We would like to thank the referee for the comments and for the positive feedback for the submitted data article.

We were not familiar with the paper by Davies et al. 2023, but we agree that the given paper is worth of citing, as it presents spectral library of various benthic vegetation species/classes measured in the field.

We also agree that the number of measured specimens remains sometimes low. We acknowledge that the published database is not complete and needs to be complemented with additional species and/or substrate types using similar approach presented in the paper. Still, we believe that in the present form, the dataset may lead to several implications to current and future satellite missions.

In the current paper we targeted the most dominant and characteristic submerged aquatic species (SAV) in the Baltic Sea. The Baltic Sea is an enclosed non-tidal water body, therefore missing the intertidal zone. All the SAV species in the current paper mostly grow submerged (except some narrow coastal areas during low water level).

The referee is correct. The species name Polysimphonia fucoides needs to be corrected to Polysiphonia fucoides.

We had two simultaneously measuring Ramses (TriOS GmbH) sensors to capture the spectral data: irradiance sensor for measuring downwelling spectral irradiance (Ed) and radiance sensor for measuring upwelling spectral radiance (Lu). As a result, remote sensing reflectance (Rrs, sr-1) was calculated as the ratio of Lu/Ed. Referee is correct that spectral data can also be measured as radiance reflectance (R), which is the ratio of upwelling radiance to downelling radiance (Lu/Ld). Converting Ed to Ld is not so straightforward. In case of Lambertian surface Ed could be divided by a value of π to get Ld.

We would like to thank the reviwer for the comments, and we will try to response to his/her concerns.

1) The measured target samples were divided into the six broad groups: green macroalgae, red macroalgae, brown macroalgae, higher plants, bare substrate and beach cast. Green macroalgae, red macroalgae and brown macroalgae are three major taxonomic groups of macroalgae in the Baltic Sea according to their pigmentation, each of which exhibits its own characteristic spectral features (groups 1-3, Table 1). In addition to macroalgae, the Baltic Sea also hosts higher plants or vascular plants (group 4, Table 1). Beside to the vegetated habitats, the benthic environment of the Baltic Sea includes unvegetated bare substrates (group 5, Table 1). Finally, the last group is beach cast, which consists of decaying vegetation material (group 6, Table 1). Several benthic vegetation species and substrate types were measured under each of the given six groups. In the current paper we targeted the most dominant and characteristic submerged aquatic species (SAV) and substrate types in the Baltic Sea.

Such a library of benthic endmembers gives a basic understanding about the spectral features of most common species/bare substrate types occurring in the temperate geographic region and therefore should allow analysing spectral differences/similarities between various taxonomic groups and between species. Reviewer is correct that since the current dataset does not include high number of measured spectra on species level (mostly between 1-6 measurements for each species), then the current dataset does not really allow analysing within species spectral variance. However, detecting within-species variation often tends to remain out of the scope of the satellite based benthic mapping due to restrictions in spectral and spatial resolution.

We agree that the number of measured specimens remains sometimes low. We acknowledge that the published database is not complete and needs to be complemented with additional species and/or substrate types using similar approach presented in the paper. Still, we believe that publishing such a library of benthic endmembers in the present form, the dataset may lead to several implications to current and future satellite missions.

2) We cannot agree with the reviewer on the current matter. Each additional centimetre of water column has a great influence on the benthic endmember spectra. Other users would not be able to benefit on benthic spectra measured together with the water column influence, as those spectra would then only be characteristic to the given water depth and given water quality. We find that it is highly important to measure the spectral signatures of benthic endmembers without the influence of the water column. The collected spectra can then serve as endmembers in various bio-optical forward (e.g. Hydrolight) and inversion (e.g. WASI-2D, BOMBER) models, where they can be used together with suitable inherent water optical properties and water depths in numerical simulations. Those modelled spectra can then be used for image classification.

3) Again, we cannot agree with the reviewer on this issue. Optical remote sensing from satellites, aircrafts, drones etc. relays on the natural light source – sun. All remote sensing applications in the natural environment are performed by using the light from the sun. That is why our spectral measurements were also performed under the natural sun light. In the current paper, we measured the reflectance of the target samples – the radiance from the sample was divided by the irradiance from the light source. As a result, we acquired the reflectance from the target sample which is not dependant on the light source. Weak spectral signal is related to the darker colour of the target sample. The darker the target sample - the lower the reflectance. In contrary, the brighter the target sample, the higher the reflectance.