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Inland water algorithmsCandidates and challenges for Diversity 2
Daniel Odermatt, Petra Philipson, Ana Ruescas, Jasmin Geissler, Kerstin Stelzer, Carsten Brockmann
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Chapt Processing step Methods/Algorithms/Tools
Chapter 3: Atmosp
heric
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
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Chapt Processing step Methods/Algorithms/Tools
Chapter 4:
Lakes
Processing
Pre-classificatio
n4.1 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 arithmeticFLH / MCI
Band ratio algorithms
4.2.4 Valid Pixel RetrievalFlagging
Statistical filtering
4.2.3 Lakes Water Temperature ARC-Lake Data Set
Quantity
4.3.1 Water Extent SAR Water Body Data Set
4.3.2 Lakes Water Level ESA river and lake dataset
Context
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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
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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
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Introduction
mg/m3 g/m3 m-1
1 0-3 0-3 0-0.82 3-10 3-30 0.8-23 >10 >30 >2
7Schroeder (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
8Doerffer & 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 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
Aerosol retrieval over water: C2R
9Odermatt et al. (2010)
Aerosol retrieval over water: C2R
10Guanter 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 km2 lake area and 20 km shore distance
Aerosol retrieval over land: Scape-M
11Guanter et al. (2010)
Aerosol retrieval over land: Scape-M
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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
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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
14Gitelson et al., 2011: Azov Sea
Red-NIR band ratios for CHL
Red
edg
e!
15Doxaran et al. (2002): Gironde estuary, Bordeaux
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
16Watanabe 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
17Odermatt 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 R2
– Published in ISI listed journals
– Between Jan 2006 and May 2011
These criteria apply to a total of 52 papers.
Algorithm validation ranges review
18Odermatt 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
Authors Oligotrophic Mesotrophic Eutrophic Hypereutr.
Chapra & Dobson (1981) 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
This study 0-3 3-10 >10 ?
19Odermatt et al. (2012)
CHL band ratios
5 SeaWiFS2 MODIS1 GLI
8 MERIS2 MODIS1 HICO
2 TM/ETM+1 MERIS
20Odermatt et al. (2012)
TSM band ratios
5 empirical5 semi-analytical
21Odermatt et al. (2012)
CDOM band ratios
22Odermatt et al. (2012)
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
Minghelli-Roman et al. (2011) NN algal_2 9.0 0.0 - - - -
Binding et al. (2011) NN C2R 70.5 1.9 19.6 0.8 7.1 0.5
Giardino et al. (2010) NN C2R 74.5 11.7 - - 4.0 1.3
Matthews et al. (2010) NN C2R 247.0 69.2 60.7 30.0 7.1 3.4
Odermatt et al. (2010) NN C2R 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
Giardino et al. (2007) MIM 2.2 1.3 2.1 0.9 - -
Odermatt et al. (2008) MIP 4.0 0.6 - - - -
Santini et al. (2010) 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
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
23Odermatt et al. (2012)
Variability range scheme
medium
low
high
Retrieved constituent
concentration level
type
contravariance
Reading example:D‘Sa et al. (2006)retrieve low CDOMwith 510, 565 nm bandsat 0.3-13.0 mg/m3 CHLand 0.5-5.5 g/m3 TSM
TSMCD
OM
510, 565 nmD‘Sa et al., 2006
CHL TSM CDOM
24Odermatt 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 constituents
band ratiosfor CDOM
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red-NIR bands
C2R & FUB
FUB & blue-green bands
blue-green bands
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
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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
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Thank you
28Schroeder 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
whereRSTOA is TOA reflectance in 12 MERIS bands
x, y, z are transformed observation anglesθs is the illumination angle
P 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
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CoastColour – Rio de la Plata
IGARSS * Munich * 24.07.2012
CoastColour AC
L1b RGB
SeaDAS l2genL2 3rd reprocessing
Case2RL1b band 13 (865nm)
30Doerffer 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*apig1.04 for 0.01-100 mg/m3
TSM = (bp1+bp2)*1.73 for 0.01-1000 g/m3
CDOM= ad+ag for 0.01-4 m-1
kd calculated from IOPs for all 10 bands
z90 is the average of the 3 lowest kd
Corresponds roughly to Secchi disc depth
31Schroeder, 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 RSTOA IOP inversion
– NN for indirect RSTOA RSBOA IOPperformed similarly
FUB neural network
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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/m3
– TSM: 0.05-50 g/m3– CDOM: 0.005-1 m-1
Partially covarying constituents assumed
FUB neural network
33Odermatt 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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