Mapping water constituents in Lake Constance using CHRIS/Proba

Transcription

1 S. Miksa, T. Heege, V. Kisselev and P. Gege Mapping water constituents in Lake Constance using CHRIS/Proba 3rd ESA CHRIS/Proba Workshop Frascati,, March 2005

2 Overview - Test site and in-situ data - The model MIP - Accuracy of inverse modeling - Maps of water constituents - Plans for Summary and outlook

3 Lake Constance (Bodensee)

4 Available Data Parameter Sensor 10 July July Sept Apr Jun 04 Lu (TOA) CHRIS-PROBA x x x x x Lu (TOA ) MERIS - - x - - Chl-a, TSM x x x - - OD Atmosphere x x x x x Additional data without in-situ: 12 Jan 05, 5 Feb 05 Littoral site: 14 July 04, 25 Dec 04, 6 Feb 05 to be processed...

5 Forward calculation MIP: Modular inversion program (Heege, Kisselev, Miksa) Coupled model for water and atmosphere Atmosphere radiative transfer calculated accurately using Finite element method (FEM: Kisselev, Bulgarelli, Roberti) several layers, 3 aerosol types (maritime, urban, rural) Water irradiance reflectance parameterised with a and b b after Gordon et al. conversion to radiance using Q data base specific inherent optical properties were measured at Lake Constance

6 Process Diagram MIP Grid Definition grid parameter grid of RT database System Initialization Atmosphere atmosph. model & opt. properties Optical models spec. opt. properties water, land, lake bottom Sensors sensor parameters RT model Radiative Transfer Data extraction Mission extraction MDB RT main database atmosph. & water atmosph. & waterq atmosph. & land* MissionDB RT database Mission Initialization Optical closure calc. Sensor calibration test Water constituents Primary production Retrieval modules Aerosol retrieval Atmospheric correction Sunglitter correction Water body correction Coverage detection MISSION navigation data auxiliary data image data Image Processing

7 Process Diagram MIP Grid Definition grid parameter grid of RT database System Initialization Atmosphere atmosph. model & opt. properties Optical models spec. opt. properties water, land, lake bottom Sensors sensor parameters RT model Radiative Transfer Data extraction Mission extraction MDB RT main database atmosph. & water atmosph. & waterq atmosph. & land* MissionDB RT database Mission Initialization Optical closure calc. Sensor calibration test Water constituents Primary production Retrieval modules Aerosol retrieval Atmospheric correction Sunglitter correction Water body correction Coverage detection MISSION navigation data auxiliary data image data Image Processing

8 Process Diagram MIP Grid Definition grid parameter grid of RT database System Initialization Atmosphere atmosph. model & opt. properties Optical models spec. opt. properties water, land, lake bottom Sensors sensor parameters RT model Radiative Transfer Data extraction Mission extraction MDB RT main database atmosph. & water atmosph. & waterq atmosph. & land* MissionDB RT database Mission Initialization Optical closure calc. Sensor calibration test Water constituents Primary production Retrieval modules Aerosol retrieval Atmospheric correction Sunglitter correction Water body correction Coverage detection MISSION navigation data auxiliary data image data Image Processing

9 Inversion algorithm Spectrum L( λ ) τ, τ τ Fit of + sm >750nm 2 3 (Radiance) 1, Fit of chl + sm +y < 750nm (Reflectance & Absortion) Fit all parameters at all wavelengths (radiance) atm. param. + water constituents

10 Comparison TOA radiances Radiances CHRIS 19 Sept all stations at 0, 36 and a A1 100 b B2 Radiance [mwm -2 sr -1 nm -1 ] c C3 d 4 D e 5 E f F 6 g G7 H h 8 i I 9 j 10 J k 11 K l 12 L m 13 M 14 n N measured spectra fitted spectra o O 16 p P 17 q Q Wavelength [nm]

11 Comparison of reflectances 7 6 Reflectance 19 Sept all stations at 0 and 36 - Reflectance L u /E d in % fitted spectra corrected spectra Wavelength [nm]

12 Problems with reflectances 10 Reflectance 19 Sept all stations at Reflectance L u /E d Wavelength [nm] fitted spectra corrected spectra

13 Accuracy of inverse modeling CHRIS/PROBA Retrieval of water constituents and all stations, 0 and 36 CHL Mean relative difference: 33% N=22 TSM Mean relative difference: 24% N= Chlorophyll-a + Phaeophytin [µg/l] 2,4 2,2 2,0 Total Suspended Matter [mg/l] Modular Inversion Program Modular Inversion Program 1,8 1,6 1,4 1,2 1,0 0,8 0,6 0, , in-situ 0,0 0,0 0,5 1,0 1,5 2,0 2,5 in-situ

14 19 Sept and 36 original data * land masked

15 19 Sept suspended matter (SM) and chlorophyll (CHL) * Shallow water areas not masked

16 19 Sept and 36 suspended matter (SM) and chlorophyll (CHL) SM [mg/l] * Shallow water areas not masked CHL [µg/l]

17 17 June /36 /-36 original data * land masked

18 17 June /36 /-36 suspended matter (SM) and chlorophyll (CHL) SM [mg/l] CHL [µg/l] *Shallow water areas not masked

19 Summary and outlook -retrieval of total suspended matter is within the error margin of in-situ data, separation of chlorophyll and gelbstoff causes problems -implementation of flourescence to water model didn t improve separability of chlorophyll and gelbstoff as no influence seen in Lake Constance -distribution maps of chlorophyll and suspended matter give reasonable results - implementation of sunglitter correction and cloud screening -parametrisation of column water vapor and ozone content in MIP -investigation on role of absorption in aerosol model

20 Plans for Lake Constance littoral (additional site) 4 3 scenes in July 2. Lake Sevan (Armenia) (new site) /40 49 N /45 42 E 4 3 scenes in July/Ausgust 3. Lake Constance pelagial (actual site) 4 2 scenes in March/April