Snow Cover Applications: Major Gaps in Current EO Measurement Capabilities

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1 Snow Cover Applications: Major Gaps in Current EO Measurement Capabilities Thomas NAGLER ENVEO Environmental Earth Observation IT GmbH INNSBRUCK, AUSTRIA Polar and Snow Cover Applications User Requirements Workshop,, Brussels

2 Outline Motivation for EO monitoring snow Users of snow products Observation Requirements Status and gaps of main snow cover parameters Capabilities of Copernicus Sentinel and MetOP-SG Summary

3 Motivation for EO snow monitoring The need for improved snow observations is addressed in several international programs and their strategy documents (IPCC; UNEP; ACIA; GCOS; WMO GCW) Main Questions: role of seasonal snow cover on the water cycle including impact on water resources and effects on energy and radiation budget parameterization and downscaling of snow and ice processes for land surface and climate models Impact of snow cover on permafrost evolution and carbon exchange in high latitudes Annual snow accumulation divided by runoff Barnett et al., 2005 Ratio CANDIDATE snowfall/runoff EARTH 50% EXPLORER in basin TO OBSERVE SNOW AND ICE FOR A BETTER UNDERSTANDING OF THE WATER CYCLE Basins with 50% runoff coming from snowmelt regions 3

4 Needs for Snow Services in Europe CRYOLAND USER GROUP: 60 Organisations from 15 European countries +and 3 EU organisations: Climate monitoring institutions Hydropower companies Energy traders Hydrological service Meteorological services Avalanche warning centres Road, Railway and River Authorities Geotechnical & Construction companies Ecologists Reindeer herders Environmental agencies

5 Observation Requirements for main land snow products Parameter Spatial Resolution Time Interval Accuracy Total Snow Area 1 km (T) 100 m (G) 1 day 12 hr 10 % (T) 5 % (G) Snow Mass (SWE) on land 1 km (T)* 100 m (G) 6 day (T) 1 day (G) 10 mm * Melting Snow Extent** 500 m (T) 100 m (G) 3 d (T) 1 d (G) 10 % (T) 5 % (G) Snow / Ice Albedo 8 km (T) 5 km (G) 6 day (T) 1 hr (G) 1% (T) of irradiance 0.5 % (G) Snow / Ice Surface Temperature 1 km (T) 100 m (G) 1 d (T) 12 h (G) - Snow grain size Optical grain size: 0.25 mm Threshold, Goal Requirement (IGOS Cryosphere, 2007, *GCOS 2016 Draft, **Proposed for parameter)

6 Total Snow Area Current daily Snow Extent product are primarily based on medium resolution optical satellite data using VIS and SWIR bands for detecting snow. Mature algorithms available: Binary Snow Product map of snow / no snow / clouds Fractional Snow Cover Product provides estimate of fractional snow extent within pixel Two types of products: canopy adjusted (snow on ground); viewable snow Issues of optical snow products Disagreement of individual snow extent products (assessed within ESA SnowPEx) Higher uncertainty of snow cover fraction in mountains and forests Problems in cloud / snow screening leads to errors in snow maps Improvement of resolution in complex terrain (mountains) needed

7 Snow Extent Products in EASE-GRID 2.0 MEaSUREs JASMES MDS10C JASMES GHRM5C AVHRR Pathfinder CryoClim NOAA IMS AutoSnow GlobSnow MOD10_C5 SCAG

8 Unbiased RMSE [%] Snow Extent - accuracy and agreement of NH snow products Intercomparison of snow products show differences in the fraction of snow cover Jan-Feb-Mar 2008, all pixels unforested Global / NH Snow Products versus a high resolution snow reference data set Landsat Snow Algorithm:

9 Snow Extent - S3 SLSTR & OLCI FSC MERIS AATSR + AATSR 1000 m 300 m

10 Snow Melt Area Product derived from current satellitebased C-band SAR Systems: Extent of snow melt area based on backscatter sensitivity to wet snow Issues of Snow Melt Mapping using C-Bd SAR: Limited applicability in forests current SAR sensors provide snapshot of melt extent at the acquisition time (ascending: morning; descending evening), but high variability of snow melt area due to weather and illumination

11 Sentinel-1 Snow Melt Extent Product SAR - Sentinel-1: Algorithm based on change detection is mature. (Nagler et al. 2016; Nagler & Rott 2000) Current acquisition planning provides snapshot of melting snow extent (ascending: morning; descending: evening) S1A Snow Melt Product: 100 m pixel spacing S1A observation interval (mid lat): ~6 d S1A+B observation interval (mid lat): ~3 d To observe diurnal melt / refreeze cycle additional images acquired during the day are needed. Retrieval not possible in dense forested areas (forests are masked out) Transition to Continental / NH monitoring requires proper acquisition planning.

12 Mass of Terrestrial Snow SWE Products from Passive Microwave data Uses multi frequency brightness temperatures 18.7 & 37 GHz, (10.6 & 32 GHz) Advanced products: assimilation of in-situ snow data in retrieval GlobSnow 2.0 SWE Product Daily global coverage, independent day / night / clouds Long data records Issues of current SWE products Coarse resolution products (~25 km), problems in mountain terrain and forests. Significant disagreement of available products (high uncertainty in SWE retrieval, and saturation at ) Need for high resolution SWE product (esp. for complex terrain) has been identified by GCOS 2016 (draft)

13 EO Concepts for SWE Monitoring Approach Strengths Weaknesses Radar (Scat or SAR): Dual: Ku & Ka Single: Ku, Ka InSAR L-, C-Band LIDAR sensitive to SWE & melt; high resolution; independent of clouds/illumination direct SWE sensitivity; high resolution avoids volume scattering issues direct observation of snow depth; very high resolution, minor forests and topographic issues algorithm maturity, coverage, SWE saturation, forests forests, complexity; requires advanced acquisition plan SWE retrieval requires snow densit Radar (Scat or SAR) InSAR SWE Retrieval Sensitivity of backscatter to SWE depends on scattering albedo: snow 1.6k SWE cos i Dual F: Ku + Ka Single F: Ku, Ka for i <50 See: CoReH2O - EE7 Mission proposal

14 InSAR SWE Retrieval Phase Sensitivity on SWE Continues Groundbased SAR measurements Wattener Lizum Austria Time series of interferometric phase Time series of retrieved SWE (C-Bd) at one point =2p, =40 : SWE Snow Depth (0.3 g/cm3) L-Band: 120 mm C-Band: 28 mm 0.40 m 0.09 m

15 Snow Albedo Snow albedo is needed for accurately determination of the radiation and energy budget and is observed using medium resolution optical sensors. Snow Albedo depends on - metamorphic snow properties (grain size, dust contamination, ) - strong angular reflectance distribution varies significantly with wavelength and snow microphysical properties Conversion of from directional albedo measurements by satellite sensors to spectral hemispheric albedo requires accurate characterization of the angular reflectance distribution 0.6 mm v =30 Zenith Angle = mm SOLUTION: Multi-directional measurements are needed to determine angular reflectance characteristics Hemispheric Albedo =

16 Capabilities of European S1, S2 and S3 and MetOP-SG S1 S2 S3 MetOP-SG SAR MSI OLCI SLSTR SRAL VII IRS SCA MWI 3MI Snow Extent Snow Melt Area coarse coarse Snow Water Equiv. coarse coarse Snow Albedo Snow Surf. Temp.

17 Summary on EO Snow Observations Parameter Status Gaps Total Snow Area Snow Mass (SWE) on land Snow Melt Extent Snow / Ice Albedo VIS, NIR & TIR imager, some problems in cloud / snow discrimination; available products show significant differences Low spatial resolution SWE maps available from IMWR, but at comparatively large uncertainty. C Band SAR provide snapshot, algorithms mature for mountain regions. Hemispheric snow albedo derived from medium resolution spectral imagers (Sentinel-3, MODIS, VIIRS) higher resolution required for complex terrain (mountains) cloudiness / polar night; in some products filled with coarse IMWR; IMWR SWE: accuracy needs to be improved; problems with spatial resolution in complex terrain, forests, saturation over deep snow. High resolution product needed. Problems in forests. Melt extent depends on acquisition time; Accuracy impaired by angular effects of surface reflection (BRDF) and of atmosphere (aerosol scattering), requiring multi-angular measurements