University of Texas at San Antonio
S5053/Remote Sensing
Assessment of Sea Ice and Ice Elevation Changes in Ross Region using ICESat
ASSESSMENT OF SEA ICE AND ICE ELEVATION
CHANGES IN ROSS REGION USING ICESAT
Burcu Ozsoy Cicek
Earth and Environmental Sciences, University of Texas at San Antonio
San Antonio, TX, 78249
ABSTRACT:
Most of the fresh water on Earth is stored in Greenland and Antarctic ice sheets. The most critical
aspect of the ice sheets is their potential in affecting global sea level. These ice sheets also
influence the global climate system through their albedo and elevated topography. In this study,
both GLAS12 Ice sheet data and GLAS13 Sea Ice data of The Ice, Cloud, and Land Elevation
Satellite (ICESat) were used to determine the elevation change. Sea ice and ice sheet elevation
change detection over the Ross Region ice sheets has been carried out by interpolation;
calculating the difference in elevation between the fall and the spring time in 2003 for the Ross
region. Data were obtained from NSIDC for both spring and fall season. LABVIEW was used for
formatting, processing, and analyzing the data. This study was focused on the Ross Ice Shelf
region on West Antarctic since most of the melting occurs in West Antarctic. The subset data for
the Ross Region were extracted from each raw ICESat data, which covers the data for all regions
that ICESat passes, and the coordinate system of the subset data were transferred to the GIS
coordinate system. The subset data, analyzed in Arc MAP using IDW and Kriging methods,
showed seasonal melting from Spring to Fall season of 2003. The results of this study are
presented in this paper.
KEYWORDS: ICESat, elevation change, Ross iceshelf
1. INTRODUCTION
While most small glaciers around the
world have been shrinking rapidly and
contributing to sea level rise in recent
decades, we do not know whether the
polar ice sheets are growing or shrinking
especially in Antarctic region. Each year
about 8 mm (0.3 inches) of water from
the entire surface of the oceans goes into
the Antarctica and Greenland ice sheets
as snowfall. If no ice returned to the
oceans, sea level would drop 8 cm (3
inches) every 10 years. Although
approximately the same amount of water
returns to the ocean in icebergs and from
ice melting at the edges, we do not know
The effect of polar ice sheets on global
sea level change is a major category of
scientific uncertainty, and baseline
information on ice sheet mass balance is
needed before significant greenhouse
warming occurs. The Ice, Cloud, and
Land Elevation Satellite (ICESat)
mission would provide data that
contributes to our knowledge and
understanding of the Earth’s cryosphere,
atmosphere, and the land processes and
provides a better tool for the assessment
of factors involved in or affected by
global climate change [1].
1
University of Texas at San Antonio
S5053/Remote Sensing
Assessment of Sea Ice and Ice Elevation Changes in Ross Region using ICESat
which is greater—the ice going in or the
ice coming out. The difference between
the ice input and output is called the
mass balance and is important because it
causes changes in the global sea level.
ICESat will answer fundamental
questions about the growth or shrinkage
of the Earth’s polar ice sheets and also
future global sea-level rise or fall. The
Greenland and Antarctic ice sheets cover
10 percent of the Earth’s land area, and
contain 77 percent of the Earth’s fresh
water and 99 percent of its glacier ice.
Measurements of the ice sheets are
essential for assessing whether future
changes in ice volume will add to the
sea-level rise, which is already
occurring, or whether the ice sheets
might grow and absorb a significant part
of the predicted sea-level rise.
Greenland and Antarctic ice sheets with
appreciable accuracy to assess the
impact of ice sheets on global sea level
and to determine and explain trends in
seasonal and inter-annual variability of
the surface elevation of ice sheets and
sea level. The satellite has a unique and
sophisticated laser altimeter, the
geoscience laser altimeter system
(GLAS). GLAS has been designed at
NASA Goddard Space Flight Center for
the purpose of measuring ice-sheet
topography and associated temporal
changes, as well as cloud and
atmospheric properties. The mission is
unique of the scientific goals of not only
mapping the surface of the Earth, but
specifically providing an accurate
(centimeter level) assessment as to the
change
in
surface
elevation,
predominantly in the Polar Regions.
Previous Earth orbiting observatories
have not been equipped to provide the
ICESat level of altimeter accuracy [3].
Ice sheets comprise the coldest regions
of the planet owing to a combination of
their height, their highly reflective
surface, and the diminished amount of
sunlight received in the Polar Regions.
Albedos of ice sheets can exceed 90% in
the visible part of the spectrum, making
them the brightest object on Earth. Their
low temperature, combined with the heat
of the tropics, creates a temperature
gradient that contributes to the
meridional exchange of heat by
atmospheric circulation [2].
Surface altimetry is accomplished using
the 1064 nm pulse. This laser pulse, after
reflection from the surface, will be
digitized
within
the
GLAS
instrumentation. The digitized waveform
will enable investigation of other surface
properties, such as surface roughness,
vegetation canopy heights and other
characteristics. The on-board tracking
algorithm will use a digital elevation
model to assist with the detection of the
surface [4].
ICESat is an Earth Science Enterprise
mission. It was launched from
Vandenberg Air Force Base on 13
January 2003. ICESat is a Lidar
technique and its data is now widely
used for a number of applications,
particularly those needing a digital
elevation model. The ICESat helps to
understand Earth’s global processes and
to provide information on long-term
changes in the volume and mass of the
The concept of using satellite-altimeters
to measure ice elevation changes and
determine icesheet mass balance is based
on the simple relationship of ice surface
elevation changes to changes in ice
thickness and therefore ice mass [5].
2
University of Texas at San Antonio
S5053/Remote Sensing
Assessment of Sea Ice and Ice Elevation Changes in Ross Region using ICESat
The purpose of this study is to detect the
elevation change over the Ross Ice Shelf
region in West Antarctic from the spring
season to fall season using ICESat data.
The future of the West Antarctic Ice
Sheet remains uncertain, with its marinebased
configuration
raising
the
possibility of important losses in the next
centuries [6]. Therefore, it is crucial to
observe the unusual elevation changes.
laser 2 sea ice (GLA13) products were
obtained from NSIDC1. The latest
release for these products is release 21.
The release 18 covers data from 200302-20 to 2003-03-20 and the release 21
covers data from 2003-09-25 to 200311-18. That essentially allows looking at
the short-term semi-annual elevation
changes between spring and fall of 2003.
In Polar Regions generally four seasons
are recognised: summer, winter, autumn
(fall), and spring. Figure 1 shows that
the seasons in the southern hemisphere
are reverse as in the northern
hemisphere.
2. STUDY AREA AND DATA USED
In this project Ross Ice Shelf region
which is in the West Antarctic was
chosen as the study area. Ice shelves are
permanent floating ice sheets that are
attached to land and are constantly fed
by glaciers. The largest individual shelf
is the Ross Ice Shelf in West Antarctica,
also called the Great Ice Barrier which is
600 – 3000 feet thick and about 600
miles long. Also scientists recently
confirmed that there is accelerated
movement of glaciers on the West
Antarctic ice sheet and the Antarctic
Peninsula (located at about 10 o’clock)
following the breakup of the floating ice
shelf onto which the glaciers flowed.
Table 1 Month ranges of seasons for
Southern Hemisphere.
Southern Hemisphere
Month
Meteorological
Astronomical
January
Summer
February
Summer
March
April
Autumn
May
Autumn
June
July
Winter
Winter
August
September
The data acquired for this project is from
both the Laser 1 and the first Laser 2
mission phases; 2/20/03 to 3/29/03 and
9/25/03 to 11/18/03, respectively. The
National Snow and Ice Data Center
(NSIDC) archives and distributes 15
GLAS products, including levels 1A, 1B
and 2 laser altimetry and atmospheric
lidar data. GLAS sends out pulses in two
wavelengths: green (532 nm) for
atmospheric properties and near-infrared
(1064 nm) for the land surface and ice
altimetry. Since the research interest was
in sea ice and ice sheet, laser 1 and laser
2 ice sheet (GLA12) and laser 1 and
October
Spring
November
December
Spring
Summer
The following are the list of the data that
is obtained from NSIDC and used in this
study:
GLA13 Spring time- from
09.27.2003 to 11.17.2003 (56 file)
GLA12
Spring
time-from
09.26.2003 to 11.17.2003 (57 file)
1
3
http://nsidc.org/data/gla13.html.
University of Texas at San Antonio
S5053/Remote Sensing
Assessment of Sea Ice and Ice Elevation Changes in Ross Region using ICESat
GLA13 Fall time- from 02.20.2003
to 03.20.2003 (31 file)
GLA12 Fall time- from 02.20.2003
to 03.20.2003 (31 file)
uses C/C++ algorithms but the code
objects are represented with icons in a
coding window called block diagram.
The
program
developed,
called
“ICESAT Data Formatter” performs the
following tasks:
- The software first reads in the text
file created using IDL Reader. Each
line in a text file consists of ID, Date,
Time,
Latitude,
Longitude,
Elevation, and GeoID, separated
with formatted spaces in between.
- Extracts the lines that are of interest.
Specifically, the following rule was
applied to extract the data for the
Ross Region:
220  Long i  150, and
3. METHODOLOGY
The methods and tools used to format,
process, and analyze the data is
presented in the below sections. The
methodology is covered in four steps, as
shown in Figure 1.
3.1 Step 1 – Converting Raw Binary
File into a Text File: The raw ICESAT
file is a binary file that includes the data
that we are interested and other sensor
specific data. After acquiring the data
from NSIDC, the first step was
converting binary ICESat data to a text
file. The data that we are interested are
written in lines, each line having the
following variables separated with
spaces: ID, Date, Time, Latitude,
Longitude, Elevation, and GeoID. For
this step IDL reader was downloaded
form NSIDC’s web site and is used to
convert each data for fall and spring to a
text file. An example filename for these
files is:
“GLA13_018_1102_001_0071_0_01_00
01.txt”
3.2 Step 2 – Formatting and Creating
the Subset of the Data for the ROSS
Region: The raw ICESat data covers all
the data for the areas that it passes. To
focus on the study area, subset of each
file was created using a program
developed in LabVIEW. LabVIEW is a
unique
graphical
programming
environment that allows developing
application programs like the other
compilers do. However, the extensive
libraries and debugging tools reduces the
development time noticeably. LabVIEW
 70  Latitudei  85
where “i” is the number of lines in the
file that is being processed.
- Converts the coordinate system to
GIS coordinate system with the
following comparison:
If Long i  180 Then {
Long i  Long i  360
}
Note that this step is performed so that
the data can be populated in a GIS
software.
- Writes the data to an ASCII file by
separating the variables in lines with
tab spaces. The output data filenames
are
appended
with
“subsetRossRegion” phrase to keep it
consistent with the original files. The
program also creates a log file with
“-subsetRossRegion-log” appended
in the filename to keep the
processing statistics for reference.
3.3 Step 3 – Analyzing Data in ArcGIS
Software: The subset of each file was
imported as shape file into the ArcGIS
software and each ones were specified as
the Projected Coordinate System as
WGS 1984 Stereographic South Pole.
4
University of Texas at San Antonio
S5053/Remote Sensing
Assessment of Sea Ice and Ice Elevation Changes in Ross Region using ICESat
STEP 1: CONVERT RAW BINARY FILE TO TEXT FILE
Acquired Raw
Binary
ICESat Data
Text File
ICESat Raw Data
Downloaded IDL From
NSIDC's Web Site
(Filename.txt)
Read Text File
X {ID,Date, Time, Lat, Long, Elev,
GeoID)
STEP 2:
SUBSET ICESat
DATA
USING "ICESat
DATA FORMATTER"
(Developed in
LabVIEW)
Extract Subset of Data
for the ROSS Region
220  Longi  150
( 70)  Lati  ( 85)
TRUE
Longi  180
FALSE
Perform GIS Conversion
Longi  Longi  360
File Writer
Subsetted ASCII file
Log File for the Subset File
(Filename-subsetROSS.txt)
(Filename-subsetROSS-log.txt)
STEP 3: ANALYZE DATA IN GIS SOFTWARE
Import ICESat Data
(ROSS Region) into
GIS Software
Reproject to
Stereographic
South Pole
Create New
Shape Files
Stack Files for
Eight days
Repeat Cycle
Perform
Interpolation
STEP 4: EVALUATE/PRESENT RESULTS
Figure 1 Flowchart for the ICESat Data File Processing and Analysis Methodology
5
University of Texas at San Antonio
S5053/Remote Sensing
Assessment of Sea Ice and Ice Elevation Changes in Ross Region using ICESat
The data was stacked for 8-day repeat
cycle orbit purpose for each season and
for each GLAS product. This process
yielded 7 cycle stacked file for fall 2003
and 4 cycle stacked file for spring 2003.
Refer to Appendix A for the detail of the
compiled files. Analysis of data in
ArcGIS Software is provided in detail in
Section 4.
3.4 Step 4 – Evaluating and Presenting
Results: The results from the ArcGIS
software is evaluated and discussed.
Microsoft Excel is used to show the
results in graphs. This step is further
shown in Section 5.
interpolation technique is based on
statistics and is used for more advanced
prediction surface modeling that also
includes errors or uncertainty of
predictions. Geostatistical methods
create surfaces incorporating the
statistical properties of the measured
data. Because geostatistics is based on
statistics, these methods produce not
only prediction surfaces but also error or
uncertainty surfaces, giving the user an
indication of how good the predictions
area is [7].
Even geostatistical gives the better result
to most of the studies, geostatistical and
deterministic interpolation techniques
were compared in
this study.
Comparison of IDW against Ordinary
Kriging technique showed that IDW
technique missed an iceberg on the
surface. For the validation purpose, the
iceberg was confirmed from the ground
track pass as shown in Figure 2.
4 ICESAT DATA ANALYSIS IN
ARCGIS SOFTWARE
Interpolation method was used to create
surface and to see the differences
between the surfaces created. ArcGIS
Geostatistical Analyst provides with a
variety of interpolation methods for the
creation of an optimal interpolated
surface. There are two main groupings
of interpolation techniques: deterministic
and
geostatistical.
Deterministic
interpolation techniques are used for
creating surfaces from measured points
based on either the extent of similarity
(e.g., Inverse Distance Weighted
referred as IDW) or the degree of
smoothing
(e.g.,
Radial
Basis
Functions). Geostatistical interpolation
techniques are based on statistics and are
used for more advanced prediction
surface modeling, which also includes
error or uncertainty of predictions. IDW,
Ordinary Kriging, Simple Kriging
interpolation methods were used to
create surface for this study. Kriging is a
geostatistical interpolation technique that
considers both the distance and the
degree of variation between known data
points when estimating values in
unknown areas. The geostatistical
IDW
?
(a)
Ordinary
Kriging
(b)
Figure 2 Comparing two techniques: (a)
IDW and (b) Ordinary Kriging.
6
University of Texas at San Antonio
S5053/Remote Sensing
Assessment of Sea Ice and Ice Elevation Changes in Ross Region using ICESat
After eliminating IDW, ordinary kriging
and simple kriging were compared. The
difference between these two techniques
is that in simple kriging method, mean
elevation value is used. Setting the
calculated mean elevation value for the
study area gave more small errors. The
mean elevation value was calculated
from the DEM that is downloaded from
the NSIDC’s web site. According to the
DEM, the elevation mean was 539 m.
The system was setting the mean
elevation value more than 1000 m. After
eliminating ordinary kriging, simple
kriging method was used with 8 different
variations to find a better technique for
the study area. The details of the
variations were provided in Appendix B.
The models were compared after the
surface is created to ensure the optimal
model was chosen. Errors comparison
showed that the Simple kriging method2 had the better prediction error. The
results are shown in Figures 3 and 4.
“SK” on the x-axis in these figures
stands for Simple Kriging method.
Spring Data, Simple Kriging, Root-Mean Square
of Prediction Errors for Different Methods
Root-Mean Square
19.6
19.4
19.2
19
18.8
18.6
18.4
SK8
SK7
SK6
SK5
SK4
SK3
SK2
SK1
18.2
Methods
(b)
26.4
26.35
26.3
26.25
26.2
SK8
SK7
SK6
SK5
SK4
SK3
SK2
26.15
SK1
Average Standard Error
Spring Data, Sim ple Kriging, Average
Standard Prediction Errors for Different
Methods
Methods
(c)
Spring Data, Simple Kriging, Mean
Standardized of Prediction Errors for
Different Methods
0.002
0.0015
0.001
0.0005
Methods
Methods
(d)
(a)
7
SK8
SK7
SK6
SK5
SK4
SK3
SK2
0
SK1
Mean Standardized
SK8
SK7
SK6
SK5
SK4
SK3
SK2
0.13
0.125
0.12
0.115
0.11
0.105
0.1
0.095
SK1
Mean Error
Spring Data, Simple Kriging, Mean
Prediction Errors for Different Methods
University of Texas at San Antonio
S5053/Remote Sensing
Assessment of Sea Ice and Ice Elevation Changes in Ross Region using ICESat
Fall Data, Simple Kriging, Average Standard
Prediction Errors for Different Methods
26.815
26.81
26.805
26.8
Methods
SK8
SK7
SK6
SK5
SK4
SK3
SK1
SK2
26.795
SK8
SK7
SK6
SK5
Methods
(e)
(c)
Figure 3 Simple Kriging Prediction Error
Results for 8 Different Variations for
Spring 2003: (a) Mean Error, (b) RootMean Square, (c) Average Standard
Prediction Errors, (d) Mean
Standardized, (e) Root Mean-Square
Standardized
SK8
SK7
SK6
SK5
SK3
SK2
0.00164
0.00162
0.0016
0.00158
0.00156
0.00154
0.00152
SK1
Mean Standardized
Fall Data, Simple Kriging, Mean Standardized
of Prediction Errors for Different Methods
SK4
SK4
SK3
SK2
Average Standard
Error
0.428
0.426
0.424
0.422
0.42
0.418
0.416
0.414
SK1
Root Mean-Square
Standardized
Spring Data, Simple Kriging, Root MeanSquare Standardized of Prediction Errors
for Different Methods
Methods
(d)
Fall Data, Simple Kriging, Root MeanSquare Standardized of Prediction Errors
for Different Methods
0.188
0.186
Root Mean-Square
Standardized
Mean Error
Fall Data, Sim ple Kriging, Mean Prediction
Errors for Different Methods
0.184
0.182
SK8
SK7
SK6
SK5
SK4
SK3
SK2
SK1
0.18
Methods
0.4945
0.494
0.4935
0.493
0.4925
0.492
SK1 SK2 SK3 SK4 SK5 SK6 SK7 SK8
(a)
Methods
(e)
Figure 4 Simple Kriging Prediction Error
Results, Method for 8 Different
Variations for Fall 2003: (a) Mean Error,
(b) Root-Mean Square, (c) Average
Standard Prediction Errors, (d) Mean
Standardized, (e) Root Mean-Square
Standardized
22.25
22.2
SK8
SK7
SK6
SK5
SK4
SK3
SK2
22.15
22.1
22.05
SK1
Root-Mean
Square
Fall Data, Simple Kriging, Root-Mean
Square of Prediction Errors for Different
Methods
Methods
(b)
8
University of Texas at San Antonio
S5053/Remote Sensing
Assessment of Sea Ice and Ice Elevation Changes in Ross Region using ICESat
Created surfaces also showed the
difference between Fall and Spring 2003
for each cycle as shown in Figures 6 and
7.
5. RESULTS
Simple Kriging produced better surface
for this study. Surface results legend
shows the minimum and maximum
difference between fall 2003 and spring
2003 as shown in legend figures 5a and
5b.
Spring 2003
Fall 2003-First Cycle
(a)
(a)
Fall 2003-Second Cycle
Fall 2003
(b)
Fall 2003-Third Cycle
(c)
(b)
Figure 5 Legends for the Sea Ice and Ice
Sheet Elevation Changes: (a) Spring
2003), (b) Fall 2003.
9
University of Texas at San Antonio
S5053/Remote Sensing
Assessment of Sea Ice and Ice Elevation Changes in Ross Region using ICESat
Spring 2003-Fourth Cycle
Fall 2003-Fourth Cycle
(d)
(d)
Figure 6 Created surfaces by Simple
Kriging for Fall Repeat Cycles.
Spring 2003-Fifth Cycle
Spring 2003-First Cycle
(e)
(a)
Spring 2003-Sixth Cycle
Spring 2003-Second Cycle
(f)
(b)
Spring 2003-Seventh Cycle
Spring 2003-Third Cycle
(g)
Figure 7 Created Surfaces by Simple
Kriging for Spring Repeat Cycles.
(c)
10
University of Texas at San Antonio
S5053/Remote Sensing
Assessment of Sea Ice and Ice Elevation Changes in Ross Region using ICESat
Results with the minimum prediction
errors by simple kriging show the
difference between two seasons. To
relate those results to temperature
change and global change, a further
study will be performed to observe the
relation.
where we have crossovers between laser
2 and laser 1 [8].
6. SUMMARY
References
GLAS data collected over the Ross Ice
Shelf was examined in this study.
Because of the limitation of the data, the
time series for the elevation change over
the Ross Region in Antarctic could not
be obtained.
One of the future work as the extension
of this study will be producing the time
series using ICESat. Another extension
of this study will be the comparison of
the result provided in this paper with the
ones obtained using other remote
sensing satellites. The results presented
in this paper should also be validated
with ground-based GPS data.
[1] NASA Goddard Space Flight Center, 2002,
Environmental Assessment for ICESat.
The use of remote sensing in the
research studies of ice sheets are
numerous and increasing. The ice sheets
in Antarctica can be investigated in
detail with ICESat altimetry data
supported by MODIS imagery and/or
AMSR-E imagery.
[5] H.J. Zwally, B. Schutz, W. Abdalati, J.
Abshirea, C. Bentley, A. Brenner, J. Bufton, J.
Dezio, D. Hancock, D. Harding, T. Herring, B.
Minster, K. Quinn, S. Palm, J. Spinhirne, R.
Thomas, 2002, Journal of Geodynamics 34
(2002) 405–445.
Acknowledgements
Special thanks go to Dr. Xie for his
support during this study.
[2] Robert Bindschadler, Monitoring Ice Sheet
Behavior from Space, Reviews of Geophysics,
36, 1 / February 1998, pages 79–104, Paper
number 97RG02669.
[3] L. A. Magruder, M. A. Suleman and B. E.
Schutz,
2003,
ICESat
laser
altimeter
measurement time validation system, Meas. Sci.
Technol. 14 (2003) 1978–1985.
[4] B. E. Schutz, J. Zwally, J. Abshire and J.
Spinhiine, , 2000, The Ice, Cloud and Land
Elevation Satellite Mission: Laser Radar Science
for the NASA Earth Observing System ,0-78036359-0/00, IEEE.
[6] Richard B. Alley, Peter U. Clark, Philippe
Huybrechts, Ian Joughin, 21 OCTOBER 2005,
Ice-Sheet and Sea-Level Changes, VOL 310
SCIENCE.
Another future work could be the
crossover analysis; calculating the
difference in elevation between an
ascending and descending pass when it
crosses over the same location but is
separated temporally. Precise elevation
changes can be calculated using both
crossovers and repeat track analysis over
the ice sheets with GLAS data. Short
term elevation change calculations from
Spring to Fall of 2003 can be made
[7] ESRI White paper, August 2001, ArcGIS
Geostatistical Analyst: Statistical Tools for Data
Exploration, Modeling and Advanced Surface
Generation.
[8] A. C. Brenner, H. J. Zwally, Chris Shuman,
and Donghui Yi, January, 2004, ICESat
Capability for Elevation Change Studies with
Sets
of
33-day
and
8-day
Cycles.
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APPENDIX A DETAILED GLAS PRODUCTS WITH DATE AND TIME
Table A.1 Antarctic Fall GLA13
No
01
02
03
04
05
06
07
08
09
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
Starting Date
02.20.2003
02.21.2003
02.22.2003
02.23.2003
02.24.2003
02.25.2003
02.26.2003
02.27.2003
02.27.2003
02.28.2003
03.01.2003
03.02.2003
03.03.2003
03.04.2003
03.05.2003
03.06.2003
03.06.2003
03.07.2003
03.08.2003
03.09.2003
03.10.2003
03.11.2003
03.12.2003
03.13.2003
03.14.2003
03.14.2003
03.15.2003
03.16.2003
03.17.2003
03.18.2003
03.19.2003
ANTARCTIC Fall (Feb- Mar) GLAS13
Time
22:25
19:18
17:46
16:19
03:41
02:10
00:45
23:23
21:56
20:30
19:03
17:31
16:06
03:24
01:55
00:30
23:06
22:39
20:14
18:47
17:16
15:49
03:10
01:39
01:15
22:50
21:24
19:59
18:32
17:00
15:33
12
Finishing Date
02.21.2003
02.22.2003
02.23.2003
02.24.2003
02.25.2003
02.26.2003
02.26.2003
02.27.2003
02.28.2003
03.01.2003
03.02.2003
03.03.2003
03.04.2003
03.05.2003
03.05.2003
03.06.2003
03.07.2003
03.08.2003
03.09.2003
03.10.2003
03.11.2003
03.12.2003
03.13.2003
03.13.2003
03.14.2003
03.15.2003
03.16.2003
03.17.2003
03.18.2003
03.19.2003
03.20.2003
Time
18:43
17:45
15:48
03:08
01:41
00:14
22:48
21:20
19:52
18:27
17:30
15:31
02:51
01:24
23:58
22:32
21:06
19:39
18:12
17:14
15:19
02:38
01:11
23:43
22:15
20:51
19:22
17:57
16:59
15:03
02:21
University of Texas at San Antonio
S5053/Remote Sensing
Assessment of Sea Ice and Ice Elevation Changes in Ross Region using ICESat
Table A.2 Antarctic Spring GLA13
No
01
02
03
04
05
06
07
08
09
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
Starting Date
09.27.2003
09.28.2003
09.29.2003
09.30.2003
10.01.2003
10.02.2003
10.03.2003
10.04.2003
10.04.2003
10.05.2003
10.07.2003
10.07.2003
10.08.2003
10.08.2003
10.09.2003
10.10.2003
10.11.2003
10.12.2003
10.13.2003
10.14.2003
10.15.2003
10.16.2003
10.17.2003
10.18.2003
10.19.2003
10.20.2003
10.21.2003
10.21.2003
10.22.2003
10.23.2003
10.24.2003
10.25.2003
10.26.2003
10.27.2003
10.28.2003
10.29.2003
10.30.2003
10.31.2003
11.01.2003
11.02.2003
11.03.2003
11.03.2003
11.04.2003
11.05.2003
11.06.2003
11.07.2003
11.08.2003
11.09.2003
11.10.2003
11.11.2003
11.12.2003
11.13.2003
ANTARCTIC Spring (Sep-Oct-Nov) GLAS13
Time
Finishing Date
20:47
09.28.2003
19:18
09.29.2003
17:53
09.30.2003
16:28
09.30.2003
15:02
10.01.2003
13:37
10.03.2003
12:10
10.03.2003
10:39
10.04.2003
13:57
10.05.2003
04:26
10.05.2003
01:29
10.07.2003
01:29
10.08.2003
00:00
10.08.2003
22:36
10.09.2003
21:10
10.10.2003
19:45
10.11.2003
18:14
10.12.2003
16:50
10.13.2003
15:24
10.14.2003
13:57
10.15.2003
12:32
10.16.2003
11:05
10.17.2003
09:32
10.18.2003
08:06
10.19.2003
06:39
10.20.2003
05:16
10.21.2003
03:50
10.21.2003
19:55
10.22.2003
18:25
10.23.2003
17:02
10.24.2003
15:34
10.25.2003
14:07
10.26.2003
12:43
10.27.2003
11:15
10.28.2003
09:42
10.29.2003
08:16
10.30.2003
06:51
10.31.2003
05:24
11.01.2003
04:00
11.02.2003
02:33
11.03.2003
01:03
11.03.2003
23:37
11.04.2003
22:07
11.05.2003
20:49
11.06.2003
19:18
11.07.2003
17:48
11.08.2003
16:25
11.09.2003
14:58
11.10.2003
13:30
11.11.2003
12:06
11.12.2003
10:39
11.13.2003
09:06
11.14.2003
13
Time
18:49
11:36
01:23
23:02
21:39
17:27
20:15
13:22
02:50
14:21
21:06
00:00
13:05
14:45
19:11
17:45
16:17
14:51
13:24
11:56
10:56
03:16
07:35
06:08
04:41
03:13
19:23
17:56
16:26
15:00
13:35
12:07
10:39
09:42
07:44
06:19
14:53
03:11
01:58
00:32
23:06
22:07
20:13
18:45
17:18
15:51
14:25
12:58
11:30
10:03
09:06
07:09
University of Texas at San Antonio
S5053/Remote Sensing
Assessment of Sea Ice and Ice Elevation Changes in Ross Region using ICESat
53
54
55
56
11.14.2003
11.15.2003
11.16.2003
11.17.2003
07:40
06:14
04:50
03:23
11.15.2003
11.16.2003
11.17.2003
11.18.2003
14
05:43
04:16
02:46
01:19
University of Texas at San Antonio
S5053/Remote Sensing
Assessment of Sea Ice and Ice Elevation Changes in Ross Region using ICESat
Table A.3 Antarctic Fall GLA12
No
01
02
03
04
05
06
07
08
09
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
Starting Date
02.20.2003
02.21.2003
02.22.2003
02.23.2003
02.24.2003
02.25.2003
02.26.2003
02.27.2003
02.27.2003
02.28.2003
03.01.2003
03.02.2003
03.03.2003
03.04.2003
03.05.2003
03.06.2003
03.06.2003
03.07.2003
03.08.2003
03.09.2003
03.10.2003
03.11.2003
03.12.2003
03.13.2003
03.14.2003
03.14.2003
03.15.2003
03.16.2003
03.17.2003
03.18.2003
03.19.2003
ANTARCTIC Fall (Feb- Mar) GLAS12
Time
23:10
19:24
17:58
17:14
03:42
02:17
01:38
00:12
22:44
20:35
19:09
17:43
16:59
03:26
02:02
01:23
23:56
22:29
20:20
18:53
17:27
16:44
03:11
01:47
01:08
23:41
22:15
20:04
18:38
17:12
16:29
15
Finishing Date
02.21.2003
02.22.2003
02.23.2003
02.24.2003
02.25.2003
02.26.2003
02.26.2003
02.27.2003
02.28.2003
03.01.2003
03.02.2003
03.03.2003
03.04.2003
03.05.2003
03.05.2003
03.06.2003
03.07.2003
03.08.2003
03.09.2003
03.10.2003
03.11.2003
03.12.2003
03.13.2003
03.13.2003
03.14.2003
03.15.2003
03.16.2003
03.17.2003
03.18.2003
03.19.2003
03.20.2003
Time
18:41
17:13
15:44
03:03
01:37
00:11
22:24
21:18
19:52
18:25
16:58
15:29
02:48
01:22
23:56
22:29
21:03
19:36
18:10
16:43
15:14
02:33
01:07
23:41
22:13
20:48
19:21
17:55
16:27
14:58
02:17
University of Texas at San Antonio
S5053/Remote Sensing
Assessment of Sea Ice and Ice Elevation Changes in Ross Region using ICESat
Table A.4 Antarctic Spring GLA12
No
01
02
03
04
05
06
07
08
09
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
Starting Date
09.26.2003
09.27.2003
09.28.2003
09.29.2003
09.30.2003
10.01.2003
10.02.2003
10.03.2003
10.04.2003
10.04.2003
10.05.2003
10.06.2003
10.07.2003
10.08.2003
10.08.2003
10.09.2003
10.10.2003
10.11.2003
10.12.2003
10.13.2003
10.14.2003
10.15.2003
10.16.2003
10.17.2003
10.18.2003
10.19.2003
10.20.2003
10.21.2003
10.21.2003
10.22.2003
10.23.2003
10.24.2003
10.25.2003
10.26.2003
10.27.2003
10.28.2003
10.29.2003
10.30.2003
10.31.2003
11.01.2003
11.02.2003
11.03.2003
11.04.2003
11.04.2003
11.05.2003
11.06.2003
11.07.2003
11.08.2003
11.09.2003
11.11.2003
11.10.2003
11.12.2003
ANTARCTIC Spring (Sep-Oct-Nov) GLAS12
Time
Finishing Date
11:06
09.27.2003
20:50
09.28.2003
19:25
09.29.2003
18:46
09.30.2003
17:19
10.01.2003
15:52
10.02.2003
13:43
10.03.2003
12:17
10.04.2003
10:51
10.04.2003
14:03
10.05.2003
05:14
10.06.2003
03:47
10.07.2003
02:21
10.07.2003
00:52
10.08.2003
22:36
10.09.2003
21:12
10.10.2003
19:49
10.11.2003
19:08
10.12.2003
17:42
10.13.2003
16:14
10.14.2003
14:47
10.15.2003
12:37
10.16.2003
11:11
10.17.2003
10:27
10.18.2003
09:00
10.19.2003
07:31
10.20.2003
06:04
10.21.2003
04:37
10.21.2003
19:57
10.22.2003
18:32
10.23.2003
17:53
10.24.2003
16:25
10.25.2003
14:57
10.26.2003
12:48
10.27.2003
11:21
10.28.2003
09:55
10.29.2003
09:12
10.30.2003
07:43
10.31.2003
06:16
11.01.2003
04:49
11.02.2003
03:21
11.03.2003
01:54
11.03.2003
00:27
11.04.2003
22:14
11.05.2003
20:45
11.06.2003
19:20
11.07.2003
17:55
11.08.2003
17:16
11.09.2003
15:48
11.10.2003
12:11
11.12.2003
14:21
11.11.2003
10:45
11.13.2003
16
Time
08:52
18:45
17:19
15:52
14:26
12:59
11:33
10:06
13:20
03:48
02:21
00:56
23:26
22:00
20:34
19:07
17:41
16:14
14:48
13:21
11:54
10:27
08:57
07:30
06:04
04:38
03:11
19:18
17:52
16:26
14:58
13:32
12:05
10:38
09:10
07:41
06:15
04:49
03:21
01:54
00:30
22:59
21:34
20:08
18:42
17:15
15:49
14:21
12:55
10:01
11:28
08:33
University of Texas at San Antonio
S5053/Remote Sensing
Assessment of Sea Ice and Ice Elevation Changes in Ross Region using ICESat
53
54
55
56
57
11.13.2003
11.14.2003
11.15.2003
11.16.2003
11.17.2003
09:19
08:34
07:06
05:38
04:11
11.14.2003
11.15.2003
11.16.2003
11.17.2003
11.18.2003
17
07:04
05:37
04:11
02:45
01:18
University of Texas at San Antonio
S5053/Remote Sensing
Assessment of Sea Ice and Ice Elevation Changes in Ross Region using ICESat
APPENDIX B SIMPLE KRIGING VARIATIONS
Table B.1 Spring Data Mean Prediction Error Values
Mean Prediction
Error (Absolute)
Use/Method
SK1
Simple Kriging (1)
1047.4/Neighboorhood (5-2)
0.1239
SK2
Simple Kriging (2)
539/Neighboorhood (5-2)
0.1066
SK3
Simple Kriging (3)
1047.4/Neighboorhood (10-2)
0.1113
SK4
Simple Kriging (4)
539/Neighboorhood (10-2)
0.1091
SK5
Simple Kriging (5)
1047.4/Neighboorhood (10-10)
0.1113
SK6
Simple Kriging (6)
539/Neighboorhood (10-10)
0.1091
SK7
Simple Kriging (7)
1047.4/Neighboorhood (15-10)
0.1128
SK8
Simple Kriging (8)
539/Neighboorhood (15-10)
0.1108
Table B.2 Spring Data Prediction Error – Root Mean SquareValues
Prediction Error Root Mean Square
Use/Method
SK1
Simple Kriging (1)
1047.4/Neighboorhood (5-2)
19.41
SK2
SK3
Simple Kriging (2)
539/Neighboorhood (5-2)
18.65
Simple Kriging (3)
1047.4/Neighboorhood (10-2)
18.84
SK4
Simple Kriging (4)
539/Neighboorhood (10-2)
18.84
SK5
Simple Kriging (5)
1047.4/Neighboorhood (10-10)
18.84
SK6
Simple Kriging (6)
539/Neighboorhood (10-10)
18.84
SK7
Simple Kriging (7)
1047.4/Neighboorhood (15-10)
18.88
SK8
Simple Kriging (8)
539/Neighboorhood (15-10)
18.87
Table B.3 Spring Data Prediction Error –Average Standars Error Values
Prediction Error Average Standard
Error
Use/Method
SK1
Simple Kriging (1)
1047.4/Neighboorhood (5-2)
26.35
SK2
Simple Kriging (2)
539/Neighboorhood (5-2)
26.37
SK3
Simple Kriging (3)
1047.4/Neighboorhood (10-2)
26.24
SK4
Simple Kriging (4)
539/Neighboorhood (10-2)
26.37
SK5
Simple Kriging (5)
1047.4/Neighboorhood (10-10)
26.24
SK6
Simple Kriging (6)
539/Neighboorhood (10-10)
26.37
SK7
Simple Kriging (7)
1047.4/Neighboorhood (15-10)
26.23
SK8
Simple Kriging (8)
539/Neighboorhood (15-10)
26.37
Table B.4 Spring Data Prediction Error –Mean Standardized Values
Prediction Error Mean Standardized
(Absolute)
Use/Method
SK1
Simple Kriging (1)
1047.4/Neighboorhood (5-2)
0.001443
SK2
SK3
Simple Kriging (2)
539/Neighboorhood (5-2)
0.0009916
Simple Kriging (3)
1047.4/Neighboorhood (10-2)
0.001119
SK4
Simple Kriging (4)
539/Neighboorhood (10-2)
0.001051
SK5
Simple Kriging (5)
1047.4/Neighboorhood (10-10)
0.001119
SK6
Simple Kriging (6)
539/Neighboorhood (10-10)
0.001051
SK7
Simple Kriging (7)
1047.4/Neighboorhood (15-10)
0.001135
SK8
Simple Kriging (8)
539/Neighboorhood (15-10)
0.001074
18
University of Texas at San Antonio
S5053/Remote Sensing
Assessment of Sea Ice and Ice Elevation Changes in Ross Region using ICESat
Table B.5 Spring Data Prediction Error –Mean Standardized Values
Prediction Error Root Mean-Square
Standardized
Use/Method
SK1
Simple Kriging (1)
1047.4/Neighboorhood (5-2)
0.4196
SK2
Simple Kriging (2)
539/Neighboorhood (5-2)
0.4182
SK3
Simple Kriging (3)
1047.4/Neighboorhood (10-2)
0.4249
SK4
Simple Kriging (4)
539/Neighboorhood (10-2)
0.4228
SK5
Simple Kriging (5)
1047.4/Neighboorhood (10-10)
0.4249
SK6
Simple Kriging (6)
539/Neighboorhood (10-10)
0.4228
SK7
Simple Kriging (7)
1047.4/Neighboorhood (15-10)
0.4255
SK8
Simple Kriging (8)
539/Neighboorhood (15-10)
0.4233
Table B.6 Fall Data Mean Prediction Error Values
Mean Prediction
Error (Absolute)
Use/Method
SK1
Simple Kriging (1)
1047.4/Neighboorhood (5-2)
0.1866
SK2
SK3
Simple Kriging (2)
539/Neighboorhood (5-2)
0.1837
Simple Kriging (3)
1047.4/Neighboorhood (10-2)
0.1868
SK4
Simple Kriging (4)
539/Neighboorhood (10-2)
0.184
SK5
Simple Kriging (5)
1047.4/Neighboorhood (10-10)
0.1868
SK6
Simple Kriging (6)
539/Neighboorhood (10-10)
0.184
SK7
Simple Kriging (7)
1047.4/Neighboorhood (15-10)
0.1857
SK8
Simple Kriging (8)
539/Neighboorhood (15-10)
0.1832
Table B.7 Fall Data Prediction Error – Root Mean SquareValues
Prediction Error Root Mean Square
Use/Method
SK1
Simple Kriging (1)
1047.4/Neighboorhood (5-2)
22.15
SK2
Simple Kriging (2)
539/Neighboorhood (5-2)
22.13
SK3
Simple Kriging (3)
1047.4/Neighboorhood (10-2)
22.21
SK4
Simple Kriging (4)
539/Neighboorhood (10-2)
22.19
SK5
Simple Kriging (5)
1047.4/Neighboorhood (10-10)
22.21
SK6
Simple Kriging (6)
539/Neighboorhood (10-10)
22.19
SK7
Simple Kriging (7)
1047.4/Neighboorhood (15-10)
22.21
SK8
Simple Kriging (8)
539/Neighboorhood (15-10)
22.2
Table B.8 Fall Data Prediction Error –Average Standars Error Values
Prediction Error Average Standard
Error
Use/Method
SK1
Simple Kriging (1)
1047.4/Neighboorhood (5-2)
26.81
SK2
Simple Kriging (2)
539/Neighboorhood (5-2)
26.81
SK3
Simple Kriging (3)
1047.4/Neighboorhood (10-2)
26.8
SK4
Simple Kriging (4)
539/Neighboorhood (10-2)
26.8
SK5
Simple Kriging (5)
1047.4/Neighboorhood (10-10)
26.8
SK6
Simple Kriging (6)
539/Neighboorhood (10-10)
26.8
SK7
Simple Kriging (7)
1047.4/Neighboorhood (15-10)
26.8
SK8
Simple Kriging (8)
539/Neighboorhood (15-10)
26.8
19
University of Texas at San Antonio
S5053/Remote Sensing
Assessment of Sea Ice and Ice Elevation Changes in Ross Region using ICESat
Table B.9 Fall Data Prediction Error –Mean Standardized Values
Prediction Error Mean Standardized
(Absolute)
Use/Method
SK1
Simple Kriging (1)
1047.4/Neighboorhood (5-2)
0.001627
SK2
Simple Kriging (2)
539/Neighboorhood (5-2)
0.00156
SK3
Simple Kriging (3)
1047.4/Neighboorhood (10-2)
0.001633
SK4
Simple Kriging (4)
539/Neighboorhood (10-2)
0.001576
SK5
Simple Kriging (5)
1047.4/Neighboorhood (10-10)
0.001633
SK6
Simple Kriging (6)
539/Neighboorhood (10-10)
0.001576
SK7
Simple Kriging (7)
1047.4/Neighboorhood (15-10)
0.001629
SK8
Simple Kriging (8)
539/Neighboorhood (15-10)
0.001578
Table B.10 Fall Data Prediction Error –Mean Standardized Values
Prediction Error Root Mean-Square
Standardized
Use/Method
SK1
Simple Kriging (1)
1047.4/Neighboorhood (5-2)
0.4929
SK2
SK3
Simple Kriging (2)
539/Neighboorhood (5-2)
0.4929
Simple Kriging (3)
1047.4/Neighboorhood (10-2)
0.494
SK4
Simple Kriging (4)
539/Neighboorhood (10-2)
0.494
SK5
Simple Kriging (5)
1047.4/Neighboorhood (10-10)
0.494
SK6
Simple Kriging (6)
539/Neighboorhood (10-10)
0.494
SK7
Simple Kriging (7)
1047.4/Neighboorhood (15-10)
0.4942
SK8
Simple Kriging (8)
539/Neighboorhood (15-10)
0.4941
20