The paper is well-written and the idea is ueful for the community. however, there are some doubts about reliability of the new proposed index which require more debates statistically. Here are my comments:

RABD670: There is a review from Van Exem et al. 2022 which they reviewed all the chl indices and they found out that RABAs work better than RABDs. Have you tried to implement that and compare the results since it seems to be possible with your data.

Line 160: how did you determine the percentage reflectance values to remove cracks and mineral reflections? it seems it is based on practical excercise on your core and your hyperspectral scanner which it should be noted in the text.

Fig 2: since RABD710-730 is located in the right shoulder of chl-a and phaephytin-a absorption bands, how would they effect this index? with bigger amount of chl-a and Phae-a it is expected that GSB-related signal disapear. And based on your data was there any detection limitation on this index?

Fig3: It would be better to remake all the plots in a way that the whole dots and the range are observed. 

Fig 3C: There are some RABD710-730 dots which are valued less than one, how do you interpret them? moreover, considering RABD710-730, I am curios to see what correlation you will get if you remove Isorenieratene which have big values ( over 1000) and then recalculate the correaltion. It seems the correlation for the samples with Iso under 1000 are weak. Maybe, it can be discussed in terms of limitations of the index!

Fig4: this figure is a bit unclear. suggest to change the caption and specify y-axes is  related to which RABD.

Finally, it is always a question that a model or here an index which is applied on one core can be applied on any other cores? what would be the limitations and maybe a discussion on this in the paper would be useful for reader. 

Overview

Zander et al. use recently developed hyperspectral imaging methods, including one first introduced in this manuscript, to quantify the abundance of sedimentary photopigment produced by oxygenic phototrophs and anoxygenic phototrophs - specifically purple and green sulfur bacteria (PSB and GSB, respectively). They apply this technique to Lake Cadagno, a lake that has been well studied for both modern and paleolimnology. The authors find that total chloropigments (TChl) and purple sulfur bacteria (PSB) are readily detectable using key absorption troughs in hyperspectal imaging, and calibrate their abundance using concentrations measured on samples using extraction and spectrophotometry and HPLC. They argue that GSB are detectable by a characteristic absorption trough, and around 700 nm, however it is difficult to calibrate as the pigments are not readily measured by other techniques. Instead they use co produced carotenoids to validate the detection of GSB, but do not attempt a calibration, instead they interpret the index as an indicator of relative abundance. 

The detection and interpretation of these pigments by hyperspectral imaging has great potential for paleolimnological studies. HSI methods are rapid, high-resolution and non destructive, and analyzing these pigments provides important and direct inference on the past light and oxygenation structure of the lake. GSB is also quite difficult to measure, and it’s possible that in situ detection via HSI may actually work better than other approaches, although this is difficult to prove. In the case of Lake Cadagno, the pigment records provide important insights on both long-term lake evolution, and response to external forcings and pressures, but also the response of the lake to flood events, highlighting the value of the high resolution detection. 

The manuscript is well written and well illustrated, and effectively articulates the importance and value of the approach. It’s an important contribution to the paleolimnologic literature, and I recommend it for publication following the correction of a minor concerns.

Minor concerns:

Non-normal distributions of pigment and RABD data

My only substantial concern with analysis and the results has to do with the treatment of the data in the calibration and reconstruction approaches. Specifically, I worry about non-normality in both the the pigment concentrations measured by spectroscopy and HPLC, and in the RABD indices. Both datasets are left bounded, and tend to be skewed right. Some strongly so. On line 236 the authors note that this may hamper their interpretations. The authors use linear regression to relate RABD indices to TChla and PSB, and to quantify their uncertainties, but this approach assumes normality in both the predictor and predictand. Looking at figure 3, this seems reasonable for TChl-a and RABD670, but the PSB data look right skewed, and potentially bimodal. And looking at figure 5, all the downcore HSI data, and especially the GSB index and PSB reconstruction both look left bounded and right skewed, as expected. I’d encourage the authors to either demonstrate normality for these data, or consider transforming them before calibration, or use a different approach that does not require normally-distributed data. 

For the Bphe-a and RABD842, I wonder whether the data really support a continuous calibration, or whether a on/off, presence/absence type calibration would be more appropriate. To my eye (Fig 3A), there are basically two populations of Bphe-a concentrations. Those close to zero, and those around 1000 ug/g. The lower concentration population does suggest a relationship, but the trend is notably lower than that inferred from the dataset as a whole. The two highest Bphe-a concentrations do also stand out, but it’s hard to know how much to weight those outliers. I’m open to the argument for a continuous relationship, but I think it needs to be made as it seems plausible that we’re mostly seeing presence/absence of PSB in these data. 

Other Concerns

Figure 4. 

The comparison between the aeDNA data and the PSB and GSB indices is very interesting. I like the presentation in figure 4, but would be very keen to see a direct comparison of the aeDNA sample data and the HSI-based ratio. Could you add a scatter plot where the PSB/GSB indices over the DNA sample depth ranges are averaged and the plotted against a similar metric in the DNA data? There are intervals where it seems to agree quite well, but others where it doesn’t, and I’d like to see the comparison explicitly, while also accounting for the different resolution of the datasets. 

Figure 5. Add a Tchl label to panel A to match panels B and C.

Figure 6. Continuing the Tchl, PSB and GSB labeling on the curves would benefit figure 6 too.