Inland water algorithms Candidates and challenges for Diversity 2

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Inland water algorithms Candidates and challenges for Diversity 2. Daniel Odermatt, Petra Philipson , Ana Ruescas , Jasmin Geissler, Kerstin Stelzer, Carsten Brockmann. Context. Context. Content. Introduction Algorithm requirements State of the art Atmospheric correction - PowerPoint PPT Presentation

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Inland water algorithmsCandidates and challenges for Diversity 2Daniel Odermatt, Petra Philipson, Ana Ruescas, Jasmin Geissler, Kerstin Stelzer, Carsten Brockmann#ChaptProcessing stepMethods/Algorithms/ToolsChapter 3: Atmospheric Correction3.6.1Atmospheric Correction over LandGlobAlbedo atmospheric correctionSCAPE-M for landATCOR 2/3Durchblick3.6.2Atmospheric Correction over Inland WaterMERIS lakes and C2RCoastColourSCAPE-M for inland watersNormalizing at-sensor radiancesMERIS lakes and C2R3.6.3Adjacency Effect Correction (Water)ICOLSIMECContext#ChaptProcessing stepMethods/Algorithms/ToolsChapter 4: Lakes ProcessingPre-classification4.1Differentiation of water typesSeparation by EcoregionOptical Water Type classification using Fuzzy LogicApplicability ranges of water constituents Quality 4.2.1IOP retrieval by spectral inversion algorithmsC2R / CCFUBMIP HYDROPT Linear matrix inversion approaches 4.2.2Feature specific band arithmeticFLH / MCIBand ratio algorithms4.2.4Valid Pixel RetrievalFlaggingStatistical filtering4.2.3Lakes Water TemperatureARC-Lake Data SetQuantity4.3.1Water ExtentSAR Water Body Data Set4.3.2Lakes Water LevelESA river and lake datasetContext#IntroductionAlgorithm requirementsState of the art

Atmospheric correctionNormalizing TOA radiancesAerosol retrieval over water:C2RAerosol retrieval over land:Scape-M

Water constituent retrievalBand ratiosC2R

ConclusionsContent#Atmospheric correction requirementsUnsupervised, automatic processingComputationally feasibleValidated use with MERIS images

Water constituent retrieval requirements as aboveApplicable to optically deep inland watersChlorophyll-a and suspended matter productsIntroduction#Introduction

mg/m3g/m3m-110-30-30-0.823-103-300.8-23>10>30>2#Schroeder (2005), Binding et al. (2010), Matthews et al. (2010)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

Normalizing TOA radiances

#Doerffer & Schiller (2008)Explicit atmospheric correction: MERIS Lakes ATBDTOA is corrected for pressure and O3 variations with MERIS metadataTOSA consists of aerosols, cirrus clouds, surface roughness variations BOA RLw is calculated by a forward-NN2 different models for atmosphere and water training dataset

C2R background reflectance training range:0.01-100 g/m3 TSM0.01-43 mg/m3 CHL0.003-9.2 m-1 aCDOM

Extended CoastColour training range:1000 g/m3 TSM100 mg/m3 CHL

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

Aerosol retrieval over water: C2R#Guanter et al. (2010)Scape-MModtran compiled LUTsDEM input for topographic correctionsRetrieving rural aerosols and water vapour over landUsing 5 vegetation-soil mixture pixels per 30x30 kmAtmospheric properties are interpolated over lakesMax. 1600 km2 lake area and 20 km shore distance

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

Aerosol retrieval over land: Scape-M#Automatic and accurate correction required in most cases

Aerosol retrieval over water Provides RT-based, accurate estimates for low reflectancesApplication limited by training ranges

Aerosol retrieval over landValuable backup for certain niches, e.g. retrieval of secondary chl-a peakLimited by atmospheric, geographic and limnic constraints Summary: Atmospheric correction#Derived through empirical regression or bio-optical modeling

Retrieve CHL, TSM, CDOM individually

Primary CHL-absorption (OC) algorithms (400-550 nm) not applicable

Secondary CHL-absorption feature shifting with concentration (681 nm)Band ratio algorithms#Gitelson et al., 2011: Azov Sea Red-NIR band ratios for CHL

Red edge!

#Doxaran et al. (2002): Gironde estuary, Bordeaux TSM sensitive bands

a13 g/m3b23 g/m3c62 g/m3d355 g/m3e651 g/m3f985 g/m3

#Watanabe et al., 2011; Kusmierczyk-Michulec & Marks, 2000 CDOM absorption properties

Maritime vs. inland aerosolsMaritime vs. Baltic sea aerosolsAmbiguity 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#Odermatt et al. (2012)To which optically complex waters do recent Case 2 algorithms apply? The literature review includes:Matchup validation studiesConstituent retrieval from satellite imageryOptically deep and complex watersExplicit concentration ranges and R2Published in ISI listed journalsBetween Jan 2006 and May 2011These criteria apply to a total of 52 papers.Algorithm validation ranges review#Odermatt et al. (2012)The literature review aims to:Quantify the use of recent algorithms and sensorsDerive algorithm applicability ranges for coastal and inland watersClarify the ambiguous use of attributes like turbid and clearAlgorithm validation ranges reviewAuthorsOligotrophicMesotrophicEutrophicHypereutr.Chapra & Dobson (1981)0-2.92.9-5.6>5.6n.a.Wetzel (1983)0.3-4.53-113-78n.a.Bukata et al. (1995)0.8-2.52.5-66-18>18Carlson & Simpson (1996)0-2.62.6-2020-56>56Nrnberg (1996)0-3.53.5-99-25>25This study0-33-10>10?#Odermatt et al. (2012) CHL band ratios

5 SeaWiFS2 MODIS1 GLI8 MERIS2 MODIS1 HICO2 TM/ETM+1 MERIS#Odermatt et al. (2012) TSM band ratios

5 empirical5 semi-analytical#Odermatt et al. (2012) CDOM band ratios

#Odermatt et al. (2012) Spectral inversion algorithmsAuthorsAlgorithmCHL [mg/m3]TSM [g/m3]CDOM [m-1]maxminmaxminmaxminBinding et al. (2011)NN algal_270.51.919.60.87.10.5Cui et al. (2010)NN algal_216.10.767.81.52.00.7Minghelli-Roman et al. (2011)NN algal_29.00.0----Binding et al. (2011)NN C2R70.51.919.60.87.10.5Giardino et al. (2010)NN C2R74.511.7--4.01.3Matthews et al. (2010)NN C2R247.069.260.730.07.13.4Odermatt et al. (2010)NN C2R9.00.0----Schroeder et al. (2007)NN FUB12.60.114.32.72.00.8Shuchman et al. (2006)coupled NN2.50.12.71.33.50.0Giardino et al. (2007)MIM2.21.32.10.9--Odermatt et al. (2008)MIP4.00.6----Santini et al. (2010)2 step inv5.01.813.03.00.80.1Van der Woerd & Pasterkamp (2008)Hydropt20.00.030.00.01.60.0Validation of C2R/algal_2/(FUB):Numerous and independentAdequate for low to medium concentrationsInadequate for high concentrationsValidation of other algorithms:Limited in number and independenceOften restricted to domestic usevalidated | falsified | threshold R2=0.6#Odermatt et al. (2012) Variability range scheme

mediumlowhighRetrieved constituent

concentration level

type

contravariance

Reading example:DSa et al. (2006)retrieve low CDOMwith 510, 565 nm bandsat 0.3-13.0 mg/m3 CHLand 0.5-5.5 g/m3 TSMTSMCDOM510, 565 nmDSa et al., 2006CHLTSMCDOM#Odermatt et al. (2012) Variability range scheme

red-NIR band ratiosfor very turbid TSM

red-NIR band ratiosfor eutrophic CHL

NN for intermediate concentrations

OC band ratiosfor oligotrophic CHL

Representing coastal waters of mostly co-varying constituentsband ratiosfor CDOM#

red-NIR bandsC2R & FUBFUB & blue-green bandsblue-green bandswc retrieval:FLH, MCIGitelson 2/3-band

atm. correction:noneSCAPE-M

wc retrieval:FUBblue-green bands

atm. correction:C2R(+ICOL!)FUB(+ICOL?)

wc retrieval & atm. correction:C2RFUBDiversity recommendations#Conclusions from the validation review:Algorithm validity ranges are defined at high confidence (52 papers)MERIS neural networks are sufficiently and independently validatedMERIS 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#Thank you#Schroeder et al. (2007), Schroeder (2005)Implicit atmospheric correction: FUBCoupled water-atmosphere RT model MOMOSimulation of 5 optical thicknesses of 8 aerosol types, 4 rel. humiditiesInversion of TOA reflectance

whereRSTOA is TOA reflectance in 12 MERIS bandsx, y, z are transformed observation angless is the illumination angleP is surface pressure for Rayleigh correctionW is wind speedT is transmissivityAnd 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

#CoastColour Rio de la PlataIGARSS * Munich * 24.07.2012

CoastColour ACL1b RGBSeaDAS l2genL2 3rd reprocessingCase2RL1b band 13 (865nm)

#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=0-36C, S=0-42 ppt

Random variation spacing for bulk a and b instead of individual IOPs CHL = 21*apig1.04 for 0.01-100 mg/m3

TSM = (bp1+bp2)*1.73 for 0.01-1000 g/m3

CDOM= ad+agfor 0.01-4 m-1kd calculated from IOPs for all 10 bands

z90 is the average of the 3 lowest kd

Corresponds roughly to Secchi disc depth #Schroeder, 2005; Schroeder et al., 2007Some conceptual differences

Uses MERIS level1 B TOA bands 1-7, 9-10, 12-14

Static 3-component IOP model

Forward simulations based on coupled atmosphere-ocean model(MOMO)

NN for direct RSTOA IOP inversion

NN for indirect RSTOA RSBOA IOPperformed similarly FUB neural network

#Alternative architechtureUses MERIS level1 B TOA bands 1-7, 9-10, 1