CHAPTER
6
ASTER datasets and derived products for
global glacier monitoring
Bhaskar Ramachandran, John Dwyer, Bruce H. Raup, and Jeffrey S. Kargel
ABSTRACT
This book investigates a wide selection of the
world’s glaciers and the status of remote-sensing
and GIS technologies designed to address their
global monitoring in this age of rapid climate
change impacts on glaciers and increasing awareness of the policy and economic relevance of
glaciers in areas as diverse as water resources and
geohazards. This chapter focuses on an important
part of the data component, especially data from
the Advanced Spaceborne Thermal Emission and
Reflection radiometer (ASTER) project, which also
spawned the Global Land Ice Measurements from
Space (GLIMS) project as an ASTER Science
Team member project (see Foreword by Hugh
Kieffer). ASTER’s combination of sensor systems,
spanning the visible through thermal infrared and
its stereo-imaging capability, the high radiometric
and geometric fidelity of the cameras, combined
with a liberal data dissemination policy for glacier
images, have made it a favored instrument for
glacier remote-sensing studies. Operational use of
the instrument with on-demand targeting has also
aided specific studies ranging from preplanned field
campaigns to rapid response to glacier-related
disasters.
6.1
INTRODUCTION
GLIMS is an international consortium established
to facilitate the acquisition and analysis of remotely
sensed satellite images of glaciers worldwide. These
images are used to monitor and evaluate changing
glacier extent and dynamics, and the implications
of these changes for people and the environment.
Although GLIMS has benefited from data derived
from many passive and active remote-sensing
instruments, and ground-based observations as
well, ASTER data remain a primary source. The
instrument has three telescopes and associated sets
of sensors, one for each of the wavelength ranges,
VNIR, SWIR, and TIR (visible and near-infrared,
shortwave infrared, and thermal infrared, respectively), an image swath width of 60 km, and ground
resolutions of 15 m/pixel for VNIR, 30 m/pixel for
SWIR, and 90 m/pixel for TIR. The instrument is
described elsewhere in detail (Ramachandran et al.
2011), but hallmarks of its capabilities include its
broad multispectral and thermal imaging range, its
exquisite pointing stability (which partly stems from
the Terra spacecraft’s high mass), and its systematic
stereoscopic imaging in VNIR band 3.
The multispectral response of the sensor system
to glacier target materials is described theoretically
in Chapter 3 of this book by Furfaro et al., in
practice in Chapter 4 by Kääb et al., and as used
for glacier mapping in Chapter 2 by Bishop et al.
Fig. 4.2 depicts ASTER’s spectral wavelengths
for all three sensors juxtaposed alongside those of
Landsat ETMþ and widely used radar bands.
The on-demand nature of ASTER data acquisition and the ability to optimize acquisitions (via
seasonality, telescope pointability, instrument gain
146
ASTER datasets and derived products for global glacier monitoring
settings, etc.) to suit glacier monitoring help
support the GLIMS project’s data requirements.
ASTER VNIR and SWIR data have been invaluable in glacial landscape classification, glacier mapping, flow velocity vector extraction, glacier lake
mapping, snow line determinations, and other fundamental investigations of glacier dynamics. Digital
terrain data, used both to orthorectify satellite
images and to derive and analyze three-dimensional
glacial and landscape parameters, holds special
relevance to the GLIMS project, and thermal data
from TIR are increasingly being applied to glacier
studies. Kieffer et al. (2000), Bishop et al. (2004),
and Raup and Kargel (2012) describe details of the
GLIMS project, what it entails, its objectives and
limitations, its organization, and its reliance on
ASTER, Landsat, and other complementary data
sources used to analyze, monitor, and map glaciological phenomena worldwide. Raup et al. (2000)
describe the initial ASTER image acquisition plans.
Kargel et al. (2005) provide an assessment of how
satellite-derived multispectral data contribute to
GLIMS, and, along with Kääb et al. (2003a) and
Raup et al. (2007), describe and showcase some of
the leading technologies used for ASTER image
processing and glacier data extraction from such
imagery. Kääb et al. (2003b) and Kargel et al.
(2011) highlight the value and role of ASTER data
in the analysis of glacier hazards, and Kargel et al.
(2010, 2012a, b) and Bolch et al. (2012) show the
central relevance of ASTER data and GLIMS
glacier analysis to pressing scientific matters related
to public education, public safety, and policy.
These, among many other studies and reviews,
many of them cited in this book or comprising
the chapters of this book, show the wide versatility
and importance of ASTER and other multispectral
imaging data in glaciological monitoring and
studies.
In this chapter we briefly review ASTER’s liberal
data access and use for glacier studies in GLIMS,
summarize the technical calibrations and corrections and various standard products, including
several important higher level products, and summarize the types of ASTER data acquisitions
involved in the GLIMS project.
6.2
ASTER DATA ACCESS AND
USE POLICY
The National Aeronautics and Space Administration’s (NASA) data and information policy advo-
cates full and open sharing of all data with the
research and applications communities, academia,
private industry, and the general public. In line with
such a policy, the following ASTER products are
available to the general public at no charge through
NASA’s Reverb interface: http://reverb.echo.nasa.
gov/reverb/
. ASTER L1B Registered Radiance at the Sensor
(only for geographical coverage over the U.S. and
its territories).
. ASTER Global Digital Elevation Model
(GDEM).
. North American ASTER Land Surface Emissivity Database (NAALSED).
The ASTER L1B datasets over the U.S. and its
territories are also accessible via the LP DAAC
(Land Processes Distributed Active Archive Center)
data pool: https://lpdaac.usgs.gov/get_data/data_
pool and also through the GloVis interface:
https://lpdaac.usgs.gov/get_data/glovis
All NASA-funded researchers and their affiliates
as well as approved educational users may order
all ASTER data products (L1A, L1B, and higher
level) directly from the LP DAAC at no charge.
The LP DAAC maintains a list of people eligible
to receive ASTER data at no cost; the list is corroborated and approved both by the GLIMS project
PI (Jeff Kargel, University of Arizona) and a project scientist at NASA Headquarters (Woody
Turner).
All ASTER data products (except NAALSED)
are orderable, for a charge, through the Japanese
Space Systems Earth Remote Sensing Division
WWW IMS interface at http://ims.aster.ersdac.
jspacesystems.or.jp/ims/html/MainMenu/MainMenu.
html Specific redistribution policies apply to the
different ASTER products acquired from LP
DAAC. NAALSED data are not subject to any
redistribution restrictions. ASTER GDEM
products are subject to certain redistribution and
citation requirements. NASA-affiliated and educational users of ASTER data received from LP
DAAC are bound by certain restrictions to only
redistribute acquired data to other researchers
and educators. No restrictions on subsequent use,
sale, or redistribution exist for ASTER data products purchased from LP DAAC. Consult the following site for additional details: https://lpdaac.
usgs.gov/products/aster_policies
ASTER data 147
6.3
ASTER DATA
The ASTER instrument, launched as part of the
NASA Earth Observing System’s (EOS) Terra platform’s payload of instruments in December 1999, is
a unique multispectral sensor system that evolved
through a well-cultivated U.S.–Japan collaboration. Plafcan (2011), who studied the international
political underpinnings of this collaboration, calls
this process ‘‘technoscientific diplomacy’’, which
led to the design and launch of a successful tripartite sensor suite that defines the ASTER instrument.
ASTER’s VNIR, SWIR, and TIR sensor data cater
to a wide variety of geophysical and biophysical
applications (Ramachandran et al. 2011), including
science team investigations in volcanology, urban
development, coastal change, forestry, and other
applications areas; GLIMS is the official ASTER
glacier investigation project. ASTER’s uniqueness
stems from its additional backward-viewing VNIR
band (band V3B) that enables stereoscopic data
observations, and an unprecedented multispectral
capability in the SWIR (six bands) and TIR (five
bands) wavelengths. Tables 6.1 and 6.2 provide
ASTER’s baseline performance requirements.
The ASTER Ground Data System (GDS) facility
at the Earth Remote Sensing Data Analysis Center
(ERSDAC) in Tokyo, Japan processes ASTER
Level 1 data (L1), which is transmitted to the Land
Processes Distributed Active Archive Center (LP
DAAC) in Sioux Falls, SD. LP DAAC archives
the L1 data and creates higher level products for
its nonpaying users and paying federal partners,
while all other paying user orders are handled by
ASTER GDS. Watanabe et al. (2011) provide a
succinct account of the various elements of the joint
U.S.–Japan ASTER mission including the mission
operations, production, data product suites, and
data dissemination in both countries. Daucsavage
et al. (2011) describe the historical and contemporary ASTER data management experience at LP
DAAC.
6.3.1 Performance of ASTER VNIR,
SWIR, and TIR
6.3.1.1
Embeo_h, lm jombeo_h (doveryai, no
proveryai: Trust, but verify)
This Russian proverb, made famous in English by
a former American President, applies to scientific
uses of any remote-sensing dataset. The ASTER
science and engineering teams have worked hard
with calibrations and corrections to make ASTER
data (both lower level and higher level standard
data products, defined below) as quantitatively
reliable and useful as possible, as the sections below
indicate. Nevertheless, glaciologists and other
researchers have applied many validation tests to
assess the reliability and accuracy of glacier measurements using ASTER and other remote-sensing
data. These efforts helped to characterize the behavior of the operational ASTER system, to identify
problems with early data acquisitions, to improve
geometric correction algorithms, and to understand
lower level and higher level datasets, their uses, and
artifacts. In fact, most validation work conducted
for GLIMS (some reported in this volume) has
supported the high quality of ASTER image and
higher level data products; however, we must constantly ensure that we practice quantitatively accurate glacier applications using ASTER or any
remote-sensing data. Below, we will take as one
example a simple validation test of a single TIR
scene—not a definitive and full validation, but an
anecdote sufficient to illustrate the need for ASTER
users (indeed, users of any remote-sensing dataset)
to take charge of these data, to understand them, to
test them to their limits, and to validate final glacier
assessments, measurements, and products derived
from ASTER and other remote-sensing data.
This chapter deals strictly with the sensor systems
and standard data-processing stream, and we do
not consider at all the additional data processing
and human links involved with extraction of glacier
information. Chapter 7 is mainly a test of the
human element as well as higher level analysis algorithms in the delineation of glacier boundaries,
whereas Chapters 2, 3, 4, and 5 pertain to glacier
assessments using ASTER and other remotesensing data.
6.3.1.2
Performance overview
The ASTER performance specifications for absolute radiometric accuracy of VNIR and SWIR
bands are defined as better than 4% at highlevel input radiance. The absolute accuracy of the
TIR bands is specified at 3 K at 200–240 K, 2 K
at 240–270 K, 1 K at 270–340 K, and 2 K at
340–370 K. The geometric performance details for
intratelescopic band-to-band registration is <0.1
pixel, and <0.2 pixel for intertelescopic band-toband registration. Well past its original design lifetime, the ASTER instrument (sans the SWIR sensor) continues to perform well, as its radiometry
148
ASTER datasets and derived products for global glacier monitoring
Table 6.1. ASTER: baseline performance requirements—1 (spectral range, radiometric and spatial resolution,
accuracy, and signal quantization levels).
Sensor
subsystem
Band No.
Spectral
range
(mm)
Radiometric
resolution
Absolute
accuracy
()
VNIR
1
2
3N
3B
0.52–0.60
0.63–0.69
0.78–0.86
0.78–0.86
NED
NED
NED
NED
4%
4%
4%
4%
15
15
15
15
m
m
m
m
8
8
8
8
bits
bits
bits
bits
SWIR
4
5
6
7
8
9
1.600–1.700
2.145–2.185
2.185–2.225
2.235–2.285
2.295–2.365
2.360–2.430
NED 0:5%
NED 1:3%
NED 1:3%
NED 1:3%
NED 1:0%
NED 1:3%
4%
4%
4%
4%
4%
4%
30
30
30
30
30
30
m
m
m
m
m
m
8
8
8
8
8
8
bits
bits
bits
bits
bits
bits
TIR
10
11
12
13
14
8.125–8.475
8.475–8.825
8.925–9.275
10.25–10.95
10.95–11.65
NEDT 0:3%
NEDT 0:3%
NEDT 0:3%
NEDT 0:3%
NEDT 0:3%
90
90
90
90
90
m
m
m
m
m
12
12
12
12
12
bits
bits
bits
bits
bits
and geometry are carefully calibrated and corrected. Nevertheless, CCD sensitivity is decreasing
over time and is being tracked, and radiances are
rigorously corrected accordingly (Sakuma et al.
2011). Engineering models for the future of ASTER
entail many more years of operations, with the
number of years of future longevity connected to
how the instrument is used operationally.
6.3.1.3
Radiometric calibration
The sensor electronics in any orbiting electrooptical
remote-sensing system are expected to degrade,
which manifests in their changing sensor responses
over time. The 13-year-old Terra ASTER instrument is no exception evidenced by the gradual
changes in VNIR (Arai et al. 2011) and TIR
(Tonooka 2011) radiometric responses. SWIR sensor responses had been somewhat more stable, with
sensitivity drifts that were almost linear and, thus,
predictable and correctable; however, starting in
mid-2007, its detector temperature started rising
gradually, thus impacting data quality. Since April
2008, even though the SWIR sensor continues to
gather observations, the images are fully saturated
and show severe striping, and hence do not yield
any useful data.
ASTER’s radiometric calibration (which ensures
the sensor’s known accuracy and precision) is based
0.5%
0.5%
0.5%
0.5%
3K
2K
1K
2K
(200–240
(240–270
(270–340
(340–370
Spatial
resolution
K)
K)
K)
K)
Signal
quantization
levels
on coefficients that are generated and maintained
by ASTER GDS in an evolving, periodically
updated radiometric calibration coefficients
(RCC) database. These coefficients were evaluated
during the preflight test period with the aid of
integration spheres. This was followed by in-flight
evaluation using onboard calibration (OBC) and
vicarious (ground-based) calibration data, which
are described in detail by Arai and Tonooka
(2005), Arai et al. (2011), and Tonooka (2011) for
the sensor subsystems. Both VNIR and SWIR
sensor systems have calibration units that include
a highly stable halogen lamp, optics to collect radiation and direct it as a reference beam to the
radiometer, and photodetectors to monitor lamp
radiation and reference beam flux. The TIR system
has a high emissivity reference plate (blackbody) for
onboard calibration.
The ASTER Science Team members in the U.S.
and Japan are actively involved in calibration and
validation activities, and ASTER GDS is responsible for maintaining and managing the radiometric
calibration database. The nature of changes in spectral response functions varies among the three sensors and consequently calls for specifically tailored
calibration correction mechanisms. Calibration also
varies according to the correction function applied
based on radiometric changes observed via both
vicarious and onboard sources. Table 6.3 sum-
ASTER data 149
Table 6.2. ASTER: baseline performance requirements—2 (cross-track coverage and pointing, VNIR optics, FOV,
IFOV, MTF, duty cycle etc.).
Parameter
Value
Swath width
60 km
VNIR cross-track coverage
318 km
SWIR cross-track coverage
116 km
TIR cross-track coverage
116 km
VNIR cross-track pointing
24.00
SWIR cross-track pointing
8.55
TIR cross-track pointing
8.55
Stereo base-to-height ratio
0.6 (along-track)
Setting angle between nadir and aft telescopes
27.60
Focal length of the VNIR nadir system
329 mm
Detector size of the VNIR focal plane
7 mm
Field of view
VNIR: 6.09 (nadir), 5.19 (aft), SWIR: 4.9 , TIR: 4.9
Instantaneous field of view
VNIR: 21.5 mrad (nadir), 18.6 mrad (aft),
SWIR: 42.6 mrad, TIR: 128 mrad
Modulation transfer function at Nyquist frequency
0.25 (cross-track), 0.20 (along-track)
Band-to-band registration
0.2 pixels (intratelescope), 0.3 pixels (intertelescope)
Duty cycle
8% (VNIR and SWIR), 16% (TIR)
VNIR data rate
62 mbps
SWIR data rate
23 mbps
TIR data rate
4.2 mbps
Peak data rate
89.2 mbps
Mass
406 kg
Peak power
726 W
marizes the ASTER GDS RCC databases since
February 2005 when ASTER Version 3 products
were implemented. The application of Version
3.00 through 3.11 databases varies for each sensor
as a function of the acquisition date following the
Terra ASTER launch in December 1999.
The actual performance of the sensor subsystems
has been determined by imaging of sharp edges,
such as engineered shorelines and the lunar limb.
The sensors’ responses seen in these images show
small but significant departures from square waves
(perfect focus), where the so-called modulation
transfer function (MTF) indicates what may be
described as blurring of the edge. Blurring probably
has both optical and electronic origins. For each of
the sensor subsystems, the cross-track and alongtrack responses (lines versus samples in the images)
are slightly different, with further differences occur-
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ASTER datasets and derived products for global glacier monitoring
Table 6.3. ASTER GDS radiometric calibration coefficients: versioning and formulas.
Version
Update date
(yyyy/mm/dd)
Application period
(days since launch)
Referred OBC (from–to) and formula
VNIR
SWIR
TIR
2000/02/01
to
2001/10/20
Second-order poly.
77.0 K
to
78.2 K
Linear
(offset only)
2000/03/12
to
2000/10/07
Linear
3.00
2005/02/08
1999/12/18
(0)
3.01
2005/02/08
2000/10/18
(305)
3.02
2005/02/08
2001/10/21
(673)
2001/10/21
to
2005/07/30
Exponential
3.03
2006/07/08
2006/07/08
(2,394)
2004/12/11
to
2006/04/20
3.04
2007/01/29
2007/01/29
(2,599)
77.0 K
to
81.1 K
3.05
2007/05/06
2007/05/06
(2,696)
3.06
2008/06/16
2007/06/16
77.0 K
to
81.1 K
Exponential
(offset only)
3.07
2008/04/08
2007/09/22
(2,835)
3.08
2008/07/05
2008/07/06
(3,123)
3.09
2008/09/03
2008/07/18
(3,135)
3.10
2009/09/27
2009/09/27
(3,571)
3.11
2010/07/10
2010/07/10
(3,857)
2000/10/27
to
2005/07/30
Second-order poly.
(C0)
Exponential (C1)
77.0 K
to
79.3 K
Second-order poly.
(offset only)
2002/09/15
to
2005/12/09
2004/02/18
to
2007/04/18
77.0 K
to
86.8 K
2004/12/11
to
2008/05/18
2005/06/27
to
2008/05/18
Fixed 255
2005/06/27
to
2009/07/20
2003/04/16
to
2010/05/14
2008/03/13
to
2010/05/14
All dates follow the ASTER GDS format: yyyy/mm/dd. OBC ¼ onboard calibration; second-order poly. ¼ second-order polynomial
correction; C0 ¼ offset correction coefficient (adjusted based on preobservation blackbody measurement); C1 ¼ gain coefficient derived
by measuring the blackbody at 270, 300, 320, and 340 K.
ASTER data 151
Table 6.4. ASTER: geometric performance parameters.
Parameter
a
Version 3.0 Geometric DB (database)
Intratelescopic registration
VNIR
SWIR
TIR
<0.1 pixel
<0.1 pixel
<0.1 pixel
Intertelescopic registration
SWIR/VNIR
TIR/VNIR
<0.2 pixel
<0.2 pixel
Stereo pair system (elevation) error Band 3B/3N (nadir)
<10 m
Pixel geolocation knowledge a
<15 m
<50 m
Relative
Absolute
Not terrain-corrected.
ring between different bands (indicating an electronic component of the MTF). In general most
of the image intensity transition across the imaged
edges takes place across 2 pixels (1 pixel from the
actual edge); a small amount of the transition is
further spread across 4 pixels (2 pixels from the
edge) (Arai and Tonooka 2005). For example, band
10 thermal images of sharp edges show that roughly
90% of signal intensity (related to temperature)
transition occurs within 1 pixel from the edge,
but the rest of the intensity transition is spread more
widely. The derivative of the MTF takes a Gaussian
form.
In addition, a member of the ASTER calibration
team, Hugh Kieffer (unpublished report), has
demonstrated TIR ghosting of thermal images of
the Moon, and he also reveals stray arcs extending
up to 110 pixels; the arcs are thought to be due to
minor reflections within the sensor system. However, collectively these artifacts are of very low
intensity, but they could help to explain blurred
images of thermal boundaries shown below in
Section 6.4.2.3. Although the MTF does little to
obscure the positions of edges, absolute radiometry
is sensitive to the MTF, which effectively defines the
sizes of features where reliable thermal measurements or reflectances can be accurately assessed.
6.3.1.4
Geometric corrections
ASTER’s geometric system correction primarily
involves rotation and coordinate transformation
of the detectors’ line-of-sight vectors to Earth’s coordinate system. This correction is accomplished as
part of ASTER Level 1 processing at ASTER GDS
with data derived from both the instrument and the
Terra spacecraft platform. This geometric correc-
tion also incorporates both preflight and postlaunch calibration processes. Preflight calibration
is an offline process to generate line-of-sight vectors
and pointing axes information that are evaluated
toward the spacecraft’s navigation base reference,
which reflects the instrument’s accuracy and stability. Following ASTER’s launch, these parameters
were corrected via validation with ground control
points (GCPs) and interband matching techniques.
They include pointing correction, coordinate transformations that involve spacecraft, orbital, Earth’s
inertial and Greenwich coordinates, and band-toband registration accuracies through SWIR parallax correction and intertelescope registration processes (Iwasaki and Fujisada 2005, Fujisada 2011).
Given the ASTER instrument’s complex engineering and design features to accommodate the three
different optical sensor systems, its geometric accuracy is quite good. The geometric performance
parameters, based on Version 3.0 of the geometric
correction database, are provided in Table 6.4.
The ASTER Science Team in early 2005 discovered three discrepancies that potentially affect the
accuracy of the latitude/longitude values in ASTER
data. A brief description of these three discrepancies is provided below. ASTER data downloaded
before the problems were discovered and the corrections implemented retained the errors, but all those
data were reprocessed going back to the beginning
of acquisitions.
6.3.1.5
Earth rotation angle error
The first discrepancy is an incorrect calculation of
the Earth’s rotation angle. This produced a geolocation error of up to 300 m near the poles for
daytime scenes, and less than 100 m below 70
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ASTER datasets and derived products for global glacier monitoring
latitude. The longitude error for night-time scenes is
largest at the equator, and decreases to 100 m at
the poles. This error was completely modeled and
fixed with a polynomial correction, and then
applied to all subsequent Level 1 data.
6.3.1.6
Nutation-related longitudinal error
The second discrepancy is omission of compensation for nutation in the Earth’s rotation. Nutation is
defined as a slightly irregular oscillatory movement
or wobble in the axis of the Earth’s rotation. The
omission of compensation for nutation results in a
longitude error that is dependent on the date of
ASTER data acquisition. In general, the magnitude
of error is less than 50 m before July 2003 and has
since increased to about 200 m through the end of
2004. This discrepancy is corrected in all subsequent
Level 1 data.
6.3.1.7
Earth ellipsoid-related terrain error
The third discrepancy is due to the fact that ASTER
processing uses the Earth ellipsoid (WGS 84) as the
reference datum, and does not take into account
actual surface elevation. Therefore, terrain error
is included in latitude and longitude values caused
by a difference between the WGS 84 ellipsoid and
the actual Earth’s surface. The maximum displacement is about 400 m over the Tibetan Plateau, with
an 8.5 off-nadir view angle. This error is correctible
by using a digital elevation model to reduce or
remove this discrepancy.
6.3.1.8
Loss of SWIR
The SWIR sensor operationally stopped producing
useful quality data in April 2008; since then only
VNIR and TIR data have been acquired and processed. The SWIR sensor is equipped with a cryocooler designed to regulate a threshold operational
temperature of 77 K. This temperature started
rising gradually in September 2004, and was not a
major issue until the temperature rose beyond 83 K
in mid-2007. Initial impacts were felt in the longer
wavelength bands, but this spread to include all
SWIR bands by April 2008, after which SWIR
became useless. The ASTER Instrument Team’s
efforts to resolve this crisis included enhancing
the cryocooler’s performance, and an attempt to
maintain a stable detector temperature at 77 K.
Though there were some positive results in the short
term, the goal of stabilizing the detector’s operating
temperature was not successful. A historical record
of ASTER Level 1 SWIR data from March 2000
through April 2008 exists both in the U.S. and
Japan.
The loss of SWIR data impacts a number of
dependent processes and activities. They include
cloud cover assessment that depends on radiometrically calibrated band 4 (Level 1B) data to
discriminate clouds from snow/ice and desert.
Others include SWIR parallax correction/registration, and intertelescope registration for VNIR,
SWIR, and TIR. ASTER GDS has reprocessed all
Level 1 data affected by the deterioration of the
SWIR sensor except for the band saturation issue.
Further details regarding the SWIR sensor problem
are available at http://www.science.aster.ersdac.
jspacesystems.or.jp/t/en/about_aster/swir_en.pdf
6.4
ASTER DATA-PROCESSING
STREAM
6.4.1 Standard Level 1A and Level 1B
Since ASTER’s launch in 1999, the ASTER GDS in
Japan has been responsible for Level 1 processing of
ASTER data. This includes processing virtually all
ASTER Level 0 data to Level 1A (L1A) data, as
well as processing approximately one third of the
L1A data to level 1B (L1B) data. Copies of all L1A
and L1B data processed by the ASTER GDS are
sent to the LP DAAC for archiving and distribution
to users; and those who desire L1B data unavailable
at the DAAC can order these data from the ASTER
GDS in Japan. One primary goal of the LP DAAC
is to process archived L1A data to L1B data, so that
users would have routine access to L1B data (and
higher level products) from the entire ASTER data
archive. In late 2005, an agreement was reached to
change the approach to ASTER L1 processing.
Under the new approach, the ASTER GDS continues to process ASTER Level 0 data to L1A data,
and send copies of all L1A to the LP DAAC. The
LP DAAC and the ASTER GDS each assume
responsibility, using software developed by the
ASTER GDS, for processing on-demand L1B data
they distribute to their users and/or use to produce
higher level products ordered by their users. The
ASTER GDS implemented the Level 1 on-demand
processing system on April 19, 2006, and the LP
DAAC system became operational on May 24,
2006.
Currently, 400 to 500 ASTER Level 1A products
are produced by the ASTER GDS in Japan on a
daily basis, and transmitted to the LP DAAC via
ASTER data-processing stream
153
Figure 6.1. ASTER data flow dynamics at the LP DAAC. Source: B. Ramachandran et al. (Eds.), Land Remote
Sensing and Global Environmental Change (& Springer, 2011).
the Asia Pacific Advanced Network. The LP
DAAC, which maintains the same Level 1 algorithms as the ASTER GDS, generates the desired
Level 1B and higher level products for its NASA
affiliates as well as U.S. federal partners, while the
ASTER GDS serves all other customers. Fig. 6.1
depicts the dynamics of data flow from satellite data
acquisition through product generation (via production and expedited data streams), which
involves several entities.
The ASTER mission is unique in that it does not
have a routine reprocessing campaign for Level 1
data. Since its launch in December 1999, ASTER
GDS has reprocessed all acquired data one time.
This took place when the public version of data was
elevated from Version 2 to Version 3. The ASTER
GDS implemented this version change in May 2001
to accommodate substantial improvements in the
ASTER Level 1 algorithm. Since then the quality
of Level 1B and higher level data products has been
constrained by radiometric and geometric calibration parameters.
6.4.2 ASTER standard higher level
products
A hallmark of the NASA Earth Observing System
(EOS) mission is to generate not only L1 data prod-
ucts but also a suite of higher level products derived
from any given sensor to characterize various geophysical parameters. This facilitates the needs of
end users in a number of ways: It removes the
burden of preprocessing that would fall on them.
It ensures the very best algorithms are used in conformance with the best community practices, and it
also confirms that methodologically consistent
products are produced. Overall, it alleviates the
need for end users to expend a lot of time and
resources in correctly implementing complex processing steps, and, instead, allows them to readily
use these products in their research, applications,
analyses, and interpretations. A similar suite of
standard ASTER higher level products is produced
both in the U.S. and Japan (Watanabe et al. 2011).
All descriptions in this chapter pertain to the U.S.
production system and archives at the LP DAAC.
At the start of the ASTER mission, roughly half-adozen standard higher level products were produced on demand (Watanabe et al. 2011). Three
Level 2 products—including a decorrelation stretch
(for all three sensors), brightness temperature at
sensor, and polar surface and cloud classification
product—were retired in May 2010 (JPL 2001).
A couple of additional variant products (SWIR
crosstalk-corrected products and orthorectified
products) were introduced in 2006 and 2007, respec-
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ASTER datasets and derived products for global glacier monitoring
tively. Several higher level products described below
are important and highly subscribed products that
serve the glaciology community in numerous ways.
6.4.2.1
Reflectance Suite
The ASTER L2 Surface Reflectance product is a
higher level product that contains atmospherically
corrected VNIR and SWIR data. This product is
generated using the three VNIR bands (between
0.52 mm and 0.86 mm) and six SWIR bands
(between 1.60 mm and 2.43 mm) derived from an
ASTER L1B dataset. The atmospheric correction
process derives a relationship between the surface
radiance/reflectance and top-of-the-atmosphere
radiance from information on the scattering and
absorption characteristics of the atmosphere. Once
this relationship is established, it is used to convert
original ASTER VNIR and SWIR radiance values
to atmospherically corrected surface radiance and
reflectance values. The atmospheric correction algorithm is based on a lookup table (LUT) approach,
which uses results from a Gauss–Seidel iteration of
a radiative transfer code (RTC). This methodology
derives from the University of Arizona Remote
Sensing Group’s reflectance-based vicarious calibration approach (ATBD 1999). The algorithm is
based on the relationship between the angular distribution of radiance, atmospheric scattering and
absorption, and surface properties. The RTC used
to generate the LUT for atmospheric correction is
based on the following parameters: solar zenith
angle, satellite view angle, relative azimuth angle
between the satellite and Sun, molecular scattering
optical depth, aerosol scattering optical depth,
aerosol scatter albedo, aerosol size distribution
parameter, and surface reflectance. A digital
elevation model provides slope and elevation information required to accurately model surface reflectance. This suite also includes a SWIR crosstalkcorrected reflectance product. A description of the
SWIR crosstalk problem follows.
The ASTER SWIR sensor is affected by a crosstalk signal-scattering problem, a phenomenon discovered after ASTER’s launch. The source of the
crosstalk problem is the ASTER band 4 detector,
whose incident light is reflected by the detector’s
aluminum-coated parts (especially from the area
between the detector plane and bandpass filter),
and is then projected onto the other detectors (Arai
et al. 2011). The band-to-band parallax effect and
the distance between the CCD array pairs further
worsen the problem. Bands 9 and 5 are most
affected because of their closeness to the Band 4
detectors. Evidence of crosstalk, along with photon
spread and ghosting effects, is visible in images with
strong contrast, especially coastlines and islands.
The Japanese ASTER Science Team developed
the original crosstalk correction algorithm that is
used to correct an ASTER L1B dataset. The
original model is based on the fundamental understanding that incident radiation to band 4, which is
reflected or leaked to the other bands will follow a
certain pattern of line shifts in the along-track direction. The kernel function used in the convolution
(in the original algorithm) is not considered symmetrical in the cross-track direction (Tonooka and
Iwasaki 2003, Iwasaki and Tonooka 2005). Using
the Japanese crosstalk correction algorithm, the
ASTER project at JPL has implemented a crosstalk
correction process that is applied to ASTER L1B
data before deriving the reflectance product. This
correction is implemented for processing at the LP
DAAC and the ASTER GDS facility.
6.4.2.2
Temperature/Emissivity Suite
The ASTER L2 Surface Kinetic Temperature product (AST08) is generated using the five TIR bands
acquired either during daytime or night-time
between 8 and 12 mm spectral range. It contains
surface temperatures at 90 m spatial resolution
for land areas only. Derived using the same algorithm as that of Surface Emissivity, Surface Kinetic
Temperature is determined by applying Planck’s
Law using the emissivity values from the temperature emissivity separation (TES) algorithm
(Gillespie et al. 1998), which uses atmospherically
corrected ASTER Surface Radiance (TIR) data. In
response to changes in certain input variables, some
modifications were implemented in the TES algorithm. What follows is a brief description of the
original algorithm, and subsequent changes as well.
The TES algorithm first estimates emissivities in
the TIR channels using the Normalized Emissivity
Method (NEM) (Gillespie et al. 1998). These estimates are used along with Kirchhoff ’s Law to
account for land-leaving TIR radiance that is due
to sky irradiance. That figure is subtracted from
TIR radiance iteratively (pixel by pixel) to estimate
emitted radiance from which temperature is calculated using the NEM module. Estimates of surface
emissivity were derived using surrogates such as
land cover type or vegetation index. The TES algorithm is used to derive both " (emissivity) and T
(surface temperature). Its main goals include
ASTER data-processing stream
155
recovering accurate and precise emissivities for
mineral substrates, and estimating accurate and
precise surface temperatures especially over vegetation, water, and snow. A linear regression approach
replaced the original nonlinear one when it became
clear that precision was being sacrificed in favor of
accuracy.1
The TES algorithm is executed in the ASTER
processing chain after ASTER L2 (land-leaving)
Surface Radiance (TIR) data are generated.
Land-leaving radiance and downwelling irradiance
vectors for each pixel are taken into account. The
emissivity spectrum is normalized using the average
emissivity of each pixel. The minimum–maximum
difference (MMD) of the normalized spectrum is
calculated and estimates of the minimum emissivity
derived through regression analysis (Matsunaga,
1994) are made. These estimates are used to scale
normalized emissivity and compensate for reflected
skylight using the derived refinement of emissivity
(Gillespie et al., 1998). Over time, certain algorithm
modifications have been made in response to several
factors that include the ASTER TIR sensor’s
change in sensitivity response, the frequency of
updates to its calibration coefficients, and imperfections in atmospheric compensation, etc. (Gustafson
et al., 2006). The modifications include turning off
iterative compensation of spectral irradiance, onetime correction, and linear scaling of the normalized emissivity spectrum.
A key higher level TIR-derived data product is
surface kinetic temperature (AST08), which is
derived from all five ASTER TIR bands.
ASTER-derived kinetic temperature image data
are used in various chapters but are not validated
elsewhere in this book, so here we dedicate some
attention to this dataset’s performance relative to
glacier studies.
Fig. 6.2 (see also Online Supplement 6.1) includes
a kinetic temperature image of part of the Chugach
and Saint Elias Mountains, the Bagley Icefield, Bering Glacier, and Iceberg Lake area, Alaska. (See
Chapter 12 of this book by Wolfe et al. for a
detailed glaciological case study on Iceberg Lake
and environs and Chapter 13 by Kargel et al. for
glacier analysis results from the Chugach Mountains, including Bering Glacier.) The image shows
that a huge fraction of the glacier area—both snowfields and exposed glacier ice (including areas of
slightly dirty ice)—have fairly homogeneous temperatures (Fig. 6.2B). The image date was August
8, 2003 (the fourth author visited the area twice at
about the same time of year in 2008 and 2009),
when snow and ice across almost the whole region
was melting, and supraglacial ponds and streams
or wet snow covered almost the entire surface of
the glaciers (which were not debris covered), including the small accumulation areas. A reasonable
assumption emerging from this is that the homogeneous temperature field across glacier and snowfield surfaces in this image are almost everywhere
precisely at the melting point, thus 273.15 K.
Three sample areas were selected for extraction of
temperature histograms (Fig. 6.2G). The areas
include a large, melting snowfield; a large expanse
of melting glacier ice; and the small, icebergcluttered remnant of Iceberg Lake, which at that
time would have been a pond filled with melting
icebergs, bergy bits, brash ice, and ice-cold, turbid
water. Each of these areas shows histograms shifted
well to the warm side of the melting point of water
ice. For areas 1 and 2, the shift is identified as
indicating a temperature bias of about þ1.2 K;
the spread of values for areas 1 and 2 is consistent
with the formally stated 1 K uncertainty. We have
noted a comparable bias in many other scenes; this
appears to be fairly general for the ASTER TIR
kinetic temperature product in snow, ice, and water
areas, which might be related to emissivity assumptions for these materials. In the case of area 3, the
more substantial positive shift may be due to unresolved debris patches, but considering our discussion in Section 6.4.2.3, we think it could be due to
bleeding of high-temperature signals onto the cold
pixels of this lake; in other words, the lake is not
fully resolved, despite the formal 90 m resolution of
TIR and the published modulation transfer function (Arai and Tonooka 2005). Thus, anyone doing
thermal work with ASTER data should keep a
watchful eye on such bias and resolution issues.
1
Precision pertains to the level of measurement and
exactness of description within a GIS database (e.g.,
the number of decimal positions irrespective of whether
the number is accurate). Accuracy is the degree to which
information on a map or GIS database matches true or
accepted values, irrespective of the number of decimal
positions.
6.4.2.3
Detection versus full resolution of features
in VNIR, SWIR, and TIR
Users also should remain aware of another feature
of ASTER TIR data, which is its spatial resolution,
formally 90 m/pixel (versus 15 m/pixel for VNIR
and 30 m/pixel for SWIR). However, in practice it is
156
ASTER datasets and derived products for global glacier monitoring
Figure 6.2. Performance of ASTER TIR as shown in the kinetic temperature standard product for an image
acquired on August 8, 2003, over the Chugach Mountains, Alaska. Figs. 6.2E and 6.2F show some manually
delineated major, sharp material boundaries; the temperature image shows that the boundaries are diffuse over
about 5 TIR pixels (2 or 3 pixels on either side of the boundaries). Fig. 6.2H shows histogram distributions of
measured temperatures for three areas (Fig. 6.2G) that nominally should be almost pure snow or ice at the melting
point. The image within the three outlined sample areas has been stretched so that the full DN range is shown in
order to reveal thermal heterogeneity within; the fine structure is mainly noise. The place name ‘‘Kieffer Glacier’’ in
panel 6.2C is informal (see Chapter 12 of this book by Wolfe et al.). See Online Supplement 6.1 for full resolution.
DN ¼ digital number.
ASTER data-processing stream
157
Figure 6.3. Schematic illustration of ASTER image detection and spatial resolvability of rectangular and circular
features using VNIR and SWIR (approximated in panel A) and TIR (approximated in panel B). The diagrams show
some pixel lines across schematic anomalies that are, from left to right, either long and linear but subpixel in width,
subpixel in both length and width, fully resolved spatially (so that at least one pixel in each crossing transect senses
photons only from the anomaly material), or circular and fully resolved. The top and bottom panels (A and B,
respectively) show cases for either sharp, square wave responses along material discontinuities (panel A), or where
thermally hot pixels bleed the signal into adjoining pixels as far as 2 pixels away (panel B). In each case, ‘‘w’’ is the
pixel size (15 m for VNIR, 30 m for SWIR, 90 m for TIR). See text for further detailed explanation.
quite different than that. Sharply contrasting cold
and warm objects are not resolved as clearly as the
formal pixel resolution would suggest. Warm targets tend to bleed onto adjacent cold targets. This
has been documented by the ASTER Calibration
Team (Arai and Tonooka 2005), but what we have
found could suggest a slightly broader blurring of
sharp boundaries. Fig. 6.2F portrays this well, particularly along the sharply delineated shore of Iceberg Lake, which appears diffuse over about 5 TIR
pixels, rather than the 2 pixels if the response of
TIR for a sharp boundary was more nearly a square
wave across the boundary.
This brings us to a discussion of detection versus
full resolution of features. Consider a hot target
surrounded by a uniform cold body. We refer to
Fig. 6.3, which schematically illustrates some resolution issues encountered in Fig. 6.2. We start with
optical sensor resolution, which is more widely
known and can illustrate some key points applicable to thermal data as well. With VNIR and SWIR
(Fig. 6.3A) a lineament that is long but much less
than a pixel wide is not resolved in width (thus, it
consists of ‘‘mixels’’ of two materials in each pixel
falling on top of the lineament), but it may be
detectable and mappable. A subpixel patch (smaller
than a pixel in width and length) may appear as a
single anomalous pixel, where the significance
depends on background fluctuations and whether
the patch contributes enough to distinguish the
mixel from background pixel values; however, a
solitary subpixel anomaly would not likely engender much interest; for one thing, it could be a
single bad pixel.
A fully spatially resolved anomalous lineament
(e.g., a medial moraine) must have a width of at
least 2 pixels (30 m for VNIR, 60 m for SWIR) in
order to be assured that at least one pixel along any
transect encompasses the anomaly material; however, as Fig. 6.3A shows, a 2-pixel-wide lineament
will generally be resolved with an apparent width of
3 pixels, due to the presence of mixels. A circular
anomaly patch (e.g., an exposure of ice or a pond in
an otherwise debris-covered patch of a glacier) must
have a minimum diameter of 2 3=2 pixels (42 m for
VNIR, 85 m for SWIR) in order to be fully spatially
158
ASTER datasets and derived products for global glacier monitoring
resolved (to have at least one pixel that is not a
mixel).
ASTER TIR data have an added complication,
and this is our prime reason for dealing with this
issue here, since it is not discussed elsewhere. A
thermal anomaly can be as small as 1 TIR pixel
(even smaller if it is hot enough) and have its presence detectable; that is, with a sufficiently cooler
and uniform background, a subpixel patch of thermally anomalous material might be detectable, but
not resolvable; the hot object may appear noiselike, and could be confused with a bad pixel value.
For TIR, the situation is complicated because of
the bleeding of hot pixel energies onto what should
be cold pixels if we had a perfect imaging system.
We will assume that this type of thermal blurring
occurs over about 2 pixels away from a sharp
boundary. For a linear cold anomaly (e.g., a linear
patch of debris-free ice adjoined by Sun-warmed
debris-covered ice), full resolution and thus reliable
thermal measurements of the anomaly would
require it to have a width of 6 TIR pixels (i.e.,
540 m) in order to contain just 1 pixel representing
the anomaly temperature without contamination
from adjacent warm pixels (Fig. 6.3B). A homogeneous, circular, cold feature (e.g., an ice-cold lake
surrounded by debris-covered ice) would require a
diameter of (2 3=2 þ 3) pixels (525 m) to assure that
at least one TIR pixel measures the actual temperature near the center of that cold anomaly. However,
in terms of how ASTER TIR sees (detects) such an
anomaly, the blurring would cause the minimally
resolved circular feature (525 m in diameter) to
appear 11 VNIR pixels wide (990 m). Fig. 6.3B
suggests that in surveying ASTER TIR and derived
data, any thermal anomaly showing in the image
must have an apparent size of a kilometer or larger
in order to give any realistic hope of giving a direct
accurate measurement of the actual anomaly temperature; the corresponding actual critical size of an
anomaly (as measured on the ground, for instance)
would be about half a kilometer. Model-based
approaches may provide adequate estimates of
actual temperatures for slightly unresolved features.
The case in Fig. 6.2 shows Iceberg Lake near the
critical size for full spatial resolvability. However, it
is questionable, given the warm measured temperatures of Iceberg Lake (high 270s K; Fig. 6.2H),
whether even one TIR pixel in Iceberg Lake is completely uncontaminated by emissions from adjacent
warmer material.
The larger patches of melting snow and ice (areas
1 and 2 in Figs. 6.2G and 6.2H) are certainly com-
pletely resolved; their temperature histograms indicate a positive temperature bias of 1.2 K, a value
similar to that in several other TIR temperature
scenes of glaciers that we have examined. Bias correction could be applied to Fig. 6.2B (but has not
been) by subtracting 1.2 K. The spread of values in
the histograms for areas 1 and 2 (standard deviation
around 0.5 K; Fig. 6.2H) is consistent with the
ASTER project’s stated formal error of 1 K for
this temperature region (Table 6.1). Of course this is
just one image, but the validation appears reasonable and is typical. Users can rely on ASTER
kinetic temperature products but must be aware
of (and preferably correct) bias.
6.4.2.4
Elevation products
One of the unique capabilities of the ASTER VNIR
sensor is its aft-viewing telescope that enables it to
generate stereoscopic data (along with the nadirviewing telescope) to create digital elevation
models. From April 2001 to May 2006, LP DAAC
produced both ASTER relative (without GCPs)
and absolute (with user-supplied GCPs) DEMs
using ASTER L1A input data. The DEM production system involved a human operator, and was
designed to generate one or two DEMs per day.
Hence, following an extensive assessment of the
accuracy of ASTER relative DEMs produced by
a variety of ASTER DEM generation software,
the Japanese SilcAst software with a capacity to
produce 50 or more ASTER relative DEMs per
day in batch mode operation, was implemented at
LP DAAC. The accuracy of the new DEMs meets
or exceeds the specifications set for ASTER relative
DEMs by the Algorithm Theoretical Basis Document (ATBD). Toutin (2011) provides an extensive
history and description of standard ASTER DEM
production systems as they evolved in both the U.S.
and Japan.
NASA and Japan’s Ministry of Economy, Trade
and Industry (METI) jointly developed the ASTER
Global Digital Elevation Model (GDEM), Version
1 (GDEM-1), which was released in June 2009 and
provides elevation data for 99% of the Earth’s land
areas. GDEM-1 was produced in Japan by
the Sensor Information Laboratory Corporation
(SILC), a Japanese company that promotes
ASTER data use in science and industry through
the DEM, orthorectification, and L1B software it
develops exclusively for ASTER data. SILC produced GDEM-1 by automated processing of 1.5
million ASTER Level 1A scenes through stereo
ASTER data for GLIMS: STARS, DARs, gain settings, and image seasons
correlation to generate 1,264,118 individual scenebased ASTER DEMs. Further cloud masking,
stacking cloud-screened DEMs, eliminating outliers/bad values, averaging selected data to create
final pixel values, and correcting residual anomalies
yielded data that were partitioned into 1 1 tiles.
GDEM-1 covers land surfaces between 83 N and
83 S in 22,600 1 1 tiles. The vertical and horizontal accuracies estimated at preproduction were
20 and 30 m, respectively (at 95% confidence level).
GDEM-1 validation studies indicate that accuracies
vary and are not met uniformly, and also acknowledge that there exists an average global bias of
5 m. Chrysoulakis et al. (2011) found that preproduction accuracies are not met in their analysis
of GDEM-1 data over Greece. Both NASA and
METI acknowledged that though this version of
GDEM is deemed experimental its potential benefits outweigh its flaws.
GDEM Version 2 (GDEM-2) was released in
October 2011. GDEM-2 incorporates an additional
260,000 newly acquired scenes (not available during
the GDEM-1 time frame), many of which were
specifically targeted to help fill holes in ASTER’s
global coverage due to persistent cloud cover.
Major algorithm updates to GDEM-2 include the
following (Tachikawa et al. 2011):
1. A change in the window size for normalized
correlation matching to measure elevation from
9 9 pixels (in GDEM-1) to 5 5 pixels to produce finer horizontal resolutions.
2. Adjustments to the elevation offset (established
at around 5 m in GDEM-1).
3. Enhanced water body detection to help identify
those as small as 1 km 2 .
The GDEM-2 validation study performed by the
U.S. and Japanese partners revealed that its absolute vertical accuracy was within 0.20 m on average when compared against 18,000 geodetic control
points over the conterminous U.S. at an accuracy of
17 m at the 95% confidence level (Gesch et al.
2012). Both U.S. and Japanese investigations found
improvements in the horizontal resolution between
71 and 82 m; however, this improvement was
realized at the cost of some additional noise. The
prevalence of artifacts and voids in GDEM-1
were largely reduced in GDEM-2 with complete
elimination in some areas. Given some major
improvements, the GDEM Validation Team
recommended GDEM-2’s release, while acknowl-
159
edging the existence of some remaining artifacts
(Gesch et al. 2012).
Chapter 5 of this book by Quincey et al. includes
validation test results relating to digital topography
produced from ASTER stereo images, including
single scenes and GDEMs; several regional chapters
review or present data analysis that make use of
bias determination and correction. The regional
chapters include many applications of elevation
data; the most sensitive of these involve differential
elevations, whether elevation change, or highfrequency slope or roughness assessments, or shifting equilibrium line altitudes. The GLIMS community has produced many validation tests of
ASTER elevation products and analysis of glacierized terrain after application of validation tests and
corrections. For example, the scene average bias in
multitemporal elevation changes was determined
for a pair of ASTER DEMs in the Hoodoo Mountain area, British Columbia (see Chapter 15 of this
book by Kargel et al.); the bias was small enough
that a first-order correction made a small but sufficient correction related to derivation of glacier mass
balance. Other works have shown not just scene
average bias, but slope and aspect-dependent biases
(Nuth and Kääb 2011, Gardelle et al. 2012a, b),
which in some cases could be significant sources
of error if not corrected.
6.5
ASTER DATA FOR GLIMS: STARS,
DARs, GAIN SETTINGS, AND
IMAGE SEASONS
Glacier monitoring (GLIMS) was accepted as an
official ASTER Science Team investigation from
the earliest days of ASTER. Although GLIMS is
not tied strictly to ASTER, it has been a cornerstone of the project. GLIMS became operational in
2000 shortly after ASTER’s launch. Four types of
observations are conducted by ASTER that are of
use to GLIMS (Yamaguchi et al. 1998, and
modified subsequently by the ASTER Science Team
and Mission Operations Team). They include (1)
ASTER Science Team Acquisition Requests
(STARs) explicitly for GLIMS, (2) standard Data
Acquisition Requests (DARs), (3) emergency
DARs, and (4) all other imaging by other teams
and for other purposes, such as volcano STARs
and global mapping STARs.
The small field of view of the ASTER instrument
as well as its pointing capability mean that the
instrument must be scheduled to acquire images
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ASTER datasets and derived products for global glacier monitoring
over specific targets in response to users’ needs. For
ASTER to acquire imagery over a specific
glacierized region, it needs to be scheduled to turn
on at the correct time, with correct instrument settings for highly reflective material (snow), and possibly commanded to point off-nadir. For the GLIMS
STARs, we needed to create a set of approximately
1,760 data acquisition requests to cover all known
glaciers in the world. Each such request needs to
contain a number of types of information in order
to adequately schedule the instrument: geographic
location, in the form of a longitude/latitude
polygonal outline of the area (consisting of 20 or
fewer vertices); instrument gain settings (similar to
exposure in photography); optimal time of year for
imaging glaciers in that area; and information
about the requestor. We wrote dedicated software
to calculate optimal time of year and gains based on
solar geometry. These were then formatted and sent
to the ground systems personnel in Japan for input
into the master scheduling system. The process of
creating these original data requests is documented
in Raup et al. 2000.
While this first set of STARs led to good image
acquisition over many of the world’s glacierized
regions, some regions were not so lucky, due to a
variety of reasons. After the second year in the
mission, we were given the opportunity to change
the STARs to compensate for uneven past performance. Updates have been made repeatedly to try to
compensate for deficiencies in acquisitions, such as
excessive snow cover, excessive frequency of cloudy
images, or simply lack of image acquisitions due to
constraints imposed by ASTER Mission Operations (MO). MO has, for instance, for engineering
reasons required a drastic cutback in pointing,
which has eliminated most imaging of high-latitude
parts of Antarctica and has reduced other imaging
opportunities. Some large regions of the world,
such as Alaska, have been considerably underserved
due to constraints imposed on MO by competing
imaging needs in places like Iraq and Afghanistan
and for other reasons. Major recent modifications
of the GLIMS STARs were developed by GLIMS
over several years of successive installments to
help reduce the amount of saturation common in
ASTER VNIR images over equator-facing,
snow-covered slopes in some regions, as well as to
shift the imaging seasons slightly to reduce snow
cover.
While the GLIMS project has succeeded in capturing tens of thousands of high-quality glacier
images, coverage remains uneven. Other science
goals of the ASTER project, such as global mapping, have acquired tens of thousands of other useful glacier images, though usually not with gain
settings that are well suited for snow and ice targets;
hence, heavy saturation reduces the usefulness of
those scenes. However, what is lost in global mapping images over snow and ice areas is gained in
added detail over heavily debris-covered areas,
glacier lakes, and in shadowed areas. Hence, the
full suite of ASTER imagery provides for complementary coverage of different glaciers and different
parts of glaciers. Furthermore, global map scenes
are needed to help fill in the digital topography
needed for GDEM production.
The gaps in coverage (especially multitemporal
coverage) have been used to justify some special
increased local coverage, submitted either individually or by the project PI, in the form of standard
data acquisition requests. This category also
includes images obtained in support of field campaigns. Emergency acquisitions have also been
requested and fulfilled for prompt follow-up
imaging after natural disasters, such as outburst
floods, ice avalanches, and rockfall damming of
rivers (Kargel et al. 2010), as well as following other
special dynamical events, such as the breakup of the
Mertz Glacier tongue in Antarctica. As with the
STARs, these special requests have proven uneven
in their success, due mainly to the challenge of
cloudy conditions that prevail in many glacierized
areas.
In sum, though overall coverage still remains
uneven, ASTER has been revolutionary for glacier
remote sensing due both to the instrument’s exquisite capabilities and to the liberal data dissemination policies.
6.6
ACKNOWLEDGMENTS
We would like to thank Masami Hato (ERSDAC,
Tokyo), Ken Duda (LP DAAC, Sioux Falls), and
Bjorn Eng (JPL, Pasadena) for their help in tracking down certain critical details of the ASTER mission. We also thank Alan Gillespie (University of
Washington, Seattle) for his updates and clarifications regarding the TES algorithm. ASTER data
courtesy of NASA/GSFC/METI/Japan Space Systems, the U.S./Japan ASTER Science Team, and
the GLIMS project.
References 161
6.7
REFERENCES
Arai, K., and Tonooka, H. (2005) Radiometric performance evaluation of ASTER VNIR, SWIR, and TIR.
IEEE Transactions on Geoscience and Remote Sensing,
43(12), 2725–2732.
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