Application of EO for Environmental
Monitoring at the Finnish Environment
InstituteData Processing (CalFin) and Examples of Products
Markus Törmä
Finnish Environment Institute SYKE
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
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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