• LINEAR: the various linearized BPL pixels plot as straight lines over the whole depth range
• CONSISTENT: a consistent set of Ki/Kj ratios is observed among all pairs of spectral bands i and j in the visible range
• OPERATIONAL 2K: any of these ratios in the visible range is observed to allow for deriving spectral operational diffuse attenuation coefficient 2K from Jerlov's data
  • except in the 575-600 nm range where observed 2K values must be set at slightly lower values, because Jerlov data are provided by increment of 25 nm
• CoefZ: computed depths are therefore calibrated in meters,
  • and only need to be multiplied by a final depth correcting factor to be derived from seatruth

• CASI: the BPL assumption holds
  • at Gezirat reef, Red Sea
  • at Porquerolles, France
  • at Mahone Bay, Nova Scotia, Canada
  • at Rangiroa atoll, French Polynesia
• HYPERION: the BPL assumption holds
  • at Bora Bora island, French Polynesia

a wideband may be considered to be comprised
of several closely spaced narrow bands
 

• First problem: multispectral bands are very wide
  • we can't assume that the diffuse attenuation properties remain unchanged over the whole depth range
  • as the bottom depth increases, the upward flux of photons probably gets depleted of its longest wavelength component
  • as a result, the operational wavelength for wideband images would be seen to decrease as the depth increases
  • this would seem to be the case for the Red band
• Second problem: the Green band spans well over the 575-600 nm range where gradient is strongest in Jerlov's data
• As a result, the optical behaviour of the Green band of ETM and ALI images would be quite complex indeed
  • ETM and TM and ALI
  • IKONOS
  • SPOT no difficulty was observed
• Third problem: illumination conditions
  • Jerlov's dataset refers to measurements at sea level, clear blue skyes, sun high in the sky
  • this is not allways the case in real life
  • scenes at high altitude: Greenland at ~1200 m Bolivia at ~3700 m
  • overcast skies, adjacency effect: Red Sea, Persian Glulf, Bahamas
  • the contribution of the skydome to the downwelling irradiance might well be the clue to our problems
• Fourth problem: does the existence of biofilms at the bottom surface alter the spectral bottom reflectance?
• See an illustration of the problem using ETM at RasHatibah, Red Sea

• First problem: multispectral bands are very wide
  • we can't assume that the diffuse attenuation properties remain unchanged over the whole depth range
  • as the bottom depth increases, the upward flux of photons probably gets depleted of its longest wavelength component
  • as a result, the operational wavelength for wideband images would be seen to decrease as the depth increases
  • this would seem to be the case for the Red band

• Second problem: the Green band spans well over the 575-600 nm range where gradient is strongest in Jerlov's data
• As a result, the optical behaviour of the Green band of ETM and ALI images would be quite complex indeed
  • ETM and TM and ALI
  • IKONOS
  • SPOT no difficulty was observed
• Third problem: illumination conditions
  • Jerlov's dataset refers to measurements at sea level, clear blue skyes, sun high in the sky
  • this is not allways the case in real life
  • scenes at high altitude: Greenland at ~1200 m Bolivia at ~3700 m
  • overcast skies, adjacency effect: Red Sea, Persian Glulf, Bahamas
  • the contribution of the skydome to the downwelling irradiance might well be the clue to our problems
• Fourth problem: does the existence of biofilms at the bottom surface alter the spectral bottom reflectance?
• See an illustration of the problem using ETM at RasHatibah, Red Sea

• Hyperion, ETM and ALI in Greenland, altitude ~1200 m
  • the various linearized BPLs plot as straight lines over the whole depth range
  • the bottom is clearly homogeneous, made of ice
  • depths computed by the Green band match nicely those computed by the Red band
  • all that is needed is to increase WL[green] to 578 nm, away from mid-waveband.
• Hyperion and ALI in Bolivia, altitude ~3700 m
• CASI at Porquerolles, France

 We want to use the BILKO seatruth dataset at Caicos Bank, Bahamas
to try and unfold some of the complexities of underwater optics
using a time series of real life Landsat ETM images

• Depths.xls file from Bilko dataset contains 515 depth points?
  • BILKO's dataset is UTM zone 19
• USGS image LT50090451990326XXX05  is WGS83 UTM zone 18
• NPA-FUGRO have reprojected this time-series  dataset  of 23-scene subsets
  • from USGS's  UTM zone 18 
  • into BILKO's UTM zone 19
• NPA-FUGRO also verified and cleaned Bilko's original Depth.xls
  • and exported it to Depth_points_reproject.shp
    • which now contains 511 depth points
• shp2text read it into depth_points_reproject.txt text file
  • it contains 511 depth points, formated XUTM YUTM Z
• seatruth regression, and inspection of the results in the OpenEV display, show that
  • some odd depth point results are too close to land
  • some odd depth points results are not representative of good and consistent results in their immediate surrounding
    • in view of the 30 m pixel size on the ground
    • in view of the necessity to smooth the data prior to modeling
  • some depth points consistently yield badly underestimated depth: they are located over the steeply sloping edges of the Caicos Bank
  • some depth points are located over optically deep waters
  • some depth points simply don't seem to belong, for no reason
• Weeding out 43 louzy points:
  • all the above depth points are weeded out of the dataset (actually "commented")
  • the cleaned seatruth dataset is named depth_points_reproject_pruned.txt
    • it now contains 468 depth points
    • this new file is then used for seatruthing

Calibration : how this is done

 Estimated tide height relative to ETM_009_045_1999-09-20
(was estimated using CombinedZ)

• Lyzenga's method is still widely used
  • It needs an existing depth sounding dataset that covers faithfully the whole ranges of bottom brightness and shallow depth
  • It does not account for water volume reflectance: that's why it needs
    • segmentation of shallow areas into several bottom brightness classes, irrespective of depth
    • multiple linear regressions in order to specify Z=aX + bY + c for each class

• With 4SM, in order to get a similar result (and also a water column corrected spectral image suitable for bottom typing),
  • no need for atmospheric correction
  • no need for calibration into units of reflectance
  • no need for image classification/segmentation
  • no need for any field data
• With 4SM, seatruth data is only needed later on, in order to specify
  • Htide
  • CoefZ
  • ==> FinalZ = Htide+CoefZ*computedZ

• Lyzenga's method is still widely used
  • It needs an existing depth sounding dataset that covers faithfully the whole ranges of bottom brightness and shallow depth
  • It does not account for water volume reflectance: that's why it needs
    • segmentation of shallow areas into several bottom brightness classes, irrespective of depth
    • multiple linear regressions in order to specify Z=aX + bY + c for each class
• With 4SM, in order to get a similar result (and also a water column corrected spectral image suitable for bottom typing),
  • no need for atmospheric correction
  • no need for calibration into units of reflectance
  • no need for image classification/segmentation
  • no need for any field data
• With 4SM, seatruth data is only needed later on, in order to specify
  • Htide
  • CoefZ
  • ==> FinalZ = Htide+CoefZ*retrievedZ