CHALLENGES GLOBAL APPROACH TO:

Transcription

1 CHALLENGES GLOBAL APPROACH TO: SPECTRAL LIBRARIES(MACROPHYTES, MACRO_ALGAE, SEAGRASSES, MUD, SAND, RUBBLE, DETRITUS, BENTHIC MICRO_ALGAE PHYTOPLANKTON) INLAND BIO-OPTICAL DATABASES-OPEN SOURCE INLAND WATER CALIBRATION & VALIDATION DO WE NEED DEDICATED INLAND SATELLITE SENSORS? ATMOSPHERIC CORRECTION: GENERIC METHODS OR SENSOR SPECIFIC METHODS ADJACENCY EFFECT _DOES IT (ALWAYS) EXIST? IF SO-HOW DO CORRECT FOR IT? IF NOT AND YOU DO CORRECT FOR IT. POLARISATION (Can we ignore it over inland waters)?) VARIABLE SIOPS BOTTOM VISIBLITY

2 CHALLENGES What do we (as a global community-of-practice) advise organisations such as the WorldBank or national EPA s on FIT FOR PURPOSE applications.

3 Overview Estuarine and Coastal Remote Sensing in Australia Effects of varying SIOP (or water types) on (simulated) subsurface irradiance reflectance spectra

4 Examples of reflectance spectra, Fitzroy River-Keppel Bay Sep 03 FK_01 Surface Reflectance (nm ) Overview Estuarine and Coastal Remote Sensing in Australia FK_03 FK_15 FK_42

5 Effects of varying SIOP (or water types) on (simulated) subsurface irradiance reflectance spectra Fitzroy River Estuary: Fixed concentrations CHL=0.3 μg/l; NAP=1 mg/l; CDOM=0.08 m -1 ; z=30m Reflectance Irradiance subsurface reflectance R(0-) SIOPS vary from tropical river to open ocean 12% 10% 8% 6% 4% 2% 0% Wavelength (nm) SA_FK09_3 SA_FK15_3 SA_FK18_3 SA_FK34_3 SA_FK35_3 SA_FK40_3 SA_FK63_3

6 Adaptive (to SIOP variability) semianalytical inversion of ocean color radiometry in optically complex waters Vittorio E. Brando, Arnold G. Dekker, Young Je Park and Thomas Schroeder APPLIED OPTICS / Vol. 51, No. 15 / 20 May 2012

7 International Working Group on Evaluation of Physics-based Algorithms for Mapping in Optically Shallow Waters Co- Funded by: Australian Research Council Linkage Grant (Sep-07) USA-Office of Naval Research Ocean Optics & Biology University of Queensland Commonwealth (of Australia) Scientific and Industrial Research Organisation And significant in-kind contributions by all participants Led to: Dekker A.G., Phinn S.R., Anstee J.M., Bissett P. Brando V.E., Casey B. Fearns P., Hedley J., Klonowski, W., Lee Z.P., Lynch M., Lyons M., Mobley C. and Roelfsema C. (2011) Intercomparison of shallow water bathymetry, hydro-optics and benthos mapping techniques in Australian and Caribbean coastal environments; Limn. & Ocean. Methods. 9:pp

8 5 Physics-based Inversion Models Tested and 1 Semi-empirical Model 1.HOPE :Hyper-spectral Optimization Process Exemplar 2.ALLUT: Model Inversion by Adaptive Linearized Look-Up Trees 3.BRUCE: Bottom Reflectance Un-mixing Computation of the Environment 4.SAMBUCA: Semi-Analytical Model for Bathymetry, Unmixing, and Concentration Assessment 5.SMLUT: Spectrum-Matching and Look-Up-Table Inversion 6.Lyzenga Method

9 The conceptual physics-based model for optically deep waters

10 The conceptual physics-based model for optically shallow waters (...optically deep on the right)

11 Because there are actually two upwelling light streams: one from the bottom and one from the water column can be described as B and C respectively. The equation then becomes: R( 0, H ) R exp( KdH )[ Aexp( H ) R exp( H )] This equation reads as: The subsurface irradiance reflectance over a water body with bottom visibility is equal to : the subsurface irradiance reflectance of an infinitely deep water column plus the product of the vertical downward attenuation of the downwelling light stream times the difference between the vertical upward attenuated bottom irradiance reflectance and the vertically upward attenuated infinitely deep water column irradiance reflectance. B C

12 HOPE, SAMBUCA (ALLUT and BRUCE) HOPE: rrs ( ) modelled f P, G, X, B, H, S, Y is replaced in SAMBUCA (ALLUT and BRUCE) by: r model rs C CHL is the concentration of chlorophyll a C CDOM is the concentration of CDOM, ie a* CDOM (λ CDOM ) is set to 1 S C is the slope of the CDOM absorption C NAP is the concentration of NonAlgalParticulate matter S NAP is the slope of NonAlgalParticulate absorption a* NAP (λ tr ) is specific absorption of NonAlgalParticulate at λ tr, which is sample dependent b b * NAP (λ 0 ) is the specific backscattering due to NonAlgalParticulate matter b b * PHY (λ 0 ) is the specific backscattering due to phytoplankton A i, A j are the reflectances of substrate i & j [selected from a spectral library of n spectra] q ij is the ratio of substrate i to substrate j within each pixel (or q ijk for 3 substratum types) Lee et al (1998, 1999, 2001) CCHL, CCDOM, CNAP, H, qij, Ai, Aj, SCDOM, SNAP, f * * * * YPHY, YNAP, aphy, anap 0, bb PHY ( 0 ), bb NAP ( 0 )

13 R rs ( ) = F[a( ), b b ( ), ( ), H] (Hyperspectral Optimization Process Exemplar) HOPE (H, IOP, ) 0.04 (mea. vs mod. Rrs) bathymetry Optical properties Rrs (sr^-1) grass sandy Wavelength (nm) (Lee et al. 1999, 2001)

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15 Results for Bathymetry The phaonmneal pweor of the hmuan mnid, aoccdrnig to a rscheearch at Cmabrigde Uinervtisy, it dseno't mtaetr in what oerdr the ltteres in a word are, the olny iproamtnt tihng is that the frsit and last ltteer be in the rghit pclae.

16 Conclusions (1) the radiative-transfer-based methods were more accurate than the empirical approach for bathymetric retrieval (2) the accuracies and processing times of these were roughly inversely related to the complexity of the models used (3) all inversion methods provided moderately accurate retrievals of bathymetry, water column inherent optical properties, and benthic reflectance in areas less than 13 m deep with relatively clear water and homogeneous to heterogeneous benthic/substrate covers (4) higher accuracy retrievals were obtained from the more complex and locally parameterized methods; (5) no single method compared here can be considered optimal for all situations.

17 Recommendations A re-analysis of these same or additional sites with satellite hyperspectral image data with lower spatial and radiometric resolution would be instructive to establish guidelines for repeatable local to global scale shallow water mapping approaches.

18 Recommendations Useful to discriminate optically shallow from optically deep water prior to providing bathymetry and substratum maps Collecting the perfect combined satellite and/or airborne remote sensing and in situ dataset for validation is harder then realised previously-this would need; proper atcor parameterisation (incl sky and sunglint assessment) above and in water spectroradiometry; underwater benthic reflectance spectra of target material as well as polygons (preferably covering 20 pixels or more) of known heterogeneous or homogeneous composition Independent bathymetry data IOP and SIOP characterisation of water column (~thus concentrations too!) Good weather conditions Unlimited funds So..probably requires establishment of a cal-val shallow water supersite(s) with ongoing in situ instrumentation, accessibility and creation of systematic georeferenced habitat information.

19 CHALLENGES What do we (as a GEO global community-of-practice) advise organisations such as the WorldBank or national or continental EPA s on FIT FOR PURPOSE applications implementable within: 1 year 3 years 5 years Are the products we currently (sometimes) deliver the ones that end-users need? e.g. do they want chlorophyll concentration or do they want eutrophication status and trend?