Digital Imaging and Remote Sensing Laboratory Hyperspectral Water Quality 1 EOCAP-HSI FINAL Briefing RIT Technical Activities John Schott, RIT PI (716) Rolando Raqueno, RIT January 16-17, 2001

Digital Imaging and Remote Sensing Laboratory Hyperspectral Water Quality 2 Bottom Type ABottom Type B particles & algae CDOM phytoplankton Agriculture Urban macrophytes bacteria Airborne Hyperspectral Imagery Analysis Assessing Near Shore Water Quality

Digital Imaging and Remote Sensing Laboratory Hyperspectral Water Quality 3 Airborne Hyperspectral Imagery Analysis Assessing Near Shore Water Quality ALGE Model Bottom Type ABottom 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

Digital Imaging and Remote Sensing Laboratory Hyperspectral Water Quality 5 Hyperspectral Imagery

Digital Imaging and Remote Sensing Laboratory Hyperspectral Water Quality 6 Overview: Big Picture Model Inherent Optical Properties [ ] Concentrations Digital Counts Model Atmosphere Radiance, L Reflectance, r( 

Signal Sources Air/Water Transition Water/Air Transition In Water Atmosphere to Sensor 10% 80% 10%

Digital Imaging and Remote Sensing Laboratory Hyperspectral Water Quality 8 Remote Sensing Water Quality Tool: HydroMod

Digital Imaging and Remote Sensing Laboratory Hyperspectral Water Quality 9 absorption IOPs Absorption Wavelength Water DOC Chlor a Total suspended material

Normalized Scattering Distribution of the Fournier-Forand Phase Function with Parameters (nu,n)

Digital Imaging and Remote Sensing Laboratory Hyperspectral Water Quality 11 Example LUT Entries [C]=13 [SM]=0 [CDOM]=0 [C]=0 [SM]=0 [CDOM]=0 [C]=0 [SM]=0 [CDOM]=50

Digital Imaging and Remote Sensing Laboratory Hyperspectral Water Quality 12 Look 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 L λ (h), - R λ0 or + R λ0. k i j [ C ] [ SM ] [ CDOM ] LUT

Simple Fitting min [(S T - S P ) 2 ] Final [CHL] [CDOM] [TSS] λ S p predicted S T truth data S Q Error [CHL] [TSS] [CDOM] [CHL] TRUE FALSE [CDOM][TSS] [ C ] [ SM ] [ CDOM ] LUT k i j

Digital Imaging and Remote Sensing Laboratory Hyperspectral Water Quality 14 Squared Error Iterate using a downhill simplex (Amoeba) algorithm to minimize squared error term. Squared Error = Σ (R LUT - R obs ) 2 Interpolated LUT values observation

Trilinear Interpolation SM l,C m,CDOM n SM l,C m,CDOM k+1 SM l,C m,CDOM k SM i,C j+1,CDOM k+1 SM i,C m,CDOM k+1 SM i,C j,CDOM k+1 SM i+1,C m,CDOM k+1 SM i+1,C j+1,CDOM k+1 Sm i+1,C j,CDOM k+1 Sm i+1,C j,CDOM k SM i+1,C m,CDOM k SM i+1,C j+1,CDOM k SM i,C j,CDOM k SM i,C m,CDOM k SM i,C j+1,CDOM k CDOM SM CCCC

Digital Imaging and Remote Sensing Laboratory Hyperspectral Water Quality 16 Sample Comparison of Spectral Curve Fit CHL=0.0006, TSS=3.09, CDOM=5.7 CHL=6.3, TSS=2.0, CDOM=4.8 ASD Spectra

Digital Imaging and Remote Sensing Laboratory Hyperspectral Water Quality 17 Calibrating AVIRIS Images Figure 1: AVIRIS and Ground Truth Estimates for HYDROMOD Based ELM High Signal Pixel Low Signal Pixel

Digital Imaging and Remote Sensing Laboratory Hyperspectral Water Quality 18 ELM Including 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 atmos- phere, but for any first order model-atmosphere-sensor mismatch

Digital Imaging and Remote Sensing Laboratory Hyperspectral Water Quality 19 After ELM Calibration Braddock Bay Wavelength Reflectance Cranberry Pond Wavelength Reflectance Long Pond Wavelength Reflectance Lake Ontario Wavelength Reflectance AMOEBA FIT

Digital Imaging and Remote Sensing Laboratory Hyperspectral Water Quality 20 Long Pond ELM Control Point Reflectance Simulated by HydroMod using Lab Measured Concentrations CHL = microgram/L TSS = milligram/L CDOM = 6.12 scalar

Digital Imaging and Remote Sensing Laboratory Hyperspectral Water Quality 21 ELM Including 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 atmos- phere, but for any first order model-atmosphere-sensor mismatch

Digital Imaging and Remote Sensing Laboratory Hyperspectral Water Quality 22 Atmospheric Compensation Improvement with Addition of Ground Truth Data Point

Weighted Fitting MIN [(S T - S P ) 2 ] Final [CHL] [CDOM] [TSS] [CHL] TRUE FALSE [CDOM][TSS] [ C ] [ SM ] [ CDOM ] LUT k i j Weighting function S Q Error S p predicted S T truth data

Digital Imaging and Remote Sensing Laboratory Hyperspectral Water Quality 24 Northwest Ponds of Rochester Embayment Lake Ontario Braddock Bay Cranberry Pond Long Pond Buck Pond Round Pond Russell Station AVIRIS (Color Infrared) May 20, 1999 Lake Ontario Bathymetry (feet)

Digital Imaging and Remote Sensing Laboratory Hyperspectral Water Quality 25 AVIRIS Flightlines May 20, :45 AM Digital Imaging and Remote Sensing Laboratory solar glint to quantify multiple water quality parameters (chlorophyll, suspended solids, & yellowing organics). Hyperspectral data:

Digital Imaging and Remote Sensing Laboratory Hyperspectral Water Quality 26 May 20, 1999 AVIRIS-MISI Flight AVIRIS Study Area

Phenomenology/Ground Truth Digital Imaging and Remote Sensing Laboratory 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, 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, 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 spectral measurements in-water optical properties field support MISI underflight image of Ginna Power Plant

Digital Imaging and Remote Sensing Laboratory Hyperspectral Water Quality 28 Aviris GT

Digital Imaging and Remote Sensing Laboratory Hyperspectral Water Quality 29 CHL Ground Truth Comparison RMS = 11.6 mg/m3 18% of [CHL] range

Digital Imaging and Remote Sensing Laboratory Hyperspectral Water Quality 30 TSS Ground Truth Comparison RMS = 4.0 g/m3 17.8% of [TSS] range Glint Area

Digital Imaging and Remote Sensing Laboratory Hyperspectral Water Quality 31 CDOM Ground Truth Comparison RMS = 2.2 [scalar] 17.2% of [CDOM] range Glint Area

Digital Imaging and Remote Sensing Laboratory Hyperspectral Water Quality 32 Evidence of solar glint AVIRIS Rochester Embayment May 20, 1999 slicks

Digital Imaging and Remote Sensing Laboratory Hyperspectral Water Quality 33 Scalar Concentration of CDOM CDOM(350 nm)=1.0 CDOM(350 nm)=0.2 CDOM(350 nm)=5.0

Digital Imaging and Remote Sensing Laboratory Hyperspectral Water Quality 34 CHL Model Prediction Means vs. Ground Truth

Digital Imaging and Remote Sensing Laboratory Hyperspectral Water Quality 35 CDOM Model Prediction Means vs. Ground Truth

Digital Imaging and Remote Sensing Laboratory Hyperspectral Water Quality 36 TSS Model Prediction Means vs. Ground Truth

Lake Bottom at Different Spatial Resolutions AVIRIS: 20 meter pixels Rochester Embayment May 20, 1999

Digital Imaging and Remote Sensing Laboratory Hyperspectral Water Quality 38 Lake Bottom at Different Spatial Resolutions MISI with 9ft pixels AVIRIS with 20m pixels Region: Lake Ontario North of Irondequoit Bay

Hyperspectral Imaging for Bottom Type Classification and Water Depth Determination M.S. Thesis Defense Nikole Wilson 10 Aug 2000

Digital Imaging and Remote Sensing Laboratory Hyperspectral Water Quality 40 Depth Varies Linearly Case 1 constant bottom Philpot’s synthetic data a|| has a parallel relationship with direction of changing depth Depth varies linearly X at 550 nm X at 650 nm

Digital Imaging and Remote Sensing Laboratory Hyperspectral Water Quality 41 Case 2 : Varied depth, bottom type Data form separate but parallel clusters in linearized space Clusters separated in linearized space by a distance relating to differences in bottom reflectances X at 550 nm X at 650 nm

Digital Imaging and Remote Sensing Laboratory Hyperspectral Water Quality 42 Data Collection Ginna Bottoms Gray rock 1 Red rock Redrock with algae Light gray rock Yellow rock Gray rock 2

Ontario Beach Qualitative Results Depth Picking up different bottom type Sand2.24 Rock2.43 Sand1.62 Rock21 BottomDepth

Lake Bottom at Different Spatial Resolutions solar glint MISI with 2ft pixelsMISI with 4ft pixels Lake Ontario at Russell Station Lake Ontario at Cranberry Pond

Lake Ontario Bathymetry