Bathymetry and water column correction
LANDSAT 8 OLIP 
at Lee Stocking Island, Bahamas
Image courtesy of the U.S. Geological Survey
7671*7841 30 m pixel size, UTM zone 18


 
Using the Panchromatic band for water column correction
to derive water depth and spectral bottom signature:

Landsat 8 OLIP bandset used for this work
Purple=1Blue=2Green=3PAN=4Red=5NIR=6 and SWIR1=7


 
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
 
    
scene LC80120442016035LGN00, February 4th 2016
Work done september 2016
home


Robustness
As I don't have seatruth data, apart from the above speaker_harris_fig_01.png,
I used results obtained on lsiOLI_20140129 as a seatruth DTM
The result is astounding,
knowing that part of the fuzziness is caused by slight misregistration of the PAN band.
No smoothing applied

Regression
February 4th 2016    vs   Jan 29th 2014
This is a test of the robustness of 4SM

ZR-0.50m - ZC
February 4th 2016   -   Jan 29th 2014
Discrepancies
Three specific sources of discrepancies must be considered
  • Cloud shadows: they cause over-estimation of retrieved depths. I could not find a way to avoid it.
  • Misregistration: the 30 m re-sampled PAN band is misregistrated by one pixel in both row and line. This causes fuzziness, and overestimated depths on occasion.
  • Variation of water optical properties: they cause local over -or under- estimation of retrieved depth
Robustness
The results are much more robust than I expected!
I intend to produce
a Combined Depth raster
 out of all these results.




Data and Deglinting

TOA TCC: raw image
logaritmmic enhancement

BOA TCC deglinted image
logaritmmic enhancement
 

 
Winter is the time of choice
  • Clear atmosphere, hardly any glint
  • Homogeneous waters, particularly Blue bands (most unusual), from Exuma Sound to Tongue Of The Ocean and to open ocean
  • No local patch of discoloured waters,
    • although the PAN band comes to near extinction between waypoints B2 and B3, where depth is 5-7 m



Top notch optical calibration
16U data are scaled to allow for comfortable screen display
RAW CALIBRATION

Calibration diagram for the whole scene
for bands Blue, Green, Red and NIR
OPTIMIZED CALIBRATION

Calibration diagram for the whole scene
for bands Blue, Green, Red and NIR

Raw calibration
The optical calibration in 4SM
assumes that BPL pixels represent
spectrally neutral bottom reflectances
Not quite true for bottom type_10,
which is brightest ooid sands

 
LsM reflectances for best fit
-LsM/337.4/350.8/310.6/328.8/326.2/292.9/680.2
-CP/0228.5/221.5/129.1/000.0/024.0/000.0/000.0

 

Optimized calibration
Optimization of the optical calibration
enforce spectrally neutral bottom reflectances
for bottom type_10, which is brightest ooid sands


See that optimization achieves it
This involves fine-tuning of LsM reflectances
-LsM/322.3/330.1/297.1/347.0/305.5/292.9/680.2
-CP/0227.3/218.4/133.7/000.0/026.5/000.0/000.0
NIR CALIBRATION

Optical calibration of the NIR band is excellent
2KNIR~=6 m-1  seems to fit OLI data
La CALIBRATION

Blue vs Red
When the atmosphere is thin,
the Vegetation Line assumption allows
for straightforward estimation of La = Lsw - Lw
in the Blue range




Ready for modeling
No Smoothing

BOA TCC: water column corrected
 

Retrieved depth
see legend

 




Bottom typing

SAM classified image
see legend for SAM

Average bottom brightness