Optical calibration, bathymetry, water column correction and bottom typing of shallow marine areas, using passive remote sensing imageries
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PURPOSE : what 4SM is intended to do

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PURPOSE : what 4SM is intended to do


1 - NO NEED for field data, nor for atmospheric correction
2 - this is demonstrated in this website, using a variety of hyper/multi spectral data
Requirements are
1 - homogeneous water body and atmosphere
2 - some coverage of optically deep water
3 - some coverage of dry land
Problems are
1 - the precision on estimated depth is found wanting, because the noise-equivalent change in radiance  of accessible data is too high for shallow water column correction work 
2 - radiance data should be preprocessed by the provider at level 1 in order to improve S/N ratio
3 - exponential decay: the deeper/darker the bottom, the poorer the performances
I keep digging
until suitable data
become available
The underwater light field is complex:
 4SM is not a "push-button" magic black box for dummies

The prospective "operator" must definitely grow up 
into a seasoned "practitioner"
  • If the imagery is suitable, a knowledgeable and seasoned 4SM practioner shall obtain satisfaction:
    • a first order atmospheric correction of the image,
    • a thoroughly deglinted image,
    • the calibration of spectral operational attenuation coefficient K in m -1
    • the water column correction of the shallow areas of the spectral image (~= a low-tide view),
    • along with an estimate of the depth in meters at each shallow pixel, which is pretty close to actual depth,
    • without the need or use of any field data .
  • If the imagery is not suitable, 4SM shall produce an output anyway: garbage in, garbage out.
  • If the practioner is not suitable, 4SM shall produce an output anyway.
the deep water radiance Lsw
  • Unsuitable deep water radiance Lsw values shall result in unacceptable output
  • Deglinting: in many cases, it is necessary to remove the "glint" from the imagery to the extent possible
    • This also efficiently removes the haze that surrounds dense clouds
    • This also efficiently removes the atmospheric adjacency effect where the landmass is bright and poorly vegetated.
    • Unfortunately though, this tends to damage radiances of very shallow bottoms
    • Hopefully the image, or part of it, is now suitable for shallow water modeling. Alternately , that image must be discarded
    • The outcome is a set of spectral deep water radiances Lsw which are applicable to the whole sub-area under study

the Soil Line (SL)
  • A set of spectral LsM values : this represents the brightest shallow substrate which exists inside the sub-area under study
  • A set of spectral La values : this intends to represent the atmospheric path radiance
  • Spectral LsM and spectral La values make up a spectral radiometric model of pixels at null depth: Z=0

the Brightest Pixels Line (BPL)
  • Ratios K i/K j for all pairs of wavebands i and j are estimated: the slope of the linearized BPL is the ratio Ki/Kj
  • Their internal consistency as this system of ratios is required and verified: we found that they need to fit Jerlov's optical classification of marine and coastal water types worldwide.


the atmospheric path radiance La
  • Combined with Lsw, the Soil Line is used to estimate the atmospheric path radiance La
  • This achieves for a first order  atmospheric correction of the imagery (cf the "dark pixel" assumption)
    • this does not correct for the atmospheric adjacency effect, though.

the color of optically deep water Lw
  •  The BOA deep water radiance  is then estimated as Lw=Lsw-La
  • This is the color of the optically deep water column, which is also called water volume reflectance.
the two-ways spectral effective attenuation coefficients K
  • The ratio K i/K j  for one pair of visible bands i and j, along with  Jerlov's data, are used to approximate  effective spectral K val
  • Spectral K values provide a consistent estimation of the optical properties of the clearest waters that are present inside the sub-area under study.
  • This last statement means that results obtained for areas that have less clear waters may be erroneous: this shows in the bottom reflectance results.

Once a satisfactory fit has been achieved  
between observed image data and optical calibration model
  • 4SM operates the simplified radiative transfer equation on each shallow water pixel, through an iteration of inverse modeling steps.
    • the spectral water-leaving radiance is then reverse-modeled into a spectral shallow "water-column corrected" bottom signature that is compatible with the Soils Line assumption.
    • this is achieved by increasing the depth Z until spectral LB is deemed acceptable.
  • This yields an estimate of both
    • the shallow "water column corrected" bottom spectral signature
    • the shallow water depth
  • Tide correction: as an option, the estimated depth is then corrected for Height of Tide.
  • Seatruth: the estimated depth is then multiplied by a final Depth Correcting Factor to be derived from some existing sea truth.


4SM then operates a fully supervised bottom typing scheme

4SM has thresholds
that need to be given appropriate values in order to alleviate the most conspicuous artifacts in the output:
  • Threshold on  inconsistent high radiance (high -saturated- radiance is bad data)
  • Threshold on inconsistent low radiance   (low  radiance is bad data)
  • Threshold on low radiance                          (bottom contrast Ls-Lsw is too faint)