the Self-calibrated Supervised Spectral Shallow-sea Model

Passive optical calibration, satellite derived bathymetry, water column correction
and bottom typing of shallow water areas using remote sensing imageries.


Use passive hyperspectral or multispectral satellite images to retrieve both
SDB depth and spectral reflectance of shallow bottom, ready for bottom typing, ahead of any field work.


     
r   Empirical vs Analytical

   I am developing a 4SM_2K_Jerlov page: it is time you get updated!
home page continued



 


 
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
 
So
I keep digging
until suitable data
become available
 
 download a peer-reviewed article july 2017
4SM: a Novel Self-calibrated Algebraic Ratio Method for Satellite Derived Bathymetry and Water Column Correction

download a preprint article august 2017
4SM Method Tested in the Gulf of California Suggests Field Data are Not Needed to Derive Satellite Bathymetry






This is the end
December 2016
A time series of Landsat 8 at La Paz, Baja California

15 m GSD NO Smoothing required   RMSE~=1 m


       


 

October 2016
two Landsat 8 time series in the Bahamas

No need for formal atmospheric correction                  No use of field data for optical calibration
Water column corrected signatures calibrated into reflectance (0-1) for OLI and WV2
This also works with PAN-sharpenned images


 
  4SM now sports its own PAN sharpening routine