river. With this information, we could determine where and when to remove the effects of land adjacency. Land has a general effect on the river pixels in our methodology, one effect of which is the "land adjacency effect." We will add a small section in the outlook addressing all issues stemming from land adjacency.

This manuscript uses satellite images and field data to generate SSC profiles along a relatively un-impacted river to identify potential sediment sinks and sources and calculate sediment flux. I think this is a nice proof of concept showing the use of SSC profiles. The methods, analysis, and interpretation are sound. I think the most impactful piece is the ability to identify sources/sinks from a SSC profile. No need to work it out further in this proof of concept paper, but I do recommend further emphasizing/exploring sources/sinks if possible.

There are a few points that need clarifying in writing (and potentially analysis). One, I was expecting the kayak-based turbidity data to be more important to the manuscript since it was in the title. The kayak data is super cool, but my understanding is this data wasn’t used for anything and only appears in 1 paragraph. I may recommend simplifying the title (i.e. removing “kayak-based lagrangian”) and/or finding a way to integrate the lagrangian data more into the results/interpretation. See these papers for more info on how to interpret snapshot vs lagrangian data.

We thank the reviewer for their assessment. And we will consider either revising the title or incorporating the kayak data further into the results.

Ensign, Scott H., Martin W. Doyle, and John R. Gardner. "New strategies for measuring rates of environmental processes in rivers, lakes, and estuaries." Freshwater Science 36.3 (2017): 453-465.

Hensley, Robert T., Matthew J. Cohen, and Larry V. Korhnak. "Inferring nitrogen removal in large rivers from high‐resolution longitudinal profiling." Limnology and Oceanography 59.4 (2014): 1152-1170.

See specific comments for a few other points that could be clarified (mostly the water masking/deep water masking).

Specific comments

Line 34: Be careful about framing SSC as pollutant, which it can be in small/clear/pristine streams, but in this river, I expect SSC is not a pollutant, correct? Also, the conclusion highlights how SSC in mountain areas is an important sediment source for downstream sites and this may confuse readers if SSC was presented as a pollutant in the intro. Consider reducing language about SSC as pollutant.

We will revise the language used here.

Line 44. Consider breaking this into 2 sentences.

Line 72: Technically, colored dissolved organic matter (CDOM) since not all DOM is visible. Consider adding “colored” or “chromophoric” in front of organic matter.

We will incorporate these last two suggestions.

Line 165: Please clarify the water masking procedure that was used. Also, please clarify how the deep water threshold was used, was this used to filter “shallow water” so only deep water pixels were used in analyses?

The water masking procedure was used to eliminate the shallow water and only keep the deep water. In the Sentinel-2 image SWIR band, we simply selected a cutoff value (pixel values are from 0-1, cutoff value was selected based on visual inspection of known deep sections of the river).

Line 170: what cloud cover % threshold was used? I think you said cloud free, but does this mean only imaged with 0% cover? Please show threshold used.

A cloud probability threshold of 20% was used in this processing; we used the default parameters in FMask for Sentinel-2 images. The mask does not work by selecting images with a certain percentage of cloud cover in the image (e.g. an image with 30% cloud cover is still used in our analysis). The mask works by detecting the clouds (and cloud shadows) present in the image and removing these parts of the image (or “masking” them). We will add a line with this information.

Line 178: What was the maximum time offset you allowed between satellite image and in-situ turbidity? Please state this.

The maximum time offset between the satellite image in situ turbidity acquisition times is 15 minutes.

Line 222: If reporting all other concentrations in mg/L, consider changing the g/L to mg/L.

Thank you, we will do so.

Line 345: I do not understand why SSC measurements from satellites from 2019-2021 were combined with flow data from 1958-1989, please clarify. This assumes seasonal SSC is the same from 1958-2021.  Was there a discharge data limitation, why not use flow data 2019-2021? If there is a data limitation and this calculation is just for proof of concept, just state that.

Yes, the discharge data was only available for the 1958-1989 period. We will make it clear that these were proof-of-concept calculations.

General Comments

This paper characterizes the concentration and change of turbidity profiles and fluxes using in situ field measurements and built relationships to Sentinel-2 images supplemented by two lagrangian field studies on the Vjosa River. In general, this is an interesting combination of methods with promise when including point and longitudinal measurements as well as remote sensing to better understand river water quality dynamics for an entire main stem river. I believe that the article is well written and overall clear, but I have some specific comments and questions for further clarification going forward.

Specific Comments

The use of the Lagrangian profile is a nice addition, but I would appreciate more in both the Introduction and Discussion concerning this kind of sediment assessment. For example, is your use of a Lagrangian profile for sediment validation novel? How do others use or incorporate this kind of data?

We can include more of an assessment of lagrangian profiles used for sediment measurements.

The introduction should be more explicit on the connection between riverine sediment inputs and coastal/ocean nutrient processing and productivity. For this kind of study, which focuses on a mostly free-flowing river with direct inputs to the Adriatic sea, this should be a major reason why understanding sediment processes are important.

Thank you for this suggestion. We will consider stressing the connection between riverine sediment inputs and coastal nutrients.

Line 181: Have you considered or tested certain band ratios in your OLS regression? Many studies test different combinations as well and it is unclear if you do the same.

Yes, we have done the same. We will clarify this. Thank you.

Line 342: I am confused on the number of water grab samples, their locations, and field/processing methodology for these samples over the field campaigns. It seems like you used them for the Dorez section, but the samples are from the entire main stem? Do you have any sediment-turbidity relationships to validate other sections and your source/sink conclusions on other sections of the river?

We created a sediment-turbidity relationship from samples taken across the entire main stem. We did this because we want to create one relationship (sentinel_bands-turbidity) that can be applied to the entire main stem (as opposed to different relationships applied to different reaches, as explained in Section 3.1). When estimating the yield at Dorez, we similarly applied an catchment-wide equation of turbidity-SSC. This relationship can be applied to any section of the river but of course, a relationship with turbidity and SSC taken at only Dorez would be more accurate (however, we have neither turbidity nor SSC at this reach). We will clarify the processing methodology and the number of sediment samples and from where they were taken.

Line 354: While there are comparisons between total sediment yield on the Vjosa to other major rivers and past work, it would be helpful to compare your results to other more similarly sized mountainous catchment rivers (if mention this if it doesn’t exist).

Thank you for the comment. We will incorporate it.

Line 357: I appreciate the contextual information about a potential Vjosa National Park and its implications (no hydropower; uninterrupted sediment flow) for this study.

Thank you.

Technical Corrections

Figure 1: Add a secondary line indicating the kayak profiled reaches of the river and the long-term discharge measurement location

This is a good idea. Thank you.

Table 1: Consider adding a figure plotting NTU vs Rrs reflectance with the regression equation to help contextualize the equation and fit (how does it perform at low and high turbidity values). Also add the number of data points used to fit each equation.

We will add the number of data points used to fit each equation. And we will consider adding the 3D regression plot.

Figure 3: The map text and legend are too small, consider increasing the font size for easier interpretation.

We will increase the font text size.

Line 165: What are the average number of pixels, or was there a threshold used to calculate means at each ROI, reach, and tributary? If these are narrower river reaches, how many pixels constitute deep water reaches? This can help determine whether to use the mean or median band values. Since this is a smaller scale study and you can visually examine each image for cloud/shadow contamination the mean should work. Other studies use the median.

The average number of pixels within each ROI for every image was 280 pixels (after removing the clouds, cloud-shadows, and non-deepwater pixels). After taking the average of the pixels within the ROI, we could obtain an average Rrs for each band (at each location, on every acquisition day).

Figure 7: Do you have any theories for the large and opposite trends to the rest of your data in the Fall 2020 lagrangian profile in the upstream portion of the reach? It is also unclear if highest boxplots are reaches or tributaries.

Yes, we theorize that the river is too narrow in the upstream section to extract proper reflectance data. Also, the river is much more clear upstream so our measurements may fail. And we will make clear whether the last two boxplots are tributaries or reaches.