S. Muster1 , M. Langer1, A. Abnizova2, K. L. Young2 , J. Boike1
1Alfred Wegener Institute for Polar- and Marine Research, Potsdam, Germany; 2York University, Toronto, Ontario, Canada
Spatio-Temporal Sensitivity of MODIS Land Surface Temperature Anomalies
Indicates High Potential for Large-Scale Land Cover Change Detection in Permafrost Landscapes
GC51D -0444
Corresponding author: Sina Muster, Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Potsdam, Germany Sina.Muster@awi.de, www.awi.de/en/go/spac
We gratefully acknowledge financial support by a Helmholtz Postdoc Grant (PD-101) awarded to Sina Muster.
Outlook
• Presented summer MODIS LST anomalies can serve as a baseline against which to evaluate past and future changes in land surface properties with regard to the surface energy balance.
• A multi-sensor approach combining MODIS LST measurements in conjunction with other MODIS products (NDVI, albedo, fire, snow) and high- resolution optical and radar imagery promises to be an effective tool for a dynamic, process-based ecosystem monitoring scheme.
The accelerated warming of the Arctic climate may alter the surface energy balance locally and regionally of which a changing land surface temperature (LST) is a key indicator. Modelling current and anticipated changes of the surface energy balance requires an understanding of the spatio-temporal interactions between LST and land cover.
Motivation
Study area
The investigated region in Central Yakutia is characterized by a thermokarst landscape, with thermokarst lakes, thermokarst valleys, and alases on deep, continuous permafrost dominated by larches.
Key findings
• Between 2002 and 2011 the region showed strong differences of LST anomalies ranging from -7.6 °C to 4.5°C.
• Changes in LST anomaly patterns could be linked to occurrence and age of fires in the taiga zone.
Land surface temperature anomalies at Polar Bear Pass, Bathurst Island (CA) Goals
(1) Assess the accuracy of MODIS LST V5 1 km level 3 product
(2) Investigate MODIS LST spatio-temporal sensitivity to land cover properties
-
+
p l a n t c o v e r
s u r f a c e w e t n e s s
Study area
Polar Bear Pass (98° 30’W, 75°40’N) is a low-lying tundra wetland within a barren polar desert environment in the Canadian High Arctic.
Key findings
• Summer LST anomalies showed a robust spatio-temporal pattern taking into account the found uncertainty and different atmospheric conditions in the three years.
• Land cover and albedo explained most of the variance in LST anomalies: Dry ridge areas heat up most whereas dry barren surfaces with high albedo and wetland areas were coolest.
• Spatial pattern showed fewer positive anomalies in 2010 suggesting differences in surface moisture due to inter-annual differences in the amount of end-of- winter snow.
Land surface temperature anomalies in Central Yakutia, Siberia (RU)
130°0'0"E 130°0'0"E
125°0'0"E 125°0'0"E
120°0'0"E 120°0'0"E
64°0'0"N 64°0'0"N
63°0'0"N 63°0'0"N
62°0'0"N 62°0'0"N
0 50100 200 km
130°0'0"E 130°0'0"E
125°0'0"E 125°0'0"E
120°0'0"E 120°0'0"E
64°0'0"N 64°0'0"N
63°0'0"N 63°0'0"N
62°0'0"N 62°0'0"N
0 50100 200 km
Fires 2000 to 2011 Cumulative
2000 to 2002 2003 to 2005 2006 to 2008 2009 to 2011 LST anomaly difference 2011-2002 [°C]
-7.6 - -4 -3.9 - -2 -1.9 - 0 0.1 - 2 2.1 - 4.5 Rivers LST anomaly difference 2011-2002 [°C]
-7.6 - -4 -3.9 - -2 -1.9 - 0 0.1 - 2 2.1 - 4.5
¯
¯
MODIS LST summer anomaly difference 2011-2002
Cumulative MODIS fire map from 2000 to 2011
Data & Methods
• Spatial LST anomalies were calcuated as the difference between mean daily LST for each pixel and the daily regional mean of the study area.
• LST anomalies of summer periods were averaged for all scenes with regional means larger than 5°C at Bathurst Island (with 19 to 28 observations per pixel) and 10°C in Central Yakutia (with 11 to 65 observations per pixel).
Fires were derived from the MODIS thermal anomaly/fire product MOD14A2 8-day composite at 1 km resolution.
^ _
^
Central Yakutia
_
Polar Bear Pass
Asia
Europe North America
60 N
90E
90W
30W 30E
0 180W
Permafrost continuous ( 90-100%) discontinuous (50-90%) isolated patches 0-10%) sporadic (10-50%)
