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

Sari Metsämäki1) , Kari Luojus 2) , Jouni Pulliainen2), Mwaba Hiltunen2) , Andreas Wiesmann3)

1) Finnish Environment Institute, SYKE2) Finnish Meteorological Institute, FMI

3) Gamma Remote Sensing

7th EARSeL Workshop on Remote Sensing of Land Ice & Snow3-6 February, 2014 Bern, Switzerland

AATSR is not operating any more NPP Suomi/VIIRS employed in GlobSnow NRT SE-production Gap-filler for Sentinel-3

Northern Hemisphere covered daily (2013 onwards) Processing software by Gamma, operated in NRT at FMI, Sodankylä

FSC retrieval algorithm (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

Cloud masking with SYKE-made algorithm SCDA 2.0, employing BT3.7, BT11, BT12, R550, R1.6

GlobSnow SE-product from VIIRS

Developed for GlobSnow FSC purposes; works for ATSR-2 /AATSR, MODIS and VIIRS

No need to identify all (small/semi-transparent) clouds in confident snow-free areas

Cloud screening is targeted, not cloud classification binary information, no classes, no propabilities

Must be relatively simple and computationally fast

Must be applicable for Northern Hemisphere, no regional/local tuning

Must not confuse between fractional snow and clouds (it’s the fractional snow we want to see!)

Should work particularly over seasonally snow-covered areas and throughout the potential snow season

Cloud Detection SCDA2.0 for VIIRS

Simple Cloud Detection Algorithm

Designed to work with only a few spectral bands

common for Terra/MODIS, Envisat/AATSR,

ERS-2/ATSR-2, NPP Suomi/VIIRS R550, R1.6,

BT3.7, BT11, BT12

Based on empirically determined thresholds for

single bands and their ratios

Driven by BT11-BT3.7

Ratio NDSI / R550 important in avoiding false cloud

commissions

Several other test for BT12, R550 and NDSI

Major features of SCDA2.0

28 Feb, 2003

Cloud

Snow-covered forest and tundra

AATSR cloud (operational)

Cloud

Snow-covered forest and tundra

MODIS cloud(from MOD10_L2)

Cloud

Snow-covered forest and tundra

GlobSnow-2 cloudSCDA v2.0

Weather stations and snow courses in Finland.

FSC from the three 8-days period plus one day in September compared with in situ Snow Depth

First binary snow data is generated:

IF Snow Depth > 1cm ’snow’

IF FSC>0 ’snow’

OR

IF FSC>0.15 ’snow’

Statistical measures provided

Hit Rate, Probability of detection,

False alarm rate, Kuiper skill score

VIIRS SE-product vs. in situ Snow depth

Threshold for assigning the pixel as ‘snow’ : FSC >=0.15 (15%). From WS: snow depth >1 cm, N=1418 

estim.

in-situ

snow Non-snow

#in situ   POD FAR HR KSS

Snow 657 7 664 Snow 0.99 0.13 0.92 0.86

Non-snow 101 653 754 Non-snow

0.87 0.01

Threshold for assigning the pixel as ‘snow’: FSC > 0. From WS: snow depth >1 cm, N=1418  estim.

in-situ

snow Non-snow

#in situ   POD FAR HR KSS

Snow 659 5 664 Snow 0.99 0.16 0.91 0.83

Non-snow 124 630 754 Non-snow

0.84 0.01

VIIRS SE-product vs. WS Snow depth

Comparison against Landsat-8 data

VIIRS Fractional snow compared with Landsat fractional snow RMSE, bias

Data set preparation in progress

• Motivation:

• MOD10C2 commonly used interesting to see what new can VIIRS and SCAmod bring into NH Fractional snow mapping

• VIIRS provides good gap-filler data for Sentinel-3 SLSTR

• Earlier: GlobSnow SE (AATSR-based) difficult to evaluate due to data gaps

• Problems with weekly and monthly product: artifacts due to very limited number of observations

• Now: VIIRS provides global coverage possible to produce 8-days composites comparable with MOD10C2

• Demonstration for three 8-day periods in melting season 2013

• Doy 89-96 (March30 - April06)

• Doy 105-122 (April15 - April22)

• Doy 121-128 (May01 - May08)

VIIRS FSC vs. MOD10C2

VIIRS SE March 30 – April 06 2013

MOD10C2

•FSC in Climate modeling grid (CMG, 0.05º× 0.05º)

•based on binning the 500m 8-day maximum snow cover (MOD10A2) to the CMG

•MOD10A2 based on binary algorithm FSC biased at the transitional zone?

VIIRS FSC vs. MOD10C2

VIIRS FSC vs. MOD10C2

GS-2 VIIRS FSC

NASA MOD10C2

MOD10C2 and GlosSnow SE mostly agree well:o RMSE 12% (first period) to 15% (last period)

MOD10C2 shows less snow for forest areas, even 10% less for 80-100% forest-covered areas

VIIRS FSC vs. MOD10C2

Distinctive difference between GS SE and MOD10C2: width of the melting zone

MOD10C2 based on aggregation of binary snow data FSC is also ’binarized’ i.e. underestimations of low snow fractions and overestimations of high snow fractions

Snow ablation during spring

VIIRS FSC vs. MOD10C2

Fusion of GlobSnow SE and SWE

GlobSnow SWE NRT-product may have difficulties in detecting snow-free areas. Solution: snow line identification from SE-product

Will be in use in 2014 SWE-production