Digital Imaging and Remote Sensing Laboratory Hyperspectral Water Quality 1 EOCAP-HSI FINAL Briefing RIT Technical Activities John Schott, RIT PI [email protected]

Hyperspectral Water Quality 1Digital Imaging and Remote Sensing Laboratory

EOCAP-HSI FINAL BriefingEOCAP-HSI FINAL BriefingRIT Technical ActivitiesRIT Technical Activities

John Schott, RIT [email protected] (716)475-5170

Rolando Raqueno, [email protected](716)475-6907

http://www.cis.rit.edu/~dirsJanuary 16-17, 2001


Bottom Type A Bottom Type B

particles & algae

CDOMphytoplankton

Agriculture Urban

macrophytes

bacteria

Airborne Hyperspectral Imagery Analysis Assessing

Near Shore Water Quality


Airborne Hyperspectral Imagery Analysis Assessing Near Shore Water

QualityALGE Model

Bottom Type A Bottom Type B

particles & algae

CDOM phytoplankton

MODTRAN

Modeling Strategy•Solar Spectrum Model (MODTRAN)•Atmospheric Model (MODTRAN)•Air-Water Interface (DIRSIG/Hydrolight)•In-Water Model (HYDROMOD= Hydrolight/OOPS + MODTRAN)•Bottom Features(HYDROMOD/DIRSIG)

HydroLight

Agriculture Urban

macrophytes

bacteria

Long Term Approach: Long Term Approach: Integrated hybrid physical models validated and fine tuned by real imagery

ALGE:Hydrodynamic

Real Image Simulated Image

DIRSIG

Hydrolight

Modtran

difference

RMS


Hyperspectral ImageryHyperspectral Imagery


Overview: Overview: Big PictureBig Picture

ModelInherent Optical Properties

[ ] Concentrations

Digital Counts

ModelAtmosphere

Radiance, L

Reflectance, r(

-0.01

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

350 450 550 650 750 850

Part_abs (1/m)

Part Abs (Extract)

-0.01

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

350 450 550 650 750 850

Part_abs (1/m)

Part Abs (Extract)

Signal SourcesSignal Sources

Air/Water Transition Water/Air Transition

In Water

Atmosphere to Sensor

10%

80%

10%


Remote Sensing Water Quality Remote Sensing Water Quality Tool: HydroModTool: HydroMod


absorption IOPsabsorption IOPsA

bso

rpti

on

Wavelength

Water

DOCChlor a

Total suspended material

Normalized Scattering Distribution of theNormalized Scattering Distribution of theFournier-Forand Phase Function with Fournier-Forand Phase Function with

Parameters (nu,n)Parameters (nu,n)


Example LUT EntriesExample LUT Entries

[C]=13[SM]=0

[CDOM]=0

[C]=0[SM]=0

[CDOM]=0

[C]=0[SM]=0

[CDOM]=50


Look Up TableLook Up Table

Each entry in the LUT [i.e. LUT (i,j,k)] corresponds to a particular output of the Hydrolight code in the form of a spectral vector.

These may be in terms of Lλ(h), -Rλ0 or +Rλ0.

k

i

j

[ C ]

[ SM ]

[ CDOM ]

LUT

Simple FittingSimple Fitting

min [(ST - SP)2 ]

Final [CHL] [CDOM] [TSS]

λ

Sp predicted

ST truth data

SQ Error[CHL] [TSS] [CDOM]

[CHL]

TRUE

FALSE

[CDOM] [TSS]

[ C

]

[ SM ]

[ CD

OM

]

LUT

k

i

j


Squared ErrorSquared Error

R

Iterate using a downhill simplex (Amoeba) algorithm to minimize squared error term.

Squared Error = Σ (RLUT - Robs)2

Interpolated LUT valuesInterpolated LUT values

observationobservation

Trilinear InterpolationTrilinear Interpolation

SMl,Cm,CDOMn

SMl,Cm,CDOMk+1

SMl,Cm,CDOMk

SMi,Cj+1,CDOMk+1

SMi,Cm,CDOMk+1

SMi,Cj,CDOMk+1

SMi+1,Cm,CDOMk+1

SMi+1,Cj+1,CDOMk+1

Smi+1,Cj,CDOMk+1

Smi+1,Cj,CDOMk

SMi+1,Cm,CDOMk

SMi+1,Cj+1,CDOMk

SMi,Cj,CDOMk

SMi,Cm,CDOMk

SMi,Cj+1,CDOMk

CDOM

SM

C C

CC


Sample Comparison of Spectral Sample Comparison of Spectral Curve FitCurve Fit

CHL=0.0006, TSS=3.09, CDOM=5.7

CHL=6.3, TSS=2.0,CDOM=4.8

ASD Spectra


Calibrating AVIRIS ImagesCalibrating AVIRIS Images

Figure 1: AVIRIS and Ground Truth Estimates forHYDROMOD Based ELM

High Signal PixelLow Signal Pixel


ELMELMIncluding Model correctionIncluding Model correction

•Assume cloud R 0.9 Estimate water constituents in clear water (use ground truth if available) to predict R using HydroMod for the specific conditions under study•Perform Linear transform of Radiance to reflectance, L=mR+b•NB accounts not only for atmosphere, but for any first order model-atmosphere-sensor mismatch


After ELM CalibrationAfter ELM Calibration

Braddock Bay

0.02

0.04

0.06

400 500 600 700Wavelength

Ref

lec

tan

ce

Cranberry Pond

0.02

0.04

0.06


Ref

lec

tan

ce

Long Pond

0.02

0.04

0.06


Ref

lec

tan

ce

Lake Ontario

0.02

0.04

0.06


Ref

lec

tan

ce

AMOEBA FIT

AMOEBA FIT

AMOEBA FIT

AMOEBA FIT


Long Pond ELM Control PointLong Pond ELM Control PointReflectance Simulated by HydroMod using Lab Measured ConcentrationsCHL = 62.96 microgram/LTSS = 22.44 milligram/LCDOM = 6.12 scalar


ELMELMIncluding Model correctionIncluding Model correction

•Assume cloud R 0.9 Estimate water constituents in clear water (use ground truth if available) to predict R using HydroMod for the specific conditions under study•Perform Linear transform of Radiance to reflectance, L=mR+b•NB accounts not only for atmosphere, but for any first order model-atmosphere-sensor mismatch


Atmospheric Compensation Improvement with Atmospheric Compensation Improvement with Addition of Addition of

Ground Truth Data PointGround Truth Data Point

RMS Error Improvement with Ground Truth Data Point

0

5

10

15

20

25

30

35

CHL [ppb] TSS [ppm] CDOM[scalar]

Constituent

Co

nce

ntr

atio

n RMS Error w/oGround Truth

RMS Error w/Ground TruthPoint

RMS Error Improvement with Ground Truth Data Point

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

CHL[ppb]

TSS[ppm]

CDOM[scalar]

Constituent

% o

f M

axim

um

Lab

Val

ue

RMS Error w/oGround Truth

RMS Error w/Ground TruthPoint

Weighted FittingWeighted Fitting

MIN[(ST - SP)2 ]

Final [CHL] [CDOM] [TSS]

[CHL]

TRUEFALSE

[CDOM] [TSS]

[ C

]

[ SM ]

[ CD

OM

]

LUT

k

i

j

Weighting function

SQ Error

Sp predicted

ST truth data


Northwest Ponds of Rochester EmbaymentNorthwest Ponds of Rochester EmbaymentLake OntarioLake Ontario

Braddock Bay

Cranberry Pond

Long Pond

Buck Pond

Round Pond

Russell Station

AVIRIS (Color Infrared) May 20, 1999

Lake Ontario Bathymetry (feet)


AVIRIS FlightlinesMay 20, 1999

11:45 AM

Digital Imaging and Remote Sensing Laboratory

solar glint

to quantify multiple water quality parameters (chlorophyll, suspended solids, & yellowing organics).

Hyperspectral data:Hyperspectral data:


May 20, 1999 AVIRIS-MISI FlightMay 20, 1999 AVIRIS-MISI Flight AVIRIS Study

Area

Phenomenology/Ground TruthPhenomenology/Ground Truth

Digital Imaging and Remote Sensing Laboratory

Reference: Reference: •Schott, Barsi, de Alwis, Raqueno. “Application of LANDSAT 7 to Great Lakes Water Resource Assessment,” presented at the International Association for Great Lakes Research 43 rd Conference on Great Lakes and St. Lawrence River Research, Cornwall, Ontario, May, 2000.•Schott, Gallagher, Nordgren, Sanders, Barsi. “Radiometric calibration procedures and performance for the Modular Imaging Spectrometer Instrument (MISI).” Proceedings of the Earth Intl. Airborne Remote Sensing Conference, ERIM, 1999.•Schott, Nordgren, Miller, Barsi. “Improved mapping of thermal bar phenomena using remote sensing,” presented at the International Association for Great Lakes Research (IAGLR) Annual Conference, McMaster University, Hamilton, Ontario, May 1998.

spectral measurements

in-water optical properties

field support

MISI underflight image of Ginna Power Plant


Aviris GTAviris GT


CHL Ground Truth ComparisonCHL Ground Truth Comparison

CHL Prediction vs. Ground Truth

0

5

10

15

20

25

30

35

40

45

50

55

60

65

0 5 10 15 20 25 30 35 40 45 50 55 60 65

Lab Concentrations

Pre

dic

ted

Co

nc

en

tra

tio

ns

Braddock Bay

CranberryPondLong Pond

A25 (In Plume)

IrondequoitBay

RMS = 11.6 mg/m3

18% of [CHL] range


TSS Ground Truth ComparisonTSS Ground Truth Comparison

RMS = 4.0 g/m3

17.8% of [TSS] range

TSS Prediction vs. Ground Truth

0

5

10

15

20

25

0 5 10 15 20 25

Lab concentrations

Pre

dic

ted

Co

nc

en

tra

tio

ns Braddock

Bay

CranberryPond

Long Pond

A25 (InPlume)

IrondequoitBay

Glint Area


CDOM Ground Truth ComparisonCDOM Ground Truth Comparison

RMS = 2.2 [scalar]

17.2% of [CDOM] range

CDOM Prediction vs. Ground Truth

0

2

4

6

8

10

12

14

0 2 4 6 8 10 12 14

Lab Concentrations

Pre

dic

ted

Co

nc

en

tra

tio

ns

Braddock Bay

CranberryPondLong Pond

A25 (In Plume)

IrondequoitBay

Glint Area


Evidence of solar glintEvidence of solar glint

AVIRIS Rochester Embayment May 20, 1999

slicks


Scalar Concentration of CDOMScalar Concentration of CDOM

CDOM(350 nm)=1.0

CDOM(350 nm)=0.2

CDOM(350 nm)=5.0


CHL Model Prediction MeansCHL Model Prediction Meansvs. Ground Truthvs. Ground Truth

Mean CHL Lab vs Mean CHL Predicted Concentrations

0

10

20

30

40

50

60

70

Long Pond CranberryPond

BraddockBay

A25 (InPlume)

IrondequoitBay

Site Location

CH

L C

on

cen

trat

ion

[m

g/m

^3]

CHL Lab

CHL Predicted


CDOM Model Prediction MeansCDOM Model Prediction Meansvs. Ground Truthvs. Ground Truth

Mean CDOM Lab vs Mean CDOM Predicted Concentrations

0

2

4

6

8

10

12


BraddockBay

A25 (InPlume)

IrondequoitBay

Site Location

CD

OM

Co

nce

ntr

atio

n

[sca

lar] CDOM Lab

CDOM Predicted


TSS Model Prediction Means TSS Model Prediction Means vs. Ground Truthvs. Ground Truth

Mean TSS Lab vs Mean TSS Predicted Concentrations

0

5

10

15

20

25


BraddockBay

A25 (InPlume)

IrondequoitBay

Site Location

TS

S C

on

cen

trat

ion

[g

/m^

3]

TSS Lab

TSS Predicted

Lake Bottom at Different Lake Bottom at Different Spatial ResolutionsSpatial Resolutions

AVIRIS: 20 meter pixelsRochester Embayment

May 20, 1999



MISI with 9ft pixels

AVIRIS with 20m pixels

Region: Lake Ontario North of Irondequoit Bay

Hyperspectral Imaging for Hyperspectral Imaging for Bottom Type Classification Bottom Type Classification

and Water Depth and Water Depth DeterminationDetermination

M.S. Thesis DefenseNikole Wilson10 Aug 2000


Depth Varies LinearlyDepth Varies Linearly

Case 1 constant bottomPhilpot’s synthetic dataa|| has a parallel relationship with directionof changing depthDepth varies linearly

X at 550 nm

X at 650 nm


Case 2 : Varied depth, bottom typeCase 2 : Varied depth, bottom type

Data form separate but parallel clusters in linearized spaceClusters separated inlinearized space by a distance relatingto differences in bottomreflectances

X at 550 nm

X at 650 nm


Data CollectionData CollectionGinna BottomsGinna Bottoms

Bottom Reflectances

0

0.1

0.2

0.3

0.4

0.5

0 200 400 600 800 1000

Wavelength (nm)

Ref

lect

ance

Ont_sand Hydro algae Groc1_we Redrock

Groc2_we ltroc_w Rro_alg yroc

Gray rock 1

Red rock

Redrock with algae

Light gray rock

Yellow rock Gray rock 2

Ontario Beach Qualitative ResultsOntario Beach Qualitative Results

Depth

Picking updifferent bottom type

12

34

Sand2.24

Rock2.43

Sand1.62

Rock21

BottomDepth


solarglint

MISI with 2ft pixels MISI with 4ft pixels

Lake Ontario at Russell Station

Lake Ontario at Cranberry Pond

Lake Ontario BathymetryLake Ontario Bathymetry

Documents

Digital Imaging and Remote Sensing Laboratory Hyperspectral Water Quality 1 EOCAP-HSI FINAL Briefing RIT Technical Activities John Schott, RIT PI [email protected]