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Modeling Snowmelt Runoff under Climate Change Scenarios
This set of responses only resolves the cloud-obstructed pixel for a snow-free
period or for a continuous snow-covered period (Gao et al. 2010a).
The application of the three approaches (combination of Aqua and Terra MODIS,
spatial filtering, and temporal filtering) over the region of Austria in the work of
Parajka and Blöschl (2008a) yielded a significant decrease in cloud coverage. For
example, percentages of cloud coverage before the merger of the two products were
61.4% and 55.6% for Aqua and Terra, respectively. After combination, the cloud coverage of the merged product was reduced to 46.2%. The cloud coverage decreased
another 6% to 14% upon the application of a spatial filter to the merged Aqua and
Terra snow products. The largest cloud coverage reduction as stated by Parajka and
Blöschl (2008a) was achieved when temporal filtering was applied to the merged
Terra and Aqua map products, resulting in about 50% cloud coverage reduction with
1-day temporal filtering during the winter months.
In addition to the spatial and temporal filtering applied to the combined Aqua
and Terra MODIS product, Gafurov and Bardossy (2009) included the snow line
method in the list of cloud removal methodologies used over the Kokcha Basin in
the northeastern part of Afghanistan. This approach is based on the snow transition elevation. The basic idea of this method as stated by Parajka et al. (2010) is to
estimate the regional snow line elevation and reclassify all the cloud-covered pixels
based on their vertical position relative to the snow line. Above the snow line, all
pixels are assumed to be snow covered, whereas all pixels are land covered below
the snow line. This concept is used to reclassify cloud-covered pixels in such a way
that all cloud-covered pixels above the snow line are reclassified as snow; likewise,
all cloud-covered pixels below the snow line are reclassified as land. Parajka et
al. (2010) achieved an impact reduction due to cloud cover from 60% to 10% with
MODIS/Terra data with the snow line method.
Other researchers combined passive microwave sensor data with MODIS data
in order to benefit from the cloud-penetrating power of passive microwave radiometers and the high spatial and temporal resolutions of optical sensors. For instance,
Foster et al. (2007) showed that blending of snow cover products gives more accurate determination of snow cover measurements when compared to the accuracy
obtained from using either MODIS or AMSR-E alone. Foster et al. (2007) blended
Aqua MODIS together with AMSR-E, while Liang et al. (2008) blended the Terra
MODIS daily snow cover product together with the AMSR-E daily SWE product to
obtain new snow cover products at a 500-m resolution. Both studies increased the
accuracy of snow cover determination through the MODIS/AMSR-E combination.
Building on the previous works, Gao et al. (2010b) blended the Terra and Aqua
MODIS snow cover products with AMSR-E SWE. Owing to the different encoding
schemes, they started by using unifying codes to transform the original integers of
the MODIS and AMSR-E snow products into new unified codes in such a way that the
new integers in both products will have the same meaning and, therefore, be compatible. Before assigning the unifying codes, AMSR-E was resampled and reprojected
from a 25-km resolution to a 500-m resolution to enable combination. Subsequent to
the creation of the unifying coding system for the products, Terra and Aqua MODIS
images were combined first as one Terra–Aqua combined (TAC) image according to
a priority principle in which a lower integer value is replaced with a higher integer
Modeling Snowmelt Runoff under Climate Change Scenarios
This set of responses only resolves the cloud-obstructed pixel for a snow-free
period or for a continuous snow-covered period (Gao et al. 2010a).
The application of the three approaches (combination of Aqua and Terra MODIS,
spatial filtering, and temporal filtering) over the region of Austria in the work of
Parajka and Blöschl (2008a) yielded a significant decrease in cloud coverage. For
example, percentages of cloud coverage before the merger of the two products were
61.4% and 55.6% for Aqua and Terra, respectively. After combination, the cloud coverage of the merged product was reduced to 46.2%. The cloud coverage decreased
another 6% to 14% upon the application of a spatial filter to the merged Aqua and
Terra snow products. The largest cloud coverage reduction as stated by Parajka and
Blöschl (2008a) was achieved when temporal filtering was applied to the merged
Terra and Aqua map products, resulting in about 50% cloud coverage reduction with
1-day temporal filtering during the winter months.
In addition to the spatial and temporal filtering applied to the combined Aqua
and Terra MODIS product, Gafurov and Bardossy (2009) included the snow line
method in the list of cloud removal methodologies used over the Kokcha Basin in
the northeastern part of Afghanistan. This approach is based on the snow transition elevation. The basic idea of this method as stated by Parajka et al. (2010) is to
estimate the regional snow line elevation and reclassify all the cloud-covered pixels
based on their vertical position relative to the snow line. Above the snow line, all
pixels are assumed to be snow covered, whereas all pixels are land covered below
the snow line. This concept is used to reclassify cloud-covered pixels in such a way
that all cloud-covered pixels above the snow line are reclassified as snow; likewise,
all cloud-covered pixels below the snow line are reclassified as land. Parajka et
al. (2010) achieved an impact reduction due to cloud cover from 60% to 10% with
MODIS/Terra data with the snow line method.
Other researchers combined passive microwave sensor data with MODIS data
in order to benefit from the cloud-penetrating power of passive microwave radiometers and the high spatial and temporal resolutions of optical sensors. For instance,
Foster et al. (2007) showed that blending of snow cover products gives more accurate determination of snow cover measurements when compared to the accuracy
obtained from using either MODIS or AMSR-E alone. Foster et al. (2007) blended
Aqua MODIS together with AMSR-E, while Liang et al. (2008) blended the Terra
MODIS daily snow cover product together with the AMSR-E daily SWE product to
obtain new snow cover products at a 500-m resolution. Both studies increased the
accuracy of snow cover determination through the MODIS/AMSR-E combination.
Building on the previous works, Gao et al. (2010b) blended the Terra and Aqua
MODIS snow cover products with AMSR-E SWE. Owing to the different encoding
schemes, they started by using unifying codes to transform the original integers of
the MODIS and AMSR-E snow products into new unified codes in such a way that the
new integers in both products will have the same meaning and, therefore, be compatible. Before assigning the unifying codes, AMSR-E was resampled and reprojected
from a 25-km resolution to a 500-m resolution to enable combination. Subsequent to
the creation of the unifying coding system for the products, Terra and Aqua MODIS
images were combined first as one Terra–Aqua combined (TAC) image according to
a priority principle in which a lower integer value is replaced with a higher integer