ADVANCEMENTS IN SNOW MONITORING

Transcription

1 Polar Space Task Group ADVANCEMENTS IN SNOW MONITORING Thomas Nagler, ENVEO IT GmbH, Innsbruck, Austria

2 Outline Towards a pan-european Multi-sensor Snow Product SnowPEx Summary Upcoming activities

3 SEOM S1-4SCI SNOW: DEVELOPMENT OF PAN-EUROPEAN MULTI-SENSOR SNOW MAPPING METHODS EXPLOITING S1 PROJECT TEAM FMI Environmental Earth Observation IT GmbH Innsbruck AT - Principle Investigator Finnish Meteorological Institute Helsinki, FI University of Zurich, Zurich, CH Finnish Environment Institute Helsinki, FI NORUT, Tromsö, NO PERIOD: Primary Objectives Develop, implement and validate methods and tools for generating maps of snowmelt area based on SAR data of the S1 mission Combination of S1 wet snow products with snow products from optical sensors of Sentinel-3 / Sentinel-2 Use developed algorithm to generate advanced pan-european snow extent and condition (dry/wet) maps

4 Combined SAR & OPT Snow Product Addressed Tasks: - SAR snow mapping in mountains and low lands - Combination of optical and SAR - Optical Snow Products: cloud coverage

5 Sentinel-1 and Sentinel-3 Sensor Overview S1 SAR IW Mode

6 Baseline Sentinel-1 VV&VH Snow Mapping Method Track April 2015, VV (Nagler et al. 2016; Rem. Sens., 2016, 8(4), 348, doi: /rs )

7 Algorithm Intercomparison Mountains - Swiss Alps 4 May 2016 Reference Data: Sentinel-2 SAR Data: Sentinel-1 IWS S2 Klein 100 m S1 WS ENVEO 100 m S1 WS UZH 100 m S1 WS NORUT 100 m SAR Alg. Intercomparison UZH NORUT ENVEO UZH NORUT

8 Sentinel-1 Wet Snow Maps Mountain regions

9 Low Lands-Dense Forest-Cultivated Areas/Poland 16 Feb 2016 Warsaw Warsaw Landcover LS8 Salomonson 100 m S1 WS ENVEO 100 m Forests Maksed S1 WS ENVEO 100 m

10 Radar backscatter of Wet Snow Image - Europe WS image σ 0 VH [db] WS image σ 0 VV [db] 4 Apr 2017 Background image: C³ - Cryoland Cloud Cleared

11 Concept for SENTINEL-3 Snow Mapping using SLSTR (AATSR) and OLCI (MERIS) Sentinel-3: SLSTR (follow on of AATSR): , -3.7 µm + TIR 500 m / 1 km OLCI (follow on of MERIS): µm; 300 m Daily Global Coverage Fractional Snow Extent estimated using multi-spectral algorithm Synergistic SLSTR & OLCI TOA Product not available use only SLSTR TOA Product

12 Fractional Snow Cover Map 28 March 2017 Sentinel-3 SLSTR FSC Note: SLSTR Band co-location problems (1.6 µm band)

13 Assimilated Snow Cover Product Cloud-cleared Daily Cryoland Snow Extent product Observed by MODIS Assimilated Snow Extent Product FSC Generated by assimilation of observed FSC product and a snow pack model using ECMWF data as input

14 Combining fractional snow extent maps with melting snow area maps Advanced product providing Fractional Snow Extent and Snow Conditions (dry / wet) 14

15 Conclusions The Sentinel-1 satellite formation offers excellent capabilities for operational monitoring of snow melt area at high spatial resolution, but requires systematic acquisitions. An algorithm for retrieval of snowmelt area has been developed, applying the fusion of co- and cross-polarized channels to optimize the discrimination of snow vs. snow-free areas over a wide range of incidence angles, was successful applied in mountainous regions (Alps) and Iceland. Synergistic use of SAR and optical based snow maps provide information on the total snow extent (optical satellite) and on physical snow conditions (wet snow; SAR), and allows snow monitoring in lower elevated non-mountain regions. This advanced information is required by hydrological services, water management, meteorological services, geotechnical engineering, etc..

16 Snow Extent Products MEaSUREs JASMES MDS10C JASMES GHRM5C AVHRR Pathfinder CryoClim NOAA IMS AutoSnow GlobSnow MOD10_C5 SCAG

17 NH Product intercomparisons 2007/08 Jan-Feb-Mar 2008, Valid pixel (snow pixels only) unforested total area forested total area Viewable Snow Snow on Ground

18 Monthly Spatial Difference Maps May 2008 IMS24-ASNOW IMS24-JXM10 IMS24-JXM05 IMS24-CRCLIM IMS24-M10C05 IMS24-MEASU

19 Multiannual trends of monthly average snow products

20 SnowPEx Major Results There is considerable inter-dataset spread in the NH snow cover extent and snow mass derived from available satellite and modelled products. Agreement between satellite SE products depends on environment and surface classes and varies during the season. Typical: RMSE is 15 %, Bias is +/- 8 %, higher differences (up to 50% RMSE). Agreement of SE products with Landsat snow reference data is typically between 10% and 25% RMSE. But the spread of different Landsat snow algorithms requires a more detailed analysis on their performance. Satellite SWE retrievals show an RMSE between 42 mm and 72 mm. Assimilation of in-situ snow measurements in the retrieval algorithm improves performance in comparison to satellite data only algorithms. Analysis of the trends of monthly average of SE from different products from 1999 onwards show a wide spreading of different products, which should be taken into account for climate application (e.g. model spread can be compared to observational spread). SnowPEx continuation in discussion.

21 snow_cci (expected to start in Q1/Q2 2018) Primary objectives : generate a long term consistent multisensor daily global snow extent products starting in 1981 using archived satellite data from AVHRR, ATSR-2 / AATSR, MODIS, Sentinel-3 SLSTR & OLCI, SAR Improved time series of snow water equivalent products from 1980 onwards, using SMMR and SSM/I address discrepancies of trends between snow products revealed by SnowPEx by adding an independent

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23 Multitemporal Reference Image Pixelwise calculation of the statistics of backscatter values between Upper Extreme and Upper Quartile in data stack. Requires systematic acquisitions during the year AVG + Applicable for long tracks Automatic processing for large scale areas it requires sufficient long time series to cover suitable surface

24 Monthly SE Theil-Sen robust trends SE Theil-Sen robust trends of monthly average snow extent from 8 snow products for the Northern hemisphere for the period Filled symbols Mann- Kendall p-value < 0.1.