Inland water algorithms Candidates and challenges for Diversity 2

1. Inland water algorithmsCandidatesandchallengesforDiversity2 Daniel Odermatt, Petra Philipson, Ana Ruescas, Jasmin Geissler, Kerstin Stelzer, Carsten Brockmann

2. Context

3. Context

4. Content 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

5. Introduction 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

6. Introduction

7. Normalizing TOA radiances Over clear waters, Latm exceedsLw by an order of magnitude Needs correction Over very turbid waters, signalstrength increases strongly Uncorrected analysis possible Recent studies achieved betterresults with FLH, MCI on Lw Schroeder (2005), Binding et al. (2010), Matthews et al. (2010)

8. Aerosol retrieval over water: C2R 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 RLw is calculated by a forward-NN • 2 different models for atmosphere and water training dataset C2R background reflectance training range: • 0.01-100 g/m3 TSM • 0.01-43 mg/m3 CHL • 0.003-9.2 m-1 aCDOM Extended CoastColour training range: • 1000 g/m3 TSM • 100 mg/m3 CHL Doerffer & Schiller (2008)

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

10. Aerosol retrieval over land: Scape-M 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. 1600 km2 lake area and 20 km shore distance Guanter et al. (2010)

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

12. Summary: Atmospheric correction 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

13. Band ratio algorithms Derivedthroughempiricalregressionorbio-opticalmodeling Retrieve CHL, TSM, CDOM individually PrimaryCHL-absorption (OC) algorithms (400-550 nm) notapplicable SecondaryCHL-absorptionfeatureshiftingwithconcentration (681 nm)

14. Red-NIR band ratiosfor CHL Red edge! Gitelson et al., 2011: AzovSea

15. TSM sensitive bands a 13 g/m3 b 23 g/m3 c 62 g/m3 d 355 g/m3 e 651 g/m3 f 985 g/m3 Doxaran et al. (2002): Girondeestuary, Bordeaux

16. CDOM absorptionproperties • Ambiguitycanoccurwith all otheropticalparameters • Angstromvariationsovercontinents • Band ratiosmakeuse of 2-4 bands of thevisiblespectrum • Methodologicalconvergenceisnotsignificant Maritime vs. Balticseaaerosols Maritime vs. inlandaerosols Watanabe et al., 2011; Kusmierczyk-Michulec & Marks, 2000

17. Algorithmvalidationrangesreview 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 R2 • Published in ISI listed journals • Between Jan 2006 and May 2011 These criteria apply to a total of 52 papers. Odermatt et al. (2012)

18. Algorithmvalidationrangesreview 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” Odermatt et al. (2012)

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

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

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

22. Spectralinversionalgorithms 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 R2=0.6 Odermatt et al. (2012)

23. Variabilityrangescheme Retrievedconstituent concentrationlevel type contravariance Reading example: D‘Sa et al. (2006) retrievelowCDOM with 510, 565 nmbands at0.3-13.0 mg/m3 CHL and0.5-5.5 g/m3 TSM high CDOM TSM medium CHL low TSM CDOM 510, 565 nmD‘Sa et al., 2006 Odermatt et al. (2012)

24. Variabilityrangescheme band ratios for CDOM red-NIR band ratios forveryturbid TSM red-NIR band ratios foreutrophicCHL NN for intermediate concentrations OC band ratios foroligotrophic CHL Representingcoastalwatersofmostlyco-varyingconstituents Odermatt et al. (2012)

25. Diversityrecommendations blue-greenbands • wcretrieval: • FLH, MCI • Gitelson 2/3-band • atm. correction: • none • SCAPE-M • wcretrieval: • FUB • blue-greenbands • atm. correction: • C2R (+ICOL!) • FUB (+ICOL?) • wcretrieval & atm. correction: • C2R • FUB red-NIR bands FUB & blue-greenbands C2R & FUB

26. Conclusions 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 previousknowledgeor on-the-flight? • Spatio-temporallystaticordynamic? – based on previousknowledgeor iterative processing? • Should algorithm blending be applied as suggested by Doerffer et al. (2012)?

27. Thankyou

28. Aerosol retrieval over water Implicitatmosphericcorrection: FUB • Coupledwater-atmosphere RT model MOMO • Simulation of 5 opticalthicknesses of 8 aerosoltypes, 4 rel. humidities • Inversion of TOA reflectance where RSTOAis TOA reflectance in 12 MERIS bands x, y, z aretransformedobservationangles θsistheillumination angle P issurfacepressurefor Rayleigh correction W is wind speed T istransmissivity And x is a neuralnetworklearningpatterncorresponding to a set of concentrations • (Originally not meantforuseforinlandwaters, e.g. altitude)) Schroeder et al. (2007), Schroeder (2005)

29. IGARSS * Munich * 24.07.2012 CoastColour – Rio de la Plata L1b band 13 (865nm) L1b RGB Case2R SeaDAS l2gen CoastColour AC L2 3rd reprocessing

30. CoastColourneuralnetwork • CHL = 21*apig1.04for0.01-100 mg/m3 • TSM = (bp1+bp2)*1.73 for 0.01-1000 g/m3 • CDOM= ad+agfor 0.01-4 m-1 • kdcalculatedfromIOPsfor all 10 bands • z90istheaverage of the 3 lowestkd • Correspondsroughly to Secchidiscdepth • Inv NN trainedwith 6Mio. Hydrolightsimulations Rw(10 bands)IOPs • 5 IOPs: a_pig, a_gelbstoff, b_ particle; NEW: a_detritus, b_white • SIOP variationsby additional ratherthanhypotheticallyaccurateIOPs • Accountingfor T=0-36°C, S=0-42 ppt • Randomvariationspacingforbulk a and b instead of individualIOPs Doerffer et al. (2012)

31. FUB neuralnetwork Someconceptualdifferences • Uses MERIS level1 B TOA bands 1-7, 9-10, 12-14 • Static 3-component IOP model • Forward simulationsbased on coupledatmosphere-oceanmodel(MOMO) • NN fordirect RSTOA IOP inversion • NN forindirect RSTOA RSBOA IOPperformedsimilarly Schroeder, 2005; Schroeder et al., 2007

32. FUB neuralnetwork • Alternative architechture • Uses MERIS level1 B TOA bands 1-7, 9-10, 12-14 Schroeder, 2005; Schroeder et al., 2007 Bio-opticalmodeltrainingranges • CHL: 0.05-50 mg/m3 • TSM: 0.05-50 g/m3 • CDOM: 0.005-1 m-1 Partiallycovaryingconstituentsassumed

33. Validation examples Greifensee 2011 EUT/C2R/FUB 16 MERIS imageswithin 69 days CHL profilesacquiredautomatically Stratifiedcyanobacteriablooms Odermatt et al., 2012