• Production of spatially-enhanced 250-m MODIS clear-sky mosaics for

the Northern circumpolar zone (9,000 km × 9,000 km or 36,000 pix ×

36,000 lines) was initiated at the Canada Centre for Remote Sensing

(CCRS) as a contribution to the Canadian component of the International

Polar Year (IPY) Program in 2007 (Trishchenko et al., 2009);

•The clear-sky composites are obtained from swath imagery (MOD02 or

L1B) by fusion (downscaling) of MODIS bands B1–B2 observed at 250 m

spatial resolution with bands B3–B7 observed at 500 m spatial resolution;

•Time series are generated at 10-day temporal resolution. The imagery is

generated in the Lambert Azimuthal Equal-Area (LAEA) projection centered

over the North Pole;

• The warm season (April-September) snow/ice annual probability maps

and seasonal spectral reflectance aggregates (Figure 1a) are also

produced. These data are valuable for assessment of multi-annual

dynamics of the Northern cryosphere, such as snow, glaciers and ice caps

(Trishchenko, 2019; Trishchenko and Wang, 2018; Trishchenko et al., 2016);

• Visible Infrared Imaging Radiometer Suite (VIIRS) processing at CCRS

produces results in the format compatible to MODIS (Trishchenko, 2019):

Map projections compatible with CCRS MODIS formats: LAEA for

circumpolar area and LCC for Canada-centered region

10-day compositing intervals

Spatial resolution (re-mapping) for output products

250m for I-bands (originally at 375m)

500m for M-bands (originally at 750m)

In-house re-projection tool

In-house scene ID-mask

In-house corrections and compositing scheme

•Difference in seasonal minimum snow/ice (MSI) extent between MODIS

and VIIRS is usually below 1% (see Figure 4).

9th EARSeL workshop on Land Ice and Snow. 03 - 05 February 2020, Bern, Switzerland

Minimum Snow/Ice Cover Extent over Northern Circumpolar Landmass at 250-m Spatial Resolution from

MODIS and VIIRS: Climatic Trends and Suitability for Annual Updates of Glacier Inventory since 2000

Alexander P. Trishchenko Canada Centre for Remote Sensing, Natural Resources Canada, Ottawa, ON, Canada, K1A 0E4

*Contact e-mail: alexander.trichtchenko@canada.ca

Trishchenko, A.P., 2019: Clear-sky composites over Canada from visible infrared imaging

radiometer suite: Continuing MODIS time series into the future. Can. J. Rem. Sens.

45(3-4),pp.276-289. https://www.tandfonline.com/doi/full/10.1080/07038992.2019.1601006

Trishchenko, A.P.: 2019: Probability maps of the annual minimum snow and ice (MSI)

presence over April-September period since 2000 derived from MODIS 250m imagery

over Canada and neighboring regions. Data are available at

http://open.canada.ca/data/en/dataset/808b84a1-6356-4103-a8e9-db46d5c20fcf .

Trishchenko. A.P. and C.Ungureanu, 2018: Warm season snow/Ice probability maps from

MODIS and VIIRS sensors over Canada. Proceeding of 38th IEEE IGARSS, Valencia,

Spain, July 22-27, 2018. Pages: 5210-5212.

Trishchenko, A.P., 2018: Assessment of VIIRS geolocation at subpixel level using MODIS

imagery. Proceeding of 38th IEEE IGARSS, Valencia, Spain, July 22-27, 2018. Pages:

6448-6451.

Trishchenko, A.P. and S. Wang, 2018: Variations of climate, surface energy budget and

minimum snow/ice extent over Canadian Arctic landmass for 2000-2016. Journal of

Climate, vol. 31, no. 3, pp.1155-1172. doi: https://doi.org/10.1175/JCLI-D-17-0198.1.

Trishchenko, A.P., 2018: Re-projection of VIIRS SDR imagery using concurrent gradient

search. IEEE TGRS, 56(7), pp. 4016-4024, doi: 10.1109/TGRS.2018.2819825.

Trishchenko, A.P., et al., 2016: Variations of annual minimum snow and ice extent over

Canada and neighboring landmass derived from MODIS 250m imagery for 2000-2014

period. Can. J. Rem. Sens. 42(3), pp.: 214-242

Trishchenko, A.P., et al., 2009: Arctic circumpolar mosaic at 250m spatial resolution for

IPY by fusion of MODIS/TERRA land bands B1–B7. Int. J. Rem. Sensing. 30, 1635-

1641.

References:

Conclusions1) Annual time series of CCRS-derived Minimum Snow/Ice (MSI)

extent is self-consistent product that shows very good

correlation with seasonal climate conditions;

2) CCRS-derived MSI extent shows reasonable consistency with

RGI 6.0 baseline and can be recommended to Glacier

Scientific Community, as a source of annual updates and the

first order validation data despite its coarser spatial

resolution.

3) CCRS MSI maps shows that RGI 6.0 baseline requires annual

updates to account for dynamics of glacier cover that is not

available from any other sources of high-resolution data.

• Mapping of seasonal permanent snow/ice presence over the land can

be most accurately achieved through analysis of time series of snow/ice

maps over the warm season (April – September in the Northern

Hemisphere). If all data points (from the melt date to start of freezing) in

the temporal sequence for particular pixel show the presence of snow/ice,

then this pixel belongs to the “permanent snow/ice” category (Figs. 1a,b);

• Temporal sequence of snow/ice flags is converted into the warm season

snow/ice probability maps : Probability P = (Nsnow&ice)/Nmax;

•Annual Minimum Snow/Ice (MSI) Extent is derived as an area with

P=100% (or above threshold P0);

• Single composite map derived for the entire warm season either from

high-resolution (Landsat-Sentinel) imagery or coarser resolution imagery

(MERIS, MODIS) is not sufficient for the purpose of permanent

snow/ice mapping as solid precipitation events (snow/ice pellets) can

occur at any time of the year in the alpine and Arctic-type of

environment (Trishchenko and Wang, 2018);

• CCRS-derived annual Minimum Snow/Ice (MSI) are compared against

Randolph Glacier Inventory (RGI 6).

Introduction

Acknowledgments• This work is supported through the CCRS activity on Long-Term Satellite

Data Records (LTSDR) as part of Cumulative Effects Project and the

NRCan Climate Change Geoscience Program (CCGP);

• MODIS data were acquired from the NASA archive;

• VIIRS data were acquired from the NOAA CLASS archive;

• RGI 6.0 data were acquired from the Randolph Glacier Inventory;

• Author is greatly indebted to Calin Ungureanu (CCRS) for his

assistance with MODIS and VIIRS data processing.

Main Processing Features: MODIS and VIIRS

Warm Season Cycle for Circumpolar Region

Warm Season Snow/Ice Probability Maps

Poster Session II. Wednesday. February 4, 2020

Figure 1b. Example of temporal sequence of 10-day MODIS clear-sky maps for Northern

Circumpolar Area for April-September, 2014. These time series are used to

compute snow/ice probability maps.

Figure 3. Warm season snow/ice probability maps from MODIS and VIIRS over the land

areas, 2018.

Figure 2. Warm season (April-September) snow/ice probability maps for the Northern

Circumpolar Area derived from MODIS/Terra for 2000-2018 period at 250-m spatial

resolution ( 36,000 × 36,000 pixels, 9,000 × 9,000 km)

Figure 4. Statistics of the difference between MODIS and VIIRS probability maps for 2018

over land shown in Figure 3 above. The difference for permanent snow/ice category

(100% ) is less than 1% (-0.97%).

Figure 5. RGI regions within our

circumpolar projection

•13 - First Order (O1) Regions;

•Greenland (yellow) is excluded from our analysis, as land cover maps include all land ice, while RGI reports only peripheral areas;

•3 RGI regions have largest glacier/ice cap areas:

•Canadian Arctic: Green+Blue(R03 North +R04 South)

•Alaska (R01): Brown

•Russian Arctic (R09): Sea Green

• Central Europe (R11) - Coral

CCRS MODIS 2012 RGI 6.0

Figure 6. Variations in annual

minimum snow/ice (MSI) extent

from CCRS MODIS probability

maps since 2000 over 3 RGI

regions:

Combined Canadian Arctic (N+S)

Alaska and Western NA

Russian Arctic

Figure 7. Example of CCRS

MODIS MSI extent and RGI 6.0

glacier cover in Canadian Arctic.

Figure 8. Example of CCRS

MODIS MSI extent and RGI 6.0

glacier cover over Alaska.

Canadian Arctic (R03+R04)

CCRS MODIS 2012 RGI 6.0

Figure 7

Figure 8

Ala ska (R01)

Central Europe (R11)

CCRS MODIS 2001 overlaid with RGI 6.0

CCRS MODIS 2017

Figure 10

Figure 9. Variations in annual minimum

snow/ice (MSI) extent from CCRS MODIS

probability maps since 2000 over the Central

European Region (RGO region R11)

Figure 10. Example of CCRS MODIS MSI

extent and RGI 6.0 glacier cover over Central

Europe. Left panel 2001, right panel 2017.

CCRS MODIS MSI shows declining trend in land ice extent over

Alps. Significant year-to-year variability is clearly observed in

CCRS MSI annual time series ( ~ 16% of average value).

This is highly unlikely that these variations and trends do not

reflect the real snow/ice cover conditions for particular year and

long-term declining land ice extent.

Probability

%

Figure 1aMODIS/Terra 2019 warm season composite

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ESA CCI Phase 2

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Canadian Arctic (R03+R04)