jerlov

Inherent optical properties of the Jerlov optical water types, with provenance.

https://github.com/tishibashi-cpu/jerlov

pip install jerlov

Every coefficient carries the source it came from. Values that a published table got wrong are flagged rather than quietly repaired, and quantities that the data do not determine are refused rather than guessed.

What is claimed. That the published coefficients are implemented correctly, and that every shipped table can be regenerated from its primary source. The tests demonstrate both, by reproducing each paper's tables from that paper's own equations.

What is not claimed. That this predicts what a camera will record underwater. That would need measurements which, as far as we can tell, have not been made. DATA.md records what is uncertain and DECISIONS.md records where the model stops.

import jerlov

w = jerlov.water("III")          # Williamson & Hollins (2022) by default
w.a(550), w.b(550), w.c(550)

Why the source is part of the API

The same Jerlov water type has different coefficients in different papers, and the differences are not small. Solonenko & Mobley (2015) obtained a and b by inverting Kd; Williamson & Hollins (2022) measured them. At 510 nm their scattering coefficients differ by up to a factor of 2.6.

The equations differ too. Both papers use the scattering model of Haltrin (1999), but with different constants: Haltrin's Eq. (6) gives a small-particle coefficient of 1.151302, and Solonenko & Mobley print and compute with 1.513. Reproducing a published table therefore needs that paper's own constant, so constants are attached to sources.

What this package will not do

• Extrapolate. Asking for a wavelength outside the data raises.
• Fill a gap. Where a published value is wrong and could not be recovered, the value is NaN and stays NaN through interpolation.
• Supply bb. The backscattering coefficient is not determined by the Jerlov classification. Deriving it from the particle concentrations of the two sources gives answers differing by up to a factor of 31, so Water.bb requires an explicit backscatter_ratio.

Provenance warnings

Interpolating across a value that a paper got wrong gives a number that looks no different from a sound one. ProvenanceWarning is the only thing that tells them apart.

>>> w = jerlov.water("5C", source="solonenko2015")
>>> w.b(650)
ProvenanceWarning: b for Jerlov 5C rests on flagged values:
reconstructed at 650 nm. ...

Water.caveats() returns everything known to be doubtful about that water.

Sources

solonenko2015 also carries kd_hydrolight(), the Kd that HydroLight computed from its a and b; DATA.md section 19 says how it differs from kd().

jerlov.SOURCES holds the full citation, DOI and caveats for each. The two Jerlov editions differ by up to 35 percent at single wavelengths; establish which one a number came from before comparing it with either.

Looking at something through water

For a horizontal path, the observed radiance splits into the target's light that survived and the light the water added along the way:

import numpy as np, jerlov

wl   = np.arange(450., 651., 50.)
iops = jerlov.water("III")
kd   = jerlov.water("III", source="austin1986")

scene = jerlov.Scene.at_depth(iops, 10.0, np.ones_like(wl), wl, kd=kd)
b_inf = jerlov.veiling_radiance_estimate(
    iops, scene.downwelling, wl, backscatter_ratio=0.015
)

obs = scene.observe(np.full_like(wl, 0.8), distance_m=5.0,
                    veiling_radiance=b_inf)
obs.direct, obs.veiling, obs.radiance
obs.veiling_fraction        # how much of what you see is just water
obs.contrast(background)    # against another target down the same path

veiling_radiance has no default. B_inf depends on the backscattering coefficient and the phase function, and neither follows from the water type. veiling_radiance_estimate gives the usual single-scattering approximation for callers with nothing better, but it has to be asked for.

Deliberate limits, all recorded in DECISIONS.md:

• Horizontal paths only. Observer and target at the same depth.
• No forward-scatter blur. This is the radiance of one point, not the sharpness of an image.
• Lambertian targets.

Coefficients for the Akkaynak-Treibitz form

Underwater vision work usually writes the same physics as

I = J * exp(-beta_D * r) + B_inf * (1 - exp(-beta_B * r))

p = scene.attenuation_coefficients((0.5, 5.0), veiling_radiance=b_inf)
p.beta_D, p.beta_B, p.B_inf
p.are_distinct      # False
p.note              # why

are_distinct is False, and that is the honest answer. Under single scattering both coefficients are the beam attenuation coefficient c. The central claim of Akkaynak & Treibitz (2018) is that in reality they differ and vary with range, depth and target reflectance; separating them needs measurements this package does not have.

It is still useful. Implementations that fit these coefficients often bound them by guesswork for want of a physical starting point. This gives them one, and says how far it can be trusted.

distance_range_m is required and recorded. A coefficient quoted without the range it describes is not a well-defined quantity, so the API does not let you omit it.

What colour is that

white = scene.downwelling / np.pi          # a perfect diffuser at that depth
rgb   = jerlov.spectrum_to_srgb(obs.radiance, wl, white=white)

jerlov.spectrum_to_xyz(spectrum, wl)      # unnormalised CIE XYZ
jerlov.integrate_response(spectrum, wl, sensitivity, sensitivity_wl)

white has no default. A radiance spectrum has no colour until something is called white, and underwater the useful reference is usually the downwelling irradiance at that depth rather than daylight. Both are legitimate and they answer different questions:

integrate_response takes any spectral sensitivity: three camera channels, or a set of photoreceptor absorbances.

Two silent errors are checked rather than assumed:

• Coverage. A spectrum spanning 450-650 nm integrated against colour matching functions spanning 360-830 nm quietly drops the ends. Every integration reports how much of the observer it actually covered and warns when it is not essentially all of it.
• Gamut. Underwater colours often fall outside sRGB. Clipping changes them, so GamutWarning says so.

If you measured backscattering

Water.bb refuses to guess a backscattering ratio. If you have an instrument you do not have to:

r = jerlov.bb_from_vsf(beta=0.0021, angle_deg=140, wavelength_nm=532)
r.bb, r.particulate, r.water     # 1/m
r.chi_p, r.quoted_error_percent  # 1.18, 3.5

Boss & Pegau (2001). The pure sea water terms are analytic and are checked against the definition of bb rather than transcribed; only the particle conversion is tabulated. Angles outside 90-170 degrees are refused, and 170 warns: its quoted spread is 34.8 percent against 3-6 in the middle.

This gives the bb of the water your instrument was in. It still gives no bb for a Jerlov water type, because nothing does.

The type changes with depth

The classification is defined on the top 10 m, but clarity does not stay put. Water that is Jerlov I at the surface is typically IB below 40 m; turbid coastal water clears as you go down.

jerlov.water_type_at_depth("I", 60.0)     # 'IB'
jerlov.water_type_at_depth("3C", 45.0)    # 'II'
jerlov.water_type_at_depth("9C", 15.0)    # None: the paper declined to say

None means fewer than ten measurement campaigns supported a declaration, so nothing is asserted. This is a lookup, not a correction applied on your behalf.

Other entry points

# Reconstruct a Kd spectrum from one measured value (Austin & Petzold 1986).
jerlov.kd_spectrum(kd=0.06, wavelength_nm=490, at=[440, 550, 650])

# Estimate b from a transmissometer's c (Smart 2007).
jerlov.b_from_c(c=0.5, wavelength_nm=555, bw=0.0019, cw=0.0659)
jerlov.pure_water_scattering(555)   # Morel's bw, if that is what c was corrected with

# The measured points behind the williamson2022 spectra, with their spread.
m = jerlov.measured_points("III", "a")   # m.wavelengths, m.values, m.std_dev

# Use your own measurements; they take exactly the same path.
jerlov.Water.from_measurements(wavelengths, a=..., b=...)

Solar heating of the upper ocean

Ocean circulation models absorb shortwave radiation as a sum of two exponentials, with parameters per Jerlov type from Paulson & Simpson (1977):

jerlov.solar_fraction("IB", 10.0)        # 0.183 of the surface irradiance
p = jerlov.shortwave_parameters("IB")    # R, zeta1_m, zeta2_m
depths, fraction = jerlov.jerlov1968_solar_fraction("IB")   # the table itself

Broadband, 300-2500 nm; nothing to do with the spectral quantities above. The parameters are usually copied from secondary sources, so tools/ refits them from the table the paper fitted, Jerlov (1968) Table XXI, and reproduces R for every row.

They miss their own source by 46 percent at 1 m for types II and III. Two exponentials cannot follow the near-surface decay. DATA.md section 14 has the numbers; anyone heating a thin surface layer should read it.

Only the five oceanic types were fitted. Asking for a coastal type raises rather than substituting a neighbour.

Examples

python examples/sources_disagree.py            why the source is an argument
python examples/from_one_measurement.py        from an instrument reading
python examples/synthetic_underwater_images.py appearance at range and depth
python examples/solar_heating.py               for ocean circulation models
python examples/what_an_eye_sees.py            any spectral sensitivity

Five scripts, each aimed at a different reader; examples/README.md says which to start with. CI runs all of them on every push, so an example that has stopped working is a failed build. Every one ends with the assumptions it made.

Provenance and design

DATA.md records, for every shipped table, where it came from, what was verified, and what is known to be wrong with it. Nineteen entries are documented there: eight confirmed defects in the source literature, three questions the first edition of Jerlov settled, and the rest notes.

DECISIONS.md records why the package is shaped the way it is, including the alternatives that were rejected and why.

Contributing, and reporting a wrong number

CONTRIBUTING.md sets out what goes in and what it takes: a primary source, a script in tools/ that produces the table, checks inside that script against the paper's own equations, an entry in DATA.md, and a regression test.

A coefficient that disagrees with a paper you have is the most useful report this package can receive. DATA.md exists because several such disagreements turned out to be defects in the literature rather than here. Issues: https://github.com/tishibashi-cpu/jerlov/issues

CHANGELOG.md covers every release.

Licence

Apache-2.0. The Williamson & Hollins data are Crown copyright, Dstl, under the Open Government Licence v3.0; see NOTICE.

Citation

Cite the concept DOI, which always resolves to the latest version:

Ishibashi, T. jerlov: inherent optical properties of the Jerlov optical water types, with provenance. Zenodo. https://doi.org/10.5281/zenodo.22321312

Please also cite the sources the coefficients came from; they are listed with their DOIs at the top of DATA.md. The package is a carrier for other people's measurements, and this work does not replace citing them.