4SM slides

Optical calibration, bathymetry, water column correction and bottom typing of shallow marine areas, using passive remote sensing imageries

water column correction since 1994
4SM : an operational suite of tools
written in C under Ubuntu Linux (60,000 lines)

no need for field data
no need for atmospheric correction
works with DNs
"same day" service
from raw spectral data to final deliverable products
through one single commandline

view 4SM_slides below (sept 1rst 2012)

view WV2 fundamentals (sept 1rst 2012)

The simplified RTE	https://www.watercolumncorrection.com/sld016.php
JERLOV wrote	https://www.watercolumncorrection.com/sld036.php
KIRK wrote	https://www.watercolumncorrection.com/sld037.php
A family of curves	https://www.watercolumncorrection.com/4sm-illustrations-calibration-process.php#porquerolles
The SL and BPL	https://www.watercolumncorrection.com/4sm-illustrations-calibration-process.php
Calibration diagram	https://www.watercolumncorrection.com/4sm-illustrations-calibration-process.php#yes
Deglinting	https://www.watercolumncorrection.com/4sm-illustrations-the-deglinting-process.php#lsi
Modeling	https://www.watercolumncorrection.com/waimanalo-pollution.php#profiles
Seatruthing	https://www.watercolumncorrection.com/waimanalo-pollution.php#regression_west
RMSE	https://www.watercolumncorrection.com/waimanalo-pollution.php#Lmyellow=4
Bottom typing	https://www.watercolumncorrection.com/waimanalo-bottom-typing.php#July_25th
The 4SM command line	https://www.watercolumncorrection.com/waimanalo-pollution.php#commandline

slide 1
4SM: a ratio method for water column correction of shallow spectral images Following work by Lyzenga, Philpot, Maritorena and Jerlov Simplified TOA RTE : Ls – Lsw = (LsB – Lsw) / exp (2K * Z) Inverted BOA RTE : LB = Lw + (Ls – Lw ) exp (2K Z) where Lsw=La+Lw, LsB=LB+La, Ls=L+La Estimate spectral deep water radiance Lsw from the deglinted image Estimate ratio Ki/Kj in the image for one pair of bands i and j Use ratio Ki/Kj and Jerlov's data to derive spectral K in m^-1 Use bareland in the image to formulate the Soils Line: this yields a spectral model at null depth for all pairs of bands, and also the spectral path radiance La, and therefore the water volume reflectance Lw Inverse modeling by increasing Z until the ratio LBi/LBj approximately fits the spectral Soils Line for all pairs of bands i and j which exhibit bottom detection This yields both bottom depth and spectral bottom reflectance signature ready for bottom typing Since 1994 www.watercolumncorrection.com No need for field data No need for atmospheric correction: works with Dns From raw data to final deliverables “on the fly” in one single commandline Aims at “Same day” service

slide 2
Deglinting in 4SM Hedley's method Get the linear regressions of Lsi vs LsNIR for all pairs of bands i and NIR over an optically deep area The amount of glint at the current pixel is LsNIR-LswNIR Use the regressions to estimate the amount of glint in other bands, and subtract it For WV2 images Band 8 is well correlated with bands 6, 4 and 1 (new bands) Band 7 is well correlated with bands 5, 3 and 2 (legacy BGRN bands) S/N ratio is bad , heavy smoothing required *1111 circular smoothing kernel used at Waimanalo Deglinting Ron's “deglinted” image**

slide 3
WV2 at Waimanalo Bay, Oahu, Hawaii LIDAR seatruth Zcomputed-ZLidar Red tones for over-estimated depths Blue tones for under-estimated depths Dark_blue tones delineate artifacts caused by ”pollution” Profile_black Enhanced TCC of water column corrected image Deep purple tones signal "pollution"

slide 4
WV2: Profile_black at Waimanalo Bay, Oahu, Hawaii

slide 5

slide 6
WV2 at waimanalo Bay Reducing the error on estimated depth LIDAR DTM is in plain meters: I need a DTM in centimeters Away from suspected pollution from row_1 to row_1000 from 0 to 10 m along profile_black 96.2% of all estimated depths are within +-2 m of DTM RMSE=0.84-0.50=0.34 m average ZComputed-ZLidar = 0.61-0.50=0.11 m This image is a "worst case" fully glinted image Sensor is looking 37° sideways right into the sun Thanks you, Ron! September 1rst 2012