• Various calibration processes use original DNs read from the image for the estimation of the following:
  • sea-surface glint regressions: deglinting removes sea-surface glitter, and variations of the atmospheric path radiance as well
  • Lsw a uniform spectral radiance over a body of optically deep waters for the whole deglinted scene
  • La a uniform spectral path radiance for the whole deglinted scene
  • Lw a uniform BOA spectral water volume upwelling radiance
  • 2K spectral value of the effective diffuse attenuation coefficient 
  • LM spectral BOA  radiance for the brightest type of shallow bottom type
• These shall be used on the fly over the whole scene
  • up to now, I feel comfortable that La and Lw are estimated quite realistically, I think within comparable range of confidence if compared to other ways of estimation
• Now with Landsat 8 OLI imagery (see USGS's webpage "Using the USGS Landsat 8 Product"), I translate La, Lw and LM into calibrated reflectance Ra, Rw and RM (range 0-1).
  • This mean that i can write out water column corrected product in units of reflectance for Landsat 8's OLI images.
  • Don't know yet if WV2 offers a similar way.

• Subtraction of estimated glint on the fly: this includes swell-modulated glint, whitecaps, boat trails, lumps of haze (and also removes the adjacency effect where atmosphere is stuffy and land is hardly vegetated a all)
• At this stage, one obtains the DN value for the upwelling signal above the water surface for the current pixel

• either hunt for published water optical properties and libraries of bottom spectral reflectance for many (~20?) bottom type end-members,
• or send a team of trained scientists onsite to collect such site-specific data
• and then get through a long and delicate data reduction process

• Based on a spectral matching which minimizes a difference between simulated and observed spectral reflectance above the water surface
• This uses the spectral BOA reflectance product
• For each shallow pixel, this requires browsing all occurences in all LUTs: that's a great many!
• This process uses KNITRO solver for nonlinear optimization

• Based on increasing depth iteratively until the watercolumn corrected spectral signature achieves an acceptable match with the spectral Soil Line at null depth that was specified in the calibration process
• For each shallow pixel
  • read the original raw TOA DN for the current pixel
  • deglint and smart-smooth it as required
  • iterate a few ten times while increasing/optimizing Z
  • until an acceptable match is obtained

• not deglinting, other than masking alien pixels
• not applying any smoothing

• see raw TCC at SW corner of St Croix
• see image_Z on deglinted, but not smoothed
• see image_Z on deglinted, but       smoothed

• Do DG produce a watercolumn corrected view of the scene?

• Do DG produce a map of bottom types?

• Can DG add the PAN band to the MULTI bandset?

• It would seem to me like if an important bottom type is missing for your LUTs, so your spectral matching zoomed on a wrong  Rsim(x) fitted reflectance
• I have tried this sort of approach myself in 4SM, starting from Z=0: with the result that the process happily stops at first opportunity on one bottom type, whereas a slightly better (or equivalent) opportunity might be found at greater depth on another bottom type
• at 1.0 km on the white profile:
  • in 4SM: applying your estimated depth of ~13 m instead of Lidar depth ~15 m  is an incomplete watercolumn correction
    • the Green radiance is the least corrected
    • then the Blue, then the Purple
  • it would mean the bottom looks very Bright_Purple-Blue down there,
    • which is commonly seen over live corals
    • which of course is unrealistic for a sandy area

• Selling computed depth is one thing: you have to satisfy hydrographers
• Selling bottom typing is even more attractive: coastal monitoring is the biggest market

I think that, for DG to succeed
with shallow water work,
you should include a naturalist in your team

• one who is a scuba diver
• one who knows what the shallow bottoms look like,
• one who knows it all about underwater and atmospheric optics

• you don't have to start again from scratch:
  • there is no such thing as a "product" compensated for path radiance
  • just modify any parameter in the command line as required
• then run the modeling again, possibly along a profile or inside a small window
• you get the result in minutes
• go back to your commandline, etc, 
• increase or decrease any parameter, enable or disable any functionality, 
• run a profile, run another profile, try another window, process any specific pixel, etc
• then when satisfied, run the modeling again on the whole image
  • and mask out areas which are unsuitable

• In 4SM I am feeling more and more uncomfortable when setting wavelengths at mid-wavebands while accessing Jerlov's table of diffuse attenuation coefficients for marine waters
  • setting WL_green at 551 nm appears to suit most recent cases
  • as for WL_yellow and WL_red, they would appear to need decreasing away from mid-waveband: I noted that at PrincessCays, then again at BuckIslandReef from 0 to ~9 m
• I've got hold of the LIDAR DTM Bathymetry.tif
  • I'd want to work on your WV2 images, including coverage of Bay of Mayaguez

• Murky waters tend to increase the upwelling signal
• This inevitably results in under-estimation of retrieved bottom depth
  • no matter what
  • in 4SM, such situation translates into conspicuously dark bottoms, which should attract the attention of a seasonned practitioner: see 4SM at Arcachon Inlet.