Sentinel-2 MSI in the Monitoring of Lakes and Coastal Waters in Finland: Spectral and Spatial Resolution Considerations

Kari Kallio, Sampsa Koponen, Jenni Attila, Mikko Kervinen, Timo Pyhälahti

Finnish Environment Institute Carsten Brockmann, Tonio Fincke

Brockmann Consult Lena Kritten

Free University of Berlin

Sentinel-2 for Science Workshop, ESRIN, May 20-22 2014

- Global Lakes Sentinel Services

● Collaborative Project (2013-2016) funded by the EU 7th Framework Programme

● Develops processing tools for the upcoming Sentinel-2 and Sentinel-3 satellites to monitor lakes and reservoirs. ○ Algorithm development for various water types

http://www.glass-project.eu/

Outline

● Characteristics of lake and coastal waters in Finland ● Spatial resolution analyses

● Potential of MSI band wavelengths for water quality estimation

with band ratios ● Spectral inversion algorithm SIOCS (The Sensor-Independent

Ocean Colour Processor)

Length of coastal line (main land) : 1 100 km.

Number of islands along the Finnish coast: 28 000 (> 0.3 hectar). large spatial variation of water quality and a huge monitoring task.

Coastal waters and lakes in Finland – need for high resolution data

LANDSAT5 TM Archipelago sea, 2 June 2007 Turbidity

10 km

Number of coastal WFD water bodies is 215. ~ 40% of them cannot be monitored with 300 m data.

5

Lakes in Finland

Lakes not possible to monitor with 300 m resolution instruments

Number of lakes is 56 000 (> 1 hectar)

MERIS pixels

0

200

400

600

800

1000

10 pixels

Landmask1 pixelbuffer

695 15%

201 4%

300 m sensor with land mask 300 m sensor with buffered land mask: the mask is extended by one extra pixel from the shore

WFD monitoring of Finnish lakes with 300 m data (MERIS, OLCI) – Effect of spatial resolution Total number of WFD lakes (> 0.5 km2) is 4596.

With Sentinel 2: All WFD lakes and many smaller lakes

Num

ber o

f lak

es

Based on HydroLight (Version 5.2) simulations Water quality input to HydroLight: • Set 1: Stepwise data based on water quality distributions of

Finnish lakes (N=3375) for SIOCS training • Set 2: Extensive algorithm testing (band ratios and SIOCS):

based on measurements in 5553 stations SIOPs for HydroLight from Finnish lakes Algorithms • ‘Classical’ band ratios (Landsat8 OLI, MSI, OLCI) • Spectral inversion SIOCS (MSI)

Water quality algorithms: analysis of spectral configuration and test of SIOCS

Spectral inversion algorithm: Sensor Independent Ocean Color prosessor (SIOCS)

● Joint project of Brockmann Consult and Free University of

Berlin.

● Sensor bands can be selected

● User can use own IOPs/SIOPs and concentration ranges ● Planned to be available in the BEAM EO toolbox

● SIOCS is under development. SYKE has participated in

testing of SIOCS and used HydroLight in its training

8

400 450 500 550 600 650 700 7500

1

2

3

4

wavelength nm

Spectral resolutions of OLI, MSI and OLCI in 390-715 nm

S2 MSI S3 OLCI

Landsat8 OLI

MSI 650-680 nm: 2. absorption maximum of phytoplankton MSI 698-713 nm: small absorption by particles and CDOM

SIOCS: main components

IOPs (aph, aCDOM, ad, bTSM) for the bands

10

Inversion operator - cost function - stop criterium

Rrs simulations Rrs measured

IOPs at 443 nm

Concentrations

SIOPs

3375 cases, stepwise variation

HydroLight HydroLight simulated, 5553 cases, based on measured concentrations

Finnish lakes

Test with Finnish lakes using the S2-MSI bands

LUTs

Chl-a: band ratios and SIOCS

11

0.5 1 1.50

20

40

60

80

S2-MSI Chl-a

Rrs(698-713)/Rrs(650-680)

Chl

-ay = 27.0x4.81

R2 = 0.81N = 5498

0.5 1 1.50

20

40

60

80

S3-OLCI Chl-a

Rrs(704-714)/Rrs(660-670)

Chl

-a

y = 35.2x4.37

R2 = 0.81N = 5498

0 20 40 60 800

20

40

60

80

SIOCS S2-MSI Chl-a

CHL SIOCS µg/l

CH

L m

easu

red

µg/l

R2 = 0.99N = 5059R2 = 0.99N = 5059

MSI SIOCS MSI

OLCI

0 0.005 0.01 0.015 0.020

10

20

30

S3-OLCI TSM

Rrs(704-714) sr-1

TSM

R2 = 0.99N = 5497

0 0.005 0.01 0.015 0.020

10

20

30

Landsat8-OLI TSM

Rrs(640-670) sr-1

TSM

R2 = 0.89N = 5497

0 0.005 0.01 0.015 0.020

10

20

30

S2-MSI TSM

Rrs(698-713) sr-1

TSM

R2 = 0.98N = 5497

TSM: single band and SIOCS

12

0 10 20 300

10

20

30

SIOCS S2-MSI TSM

TSM SIOCS mg/l

TSM

mea

sure

d m

g/l R2 = 1.00

N = 5059R2 = 1.00N = 5059

MSI SIOCS MSI

OLI OLCI

Rrs(640-670) sr-1

CDOM: band ratios and SIOCS

13

0 1 2 3 40

5

10

15

S2-MSI CDOM

Rrs(705)/Rrs(560)

a CD

OM(4

43) 1

/my = 5.16x1.29

R2 = 0.97N = 5498

0 1 2 30

5

10

15

Landsat8-OLI CDOM

Rrs(665)/Rrs(560)

a CD

OM(4

43) 1

/m

y = 5.77x1.23

R2 = 0.98N = 5498

0 1 2 3 40

5

10

15

S3-OLCI CDOM

Rrs(709)/Rrs(560)

a CD

OM(4

43) 1

/m

y = 3.34x1.86

R2 = 0.96N = 5498

0 5 10 150

5

10

15

SIOCS S2-MSI aCDOM(443)

aCDOM(443) SIOCS 1/m

a CD

OM(4

43) m

easu

red

1/m R2 = 0.98

N = 5059R2 = 0.98N = 5059

SIOCS MSI MSI

OLI OLCI

Rrs(698-713)/Rrs(542-578)

Rrs(640-670)/Rrs(530-590) Rrs(704-714)/Rrs(555-565)

Moored automatic stations (Chl-a, turbidity) • Coastal: 2-3 stations, lakes 3 stations

Ship-of-Opportunity, Baltic Sea(Alg@line) • Chl-a, turbidity • Rrs

Lakes • Rrs, IOPs

SIOP/IOP datasets • Lakes: Ylöstalo et al. 2014 RSE 148: 190–205 • Baltic Sea: data analyses in progress • River influenced coastal waters: to be measured

Validation measurements in Finland

• WFD ecological classification (Chl-a, transparency)

• Estimation of the impact of water protection measures (Chl-a, turbidity)

• Mapping of areas influenced by river plumes (turbidity)

• Macrophyte mapping (e.g. common reed belts)

• Bottom refrectance may become a limiting factor in part of the coastal and lake environments

S2-MSI data will help in

• High resolution data of S2 MSI can cover all the Finnish coastal waters and lakes.

• S2 MSI improves the estimation accuracy of TSM

compared to the currently operational HighRes sensors and is likely to enable the estimation of Chl-a.

• SIOCS will be improved e.g. by testing different options for the initial guess values.

• The SIOCS spectral inversion algorithm will be very useful enables e.g. local SIOP adjustment improved regional/local products.

Conclusions

Thank you!