4SM on OLI at Caicos Bank, Bahamas

scene LC80090452015139LGN00 May 19th 2015
work done august 2016
home
This is spring in the Bahamas: lots of greeneries.
Sun light is plentyful
Calibration optimization now operational
Vegetation Line for Lw and La
DTM seatruth RMSE=0.62 m

ZDTM-1.1m-Z4SM see legend	Z4SM vs ZDTM+1.10 m
ZDTM and Z4SM tide correction+ ZDTM+1.10 m

Data and Deglinting

caicosOLI_20150519_TOA_TCC_linear_enhancement

TOA TCC: raw image logaritmic enhancement	TOA TCC deglinted image same logaritmic enhancement
TOA TCC deglinted chequered same logaritmic enhancement	TOA TCC water column corrected chequered same logaritmic enhancement
Glint regressions for NIR band at 866 nm There is no sea surface wave-modulated clutter. But there is a lot of aerosols, like in the Middle East, far denser in the South-East part of this scene. Most often, sea-surface glint and path radiance have very similar spectral properties, but not always though!	So what does it take to "deglint" this scene? Locate an area with lowest NIR signal, most often in a cloud shadow area (could also be some dark lake at sea level): this shall yield the atmospheric path radiance La_NIR for the NIR band, which is equal to the deep water reflectance Lsw_NIR La_NIR=Lsw_NIR Sample areas of variable "glint" signal: here from low to high path radiance Run the pair-wise regressions Knowing Lsw_NIR and the slope and intercept of the regressions, spectral Lsw_WL is computed for all visible bands Run the Deglinter, under the "dark pixel subtraction" assumption
Plot of deglinting along profile_green for Coastal, Green and NIR bands	Profile_green locator profile_green_A runs from NW towards SE at 136 km, it crosses a very dark cloud shadow profile_green_B runs from SW towards NE increase: see that the path radiance increases steadily from NW_18 towards SE_70 this is a huge increase over 150 km "island effect": see that the coastal band at 440 nm exhibits a distinct "island effect" which lowers its deep water radiance see profile_green_B at 120 km, where it extends over ~12 km noise: see that the noise is much weaker over shallow waters

Optical calibration
16U data are scaled to allow for comfortable screen display

Calibration diagram for bands Blue, Green, Red and NIR K_BLUE/K_GREEN=0.54 => Jerlov water type OIB+0.5	Calibration diagram for bands Blue, PAN, Red and NIR
Reflectance (0-1) of brightest bottom substrate for this scene Coastal=1 Blue=2 Green=3 PAN=4 Red=5 NIR=6 SWIR1=7 0.272 0.336 0.405 0.426 0.473 0.613 0.600	2K (m^-1) for Jerlov water type OIB+0.5 diffuse attenuation coefficient Coastal=1 Blue=2 Green=3 PAN=4 Red=5 NIR=6 SWIR1=7 0.114 0.100 0.183 0.627 2.908
Reflectance (0-1) of atmospheric path radiance Ra 0.107 0.082 0.056 0.050 0.037 0.024 0.011	Reflectance (0-1) of the ater volume Rw reflectance (0-1) 0.020 0.016 0.001 0.001 0.0 0.0 0.0
Vegetation Line Blue vs Red: use the "vegetation line" to estimate Lw_BLUE=~13.5	Vegetation Line Coastal vs Red: use the "vegetation line" to estimate Lw_COASTAL=~24.5

Calibration optimization
is an achievement obtained in july 2016, now hard coded in 4SM

The ratio K_blue/K_green Hopefully, assigning a value for the ratio K_blue/K_green from the image itself shall be easy enough. This rests on two assumptions: that the water body is homogeneous. that the brightest bottom type is present over the whole depth range if only as isolated patches/pixels.	Spectral LsM Hopefully, assigning a first order spectral value for LsM retrieved depths shall be quite close to correct let's call that "ball-parking" the calibration. But this is not enough for one who wants to produce a fair water column correction for bottom typing using a time series of images this rests on the assumption that the brightest bottom type sould exhibit a "flat" spectral response meaning: should not be biased toward any particular wavelength. In other words, spectral LsM should be representative of a very bright sand/mud, un-tainted by any discoloration, like clean fine grained coral/quartz sand/mud, or snow achieving this requires a very fine-tuning of LsM achieving this manually can take hours we now use an automatic procedure to do this .
Before optimization of spectral LsM Average retrieved depth 4.77 m over a ROI of 5700 pixels Reflectances are shown (0-1) Depth retrieval may be good and bottom typing for this scene might prove to be useful But, in view of time series of images or scenes, we can't accept such a "sea-saw" water column corrected signature for the brightest bottom type in a coral reef enviromment	After optimization of spectral LsM Average retrieved depth 4.82 m over the same ROI of 5700 pixels Reflectances are shown (0-1) Depth retrieval only changed by a mere 1.1% The optimization is automatic and runs for less than one minute Now we can engage into time series/scenes comparisons of shallow habitats
Calibration before optimization of spectral LsM appears to be quite good, but still needs fine-tuning though!	Calibration after optimization of spectral LsM quite a subtle fine-tuning has been achieved
Bottom types for SAM classification	Now, a complete suite of bottom type signatures may be developped, which hopefully should be suitable for bottom typing of this time series of Landsat 8 OLIP scenes over the Bahamian platform

Now ready for Modeling
Modeling by the PAN solution ; No smoothing applied

Average bottom brightness Very bright in spite of ubiquitous seagrass regrowing in spring	BOA TCC water column corrected No enhancement BOA view reveals the details
SAM mapping Noise caused by SWIR1 deglinting see legend for SAM	Retrieved depth in centimeters see legend for retrieved depth in cm