Inland water algorithms Candidates and challenges for Diversity 2

33
1 Inland water algorithms Candidates and challenges for Diversity 2 Daniel Odermatt, Petra Philipson, Ana Ruescas, Jasmin Geissler, Kerstin Stelzer, Carsten Brockmann

description

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

Transcript of Inland water algorithms Candidates and challenges for Diversity 2

Page 1: Inland water algorithms Candidates and challenges for Diversity 2

1

Inland water algorithmsCandidates and challenges for Diversity 2

Daniel Odermatt, Petra Philipson, Ana Ruescas, Jasmin Geissler, Kerstin Stelzer, Carsten Brockmann

Page 2: Inland water algorithms Candidates and challenges for Diversity 2

2

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

Page 3: Inland water algorithms Candidates and challenges for Diversity 2

3

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

Page 4: Inland water algorithms Candidates and challenges for Diversity 2

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

Page 5: Inland water algorithms Candidates and challenges for Diversity 2

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

Page 6: Inland water algorithms Candidates and challenges for Diversity 2

6

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

Page 7: Inland water algorithms Candidates and challenges for Diversity 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

Page 8: Inland water algorithms Candidates and challenges for Diversity 2

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

Page 9: Inland water algorithms Candidates and challenges for Diversity 2

9Odermatt et al. (2010)

Aerosol retrieval over water: C2R

Page 10: Inland water algorithms Candidates and challenges for Diversity 2

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

Page 11: Inland water algorithms Candidates and challenges for Diversity 2

11Guanter et al. (2010)

Aerosol retrieval over land: Scape-M

Page 12: Inland water algorithms Candidates and challenges for Diversity 2

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

Page 13: Inland water algorithms Candidates and challenges for Diversity 2

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

Page 14: Inland water algorithms Candidates and challenges for Diversity 2

14Gitelson et al., 2011: Azov Sea

Red-NIR band ratios for CHL

Red

edg

e!

Page 15: Inland water algorithms Candidates and challenges for Diversity 2

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

Page 16: Inland water algorithms Candidates and challenges for Diversity 2

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

Page 17: Inland water algorithms Candidates and challenges for Diversity 2

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

Page 18: Inland water algorithms Candidates and challenges for Diversity 2

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 ?

Page 19: Inland water algorithms Candidates and challenges for Diversity 2

19Odermatt et al. (2012)

CHL band ratios

5 SeaWiFS2 MODIS1 GLI

8 MERIS2 MODIS1 HICO

2 TM/ETM+1 MERIS

Page 20: Inland water algorithms Candidates and challenges for Diversity 2

20Odermatt et al. (2012)

TSM band ratios

5 empirical5 semi-analytical

Page 21: Inland water algorithms Candidates and challenges for Diversity 2

21Odermatt et al. (2012)

CDOM band ratios

Page 22: Inland water algorithms Candidates and challenges for Diversity 2

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

Page 23: Inland water algorithms Candidates and challenges for Diversity 2

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

Page 24: Inland water algorithms Candidates and challenges for Diversity 2

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

Page 25: Inland water algorithms Candidates and challenges for Diversity 2

25

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

Page 26: Inland water algorithms Candidates and challenges for Diversity 2

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

Page 27: Inland water algorithms Candidates and challenges for Diversity 2

27

Thank you

Page 28: Inland water algorithms Candidates and challenges for Diversity 2

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

Page 29: Inland water algorithms Candidates and challenges for Diversity 2

29

CoastColour – Rio de la Plata

IGARSS * Munich * 24.07.2012

CoastColour AC

L1b RGB

SeaDAS l2genL2 3rd reprocessing

Case2RL1b band 13 (865nm)

Page 30: Inland water algorithms Candidates and challenges for Diversity 2

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

Page 31: Inland water algorithms Candidates and challenges for Diversity 2

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

Page 32: Inland water algorithms Candidates and challenges for Diversity 2

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/m3

– TSM: 0.05-50 g/m3– CDOM: 0.005-1 m-1

Partially covarying constituents assumed

FUB neural network

Page 33: Inland water algorithms Candidates and challenges for Diversity 2

33Odermatt et al., 2012

Greifensee 2011 EUT/C2R/FUB

16 MERIS images within 69 days

CHL profiles acquired automatically

Stratified cyanobacteria blooms

Validation examples