Inland water algorithms Candidates and challenges for Diversity 2 Daniel Odermatt, Petra Philipson, Ana Ruescas, Jasmin Geissler, Kerstin Stelzer, Carsten Brockmann 1 Context Chapter 3: Atmospheric Correction Chapt Processing step Methods/Algorithms/Tools GlobAlbedo atmospheric correction 3.6.1 Atmospheric Correction over Land SCAPE-M for land ATCOR 2/3 Durchblick MERIS lakes and C2R CoastColour 3.6.2 Atmospheric Correction over Inland Water SCAPE-M for inland waters Normalizing at-sensor radiances MERIS lakes and C2R 3.6.3 Adjacency Effect Correction (Water) ICOL SIMEC 2 Context Preclassification Chapt Processing step Methods/Algorithms/Tools Separation by Ecoregion 4.1 Differentiation of water types Optical Water Type classification using Fuzzy Logic Applicability ranges of water constituents IOP retrieval by spectral inversion 4.2.1 algorithms FUB MIP Quality HYDROPT Linear matrix inversion approaches 4.2.2 Feature specific band arithmetic 4.2.4 Valid Pixel Retrieval FLH / MCI Band ratio algorithms Flagging Statistical filtering Quantity Chapter 4: Lakes Processing C2R / CC 4.2.3 Lakes Water Temperature ARC-Lake Data Set 4.3.1 Water Extent SAR Water Body Data Set 4.3.2 Lakes Water Level ESA river and lake dataset 3 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 4 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 5 Introduction 1 2 3 mg/m3 0-3 3-10 >10 g/m3 0-3 3-30 >30 m-1 0-0.8 0.8-2 >2 6 Normalizing TOA radiances Over clear waters, Latm exceeds Lw 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 Lw Schroeder (2005), Binding et al. (2010), Matthews et al. (2010) 7 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) 8 Aerosol retrieval over water: C2R Odermatt et al. (2010) 9 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) 10 Aerosol retrieval over land: Scape-M Guanter et al. (2010) 11 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 12 Band ratio algorithms 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) 13 Red-NIR band ratios for CHL Gitelson et al., 2011: Azov Sea 14 TSM sensitive bands a b c d e f 13 g/m3 23 g/m3 62 g/m3 355 g/m3 651 g/m3 985 g/m3 Doxaran et al. (2002): Gironde estuary, Bordeaux 15 CDOM absorption properties 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 Maritime vs. inland aerosols Methodological convergence is not significant Watanabe et al., 2011; Kusmierczyk-Michulec & Marks, 2000 16 Algorithm validation ranges review 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) 17 Algorithm validation ranges review 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” Authors Chapra & Dobson (1981) Oligotrophic Mesotrophic Eutrophic Hypereutr. 0-2.9 2.9-5.6 >5.6 n.a. Wetzel (1983) 0.3-4.5 3-11 3-78 n.a. Bukata et al. (1995) 0.8-2.5 2.5-6 6-18 >18 Carlson & Simpson (1996) 0-2.6 2.6-20 20-56 >56 Nürnberg (1996) 0-3.5 3.5-9 9-25 >25 0-3 3-10 >10 ? This study Odermatt et al. (2012) 18 CHL band ratios 5 SeaWiFS 2 MODIS 1 GLI 8 MERIS 2 MODIS 1 HICO 2 TM/ETM+ 1 MERIS Odermatt et al. (2012) 19 TSM band ratios 5 empirical 5 semi-analytical Odermatt et al. (2012) 20 CDOM band ratios Odermatt et al. (2012) 21 Spectral inversion algorithms Authors Algorithm CHL [mg/m3] TSM [g/m3] CDOM [m-1] max min max min max min Binding et al. (2011) NN algal_2 70.5 1.9 19.6 0.8 7.1 0.5 Cui et al. (2010) NN algal_2 16.1 0.7 67.8 1.5 2.0 0.7 Validation of C2R/algal_2/(FUB): Minghelli-Roman et al. (2011) NN algal_2 9.0 0.0 - - - - NN C2R 70.5 1.9 19.6 0.8 7.1 0.5 Giardino et al. for (2010) NN C2R – Adequate low to medium concentrations 74.5 11.7 - - 4.0 1.3 Matthews et al. (2010) NN C2R – Inadequate for high concentrations Odermatt et al. (2010) NN C2R 247.0 69.2 60.7 30.0 7.1 3.4 9.0 0.0 - - - - Schroeder et al. (2007) NN FUB 12.6 0.1 14.3 2.7 2.0 0.8 Shuchman et al. (2006) coupled NN 2.5 0.1 2.7 1.3 3.5 0.0 MIM 2.2 1.3 2.1 0.9 - - Odermatt – Limited et in al.number (2008) and independence MIP 4.0 0.6 - - - - Santini et al. (2010) to “domestic” use – Often restricted 2 step inv 5.0 1.8 13.0 3.0 0.8 0.1 Van der Woerd & Pasterkamp (2008) Hydropt 20.0 0.0 30.0 0.0 1.6 0.0 Binding et al. (2011) – Numerous and independent Validation of (2007) other algorithms: Giardino et al. Odermatt et al. (2012) validated | falsified | threshold R2=0.6 22 concentration level CDOM Variability range scheme high TSM type contravariance Reading example: D‘Sa et al. (2006) retrieve low with 510, 565 nm bands at 0.3-13.0 mg/m3 CHL and 0.5-5.5 g/m3 TSM Odermatt et al. (2012) medium 510, 565 nm D‘Sa et al., 2006 low 23 Variability range scheme red-NIR band ratios for very turbid TSM red-NIR band ratios for eutrophic CHL NN for intermediate concentrations band ratios for CDOM OC band ratios for oligotrophic CHL Representing coastal waters of mostly covarying constituents Odermatt et al. (2012) 24 Diversity recommendations 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 25 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 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)? 26 Thank you 27 Aerosol retrieval over water 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 RSTOA is TOA reflectance in 12 MERIS bands x, y, z are transformed observation angles θs 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)) Schroeder et al. (2007), Schroeder (2005) 28 CoastColour – Rio de la Plata L1b RGB CoastColour AC L1b band 13 (865nm) L2 3rd reprocessing Case2R SeaDAS l2gen IGARSS * Munich * 24.07.2012 29 CoastColour neural network Inv NN trained1.04 with 6 Mio. Hydrolight simulations Rw(10 bands) IOPs 3 CHL = 21*apig for 0.01-100 mg/m kd calculated from IOPs for all 10 bands 5 IOPs: a_pig, a_gelbstoff, b_ particle; NEW: a_detritus, b_white 3 SIOP TSM (bp1+bp2)*1.73 byofadditional 0.01-1000 rather g/mhypothetically accurate IOPs z90 is=variations the average the for 3 lowest kd than Accounting for T=0-36°C, S=0-42 ppt -1 Corresponds Secchi discmdepth CDOM= ad+agroughly tofor 0.01-4 Random variation spacing for bulk a and b instead of individual IOPs Doerffer et al. (2012) 30 FUB neural network 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 RSTOA IOP inversion – NN for indirect RSTOA RSBOA IOP performed similarly Schroeder, 2005; Schroeder et al., 2007 31 FUB neural network Alternative Bio-optical model architechture training ranges 3 – CHL: Uses MERIS level1 0.05-50 B mg/m TOA bands 1-7, 9-10, 12-14 – TSM: 0.05-50 g/m3 – CDOM: 0.005-1 m-1 Partially covarying constituents assumed Schroeder, 2005; Schroeder et al., 2007 32 Validation examples Greifensee 2011 EUT/C2R/FUB 16 MERIS images within 69 days CHL profiles acquired automatically Stratified cyanobacteria blooms Odermatt et al., 2012 33