Application of EO for Environmental Monitoring at the Finnish Environment Institute

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

2 Earth observation services at SYKE 2

3 Water quality Chlorophyll-a WWW: years , March - October Daily composite images Represents the quantity of algae in water but not directly the amount of cyanobacterium Surface algal blooms WWW: years , late June - early September Daily composite images Generalized chlorophyll-a estimate is classified to 4 classes indicating probability for the surface algae blooms 3

4 Water quality Turbidity WWW: , March - October Daily composite images Sea Surface Temperature WWW: , April - October Night images 4

5 Future with Sentinels 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? 5

6 S2 MSI Hanko harbour and beach areas Detection of algae blooms and coastal processes near popular beaches and visiting harbors

7 Snow monitoring Copernicus GlobalLand Pan-European Fractional Snow Cover Continuation of CryoLand (EU, , coordinated by ENVEO IT GmbH) Snow products in Pan-European and regional scales 0.005º grid size 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 7

8 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 Pixel is labelled non-classified if FSC-products do not identify a proper at least a few days continuous snow cover

9 Lake ice: Freeze-up Landsat 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 Sentinel Sentinels have potential, we are looking project in order to make product Sentinel

10 Lake ice: melting Lokka artificial lake, Northern Finland Sentinel-1 EW-mode IW-mode IW-mode EW-mode

11 Corine Land Cover Finnish High Resolution Corine Land Cover 2012 Raster, 20 m pixel 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? Automatic generalization European CLC2012 Vector, 25 ha MMU Download from 11

12 Agricultural information for MAVI MAVI: Agency for rural affairs Control of EU agricultural subsidies Information needs Plant classification Ploughing SEN3APP (EU FP7) 12

13 Agricultural information for MAVI Plant classification Loimaa-Nakkila test area Sentinel-1 time series, 14 dates, plant groups Winter cereals Spring cereals Peas Potato Rapeseed Grasses, pasture, fallow Anomaly detection Search parcels with nontypical NDVI or backscatter June NDVI-mosaic Red: NDVI considerably smaller than plant class mean Yellow: NDVI slightly smaller Blue: NDVI slightly higher 13

14 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 Green-up (DoY) No data < > 170 Böttcher et al. (2014). Remote Sensing of Environment, 140, Böttcher et al. (2016). Remote Sensing, 8, 580.

15 Critical points 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 15

16 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