Application of EO for Environmental

Monitoring at the Finnish Environment

InstituteData Processing (CalFin) and Examples of Products

Markus Törmä

Finnish Environment Institute SYKE

markus.torma@ymparisto.fi

2www.syke.fi/earthobservation

Earth observation services at SYKE

www.syke.fi/earthobservation

Water quality

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Surface algal blooms

● WWW: years 2012 - 2016, late

June - early September

● Daily composite images

● Generalized chlorophyll-a

estimate is classified to 4

classes indicating probability

for the surface algae blooms

Chlorophyll-a

● WWW: years 2007 - 2016, March

- October

● Daily composite images

● Represents the quantity of algae

in water but not directly the

amount of cyanobacterium

Water quality

4

Sea Surface Temperature

● WWW: 2007 - 2016, April -

October

● Night images

Turbidity

● WWW: 2012 - 2016, March -

October

● Daily composite images

● Sentinels will bring huge improvement for the water quality monitoring in comparison to the gap-filling years without optical ESA satellite instruments.

● S3 OLCI & SLSTR:○ Continuing operational production (MERIS, MODIS) for the

Baltic Sea

○ Starting operational production for large lakes

○ Chl-a, algae blooms, turbidity, CDOM, transparency, SST (with SLSTR)

● S2 MSI:○ High resolution: smaller lakes, coastal areas

○ Algae blooms, turbidity and transparency

+ Reed belts and other macrophytes?

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Future with Sentinels

S2 MSI 16.5.2016 Hanko harbour and

beach areas

Detection of algae blooms and coastal processes near

popular beaches and visiting harbors

Copernicus GlobalLand Pan-European Fractional Snow Cover

● Continuation of CryoLand (EU, 2011-2015, coordinated by ENVEO

IT GmbH)

● Snow products in Pan-European and regional scales

○ 0.005º grid size

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Snow monitoring

Data processing and

data portal still maintained

as Copernicus portal

SCAmod-algorithm

(Metsämäki et al.

2005; 2012) & MODIS

reflectance data

Sentinel-3 SLSTR

used in future

25

.11

.20

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Pixel is labelled ‘non-classified’ if FSC-products do not identify

a proper at least a few days’ continuous snow cover

FSC time series Jan-Aug

Melt-off day

is defined as

the first day

of at least

several days’

snow-free

(FSC=0%)

period, but

detection

may restart

if enough

new snow

days

appear

● Sääksjärvi, South-Western

Finland

● SW-Finland Centre for

Economic Development,

Transport and Environment

has been interested about

lake ice information

○ Freeze-up and melt dates

● Sentinels have potential, we

are looking project in order to

make product

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Lake ice: Freeze-upLandsat-8 8.11.2016

Sentinel-1 8.11.2016

Sentinel-2 12.11.2016

● Lokka artificial lake, Northern Finland

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Lake ice: melting

Sentinel-1 EW-mode 24.1.2016 IW-mode 20.3.2016

IW-mode 19.5.2016 EW-mode 23.5.2016

● Pan-Europen land cover

classification

○ Next version CLC2018

○ Start early 2017, finished

summer 2018

○ Previous versions 2000,

2006 and 2012

● Combination of existing

spatial data and image

interpretation

● Sentinels:

○ More images

○ SAR: wetlands & sparsely

vegetated mountain areas

○ Pixel size of Finnish HR

CLC 20 m → 10 m?

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Corine Land Cover

Finnish High Resolution Corine

Land Cover 2012

Raster, 20 m pixel

European CLC2012

Vector, 25 ha MMU

Automatic generalization

Download from http://www.syke.fi/en-US/Open_information/Spatial_datasets

● MAVI: Agency for rural affairs

○ Control of EU agricultural subsidies

○ Information needs

• Plant classification

• Ploughing

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Agricultural information for MAVI

SEN3APP

(EU FP7)

Agricultural information for MAVI

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Anomaly detection

● Search parcels with non-

typical NDVI or backscatter

Plant classification

● Loimaa-Nakkila test area

○ Sentinel-1 time series, 14

dates, 2015

● 6 plant groups

○ Winter cereals

○ Spring cereals

○ Peas

○ Potato

○ Rapeseed

○ Grasses, pasture, fallow

June NDVI-mosaic

Red: NDVI considerably

smaller than plant class mean

Yellow: NDVI slightly smaller

Blue: NDVI slightly higher

Green-up (DoY)

No data

< 80

80 - 90

90 - 100

100 - 110

110 - 120

120 - 130

130 - 140

140 - 150

150 - 160

160 - 170

> 170

Vegetation phenology

- Harmonized time series of Fractional Snow Cover and vegetation indices

from MODIS for the period 2001 to 2015 calculated for Finland and

surrounding areas

- Yearly maps of phenological events, e.g. the green-up of vegetation are

provided for Finland

- Continuation of MODIS time series with Sentinel-3 SLSTR is planned

from 2017 onwards using the Calvalus processing system on CalFin

Böttcher et al. (2014). Remote Sensing of Environment, 140, 625-638

Böttcher et al. (2016). Remote Sensing, 8, 580.

● In Finland, in order to get timely images due to weather,

plenty of imaging capacity is needed

○ Sentinel-1: 2 satellites

• plenty of images, processing capacity is bottleneck at the moment

○ Sentinel-2: 1 satellite, -2B 2017

• one satellite is not enough, extra capacity using Landsat-8

● Radiometric correction as automated as possible

○ Sentinel-1:

• process should be installed to Calvalus

○ Sentinel-2:

• cloud masking is difficult

• SNAP, Idepix & Sen2Cor

• Envimon by VTT

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Critical points

Thank you!

Examples from Vesa Keto, Hanna Alasalmi, Mikko Kervinen, Kari

Kallio, Saku Anttila, Timo Pyhälahti, Sari Metsämäki, Kristin

Böttcher, Pekka Härmä, Olli-Pekka Mattila, Eeva Bruun, Sofia

Junttila… (sorry to all I forgot to mention)

Project partners include

Finnish Meteorological Institute FMI

VTT Technical Research Centre of Finland Ltd

Natural Resources Institute Finalnd LUKE

MAVI: Agency of Rural Affairs

National Land Survey

NLS Finnish Geospatial Research Institute