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1 Inland water algorithms Candidates and challenges for Diversity 2 Daniel Odermatt, Petra Philipson, Ana Ruescas, Jasmin Geissler, Kerstin Stelzer, Carsten.

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Presentation on theme: "1 Inland water algorithms Candidates and challenges for Diversity 2 Daniel Odermatt, Petra Philipson, Ana Ruescas, Jasmin Geissler, Kerstin Stelzer, Carsten."— Presentation transcript:

1 1 Inland water algorithms Candidates and challenges for Diversity 2 Daniel Odermatt, Petra Philipson, Ana Ruescas, Jasmin Geissler, Kerstin Stelzer, Carsten Brockmann

2 2 Chapt Processing stepMethods/Algorithms/Tools Chapter 3: Atmospheric Correction Atmospheric Correction over Land GlobAlbedo atmospheric correction SCAPE-M for land ATCOR 2/3 Durchblick Atmospheric Correction over Inland Water MERIS lakes and C2R CoastColour SCAPE-M for inland waters Normalizing at-sensor radiances MERIS lakes and C2R Adjacency Effect Correction (Water) ICOL SIMEC Context

3 3 Chapt Processing stepMethods/Algorithms/Tools Chapter 4: Lakes Processing Pre- classification 4.1 Differentiation of water types Separation by Ecoregion Optical Water Type classification using Fuzzy Logic Applicability ranges of water constituents Quality IOP retrieval by spectral inversion algorithms C2R / CC FUB MIP HYDROPT Linear matrix inversion approaches Feature specific band arithmetic FLH / MCI Band ratio algorithms Valid Pixel Retrieval Flagging Statistical filtering Lakes Water Temperature ARC-Lake Data Set Quantity Water ExtentSAR Water Body Data Set Lakes Water LevelESA river and lake dataset Context

4 4  Introduction – Algorithm requirements – State of the art  Atmospheric correction – Normalizing TOA radiances – Aerosol retrieval over water:C2R – Aerosol retrieval over land:Scape-M  Water constituent retrieval – Band ratios – C2R  Conclusions Content

5 5  Atmospheric correction requirements – Unsupervised, automatic processing – Computationally feasible – Validated use with MERIS images  Water constituent retrieval requirements – … as above – Applicable to optically deep inland waters – Chlorophyll-a and suspended matter products Introduction

6 6 mg/m 3 g/m 3 m >10>30>2

7 7 Schroeder (2005), Binding et al. (2010), Matthews et al. (2010)  Over clear waters, L atm exceeds L w by an order of magnitude   Needs correction  Over very turbid waters, signal strength increases strongly   Uncorrected analysis possible  Recent studies achieved better results with FLH, MCI on L w Normalizing TOA radiances

8 8 Doerffer & Schiller (2008)  Explicit atmospheric correction: MERIS Lakes ATBD – TOA is corrected for pressure and O3 variations with MERIS metadata – TOSA consists of aerosols, cirrus clouds, surface roughness variations – BOA RL w is calculated by a forward-NN – 2 different models for atmosphere and water training dataset  C2R background reflectance training range: – g/m 3 TSM – mg/m 3 CHL – m -1 a CDOM  Extended CoastColour training range: – 1000 g/m 3 TSM – 100 mg/m 3 CHL Aerosol retrieval over water: C2R

9 9 Odermatt et al. (2010) Aerosol retrieval over water: C2R

10 10 Guanter et al. (2010)  Scape-M – Modtran compiled LUTs – DEM input for topographic corrections – Retrieving rural aerosols and water vapour over land – Using 5 vegetation-soil mixture pixels per 30x30 km – Atmospheric properties are interpolated over lakes – Max km 2 lake area and 20 km shore distance Aerosol retrieval over land: Scape-M

11 11 Guanter et al. (2010) Aerosol retrieval over land: Scape-M

12 12  Automatic and accurate correction required in most cases  Aerosol retrieval over water – Provides RT-based, accurate estimates for low reflectances – Application limited by training ranges  Aerosol retrieval over land – Valuable backup for certain niches, e.g. retrieval of secondary chl-a peak – Limited by atmospheric, geographic and limnic constraints Summary: Atmospheric correction

13 13  Derived through empirical regression or bio-optical modeling  Retrieve CHL, TSM, CDOM individually  Primary CHL-absorption (OC) algorithms ( nm) not applicable  Secondary CHL-absorption feature shifting with concentration (681 nm) Band ratio algorithms

14 14 Gitelson et al., 2011: Azov Sea Red-NIR band ratios for CHL Red edge!

15 15 Doxaran et al. (2002): Gironde estuary, Bordeaux TSM sensitive bands a13 g/m 3 b23 g/m 3 c62 g/m 3 d355 g/m 3 e651 g/m 3 f985 g/m 3

16 16 Watanabe et al., 2011; Kusmierczyk-Michulec & Marks, 2000 CDOM absorption properties Maritime vs. inland aerosols Maritime vs. Baltic sea aerosols  Ambiguity can occur with all other optical parameters  Angstrom variations over continents  Band ratios make use of 2-4 bands of the visible spectrum  Methodological convergence is not significant

17 17 Odermatt et al. (2012) To which optically complex waters do recent “Case 2” algorithms apply? The literature review includes: – Matchup validation studies – Constituent retrieval from satellite imagery – Optically deep and complex waters – Explicit concentration ranges and R 2 – Published in ISI listed journals – Between Jan 2006 and May 2011 These criteria apply to a total of 52 papers. Algorithm validation ranges review

18 18 Odermatt et al. (2012) The literature review aims to: – Quantify the use of recent algorithms and sensors – Derive algorithm applicability ranges for coastal and inland waters – Clarify the ambiguous use of attributes like “turbid” and “clear” Algorithm validation ranges review AuthorsOligotrophicMesotrophicEutrophicHypereutr. Chapra & Dobson (1981) >5.6n.a. Wetzel (1983) n.a. Bukata et al. (1995) >18 Carlson & Simpson (1996) >56 Nürnberg (1996) >25 This study >10?

19 19 Odermatt et al. (2012) CHL band ratios 5 SeaWiFS 2 MODIS 1 GLI 8 MERIS 2 MODIS 1 HICO 2 TM/ETM+ 1 MERIS

20 20 Odermatt et al. (2012) TSM band ratios 5 empirical 5 semi-analytical

21 21 Odermatt et al. (2012) CDOM band ratios

22 22 Odermatt et al. (2012) Spectral inversion algorithms AuthorsAlgorithmCHL [mg/m 3 ]TSM [g/m 3 ]CDOM [m -1 ] maxminmaxminmaxmin Binding et al. (2011)NN algal_ Cui et al. (2010)NN algal_ Minghelli-Roman et al. (2011)NN algal_ Binding et al. (2011)NN C2R Giardino et al. (2010)NN C2R Matthews et al. (2010)NN C2R Odermatt et al. (2010)NN C2R Schroeder et al. (2007)NN FUB Shuchman et al. (2006)coupled NN Giardino et al. (2007)MIM Odermatt et al. (2008)MIP Santini et al. (2010)2 step inv Van der Woerd & Pasterkamp (2008)Hydropt Validation of C2R/algal_2/(FUB): – Numerous and independent – Adequate for low to medium concentrations – Inadequate for high concentrations Validation of other algorithms: – Limited in number and independence – Often restricted to “domestic” use validated | falsified | threshold R 2 =0.6

23 23 Odermatt et al. (2012) Variability range scheme medium low high TSM CDOM 510, 565 nm D‘Sa et al., 2006

24 24 Odermatt et al. (2012) Variability range scheme red-NIR band ratios for very turbid TSM red-NIR band ratios for eutrophic CHL NN for intermediate concentrations OC band ratios for oligotrophic CHL Representing coastal waters of mostly co- varying constituents band ratios for CDOM

25 25 wc retrieval: -FLH, MCI -Gitelson 2/3-band atm. correction: -none -SCAPE-M wc retrieval: -FUB -blue-green bands atm. correction: -C2R(+ICOL!) -FUB(+ICOL?) wc retrieval & atm. correction: -C2R -FUB Diversity recommendations

26 26 Conclusions from the validation review: – Algorithm validity ranges are defined at high confidence (52 papers) – MERIS neural networks are sufficiently and independently validated – MERIS’ 708 nm band provides unparalleled accuracy for eutrophic waters Open issues for use of the findings in diversity 2: – How is the required preclassification applied? Based on previous knowledge or on-the-flight? Spatio-temporally static or dynamic? – based on previous knowledge or iterative processing? Should algorithm blending be applied as suggested by Doerffer et al. (2012)? Conclusions

27 27 Thank you

28 28 Schroeder et al. (2007), Schroeder (2005)  Implicit atmospheric correction: FUB – Coupled water-atmosphere RT model MOMO – Simulation of 5 optical thicknesses of 8 aerosol types, 4 rel. humidities – Inversion of TOA reflectance where RS TOA is TOA reflectance in 12 MERIS bands x, y, z are transformed observation angles θ s is the illumination angle P is surface pressure for Rayleigh correction W is wind speed T is transmissivity And x is a neural network learning pattern corresponding to a set of concentrations – (Originally not meant for use for inland waters, e.g. altitude)) Aerosol retrieval over water

29 29 CoastColour – Rio de la Plata IGARSS * Munich * CoastColour AC L1b RGB SeaDAS l2gen L2 3 rd reprocessing Case2R L1b band 13 (865nm)

30 30 Doerffer et al. (2012) CoastColour neural network  Inv NN trained with 6 Mio. Hydrolight simulations Rw(10 bands)  IOPs  5 IOPs: a_pig, a_gelbstoff, b_ particle; NEW: a_detritus, b_white  SIOP variations by additional rather than hypothetically accurate IOPs  Accounting for T=0-36°C, S=0-42 ppt  Random variation spacing for bulk a and b instead of individual IOPs  CHL = 21*apig 1.04 for mg/m 3  TSM = (bp1+bp2)*1.73 for g/m 3  CDOM= ad+agfor m -1  k d calculated from IOPs for all 10 bands  z 90 is the average of the 3 lowest k d  Corresponds roughly to Secchi disc depth

31 31 Schroeder, 2005; Schroeder et al., 2007  Some conceptual differences – Uses MERIS level1 B TOA bands 1-7, 9-10, – Static 3-component IOP model – Forward simulations based on coupled atmosphere-ocean model (MOMO) – NN for direct RS TOA  IOP inversion – NN for indirect RS TOA  RS BOA  IOP performed similarly FUB neural network

32 32  Alternative architechture – Uses MERIS level1 B TOA bands 1-7, 9-10, – Schroeder, 2005; Schroeder et al., 2007  Bio-optical model training ranges – CHL: mg/m 3 – TSM: g/m3 – CDOM: m -1  Partially covarying constituents assumed FUB neural network

33 33 Odermatt et al., 2012  Greifensee 2011 EUT/C2R/FUB  16 MERIS images within 69 days  CHL profiles acquired automatically  Stratified cyanobacteria blooms Validation examples


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