Supplementary dataset to the publication "Bi, S., and Hieronymi, M. (2024). Holistic optical water type classification for ocean, coastal, and inland waters. Limnology & Oceanography"

The NetCDF data files contain the training dataset used to develop the Optical Water Type (OWT) framework proposed by Bi and Hieronymi (2024). The dataset is available in two spectral versions:

    1.    owt_BH2024_training_data_hyper.nc: This file includes training data with a spectral resolution of 2 nm, ranging from 400 to 900 nm.    2.    owt_BH2024_training_data_olci.nc: This file contains data formatted similarly to the hyperspectral version but aligned with the nominal Sentinel-3 OLCI wavebands.

Contents of the Dataset

For each version, the dataset includes spectral inherent and apparent optical properties such as:

    •    Remote Sensing Reflectance (Rrs)    •    Pure Water Absorption (aw)    •    Absorption Coefficient of Detritus (ad)    •    Total Absorption Coefficient without Pure Water (agp)    •    Absorption Coefficient of Phytoplankton (aph)    •    Backscattering Coefficient of Total Particulate Matter (bbp)    •    Scattering Coefficient of Total Particulate Matter (bp)    •    Scattering Coefficient of Pure Water (bw)

Additionally, the dataset includes various environmental and biological parameters:

    •    Chlorophyll a Concentration (Chl)    •    Inorganic Suspended Matter Concentration (ISM)    •    Colored Dissolved Organic Matter Absorption at 440 nm (ag440)    •    Single-Scattering Albedo of Detritus at 550 nm (A_d)    •    Power Law Exponent of Detritus Attenuation (G_d)    •    Water Salinity (Sal)    •    Water Temperature (Temp)    •    Fraction for Diminished Coccolithophore Absorption (a_frac)    •    Fraction of Coccolithophore Group (cocco_frac)

Optical Water Types

The training dataset includes 10 pre-defined optical water types, with 10,000 samples for each type. Detailed descriptions of these water types can be found in Table 1 of Bi and Hieronymi (2024) or as follows,

OWT
Desciption

1
Extremely clear and oligotrophic indigo-blue waters with high reflectance in the short visible wavelengths.

2
Blue waters with similar biomass level as OWT 1 but with slightly higher detritus and CDOM content.

3a
Turquoise waters with slightly higher phytoplankton, detritus, and CDOM compared to the first two types.

3b
A special case of OWT 3a with similar detritus and CDOM distribution but with strong scattering and little absorbing particles like in the case of Coccolithophore blooms. This type usually appears brighter and exhibits a remarkable ~490 nm reflectance peak.

4a
Greenish water found in coastal and inland environments, with higher biomass compared to the previous water types. Reflectance in short wavelengths is usually depressed by the absorption of particles and CDOM.

4b
A special case of OWT 4a, sharing similar detritus and CDOM distribution, exhibiting phytoplankton blooms with higher scattering coefficients, e.g., Coccolithophore bloom. The color of this type shows a very bright green.

5a
Green eutrophic water, with significantly higher phytoplankton biomass, exhibiting a bimodal reflectance shape with typical peaks at ~560 and ~709 nm.

5b
Green hyper-eutrophic water, with even higher biomass than that of OWT 5a (over several orders of magnitude), displaying a reflectance plateau in the Near Infrared Region, NIR (vegetation-like spectrum).

6
Bright brown water with high detritus concentrations, which has a high reflectance determined by scattering.

7
Dark brown to black water with very high CDOM concentration, which has low reflectance in the entire visible range and is dominated by absorption.

Additional Information

The detailed description of the data simulation can be found in the supporting information of Bi and Hieronymi (2024). The models used for simulating the data are available on GitHub:

    •    Component IOP Model: Bio-geo-optical modelling of natural waters by Bi, Hieronymi, and Röttgers (2023)    •    OWT Package: pyOWT

References

    1.    OWT Framework: Bi, S., and Hieronymi, M. (2024). Holistic optical water type classification for ocean, coastal, and inland waters. Limnology & Oceanography, lno.12606. doi: 10.1002/lno.12606    2.    Component IOP Model: Bi, S., Hieronymi, M., and Röttgers, R. (2023). Bio-geo-optical modelling of natural waters. Front. Mar. Sci. 10, 1196352. doi: 10.3389/fmars.2023.1196352    3.    Pure Water IOP Model: Röttgers, R., Doerffer, R., McKee, D., and Schönfeld, W. (2016). The Water Optical Properties Processor (WOPP): Pure Water Spectral Absorption, Scattering and Real Part of Refractive Index Model. Technical Report No WOPP-ATBD/WRD6. Available at: https://calvalportal.ceos.org/tools    4.    Rrs Model: Lee, Z., Du, K., Voss, K. J., Zibordi, G., Lubac, B., Arnone, R., et al. (2011). An inherent-optical-property-centered approach to correct the angular effects in water-leaving radiance. Appl. Opt. 50, 3155. doi: 10.1364/AO.50.003155

Authors and Contact

    •    Author: Shun Bi, Martin Hieronymi, Rüdiger Röttgers    •    Creator: Shun Bi, [e-mail]

Example Python Code to Read Data

Here is an example of how to read the NetCDF data using Python and the xarray library:

import xarray as xr

# Load the dataset
data_hyper = xr.open_dataset("path_to_your_file/owt_BH2024_training_data_hyper.nc")

# Print the dataset to see its structure
print(data_hyper)

# Access a specific variable, e.g., remote sensing reflectance (Rrs)
rrs = data_hyper['Rrs']

# Plot a sample of Rrs
import matplotlib.pyplot as plt

# Select a sample ID, for example the first sample
sample_id = 0
plt.plot(data_hyper['wavelen'], rrs[sample_id, :])
plt.xlabel('Wavelength (nm)')
plt.ylabel('Rrs (1/sr)')
plt.title(f'Remote Sensing Reflectance for Sample ID {sample_id}')
plt.show()