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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 Brockmann Slide 2 2 Chapt Processing stepMethods/Algorithms/Tools Chapter 3: Atmospheric Correction 3.6.1 Atmospheric Correction over Land GlobAlbedo atmospheric correction SCAPE-M for land ATCOR 2/3 Durchblick 3.6.2 Atmospheric Correction over Inland Water MERIS lakes and C2R CoastColour SCAPE-M for inland waters Normalizing at-sensor radiances MERIS lakes and C2R 3.6.3 Adjacency Effect Correction (Water) ICOL SIMEC Context Slide 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 4.2.1 IOP retrieval by spectral inversion algorithms C2R / CC FUB MIP HYDROPT Linear matrix inversion approaches 4.2.2 Feature specific band arithmetic FLH / MCI Band ratio algorithms 4.2.4 Valid Pixel Retrieval Flagging Statistical filtering 4.2.3 Lakes Water Temperature ARC-Lake Data Set Quantity 4.3.1 Water ExtentSAR Water Body Data Set 4.3.2 Lakes Water LevelESA river and lake dataset Context Slide 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 Slide 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 Slide 6 6 mg/m 3 g/m 3 m -1 10-3 0-0.8 23-103-300.8-2 3>10>30>2 Slide 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 Slide 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: 0.01-100 g/m 3 TSM 0.01-43 mg/m 3 CHL 0.003-9.2 m -1 a CDOM Extended CoastColour training range: 1000 g/m 3 TSM 100 mg/m 3 CHL Aerosol retrieval over water: C2R Slide 9 9 Odermatt et al. (2010) Aerosol retrieval over water: C2R Slide 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. 1600 km 2 lake area and 20 km shore distance Aerosol retrieval over land: Scape-M Slide 11 11 Guanter et al. (2010) Aerosol retrieval over land: Scape-M Slide 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 Slide 13 13 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 Slide 14 14 Gitelson et al., 2011: Azov Sea Red-NIR band ratios for CHL Red edge! Slide 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 Slide 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 Slide 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 Slide 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)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>18 Carlson & Simpson (1996)0-2.62.6-2020-56>56 Nrnberg (1996)0-3.53.5-99-25>25 This study0-33-10>10? Slide 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 Slide 20 20 Odermatt et al. (2012) TSM band ratios 5 empirical 5 semi-analytical Slide 21 21 Odermatt et al. (2012) CDOM band ratios Slide 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_270.51.919.60.87.10.5 Cui et al. (2010)NN algal_216.10.767.81.52.00.7 Minghelli-Roman et al. (2011)NN algal_29.00.0---- Binding et al. (2011)NN C2R70.51.919.60.87.10.5 Giardino et al. (2010)NN C2R74.511.7--4.01.3 Matthews et al. (2010)NN C2R247.069.260.730.07.13.4 Odermatt et al. (2010)NN C2R9.00.0---- Schroeder et al. (2007)NN FUB12.60.114.32.72.00.8 Shuchman et al. (2006)coupled NN2.50.12.71.33.50.0 Giardino et al. (2007)MIM2.21.32.10.9-- Odermatt et al. (2008)MIP4.00.6---- Santini et al. (2010)2 step inv5.01.813.03.00.80.1 Van der Woerd & Pasterkamp (2008)Hydropt20.00.030.00.01.60.0 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 Slide 23 23 Odermatt et al. (2012) Variability range scheme medium low high TSM CDOM 510, 565 nm DSa et al., 2006 Slide 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 Slide 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 Slide 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 Slide 27 27 Thank you Slide 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 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 Slide 29 29 CoastColour Rio de la Plata IGARSS * Munich * 24.07.2012 CoastColour AC L1b RGB SeaDAS l2gen L2 3 rd reprocessing Case2R L1b band 13 (865nm) Slide 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=0-36C, S=0-42 ppt Random variation spacing for bulk a and b instead of individual IOPs CHL = 21*apig 1.04 for 0.01-100 mg/m 3 TSM = (bp1+bp2)*1.73 for 0.01-1000 g/m 3 CDOM= ad+agfor 0.01-4 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 Slide 31 31 Schroeder, 2005; Schroeder et al., 2007 Some 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 RS TOA IOP inversion NN for indirect RS TOA RS BOA IOP performed similarly FUB neural network Slide 32 32 Alternative architechture Uses MERIS level1 B TOA bands 1-7, 9-10, 12-14 Schroeder, 2005; Schroeder et al., 2007 Bio-optical model training ranges CHL:0.05-50 mg/m 3 TSM:0.05-50 g/m3 CDOM:0.005-1 m -1 Partially covarying constituents assumed FUB neural network Slide 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