the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 18 Nov 2024
Hyperspectral library of submerged aquatic vegetation and benthic substrates in the Baltic Sea
Abstract. A hyperspectral reflectance database was acquired for the Baltic Sea submerged aquatic vegetation (SAV) and bare substrates by using Ramses (TriOS) radiometers capturing the spectral data within the visible (VIS) and near infrared (NIR) spectral range. The target samples included the most dominant and characteristic SAV species in the Baltic Sea, as well as several bare substrate types and beach cast communities. Target samples were measured within the 350 to 900 nm wavelength range under sun light conditions without the water column influence i.e. samples were taken out of the water. Such library is expected to provide insight into the spectral properties of various SAV species and substrates occurring in the coastal waters of the temperate geographic regions facilitating development of algorithms for differentiation and mapping various SAV communities. Additionally, measured reflectance spectra can be used as spectral endmembers in physical models and classification algorithms for coastal vegetation mapping and quantification. Data are openly available at PANGAE online repository https://doi.pangaea.de/10.1594/PANGAEA.971518 (Vahtmäe et al., 2024).
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Status: open (until 25 Jan 2025)
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RC1: 'Comment on essd-2024-528', Anonymous Referee #1, 07 Jan 2025
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This data paper describes a hyperspectral spectral library of aquatic vegetation from the Baltic Sea, with all samples taken out of water. The methodology for acquiring the spectra is well described, and all data can be seen and downloaded from the PANGAEA website. The rationale for such a dataset is justified: development of algorithms and requirements for future missions. The authors could have cited the recent paper by Davies et al. 2023 RSE, who exploited a similar spectral library to test the loss of spectral resolution to discriminate the main macrophytes classes. A graph and a small discussion of the spectral shapes of the main taxonomic groups related to the pigmentary composition provide helpful information for the reader. The number of specimens is sometimes limited to 1, which could have been improved. It is not always clear if the species are subtidal or intertidal? Which fraction of the macrophytes biodiversity of the Baltic Sea is presented here? Some species' names should be checked, like Polysimphonia fucoides, which is probably Polysiphonia fucoides (but check WORMS as the latter name seems unaccepted). The Remote Sensing reflectance Lu/Ed is used here, but many spectral library papers use Reflectance Lu/Ld. The authors should comment on this and explicitly indicate how to shift from one to another.
To conclude, it is a valuable data paper, and I recommend accepting it with minor revisions.
Citation: https://doi.org/10.5194/essd-2024-528-RC1 -
AC1: 'Reply on RC1', Ele Vahtmäe, 09 Jan 2025
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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.
Citation: https://doi.org/10.5194/essd-2024-528-AC1
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AC1: 'Reply on RC1', Ele Vahtmäe, 09 Jan 2025
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RC2: 'Comment on essd-2024-528', Anonymous Referee #2, 17 Jan 2025
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This work documented a hyperspectral library of submerged aquatic vegetation (SAV) of Baltic Sea area by collecting the reflectance of targets using a handheld radiometer under a natural light environment. The significance of this data was well justified, and procedure for collecting the data was detailed. Dataset has also already been open to the public. My concerns of this work are: 1) six targets (Figure 1, Table 2) were measured, leading to six spectral signatures of these benthic habitats. It does not make sense that such a small dataset is called a hyperspectral library. It is a very small dataset. This dataset cannot represent the diverse spectral signatures within a species, and spectral confusion between habitats. 2) it is difficult to apply this dataset for any remote sensing classification models for mapping SAV or benthic habitats because it is measured without considering water column while SAV is located under water. Water conditions, density of each SAV species, seasonality, and the healthy condition of SAV largely impact the spectral signatures observed by airborne or spaceborne sensors. It is almost impossible to link the measured spectral signatures with any optical imaging sensor products for mapping purpose because they are measured by assuming they are not SAV (above water). 3) the natural light is often not strong enough to collect spectral reflectance of samples, leading to weak reflectance as demonstrated in Figure 3. If these targets are exposed to artificial light which is often used by ASD Spectroradiometer in lab environments, the spectral signatures will be very different from the reported here. In general, the dataset is very small, and its potential application is limited.
Citation: https://doi.org/10.5194/essd-2024-528-RC2 -
AC2: 'Reply on RC2', Ele Vahtmäe, 20 Jan 2025
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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.
Citation: https://doi.org/10.5194/essd-2024-528-AC2
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AC2: 'Reply on RC2', Ele Vahtmäe, 20 Jan 2025
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Data sets
Reflectance spectra of submerged aquatic vegetation (SAV) species and substrates from the Baltic Sea coastal waters. E. Vahtmäe et al. https://doi.org/https://doi.org/10.1594/PANGAEA.971518
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