Preprints
https://doi.org/10.5194/essd-2024-528
https://doi.org/10.5194/essd-2024-528
18 Nov 2024
 | 18 Nov 2024
Status: this preprint is currently under review for the journal ESSD.

Hyperspectral library of submerged aquatic vegetation and benthic substrates in the Baltic Sea

Ele Vahtmäe, Laura Argus, Kaire Toming, Martin Ligi, and Tiit Kutser

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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Ele Vahtmäe, Laura Argus, Kaire Toming, Martin Ligi, and Tiit Kutser

Status: open (until 16 Jan 2025)

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Ele Vahtmäe, Laura Argus, Kaire Toming, Martin Ligi, and Tiit Kutser

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

Ele Vahtmäe, Laura Argus, Kaire Toming, Martin Ligi, and Tiit Kutser

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Short summary
We collected a dataset of reflectance measurements for variety of benthic macrophyte species and substrate types naturally occurring in the Baltic Sea. This dataset provides insights into the spectral properties of macrophyte species characteristic to the temperate geographic region. Such information is often lacking in the data format, while it is essential for remote sensing algorithm development, image classification as well as defining requirements for future remote sensing missions.
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