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Northern hemisphere Fractional Snow mapping with VIIRS - First experiments in ESA DUE GlobSnow-2

Northern hemisphere Fractional Snow mapping with VIIRS - First experiments in ESA DUE GlobSnow-2 Sari Metsämäki 1) , Kari Luojus 2) , Jouni Pulliainen 2) , Mwaba Hiltunen 2) , Andreas Wiesmann 3) 1) Finnish Environment Institute, SYKE 2) Finnish Meteorological Institute, FMI

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Northern hemisphere Fractional Snow mapping with VIIRS - First experiments in ESA DUE GlobSnow-2

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  1. Northern hemisphere Fractional Snow mapping with VIIRS - First experiments in ESA DUE GlobSnow-2 Sari Metsämäki1) , Kari Luojus2) , Jouni Pulliainen2), Mwaba Hiltunen2) , Andreas Wiesmann3) 1) Finnish Environment Institute, SYKE 2) Finnish Meteorological Institute, FMI 3) Gamma Remote Sensing 7thEARSeL Workshop on Remote Sensing of Land Ice & Snow 3-6 February, 2014 Bern, Switzerland

  2. GlobSnow SE-product from VIIRS • AATSR is not operating anymore NPP Suomi/VIIRS employed in GlobSnow NRT SE-production • Gap-filler for Sentinel-3 • NorthernHemispherecovered daily (2013 onwards) • Processing software by Gamma, operated in NRT at FMI, Sodankylä • FSC retrievalalgorithm (SCAmod) and auxiliary data analogous to GlobSnow SE from ATSR-2/AATSR • VIIRS band 550nm (+ 1.6 µm for NDSI) employed: corresponding to AATSR/ATSR-2 • Cloudmasking with SYKE-madealgorithm SCDA 2.0, employing BT3.7, BT11, BT12, R550, R1.6

  3. Cloud Detection SCDA2.0 for VIIRS • Developed for GlobSnow FSC purposes; works for ATSR-2 /AATSR, MODIS and VIIRS • No need to identifyall (small/semi-transparent) clouds in confidentsnow-freeareas • Cloudscreening is targeted, notcloudclassification binaryinformation, no classes, no propabilities • Mustberelativelysimple and computationallyfast • Mustbeapplicable for NorthernHemisphere, no regional/localtuning • Mustnotconfusebetweenfractionalsnow and clouds (it’s the fractionalsnowwewant to see!) • Shouldworkparticularlyoverseasonallysnow-coveredareas and throughout the potentialsnowseason

  4. Major features of SCDA2.0 • SimpleCloudDetectionAlgorithm • Designed to work with only a fewspectralbands • common for Terra/MODIS, Envisat/AATSR, ERS-2/ATSR-2, NPP Suomi/VIIRS  R550, R1.6, BT3.7, BT11, BT12 • Based on empiricallydeterminedthresholds for single bands and theirratios • Drivenby BT11-BT3.7 • Ratio NDSI / R550 important in avoidingfalsecloudcommissions • Severalothertest for BT12, R550 and NDSI

  5. 28 Feb, 2003

  6. Snow-covered forest and tundra Cloud

  7. AATSR cloud (operational)

  8. Snow-covered forest and tundra Cloud

  9. MODIS cloud (from MOD10_L2)

  10. Snow-covered forest and tundra Cloud

  11. GlobSnow-2 cloud SCDA v2.0

  12. VIIRS SE-product vs. in situ Snow depth • FSC from the three 8-days period plus oneday in Septembercompared with in situSnow Depth • Firstbinarysnow data is generated: • IF Snow Depth > 1cm  ’snow’ • IF FSC>0  ’snow’ OR • IF FSC>0.15  ’snow’ • Statistical measuresprovided • HitRate, Probability of detection, Falsealarmrate, Kuiperskillscore Weather stations and snow courses in Finland.

  13. VIIRS SE-product vs. WS Snow depth

  14. Comparison against Landsat-8 data • VIIRS Fractional snow compared with Landsat fractional snow  RMSE, bias • Data set preparation in progress

  15. VIIRS FSC vs. MOD10C2 • Motivation: • MOD10C2 commonlyused interesting to seewhat new can VIIRS and SCAmodbring into NH Fractionalsnowmapping • VIIRS providesgoodgap-filler data for Sentinel-3 SLSTR • Earlier: GlobSnow SE (AATSR-based) difficult to evaluatedue to data gaps • Problems with weekly and monthlyproduct: artifactsdue to verylimitednumber of observations • Now: VIIRS providesglobalcoverage possible to produce 8-days compositescomparable with MOD10C2 • Demonstration for three 8-day periods in meltingseason2013 • Doy 89-96 (March30 - April06) • Doy 105-122 (April15 - April22) • Doy 121-128 (May01 - May08)

  16. VIIRS SE March 30 – April 06 2013

  17. VIIRS FSC vs. MOD10C2 MOD10C2 FSC in Climatemodelinggrid (CMG, 0.05º×0.05º) based on binning the 500m 8-day maximumsnowcover (MOD10A2) to the CMG MOD10A2 based on binaryalgorithm FSC biased at the transitionalzone?

  18. VIIRS FSC vs. MOD10C2

  19. GS-2 VIIRS FSC NASA MOD10C2

  20. VIIRS FSC vs. MOD10C2 • MOD10C2 and GlosSnow SE mostlyagreewell: • RMSE 12% (firstperiod) to 15% (lastperiod) • MOD10C2 showslesssnow for forestareas, even 10% less for 80-100% forest-coveredareas

  21. VIIRS FSC vs. MOD10C2 • Distinctivedifferencebetween GS SE and MOD10C2: width of the meltingzone • MOD10C2 based on aggregation of binarysnow data  FSC is also ’binarized’ i.e. underestimations of lowsnowfractions and overestimations of highsnowfractions Snowablationduringspring

  22. Fusion of GlobSnow SE and SWE GlobSnow SWE NRT-productmayhavedifficulties in detectingsnow-freeareas. Solution:snowlineidentificationfromSE-product Willbe in use in 2014 SWE-production

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