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lyzenga maritorena water column correction multispectral hyperspectral shallow bathymetry retrieval passive optical bathymetry shallow depth satellite image satellite bathymetry satellite derived bathymetry satellite bathymetry accuracy bathymetry mapping software bathymetric mapping software marine remote sensing bottom typing bottom type classification habitat mapping  reflectance  glint correction  coastal managment  diffuse attenuation coefficient  empirical casi  landsat  etm  tm  world view 2  wv2, ikonos  hyperion  ali panchromatic 
lee stocking  bahmas  heron island  gbr  great barrier reef  caicos bank scott reef   greenland  clipperton  hawaii ohau jamaica negril french polynesia  tuamotu usvi buck island reef  new caledonia cockburn  marmion geraldton abrolhos princess cays eleuthera florida keys bahrain gezirat siyul red sea la parguera dry tortugas whitsunday andros shark bay marawaah rangiroa tikehau tarawa alacranes chinchorro gubal bora bora moorea zirku ras hatibah takapoto tarawa marakei manihi kauehi sanaa kanehoe tanzania sabah davies reef  Passive optical calibration, bathymetry retrieval,
water column correction
and bottom typing of shallow water areas
using remote sensing imageries

4SM: the Self-calibrated Supervised Spectral Shallow-sea Modeler.
Use passive hyperspectral or multispectral satellite images
to derive both
depth and spectral reflectance of shallow bottom,
ready for bottom typing, ahead of any field work.

Breaking news May 2nd 2014:
4SM demonstration
40 times faster than best ALUT process
on the same image

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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
Leave difficult cases
to semi-analytical methods!
Further to Lyzenga et al's empirical ratio method: water volume reflectance is an important variable.
No need for atmospheric correction.
Uses Jerlov's data for optical calibration.


Optical calibration does not need
any field data:

the image itself contains all the information
that is needed for a "ratio method"
Users of Geomatica, ENVI, Erdas, TNTmips, ERmapper, Ilwis, Idrisi, etc,
no sweat: I only use open source

On the cost of dispensing with water volume reflectance

Just use a X86 or AMD64 laptop
running Linux
From importing raw data to formating deliverables in one single 4SM executable code.
Uses a bash command line, focussed on productivity.