Examples on Sentinel data applications in Finland, possibilities, plans and how NSDC will be utilized - Snow

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1 Examples on Sentinel data applications in Finland, possibilities, plans and how NSDC will be utilized - Snow Kari Luojus, Jouni Pulliainen, Jyri Heilimo, Matias Takala, Juha Lemmetyinen, Ali Arslan, Timo Ryyppö, (Finnish Meteorological Institute) Sari Metsämäki (Finnish Environment Institute)

2 Outline Sentinel-3 based Northern Hemisphere Fractional Snow Cover (FSC) Sentinel-3 augmented Snow Water Equivalent (SWE) Pan-European domain (5km resolution) Northern Hemisphere (25km resolution) Wet Snow detection and FSC-retrieval using Sentinel-1 Detection of timing of snow clearance using optical (Sentinel-3) based FSC A lot of algorithm and method development work carried out in Finland (partly with internationanal collaborators) during the past years Finnish Meteorological Institute, & Finnish Environment Institute

3 Sentinel-3 based NH FSC product Legacy from GlobSnow AATSR & VIIRS products, Preparation on-going for S3 SLSTR/OLCI

4 GlobSnow Snow Extent (SE) dataset 17 years SE data record has been produced using optical imagery from ESA ATSR-2 (1995-) and AATSR (2002-) on a hemispherical scale. NPP VIIRS from SYKE s SCAmod method for fractional snow cover mapping implemented for the Northern Hemisphere Cloud detection algorithm developed by SYKE (+ contributed by ENVEO, FMI & NR) Methodology developed especially for forested regions basically a tough challenge for optical SE retrieval Uncertainty estimate provided for each grid cell, data available as NetCDF CF Operational data production at the FMI Metsämäki, S., Pulliainen, J., Salminen, M., Luojus, K., Wiesmann, A., Solberg, R., Böttcher, K., Hiltunen, M., Ripper, E. (2015). Introduction to GlobSnow SE-products with considerations for accuracy assessment. Remote Sensing of Environment, 156 (2015)

5 Northern Hemisphere and Pan-European SWE Northern Hemisphere SWE GlobSnow-legacy (validated) 25km EASE-grid ECV + Operational NRT product Pan-European SWE GlobSnow / EUMETSAF H-SAF 5km lat-lon grid Operational NRT product Finnish Meteorological Institute

6 35 year-long CDR time-series on snow conditions of Northern Hemisphere First time reliable daily spatial information on SWE (snow cover): - Snow Water Equivalent (SWE) - Snow Extent and melt (+grain size) - 25 km resolution (EASE-grid) - Time-series for Passive microwave radiometer data combined with ground-based synoptic snow observations - Variational data-assimilation Available at open data archive ( Daily NRT production since 2010 Takala, M., Luojus, K., Pulliainen, J., Derksen, C., Lemmetyinen, J., Kärnä, J.-P, Koskinen, J., Bojkov, B., Estimating northern hemisphere snow water equivalent for climate research through assimilation of spaceborne radiometer data and ground-based measurements, Remote Sensing of Environment, Vol. 115, Issue 12, 15 December 2011, doi: /j.rse

7 Fusion of SE and SWE products on hemispheric scale NRT north-hemisphere daily snow monitoring product combining 25km NH SWE and 1km FSC products (based on SSMI-S and VIIRS+Sentinel-3) SE information from VIIRS & NOAA IMS applied for (NRT) SWE production

8 Fusion of SE and SWE products on hemispheric scale GlobSnow SWE NRT-product has challenges in detecting snow line during spring melt season -> snow line identification from SE-product Example for spring season 2013

9 Super resolution (5km) Pan-European SWE product Super resolution 5km SWE SWE retrieval based on enhanced GlobSnow approach Fractional snow cover information used in SWE retrieval for: improved snow detection during winter snow accumulation period Improved melt detection during spring Utilization of optical (VIIRS-based) FSC-data in combination with NOAA IMS (4km) product Processing for Pan-European domain in NRT since 2015 Example of super resolution SWE product 17

10 Super resolution (5km) Pan-European SWE product A prototype product using Suomi NPP VIIRS and NOAA IMS for resolution enhancement -> shows a more realistic snow line (1km spatial resolution) Currently in 5km, upgraded to 1km with Sentinel-3 data 18

11 Preparation for Snow Mapping using combined S-3 SLSTR & OLCI AATSR 1 km MERIS/AATSR 300 m Input Data for FSC: SLSTR (AATSR): mm+tir; 1 km OLCI (MERIS): mm; 300 m Algorithm: MS Unmixing by Enveo Local Adaptive Endmembers

12 Sentinel-1 C-band SAR for FSC & wet snow monitoring A method for monitoring the fraction of snow-covered area (FSC) during the spring snow-melt season using C-band SAR imagery for the boreal forest zone 2 ref. images to determine FSC (fractional snow cover extent), forest compensation and additional automatic utilization of WS data for full spring time melt cycle within Finland Will be applied to Sentinel-1 data for Finland and Pan-European domain, work in progress Methodology: Luojus et al. (2009), "Enhanced SAR-Based Snow-Covered Area Estimation Method for Boreal Forest Zone", IEEE TGRS, vol. 47, no. 3, March 2009.

13 FSC estimation method for boreal forest zone Algorithm based on radar backscattering changes during the snow melt season, applies 2 reference images FSC obtained by: FSC 100% observed snow, ref ground, ref ground, ref Satellite Data Geocoding Land-use & Stem volume Info 2 x Reference Images Forest Compensation Linear Interpolation FSC map 23

14 Snow Melt-off day maps years , based on Pan-European Fractional Snow Cover (FSC) products, now on Copernicus CryoLand transition to S3-SLSTR/OLCI

15 Before MoD comparisons: resolution change 0.005º 0.005º 0.1º 0.1º by averaging Melt-off day values within 20x20 pixel windows Result: degradation deteriorates the in-situ comparison (based on point-wise weather station data) but enables comparison vs. MWR-based melt-off day

16 One application for the information on the timing of snow clearance Moth Phenology Indicator Results for one focal species (Orthosia gothica) with peak flight in spring Predictive power in the randomly selected test set (30% of data) using linear mixed effect models Pekka Malinen Spatial predictions based on model including snow melt date for Finland ( R 2 = Coefficient of determination) Latitude: r 2 = 0.50 Snow melt date: r 2 = 0.62 Thermal sum: r 2 = 0.61 Base t = 3 C Greening date: r 2 = 0.57 All four variables: r 2 = Acknowledgement: Juha Pöyry, SYKE

17 Summary Great potential for NH & Pan-European snow products Sentinel-3 based fraction of snow cover extent Sentinel-3 augmented SWE retrieval Sentinel-1 based FSC retrieval / wet snow detection Legacy production chains are existing Adaptation for Sentinels for the winter Part of the data are received at Sodankylä, part of it via ESA archives: the processing takes place in NSDC Production and dissemination of both: Near Real Time products Historical snow data records (ECV) All data: open and free! -> Allows for further downstream services and public-private partnerships