![](media/image1.png){width="6.49375in" height="8.403472222222222in"}

ASTER User Handbook

Version 2

Michael Abrams

Simon Hook

Jet Propulsion Laboratory

4800 Oak Grove Dr.

Pasadena, CA 91109

Bhaskar Ramachandran

EROS Data Center

Sioux Falls, SD 57198

Acknowledgments

This document was prepared at the Jet Propulsion Laboratory/California
Institute of Technology. Work was performed under contract to the
National Aeronautics and Space Administration.

Table of Contents

[1.0 Introduction to ASTER 8](#introduction-to-aster)

[2.0 The ASTER Instrument 8](#the-aster-instrument)

[2.1 The VNIR Instrument 11](#the-vnir-instrument)

[2.2 The SWIR Instrument 12](#the-swir-instrument)

[2.3 The TIR Instrument 13](#the-tir-instrument)

[3.0 ASTER Level-1 Data 16](#aster-level-1-data)

[3.1 ASTER Level-1A Data 19](#aster-level-1a-data)

[3.1.1 ASTER Level-1A Browse 19](#aster-level-1a-browse)

[3.2 ASTER Level-1B Data 21](#aster-level-1b-data)

[3.2.1 ASTER Level-1B Browse 21](#aster-level-1b-browse)

[4.0 ASTER Higher-Level Products 24](#aster-higher-level-products)

[5.0 ASTER Radiometry 25](#aster-radiometry)

[6.0 ASTER Geometry 27](#aster-geometry)

[7.0 Data Acquisition Strategy 29](#data-acquisition-strategy)

[8.0 ASTER Data Search and Order of Archived Data and Products
31](#aster-data-search-and-order-of-archived-data-and-products)

[9.0 ASTER Higher-Level Data Products Ordering Mechanism
40](#aster-higher-level-data-products-ordering-mechanism)

[10.0 Data Acquisition Requests 41](#data-acquisition-requests)

[11.0 ASTER Applications 41](#aster-applications)

[11.1 Cuprite, Nevada 41](#cuprite-nevada)

[11.2 Lake Tahoe 45](#lake-tahoe)

[11.2.1 Objective 45](#objective)

[11.2.2 Introduction 45](#introduction)

[11.2.3 Field Measurements 46](#field-measurements)

[11.2.4 Using ASTER to measure water clarity
48](#using-aster-to-measure-water-clarity)

[11.2.5 Using ASTER to Measure Circulation
52](#using-aster-to-measure-circulation)

[12.0 Geo-Referencing ASTER Level-1B Data
54](#geo-referencing-aster-level-1b-data)

[12.1 Introduction 54](#introduction-1)

[12.2 Accessing ASTER Level-1B Metadata
54](#accessing-aster-level-1b-metadata)

[12.3 ASTER Level-1B Geo-Referencing Methodology
56](#aster-level-1b-geo-referencing-methodology)

[12.4 Unique Features of ASTER Level-1B Data
57](#unique-features-of-aster-level-1b-data)

[12.4.1 Pixel Reference Location 57](#pixel-reference-location)

[12.4.2 Footprint of an ASTER Level-1B Image
57](#footprint-of-an-aster-level-1b-image)

[12.4.3 Path- or Satellite-Orientation of an ASTER Level-1B Image
58](#path--or-satellite-orientation-of-an-aster-level-1b-image)

[12.4.4 Geometric Correction Table 59](#geometric-correction-table)

[12.4.5 Geodetic versus Geocentric Coordinates
61](#geodetic-versus-geocentric-coordinates)

[13.0. Frequently-Asked Questions 62](#frequently-asked-questions)

[13.1 General ASTER 62](#general-aster)

[13.2 ASTER Instrument 63](#aster-instrument)

[13.3 ASTER Level-1 Data 63](#aster-level-1-data-1)

[13.4 Acquiring and Ordering ASTER Data
69](#acquiring-and-ordering-aster-data)

[13.5 ASTER Metadata 70](#aster-metadata)

[13.6 ASTER Higher-Level Products 81](#aster-higher-level-products-1)

[13.7 ASTER Expedited Data Sets 83](#aster-expedited-data-sets)

[13.8 ASTER-Related Algorithms 84](#aster-related-algorithms)

[13.9 ASTER Documentation 85](#aster-documentation)

[13.10 HDF-EOS Data Format 86](#hdf-eos-data-format)

[Appendix I: Dump of HDF Metadata in a ASTER L1B file
87](#appendix-i-dump-of-hdf-metadata-in-a-aster-l1b-file)

[Appendix II: ASTER Higher-Level Data Products
95](#appendix-ii-aster-higher-level-data-products)

[Appendix III: Metadata Cross Reference Table
114](#appendix-iii-metadata-cross-reference-table)

[Appendix IV: Public Domain Software for Handling HDF-EOS Format
131](#appendix-iv-public-domain-software-for-handling-hdf-eos-format)

[Appendix V: LP-DAAC Data Sets Available through EDC via the EDG
133](#appendix-v-lp-daac-data-sets-available-through-edc-via-the-edg)

> Table of Figures

[Figure 1: The ASTER Instrument before Launch.
9](#__RefHeading___Toc21151255)

[Figure 2: Comparison of Spectral Bands between ASTER and Landsat-7
Thematic Mapper. 10](#__RefHeading___Toc21151256)

[Figure 3: VNIR Subsystem Design. 12](#__RefHeading___Toc21151257)

[Figure 4: SWIR Subsystem Design. 13](#__RefHeading___Toc21151258)

[Figure 5: TIR Subsystem Design. 15](#__RefHeading___Toc21151259)

[Figure 6: End-to-End Processing Flow of ASTER data between US and
Japan. 18](#__RefHeading___Toc21151260)

[Figure 9: Opening Page of the EOS Data Gateway.
32](#__RefHeading___Toc21151261)

[Figure 10: Choosing Search Keyword "DATASET."
33](#__RefHeading___Toc21151262)

[Figure 11: Choosing Search Area. 34](#__RefHeading___Toc21151263)

[Figure 12: Choosing a Date/Time Range. 35](#__RefHeading___Toc21151264)

[Figure 13: Data Set Listing Result. 36](#__RefHeading___Toc21151265)

[Figure 14: Data Granules Listing Result.
37](#__RefHeading___Toc21151266)

[Figure 15: Choosing Ordering Options. 38](#__RefHeading___Toc21151267)

[Figure 16: Order Form. 39](#__RefHeading___Toc21151268)

[Figure 17: Cuprite Mining District, displayed with SWIR bands 4-6-8 as
RGB composite. 42](#__RefHeading___Toc21151269)

[Figure 18: Spectral Angle Mapper Classification of Cuprite SWIR data.
43](#__RefHeading___Toc21151270)

[Figure 19: ASTER image spectra (left) and library spectra (right) for
minerals 44](#__RefHeading___Toc21151271)

[mapped at Cuprite. 44](#__RefHeading___Toc21151272)

[Figure 20: Outline map of Lake Tahoe, CA/NV.
45](#__RefHeading___Toc21151273)

[Figure 21: Raft Measurements. 46](#__RefHeading___Toc21151274)

[Figure 22: Field measurements at the US Coast Guard.
48](#__RefHeading___Toc21151275)

[Figure 23: Color Infrared Composite of ASTER bands 3, 2, 1 as R, G, B
respectively. 49](#__RefHeading___Toc21151276)

[Figure 24: ASTER band 1 (0.52-0.60 µm) color-coded to show variations
in the intensity of the near-shore bottom reflectance.
50](#__RefHeading___Toc21151277)

[Figure 25: Bathymetric map of Lake Tahoe CA/NV.
51](#__RefHeading___Toc21151278)

[Figure 26: Near-shore clarity map derived from ASTER data and a
bathymetric map. 52](#__RefHeading___Toc21151279)

[Figure 27: ASTER Band-13 Brightness Temperature Image of Lake Tahoe
from Thermal Data Acquired June 3, 2001.
53](#__RefHeading___Toc21151280)

[Figure 28: Upper-left pixel of VNIR, SWIR and TIR bands in an ASTER
Level-1B data set. 57](#__RefHeading___Toc21151281)

[Figure 29: ASTER L1B Footprint in the Context of the SCENEFOURCORNERS
Alignment. 58](#__RefHeading___Toc21151282)

[Figure 30: Ascending and Descending Orbital Paths.
59](#__RefHeading___Toc21151283)

[Figure 31: G-Ring and G-Polygon. 78](#__RefHeading___Toc21151284)

> List of Tables

[Table 1: Characteristics of the 3 ASTER Sensor Systems.
10](#__RefHeading___Toc21153352)

[Table 2: Specifications of the ASTER Level-1A Browse Product.
19](#__RefHeading___Toc21153353)

[Table 2: Resampling Methods and Projections Available for Producing
Level-1B products. 23](#__RefHeading___Toc21153354)

[Table 3: ASTER Higher-Level Standard Data Products.
24](#__RefHeading___Toc21153355)

[Table 4: Maximum Radiance Values for all ASTER Bands and all Gains.
25](#__RefHeading___Toc21153356)

[Table 5: Calculated Unit Conversion Coefficients.
26](#__RefHeading___Toc21153357)

[Table 6: Geometric Performance of ASTER Level-1 Data (Based on V2.1 of
the Geometric Correction Database). 28](#__RefHeading___Toc21153358)

[Table 7: Specific Metadata Attributes Required for Geo-Referencing
ASTER Level-1B Data. 55](#__RefHeading___Toc21153359)

# 1.0 Introduction to ASTER

The Advanced Spaceborne Thermal Emission and Reflection Radiometer
(ASTER) is an advanced multispectral imager that was launched on board
NASA's Terra spacecraft in December, 1999. ASTER covers a wide spectral
region with 14 bands from the visible to the thermal infrared with high
spatial, spectral and radiometric resolution. An additional
backward-looking near-infrared band provides stereo coverage. The
spatial resolution varies with wavelength: 15 m in the visible and
near-infrared (VNIR), 30 m in the short wave infrared (SWIR), and 90 m
in the thermal infrared (TIR). Each ASTER scene covers an area of 60 x
60 km.

Terra is the first of a series of multi-instrument spacecraft forming
NASA's Earth Observing System (EOS). EOS consists of a science component
and a data information system (EOSDIS) supporting a coordinated series
of polar-orbiting and low inclination satellites for long-term global
observations of the land surface, biosphere, solid Earth, atmosphere,
and oceans. By enabling improved understanding of the Earth as an
integrated system, [the EOS program has benefits for us
all](http://earthobservatory.nasa.gov/Study/WhyItMatters/). In addition
to ASTER, the other instruments on Terra are the Moderate-Resolution
Imaging Spectroradiometer (MODIS), Multi-angle Imaging
Spectro-Radiometer (MISR), Clouds and the Earth's Radiant Energy System
(CERES), and Measurements of Pollution in the Troposphere (MOPITT). As
the only high spatial resolution instrument on Terra, ASTER is the "zoom
lens" for the other instruments. Terra is in a sun-synchronous orbit, 30
minutes behind Landsat ETM+; it crosses the equator at about 10:30 am
local solar time.

ASTER can acquire data over the entire globe with an average duty cycle
of 8% per orbit. This translates to acquisition of about 650 scenes per
day, that are processed to Level-1A; of these, about 150 are processed
to Level-1B. All 1A and 1B scenes are transferred to the EOSDIS archive
at the EROS Data Center's (EDC) Land Processes Distributed Active
Archive Center (LP-DAAC), for storage, distribution, and processing to
higher-level data products. All ASTER data products are stored in a
specific implementation of Hierarchical Data Format called HDF-EOS.

# 2.0 The ASTER Instrument

ASTER is a cooperative effort between NASA and Japan\'s Ministry of
Economy Trade and Industry (METI) formerly known as Ministry of
International Trade and Industry (MITI), with the collaboration of
scientific and industry organizations in both countries. The ASTER
instrument consists of three separate instrument subsystems (Figure 1).

![](media/image2.jpeg){width="5.873611111111111in" height="4.58125in"}

[]{#__RefHeading___Toc21151255 .anchor}**Figure 1: The ASTER Instrument
before Launch.**

ASTER consists of three different subsystems (Figure 2): the Visible and
Near-infrared (VNIR) has three bands with a spatial resolution of 15 m,
and an additional backward telescope for stereo; the Shortwave Infrared
(SWIR) has 6 bands with a spatial resolution of 30 m; and the Thermal
Infrared (TIR) has 5 bands with a spatial resolution of 90 m. Each
subsystem operates in a different spectral region, with its own
telescope(s), and is built by a different Japanese company. The spectral
bandpasses are shown in Table 1, and a comparison of bandpasses with
Landsat Thematic Mapper is shown in Figure 3. In addition, one more
telescope is used to view backward in the near-infrared spectral band
(band 3B) for stereoscopic capability.

  -------------- ------ ---------------------- ------------- --------------
  Subsystem      Band   Spectral Range (μm)    Spatial       Quantization
                 No.                           Resolution, m Levels

  VNIR           1      0.52-0.60              15            8 bits

                 2      0.63-0.69                            

                 3N     0.78-0.86                            

                 3B     0.78-0.86                            

  SWIR           4      1.60-1.70              30            8 bits

                 5      2.145-2.185                          

                 6      2.185-2.225                          

                 7      2.235-2.285                          

                 8      2.295-2.365                          

                 9      2.360-2.430                          

  TIR            10     8.125-8.475            90            12 bits

                 11     8.475-8.825                          

                 12     8.925-9.275                          

                 13     10.25-10.95                          

                 14     10.95-11.65                          
  -------------- ------ ---------------------- ------------- --------------

[]{#__RefHeading___Toc21153352 .anchor}Table 1: Characteristics of the 3
ASTER Sensor Systems.

![](media/image3.png){width="6.395138888888889in"
height="3.4680555555555554in"}

> []{#__RefHeading___Toc21151256 .anchor}**Figure 2: Comparison of
> Spectral Bands between ASTER and Landsat-7 Thematic Mapper.**

(Note: % Ref is reflectance percent).

The Terra spacecraft is flying in a circular, near-polar orbit at an
altitude of 705 km. The orbit is sun-synchronous with equatorial
crossing at local time of 10:30 a.m., returning to the same orbit every
16 days. The orbit parameters are the same as those of Landsat 7, except
for the local equatorial crossing time.

## 2.1 The VNIR Instrument

## 

The VNIR subsystem consists of two independent telescope assemblies to
minimize image distortion in the backward and nadir looking telescopes
(Figure 3). The detectors for each of the bands consist of 5000 element
silicon charge-coupled detectors (CCD\'s). Only 4000 of these detectors
are used at any one time. A time lag occurs between the acquisition of
the backward image and the nadir image. During this time earth rotation
displaces the image center. The VNIR subsystem automatically extracts
the correct 4000 pixels based on orbit position information supplied by
the EOS platform.

The VNIR optical system is a reflecting-refracting improved Schmidt
design. The backward looking telescope focal plane contains only a
single detector array and uses an interference filter for wavelength
discrimination. The focal plane of the nadir telescope contains 3 line
arrays and uses a dichroic prism and interference filters for spectral
separation allowing all three bands to view the same area
simultaneously. The telescope and detectors are maintained at 296 ± 3K
using thermal control and cooling from a platform-provided cold plate.
On-board calibration of the two VNIR telescopes is accomplished with
either of two independent calibration devices for each telescope. The
radiation source is a halogen lamp. A diverging beam from the lamp
filament is input to the first optical element (Schmidt corrector) of
the telescope subsystem filling part of the aperture. The detector
elements are uniformly irradiated by this beam. In each calibration
device, two silicon photo-diodes are used to monitor the radiance of the
lamp. One photo-diode monitors the filament directly and the second
monitors the calibration beam just in front of the first optical element
of the telescope. The temperatures of the lamp base and the photo-diodes
are also monitored. Provision for electrical calibration of the
electronic components is also provided.

The system signal-to-noise is controlled by specifying the NE delta rho
(ρ) to be \< 0.5% referenced to a diffuse target with a 70% albedo at
the equator during equinox. The absolute radiometric accuracy is ± 4% or
better.

The VNIR subsystem produces by far the highest data rate of the three
ASTER imaging subsystems. With all four bands operating (3 nadir and 1
backward) the data rate including image data, supplemental information
and subsystem engineering data is 62 Mbps.

![](media/image4.png){width="6.498611111111111in"
height="4.874305555555556in"}

[]{#__RefHeading___Toc21151257 .anchor}**Figure 3: VNIR Subsystem
Design.**

## 2.2 The SWIR Instrument

## 

The SWIR subsystem uses a single aspheric refracting telescope (Figure
4). The detector in each of the six bands is a Platinum Silicide-Silicon
(PtSi-Si) Schottky barrier linear array cooled to 80K. A split Stirling
cycle cryocooler with opposed compressors and an active balancer to
compensate for the expander displacer provide cooling. The on-orbit
design life of this cooler is 50,000 hours. Although ASTER operates with
a low duty cycle (8% average data collection time), the cryocooler
operates continuously because the cool-down and stabilization time is
long. No cyrocooler has yet demonstrated this length of performance, and
the development of this long-life cooler was one of several major
technical challenges faced by the ASTER team.

The cryocooler is a major source of heat. Because the cooler is attached
to the SWIR telescope, which must be free to move to provide cross-track
pointing, this heat cannot be removed using a platform provided cold
plate. This heat is transferred to a local radiator attached to the
cooler compressor and radiated into space.

Six optical bandpass filters are used to provide spectral separation. No
prisms or dichroic elements are used for this purpose. A calibration
device similar to that used for the VNIR subsystem is used for in-flight
calibration. The exception is that the SWIR subsystem has only one such
device.

The NE delta rho will vary from 0.5 to 1.3% across the bands from short
to long wavelength. The absolute radiometric accuracy is +4% or better.
The combined data rate for all six SWIR bands, including supplementary
telemetry and engineering telemetry, is 23 Mbps.

![](media/image5.png){width="6.498611111111111in"
height="4.874305555555556in"}

[]{#__RefHeading___Toc21151258 .anchor}**Figure 4: SWIR Subsystem
Design.**

## 2.3 The TIR Instrument

## 

The TIR subsystem uses a Newtonian catadioptric system with an aspheric
primary mirror and lenses for aberration correction (Figure 5). Unlike
the VNIR and SWIR telescopes, the telescope of the TIR subsystem is
fixed with pointing and scanning done by a mirror. Each band uses 10
Mercury-Cadmium-Telluride (HgCdTe) detectors in a staggered array with
optical band-pass filters over each detector element. Each detector has
its own pre- and post-amplifier for a total of 50.

As with the SWIR subsystem, the TIR subsystem uses a mechanical split
Stirling cycle cooler for maintaining the detectors at 80K. In this
case, since the cooler is fixed, the waste heat it generates is removed
using a platform supplied cold plate.

The scanning mirror functions both for scanning and pointing. In the
scanning mode the mirror oscillates at about 7 Hz. For calibration, the
scanning mirror rotates 180 degrees from the nadir position to view an
internal black body which can be heated or cooled. The scanning/pointing
mirror design precludes a view of cold space, so at any one time only a
single point temperature calibration can be effected. The system does
contain a temperature controlled and monitored chopper to remove low
frequency drift. In flight, a single point calibration can be done
frequently (e.g., every observation) if necessary. On a less frequent
interval, the black body may be cooled or heated (to a maximum
temperature of 340K) to provide a multipoint thermal calibration.
Facility for electrical calibration of the post-amplifiers is also
provided.

For the TIR subsystem, the signal-to-noise can be expressed in terms of
an NE delta T. The requirement is that the NE delta T be less than 0.3K
for all bands with a design goal of less than 0.2K. The signal reference
for NE delta T is a blackbody emitter at 300K. The accuracy requirements
on the TIR subsystem are given for each of several brightness
temperature ranges as follows: 200 - 240K, 3K; 240 - 270K, 2K; 270 -
340K, 1K; and 340 - 370K, 2K.

The total data rate for the TIR subsystem, including supplementary
telemetry and engineering telemetry, is 4.2 Mbps. Because the TIR
subsystem can return useful data both day and night, the duty cycle for
this subsystem is set at 16%. The cryocooler, like that of the SWIR
subsystem, operates with a 100% duty cycle.

![](media/image6.png){width="6.498611111111111in"
height="4.874305555555556in"}

[]{#__RefHeading___Toc21151259 .anchor}**Figure 5: TIR Subsystem
Design.**

# 3.0 ASTER Level-1 Data

The ASTER instrument produces two types of Level-1 data: Level-1A (L1A)
and Level-1B (L1B). ASTER L1A data are formally defined as
reconstructed, unprocessed instrument data at full resolution. They
consist of the image data, the radiometric coefficients, the geometric
coefficients and other auxiliary data without applying the coefficients
to the image data, thus maintaining original data values. The L1B data
are generated by applying these coefficients for radiometric calibration
and geometric resampling.

All acquired image data are processed to L1A. On-board storage
limitations on the spacecraft limit ASTER's acquisition to about 650 L1A
scenes per day. A maximum of 310 scenes per day are processed to L1B
based on cloud coverage. The end-to-end flow of data, from upload of
daily acquisition schedules to archiving at the LP-DAAC, is shown in
Figure 6. The major steps involved in the processing of Level-1 data can
be summarized thus:

-   The one-day acquisition schedule is generated in Japan at ASTER GDS
    with inputs from both US and Japan, and is sent to the EOS
    Operations Center (EOC) at the Goddard Spaceflight Center (GSFC).

-   The one-day acquisition schedule is uplinked to Terra, and data are
    accordingly acquired.

-   Terra transmits the Level-0 data via the Tracking and Data Relay
    Satellite System (TDRSS), to ground receiving stations at White
    Sands, New Mexico in the US.

-   These data are shipped on tape to the EOS Data Operations System
    (EDOS) at GSFC.

-   EDOS, following some minimal pre-processing, ships the data on tapes
    (by air) to ASTER GDS in Tokyo, Japan

-   GDS processes Level-0 to Level-0A in the Front-End Processing Module
    which includes:

    -   ***Depacketizing Level-0 Data:*** a depacketizing function to
        recover the instrument source data. The packets for each group
        are depacketized and aligned to recover the instrument source
        data using a sequential counter, flags in the primary header,
        and time tags in the secondary header. The spectral band
        information in the instrument source data is multiplexed with
        the image in Band Interleaved by Pixel (BIP) format.

    -   ***Demultiplexing Instrument Source Data:*** a demultiplexing
        function to separate image data into spectral bands in BSQ
        format. The instrument source data are demultiplexed to separate
        image data for every spectral band in BSQ format. Each
        (Level-0A) data group (VNIR, SWIR, & TIR) contains image data,
        instrument supplementary data, & spacecraft ancillary data.

    -   ***SWIR and TIR Image Data Stagger Realignment:*** SWIR and TIR
        image data are re-aligned to compensate for a staggered
        configuration. The SWIR parallax error is caused by the offset
        in the detector alignment in the along-track direction. The
        parallax correction is done with a combination of image matching
        correlation and DEM methods.

    -   Geometric system correction: Coordinate transformation of the
        line of sight vector using the ancillary information from the
        instrument supplementary data and spacecraft ancillary data to
        identify the observation points in latitude/longitude
        coordinates on the Earth's surface defined by the WGS84 Earth
        model.

    -   Radiometric coefficients are generated using real temperature
        values in the instrument supplementary data.

-   ASTER GDS ships the final ASTER L1A and L1B data on tapes (by air)
    to the

> LP-DAAC for archiving, distribution, and processing to higher level
> data products.

![](media/image8.png){width="9.198611111111111in"
height="6.899305555555555in"}

[]{#__RefHeading___Toc21151260 .anchor}**Figure 6: End-to-End Processing
Flow of ASTER data between US and Japan.**

## 3.1 ASTER Level-1A Data

The ASTER Level-1A raw data are reconstructed from Level-0, and are
unprocessed instrument digital counts. This product contains
depacketized, demultiplexed and realigned instrument image data with
geometric correction coefficients and radiometric calibration
coefficients appended but not applied. These coefficients include
correcting for SWIR parallax as well as inter- and intra-telescope
registration. (The SWIR parallax error is caused by the offset in
detector alignment in the along-track direction and depends on the
distance between the spacecraft and the observed earth surface. For SWIR
bands the parallax corrections are carried out with the image matching
technique or the coarse DEM data base, depending on cloud cover). The
spacecraft ancillary and instrument engineering data are also included.
The radiometric calibration coefficients, consisting of offset and
sensitivity information, are generated from a database for all
detectors, and are updated periodically. The geometric correction is the
coordinate transformation for band-to-band co-registration. The VNIR and
SWIR data are 8-bit and have variable gain settings. The TIR data are
12-bit with a single gain. The structure of the data inside a Level-1A
product is illustrated in Figure 7. This is in HDF-EOS format.

### 3.1.1 ASTER Level-1A Browse

The ASTER Level-1A also contains browse images for each of the three
sensors. The browse product contains 1-scene of image data generated
based on the Level-1A data with similar radiometric corrections and
mis-registration corrections applied to Level-1B data. All image data
(VNIR, SWIR, TIR) are 24-bit JPEG compressed images stored in an HDF
file in RIS24 objects. The following table (Table 2) provides the main
characteristics of the Level-1A browse images:

  --------------- -------------- --------------- ----------- ---------- ----------- ---------
  **Telescope**   **Dimensions   **Compression   **Quality   **Blue**   **Green**   **Red**
                  (pixel x       Method**        Factor**                           
                  line)**                                                           

  VNIR            224x208        JPEG            50          Band 1     Band 2      Band 3N

  SWIR            224x208        JPEG            50          Band 4     Band 5      Band 9

  TIR             224x208        JPEG            50          Band 10    Band 12     Band 14
  --------------- -------------- --------------- ----------- ---------- ----------- ---------

[]{#__RefHeading___Toc21153353 .anchor}Table 2: Specifications of the
ASTER Level-1A Browse Product.

## 3.2 ASTER Level-1B Data

The ASTER Level-1B data are L1A data with the radiometric and geometric
coefficients applied. All of these data are stored together with
metadata in one HDF file. The L1B image is projected onto a rotated map
(rotated to "path oriented" coordinate) at full instrument resolutions.
The Level-1B data generation also includes registration of the SWIR and
TIR data to the VNIR data. And in addition, for SWIR in particular, the
parallax errors due to the spatial locations of all of its bands are
corrected. Level-1B data define a scene center as the geodetic center of
the scene obtained from the L1A attribute named "SceneCenter" in the
HDF-EOS attribute "productmetadata.0". The definition of scene center in
L1B is the actual center on the rotated coordinates (L1B coordinates)
not the same as in L1A.

The structure of the L1B data file is shown schematically in Figure 8.
This illustration is for the product generated when the instrument is
operated in full mode (all systems are on and acquiring data). In other
restricted modes, e.g. just SWIR and TIR, not all the items listed in
Figure 8 are included in the product.

### 3.2.1 ASTER Level-1B Browse

## 

The ASTER Level-1B data sets do not have dedicated browse images of
their own. Their browse link maps back to their ASTER Level-1A parent's
browse images. Occasionally, there are instances when an L1B browse link
is grayed out or inactive. This happens under two circumstances: one,
the L1B data set was sent from GDS to LP-DAAC ahead of the L1A parent,
or two, the LP-DAAC archive has not yet received the corresponding L1A
parents.

##   {#section-4 .unnumbered}

## 

The L1B data product is generated, by default, in UTM projection in
swath orientation, and Cubic Convolution resampling. An L1B in a
different projection and/or resampling method can be produced on request
from
[GDS](http://www.gds.aster.ersdac.or.jp/gds_www2000/service_e/all_guid_e/set_all_guid_e.html)
in Japan (Table 2).

[]{#__RefHeading___Toc21153354 .anchor}Table 2: Resampling Methods and
Projections Available for Producing Level-1B products.

  ----------------------------------- -----------------------------------
  Resampling methods                  Map Projections

  Nearest Neighbor (NN)               Geographic (EQRECT)

  Cubic Convolution (CC)              Lambert Conformal Conic (LAMCC)

  Bi-Linear (BL)                      Space Oblique Mercator (SOM)

                                      Polar Stereographic (PS)

                                      Universal Transverse Mercator (UTM)
  ----------------------------------- -----------------------------------

Each image contains geolocation information stored as a series of
arrays. There is one set of geolocation information (array) per nadir
telescope (3 sets total). Each geolocation array is 11 x 11 elements
size, with the top left element (0,0) in the image for the nadir views.
For the backward view the image is offset with respect to the
geolocation array. The nadir VNIR and backward-viewing VNIR images use
the same latitude/longitude array, except the backward-viewing image is
offset with respect to the nadir image.

The L1B latitude and longitude geolocation arrays are two 11 x 11
matrices of geocentric latitude and geodetic longitude in units of
degrees. The block size of the geolocation array is 420 lines by 498
samples for the VNIR bands; 210 lines by 249 samples for the SWIR bands;
and 70 lines by 83 samples for the TIR bands.

Appendix I provides a dump of the metadata contained in a L1B data
product. There are five metadata groups:

Productmetadata.0

Productmetadata.1

Productmetadata.V

Productmetadata.S

Productmetadata.T

................

# 4.0 ASTER Higher-Level Products

Table 3 lists each of the ASTER higher-level Standard Data Products and
some of their basic characteristics. More detailed descriptions of these
data products are given in Appendix II.

+----------+---+-------------+--------+------+------+------+-------+
| #        | * | **Parameter | **     | *    | **   | **   | **Ho  |
| ##### Sh | * | Name**      | Produc | *Uni | Abso | Rela | rizon |
| ort Name | L |             | tion** | ts** | lute | tive | tal** |
|          | e |             |        |      | Ac   | Ac   |       |
|          | v |             | **     |      | cura | cura | **Re  |
|          | e |             | Mode** |      | cy** | cy** | solut |
|          | l |             |        |      |      |      | ion** |
|          | * |             |        |      |      |      |       |
|          | * |             |        |      |      |      | **    |
|          |   |             |        |      |      |      | (m)** |
+----------+---+-------------+--------+------+------+------+-------+
| AST_06V  | 2 | De          | r      | none | N/A  | N/A  | 15    |
|          |   | correlation | outine |      |      |      |       |
|          |   | stretch     |        |      |      |      |       |
|          |   | -VNIR       |        |      |      |      |       |
+----------+---+-------------+--------+------+------+------+-------+
| AST_06S  | 2 | De          | r      | none | N/A  | N/A  | 30    |
|          |   | correlation | outine |      |      |      |       |
|          |   | stretch     |        |      |      |      |       |
|          |   | -SWIR       |        |      |      |      |       |
+----------+---+-------------+--------+------+------+------+-------+
| AST_06T  | 2 | De          | r      | none | N/A  | N/A  | 90    |
|          |   | correlation | outine |      |      |      |       |
|          |   | stretch     |        |      |      |      |       |
|          |   | -TIR        |        |      |      |      |       |
+----------+---+-------------+--------+------+------+------+-------+
| AST_04   | 2 | Brightness  | on-    | deg  | 1-2  | 0.3  | 90    |
|          |   | temperature | demand | rees | C    | C    |       |
|          |   |             |        | C    |      |      |       |
+----------+---+-------------+--------+------+------+------+-------+
| AST_07   | 2 | Surface     | on-    | none | 4%   | 1%   | 15,   |
|          |   | reflectance | demand |      |      |      | 30    |
|          |   | VNIR,SWIR   |        |      |      |      |       |
+----------+---+-------------+--------+------+------+------+-------+
| AST_09   | 2 | Surface     | on-    | W/m^ | 2%   | 1%   | 15,   |
|          |   | radiance    | demand | 2^/s |      |      | 30    |
|          |   | -VNIR, SWIR |        | r/µm |      |      |       |
+----------+---+-------------+--------+------+------+------+-------+
| AST_09T  | 2 | Surface     | on-    | W/m^ | 2%   | 1%   | 90    |
|          |   | radiance    | demand | 2^/s |      |      |       |
|          |   | -TIR        |        | r/µm |      |      |       |
+----------+---+-------------+--------+------+------+------+-------+
| AST_05   | 2 | Surface     | on-    | none | 0.05 | 0    | 90    |
|          |   | emissivity  | demand |      | -0.1 | .005 |       |
+----------+---+-------------+--------+------+------+------+-------+
| AST_08   | 2 | Surface     | on-    | deg  | 1-4  | 0.3  | 90    |
|          |   | kinetic     | demand | rees | K    | K    |       |
|          |   | temperature |        | K    |      |      |       |
+----------+---+-------------+--------+------+------+------+-------+
| AST13POL | 2 | Polar       | on-    | none | 3%   | 3%   | 15,   |
|          |   | surface and | demand |      |      |      | 30,   |
|          |   | cloud       |        |      |      |      | 90    |
|          |   | cla         |        |      |      |      |       |
|          |   | ssification |        |      |      |      |       |
+----------+---+-------------+--------+------+------+------+-------+
| AST14DEM | 3 | Digital     | on-    | m    | \>=  | \>=  | 30    |
|          |   | elevation   | demand |      | 7 m  | 10 m |       |
|          |   | model (DEM) |        |      |      |      |       |
+----------+---+-------------+--------+------+------+------+-------+

# 

# 

[]{#__RefHeading___Toc21153355 .anchor}Table 3: ASTER Higher-Level
Standard Data Products.

# 5.0 ASTER Radiometry

The ASTER Level-1B data are offered in terms of scaled radiance. To
convert from DN to radiance at the sensor, the unit conversion
coefficients (defined as radiance per 1 DN) are used. Radiance (spectral
radiance) is expressed in unit of W/(m^2^\*sr\*µm). The relation between
DN values and radiances is shown below:

(i) a DN value of zero is allocated to dummy pixels

(ii) a DN value of 1 is allocated to zero radiance

(iii) a DN value of 254 is allocated to the maximum radiance for VNIR
      and SWIR bands

(iv) a DN value of 4094 is allocated to the maximum radiance for TIR
     bands

(v) a DN value of 255 is allocated to saturated pixels for VNIR and SWIR
    bands

(vi) a DN value of 4095 is allocated to saturated pixels for TIR bands

The maximum radiances depend on both the spectral bands and the gain
settings and are shown in Table 4.

+-------------+-------------+-------------+-------------+-------------+
| Band No.    | Maximum     |             |             |             |
|             | radiance    |             |             |             |
|             | (W/(m^      |             |             |             |
|             | 2^\*sr\*µm) |             |             |             |
+-------------+-------------+-------------+-------------+-------------+
|             | High gain   | Normal Gain | Low Gain 1  | Low gain 2  |
+-------------+-------------+-------------+-------------+-------------+
| 1           | 170.8       | 427         | 569         | N/A         |
|             |             |             |             |             |
| 2           | 179.0       | 358         | 477         |             |
|             |             |             |             |             |
| 3N          | 106.8       | 218         | 290         |             |
|             |             |             |             |             |
| 3B          | 106.8       | 218         | 290         |             |
+-------------+-------------+-------------+-------------+-------------+
| 4           | 27.5        | 55.0        | 73.3        | 73.3        |
|             |             |             |             |             |
| 5           | 8.8         | 17.6        | 23.4        | 103.5       |
|             |             |             |             |             |
| 6           | 7.9         | 15.8        | 21.0        | 98.7        |
|             |             |             |             |             |
| 7           | 7.55        | 15.1        | 20.1        | 83.8        |
|             |             |             |             |             |
| 8           | 5.27        | 10.55       | 14.06       | 62.0        |
|             |             |             |             |             |
| 9           | 4.02        | 8.04        | 10.72       | 67.0        |
+-------------+-------------+-------------+-------------+-------------+
| 10          | N/A         | 28.17       | N/A         | N/A         |
|             |             |             |             |             |
| 11          |             | 27.75       |             |             |
|             |             |             |             |             |
| 12          |             | 26.97       |             |             |
|             |             |             |             |             |
| 13          |             | 23.30       |             |             |
|             |             |             |             |             |
| 14          |             | 21.38       |             |             |
+-------------+-------------+-------------+-------------+-------------+

[]{#__RefHeading___Toc21153356 .anchor}Table 4: Maximum Radiance Values
for all ASTER Bands and all Gains.

The radiance can be obtained from DN values as follows:

Radiance = (DN value -- 1) x Unit conversion coefficient

Table 5 shows the unit conversion coefficients of each band

+-------------+-------------+-------------+-------------+-------------+
| Band No.    | Coefficient |             |             |             |
|             | (W/(m^2^\*  |             |             |             |
|             | sr\*µm)/DN) |             |             |             |
+-------------+-------------+-------------+-------------+-------------+
|             | High gain   | Normal Gain | Low Gain 1  | Low gain 2  |
+-------------+-------------+-------------+-------------+-------------+
| 1           | 0.676       | 1.688       | 2.25        | N/A         |
|             |             |             |             |             |
| 2           | 0.708       | 1.415       | 1.89        |             |
|             |             |             |             |             |
| 3N          | 0.423       | 0.862       | 1.15        |             |
|             |             |             |             |             |
| 3B          | 0.423       | 0.862       | 1.15        |             |
+-------------+-------------+-------------+-------------+-------------+
| 4           | 0.1087      | 0.2174      | 0.290       | 0.290       |
|             |             |             |             |             |
| 5           | 0.0348      | 0.0696      | 0.0925      | 0.409       |
|             |             |             |             |             |
| 6           | 0.0313      | 0.0625      | 0.0830      | 0.390       |
|             |             |             |             |             |
| 7           | 0.0299      | 0.0597      | 0.0795      | 0.332       |
|             |             |             |             |             |
| 8           | 0.0209      | 0.0417      | 0.0556      | 0.245       |
|             |             |             |             |             |
| 9           | 0.0159      | 0.0318      | 0.0424      | 0.265       |
+-------------+-------------+-------------+-------------+-------------+
| 10          | N/A         | 6.822 x     | N/A         | N/A         |
|             |             | 10^-3^      |             |             |
| 11          |             |             |             |             |
|             |             | 6.780 x     |             |             |
| 12          |             | 10^-3^      |             |             |
|             |             |             |             |             |
| 13          |             | 6.590 x     |             |             |
|             |             | 10^-3^      |             |             |
| 14          |             |             |             |             |
|             |             | 5.693 x     |             |             |
|             |             | 10^-3^      |             |             |
|             |             |             |             |             |
|             |             | 5.225 x     |             |             |
|             |             | 10^-3^      |             |             |
+-------------+-------------+-------------+-------------+-------------+

[]{#__RefHeading___Toc21153357 .anchor}Table 5: Calculated Unit
Conversion Coefficients.

(Note: These values are given in the telescope-specific metadata -- see
Appendix I)

# 6.0 ASTER Geometry

ASTER's geometric system correction primarily involves the rotation and
the coordinate transformation of the line of sight vectors of the
detectors to the coordinate system of the Earth.

This is done as part of ASTER Level-1 processing at GDS using
engineering data from the instrument (called supplementary data) and
similar data from the spacecraft platform (called ancillary data). The
geometric correction of ASTER data has evolved through elaborate
processes of both pre-flight and post-launch calibration.

#### Pre-Flight Calibration

This is an off-line process to generate geometric parameters such as
Line of Sight (LOS) vectors of the detectors and pointing axes
information evaluated toward the Navigation Base Reference (NBR) of the
spacecraft deemed to reflect on the instrument accuracy & stability.
These data are stored in the geometric system correction database.

#### Post-Launch Calibration

Following launch of ASTER, these parameters are being corrected through
validation using Ground Control Points (GCPs) and inter-band image
matching techniques. Geometric system correction in the post-launch
phase entails the following processes:

-   Pointing correction

-   Coordinate transformation from spacecraft coordinates to the orbital
    coordinates

-   Coordinate transformation from orbital coordinates to the earth's
    inertial coordinates

-   Coordinate transformation from earth's inertial coordinates to
    Greenwich coordinates

-   Improving Band-to-Band registration accuracy through image-matching
    involves 2 processes:

    -   SWIR parallax correction

    -   Inter-telescope registration process

Based on current knowledge, the geometric performance parameters of
ASTER are summarized in Table 6.

  -------------------------- ------------------- ------------------------
  **Parameter**                                  **Version 2.1 Geometric
                                                 Db**

  Intra-Telescope            VNIR                \< 0.1 pixel
  Registration                                   

                             SWIR                \< 0.1 pixel

                             TIR                 \< 0.1 pixel

  Inter-Telescope            SWIR/VNIR           \< 0.2 pixel
  Registration                                   

                             TIR/VNIR            \< 0.2 pixel

  Stereo Pair System Error   Band 3B/3N          \< 10 m

  Pixel Geolocation          Relative            \< 15 m
  Knowledge\*                                    

                             Absolute            \< 50 m
  -------------------------- ------------------- ------------------------

\* Not Terrain-Corrected

[]{#__RefHeading___Toc21153358 .anchor}Table 6: Geometric Performance of
ASTER Level-1 Data (Based on V2.1 of the Geometric Correction Database).

**Geometric System Correction Database**

There is an evolving geometric system correction database that is
maintained at GDS. This database provides the geometric correction
coefficients that are applied to produce the Level-1B data. The
geometric correction reference in an ASTER Level-1 data set is provided
in both the HDF and ECS metadata. In the HDF file, this is present as
the GeometricDBVersion value in the ProductMetadata.0 block. In the ECS
.met file, the same attribute name and value are present as part of the
granule-level metadata. The evolving versions of the GeometricDBVersions
till date have been 01.00, 01.01, 01.02, 02.00 and 02.05.

# 7.0 Data Acquisition Strategy

## 

ASTER was not designed to continuously acquire data, and hence each
day's data acquisitions must be scheduled and prioritized. The ASTER
Science Team has developed a data acquisition strategy to make use of
the available resources. Acquisition requests are divided into three
categories: local observations, regional monitoring, and global map.

#### Local Observations

Local Observations are made in response to data acquisition requests
from authorized ASTER Users. Local Observations might include, for
example, scenes for analyzing land use, surface energy balance, or local
geologic features.

One subset of Local Observations consists of images of such ephemeral
events as volcanoes, floods, or fires. Requests for \"urgent
observations\" of such phenomena must be fulfilled in short time periods
(of a few days). These requests receive special handling.

#### Regional Monitoring Data 

Regional data sets contain the data necessary for analysis of a large
region (often many regions scattered around the Earth) or a region
requiring multi-temporal analysis. A \"Local Observation\" data set and
a \"Regional Monitoring\" data set are distinguished by the amount of
viewing resources required to satisfy the request, where smaller
requirements are defined as Local Observations and larger requirements
are defined as Regional Monitoring. The ASTER Science Team has already
selected a number of Regional Monitoring tasks. Among the most
significant are three that involve repetitive imaging of a class of
surface targets:

1.  The world\'s mountain glaciers,

2.  The world\'s active and dormant volcanoes, and

3.  The Long-Term Ecological Research (LTER) field sites.

#### Global Map 

The Global data set will be used by investigators of every discipline to
support their research. The high spatial resolution of the ASTER Global
Map will complement lower resolution data acquired more frequently by
other EOS instruments. This data set will include images of the entire
Earth's land surface, in all ASTER spectral bands and stereo.

Each region of the Earth has been prioritized by the ASTER Science Team
for observation as part of the Global Map. Currently the following
characteristics have been identified for images in the Global Map data
set:

-   One-time coverage

-   High sun angle

-   Optimum gain for the local land surface

-   Minimum snow and ice cover

-   Minimum vegetation cover, and

-   No more than 20% cloud cover (perhaps more for special sub-regions).

#### Allocation of Science Data

At the present time, it is expected that approximately 25% of ASTER
resources will be allocated to Local Observations, 50% to Regional
Monitoring, and 25% to the Global Map.

Global Map data has been further sub-divided among high priority areas
which are currently allocated 25%, medium priority areas which are
currently allocated 50%, and low priority areas which are currently
allocated 25%.

Regional Monitoring data sets and the Global Map will be acquired by
ASTER in response to acquisition requests submitted by the ASTER Science
Team acting on behalf of the science community. These Science Team
Acquisition Requests (STARs) are submitted directly to the ASTER Ground
Data System in Japan. Under limited circumstances, STARs for Local
Observations may also be submitted by the Science Team.

STARs for Regional Monitoring data are submitted by the ASTER Science
Team only after a proposal for the Regional Monitoring task has been
submitted and accepted. These \"STAR Proposals\" will be evaluated by
ASTER\'s science working groups before being formally submitted to the
Science Team.

An already-authorized ASTER User, who wants ASTER to acquire far more
data than he or she is allocated, may submit a STAR Proposal to the
Science Team.  Please note that the process for evaluating STAR
proposals is cumbersome and time-consuming.  Far fewer STAR Proposals
will be approved than ASTER User Authorization Proposals.

## 

# 8.0 ASTER Data Search and Order of Archived Data and Products

## 

The EOSDIS at the LP-DAAC archives and distributes ASTER Level-1A,
Level-1B, Decorrelation Stretch and DEM products. All other products are
produced on demand. The steps to access the EOS Data Gateway (EDG) web
site for archived products, and a tutorial on how to use it are
described below. See the following sections for higher level, on-demand
products. There is also another on-line tutorial that is available on
the EOS Data Gateway for beginning users:
<http://edcdaac.usgs.gov/tutorial/>

##### 1: Begin Search and Order Session

> Log on to the EOS Data Gateway to begin your search and order session.
> (<http://edcimswww.cr.usgs.gov/pub/imswelcome/>)

New users may click the \'Enter as guest\' link (Figure 9). If you are a
registered user, you may continue to use your account by clicking the
\'Enter as a registered user\' link and login as before. Should you wish
to register, click the \'Become a registered user\' link and follow the
registration prompts. This will take you to the \'Primary Data Search\'
screen. Registering will allow the system to remember your shipping
information. It is absolutely not necessary to register in order to
search and order.

![](media/image9.png){width="6.488888888888889in"
height="5.315972222222222in"}

[]{#__RefHeading___Toc21151261 .anchor}**Figure 9: Opening Page of the
EOS Data Gateway.**

**2: Primary Data Search - Choose Search Keywords**

> Highlight \'DATA SET\' in the \'Method 2:\' scroll window and click
> the \'SELECT -\>\' button (Figure 10). This will take you to the
> \'Keyword Selection: Data Set\' screen.

A list of data sets available are displayed in the \'Data Set list 1\'
scroll window.

Highlight the data set or sets you wish to search and click \'OK!\'.

![](media/image10.png){width="6.495138888888889in"
height="4.6402777777777775in"}

[]{#__RefHeading___Toc21151262 .anchor}**Figure 10: Choosing Search
Keyword "DATASET."**

There are two ASTER level-1 collections available from the LP-DAAC.
Therefore, you should select "ASTER L1A reconstructed unprocessed
instrument data V002 & V003"; and "ASTER L1B registered radiance at the
sensor V002 & V003". The other choice of expedited data are scenes that
were processed in expedited mode. These are temporarily stored for only
30 days in the archive, and are subsequently replaced by the standard
processed product that arrives from GDS, Japan.

**3: Primary Data Search - Choose Search Area**

The next step involves delineating your area-of-interest (Figure 11).
The default method to define your area of search is the \'Type in
Lat/Lon Range\' method. If you know the Latitude and Longitude, enter
the coordinates.

To define the geographic area on a map, click the radio button next to
your map of choice and delineate the region by clicking on the map.

![](media/image11.png){width="6.490277777777778in"
height="2.959722222222222in"}

[]{#__RefHeading___Toc21151263 .anchor}**Figure 11: Choosing Search
Area.**

**4: (optional): Primary Data Search - Choose a Time Range (not
required)**

You may now enter your Date or Time Range. You may use either a Standard
Date Range or Julian Date Range (Figure 12).

Be sure to enter the date and time by following the format provided.

To search seasonally, select the radio button next to \'Annually
Repeating\'.

![](media/image12.png){width="6.490972222222222in"
height="3.0506944444444444in"}

[]{#__RefHeading___Toc21151264 .anchor}**Figure 12: Choosing a Date/Time
Range.**

**5: Primary Data Search - Start Search!**

You are now ready to execute your search. Click the \'Start Search!\'
button to begin.

**6: Results: Data Set Listing**

If you ran a search on more than one data set (such as Level-1A and
Level-1B), the results will be displayed, along with the number of
granules returned (Figure 13). To view a list of the granules, click on
the box in the \'Select\' column for one or more data sets and click on
\'List data granules\'.

![](media/image13.png){width="6.489583333333333in"
height="3.540277777777778in"}

[]{#__RefHeading___Toc21151265 .anchor}**Figure 13: Data Set Listing
Result.**

**7: Results: Granule: Listing**

When your results are returned, you will see a table of granules (Figure
14). A granule is the smallest aggregation of data, which is
independently managed. For ASTER this is a single scene.

To view a browse image of the granule, click the \'View Image\' button.
Be sure to view the browse for each granule you intend to order, if it
is available. The browse will give a good representation of data
anomalies or cloud cover in the granule. For detailed information on a
particular granule, click on \'Granule attributes\'.

To order the data, click the box in the \'Select\' column of the desired
granule. Next, click the \'Add to cart\' button:

![](media/image14.png){width="6.497916666666667in"
height="4.347222222222222in"}

[]{#__RefHeading___Toc21151266 .anchor}**Figure 14: Data Granules
Listing Result.**

**8: Shopping Cart - Step 1: Choose Ordering Options**

Click the \'Order options\' button and choose your output media (Figure
15). You will have the option to either apply the output media and
processing parameters to all granules or to just one granule. Click the
\'OK! Accept my choice & return to the shopping cart\' button to
proceed.

You will want to repeat this for each remaining granule if you are
ordering data from more than one data set, or if you did not apply the
ordering options to all granules. When finished with your processing
parameters, click the \'Go to Step 2: Order Form\' button.

![](media/image15.png){width="5.83125in" height="3.027083333333333in"}

[]{#__RefHeading___Toc21151267 .anchor}**Figure 15: Choosing Ordering
Options.**

**9: Shopping Cart: - Step 2: Order Form**

Enter your appropriate address in the contact, shipping and billing
address fields (Figure 16). You are required to supply information in
the red fields. If you are a registered user, this page will be
completed for you. When finished, choose your affiliation and click the
\'Go to Step 3: Review Order Summary\' button.

![](media/image16.png){width="6.495833333333334in"
height="5.961111111111111in"}

[]{#__RefHeading___Toc21151268 .anchor}**Figure 16: Order Form.**

**10: Shopping Cart: - Step 3: Order Summary**

Review the order summary. If all of the information is correct, click
the \'Go to Step 4: Submit Order!\' button.

**11: Order Submitted**

Your order has now been submitted. You will receive automatic email(s)
confirming the receipt of your order. The EDC email will summarize your
order, cost, and provide information on Modes of Payment. Payment must
be received before order processing can begin. Please contact LP-DAAC to
arrange payment and include your order number on all modes of payment.

# 9.0 ASTER Higher-Level Data Products Ordering Mechanism

All the higher-level (Level-2) ASTER geophysical products (except
absolute and relative DEMs) are produced using an ASTER-L1B as its
input. The routinely-produced on-demand products including their Long
and Short names are:

#### Routinely-Produced Level-2 Products

> 1\. ASTER L2 Decorrelation Stretch (VNIR)
> [**AST_06V**](http://edcdaac.usgs.gov/aster/ast_06v.html)

2\. ASTER L2 Decorrelation Stretch (SWIR)
[**AST_06S**](http://edcdaac.usgs.gov/aster/ast_06s.html)

3\. ASTER L2 Decorrelation Stretch (TIR)
[**AST_06T**](http://edcdaac.usgs.gov/aster/ast_06t.html)

You can search, browse and order these products from the **[EOS Data
G](http://edcimswww.cr.usgs.gov/pub/imswelcome/)ateway.**

#### On-Demand Level-2 Products

1.  ASTER On-Demand L2 Decorrelation Stretch (VNIR)
    [**AST_06VD**](http://edcdaac.usgs.gov/aster/ast_06vd.html)

2.  ASTER On-Demand L2 Decorrelation Stretch (SWIR)
    [**AST_06SD**](http://edcdaac.usgs.gov/aster/ast_06sd.html)

3.  ASTER On-Demand L2 Decorrelation Stretch (TIR)
    [**AST_06TD**](http://edcdaac.usgs.gov/aster/ast_06td.html)

4.  ASTER On-Demand L2 Brightness Temperature at the Sensor
    [**AST_04**](http://edcdaac.usgs.gov/aster/ast_04.html)

5.  ASTER On-Demand L2 Emissivity
    [**AST_05**](http://edcdaac.usgs.gov/aster/ast_05.html)

6.  ASTER On-Demand L2 Surface Reflectance (VNIR & SWIR)
    [**AST_07**](http://edcdaac.usgs.gov/aster/ast_07vd.html)

7.  ASTER On-Demand L2 Surface Kinetic Temperature
    [**AST_08**](http://edcdaac.usgs.gov/aster/ast_08.html)

8.  ASTER On-Demand L2 Surface Radiance (VNIR & SWIR)
    [**AST_09**](http://edcdaac.usgs.gov/aster/ast_09vd.html)

9.  ASTER On-Demand L2 Surface Radiance (TIR)
    [**AST_09T**](http://edcdaac.usgs.gov/aster/ast_09td.html)

10. ASTER On-Demand L2 Polar Surface & Cloud Classification
    [**AST13POL**](http://edcdaac.usgs.gov/aster/ast_13pol.html)

You can order these on-demand products using the [**ASTER On-Demand
Gateway**](http://edcdaac.usgs.gov/asterondemand/index.html). You will
be led there through a link (Order higher-level product) on the EDG's
Level-1B granules search page next to the granule-listing column. This
site allows you to select a product (such as reflectance), and either
accept the default parameters, or customize by selecting appropriate
alternative options. There is a brief on-line tutorial for beginning
users on how to place an order for ASTER on-demand products:
<http://edcdaac.usgs.gov/asterondemand/aod_tutorial.html>

#### On-Demand Level-3 Products 

1.  ASTER Digital Elevation Model (DEM)
    [**AST14DEM**](http://edcdaac.usgs.gov/aster/ast14dem.html)

The only Level-3 products generated from an ASTER L1A data set are:

-   An Absolute Digital Elevation Model (DEM). This product requires you
    to submit Ground Control Points (GCPs) for your area of interest. To
    accomplish this, you must first order the L1A data, locate your
    GCP's on both the 3N and 3B images, and supply these pixel
    coordinates along with the GCP coordinate locations.

-   A Relative DEM. This does not require a requestor to provide GCPs.
    You only have to copy and paste the L1A Granule ID in the
    appropriate place on the [on-demand product order
    page](http://e0ins02u.ecs.nasa.gov:10800/), and submit the order.

You can order on-demand DEMs using the [**ASTER On-Demand
Gateway**](http://edcdaac.usgs.gov/asterondemand/index.html). You will
be led there through a link (Order custom DEM.) on the EDG's Level-1A
granules search page next to the granule-listing column. Follow the
instructions there to order a relative or absolute DEM.

#### Archived Level-3 DEM Products

You can search for and order archived DEMs from the [**EOS Data
Gateway**](http://edcimswww.cr.usgs.gov/pub/imswelcome/) by selecting
the ASTER Digital Elevation Model data set.

# 10.0 Data Acquisition Requests

## 

Data Acquisition Requests (DARs) are user requests to have ASTER acquire
new data over a particular site at specified times. If the desired ASTER
observations have not yet been acquired or even requested, a requestor
can become an authorized ASTER User, and can submit a data acquisition
request (DAR) via the DAR Tool.  To register as an authorized ASTER
User, use the link below which will take you to the web site for
registering, and explains the procedure:

<http://asterweb.jpl.nasa.gov/gettingdata/authorization/default.htm>

Once you are registered, you can go to the on-line ASTER DAR Tool web
site to enter your request:

<http://e0ins02u.ecs.nasa.gov:10400/>

Be sure to read the "Getting Started" information for help on using the
tool, and getting any plug-ins if necessary.

# 

# 

# 

# 11.0 ASTER Applications

## 11.1 Cuprite, Nevada

The Cuprite Mining District is located in west-central Nevada, and is
one of a number of alteration centers explored for precious metals.
Cambrian sedimentary rocks and Cenozoic volcanic rocks were
hydrothermally altered by acid-sulfate solutions at shallow depth in the
Miocene, forming three mappable alteration assemblages: 1) silicified
rocks containing quartz and minor alunite and kaolinite; 2) opalized
rocks containing opal, alunite and kaolinite; 3) argillized rocks
containing kaolinite and hematite. A general picture of the alteration
is shown in Figure 17, combining bands 4,6 and 8 in RGB and processed to
increase the color saturation.

![](media/image17.png){width="3.870138888888889in"
height="4.459722222222222in"}

[]{#__RefHeading___Toc21151269 .anchor}**Figure 17: Cuprite Mining
District, displayed with SWIR bands 4-6-8 as RGB composite.**

(Note: Area covered is 15 x 20 km)

Red-pink areas mark mostly opalized rocks with kaolinite and/or alunite;
the white area is Stonewall Playa; green areas are limestones, and
blue-gray areas are unaltered volcanics.

Data from the SWIR region were processed to surface reflectance by
LP-DAAC and image spectra were examined for known targets at Cuprite.
Evidence of SWIR crosstalk was apparent, making the data difficult to
use for spectral analysis using direct comparisons with library or field
spectra. To reduce the cross-talk artifacts, a spectrum of Stonewall
Playa was used as a bright target, resampled to the ASTER wavelengths,
and divided into the SWIR reflectance data. Library spectra were
compiled for minerals known to occur at Cuprite; they were then
resampled to ASTER SWIR wavelengths. These spectra were used with a
supervised classification algorithm, Spectral Angle Mapper, to map
similar spectral occurrences in the SWIR data. The result of this
classification is shown in Figure 18.

![](media/image18.jpeg){width="5.995138888888889in"
height="6.002777777777778in"}

[]{#__RefHeading___Toc21151270 .anchor}**Figure 18: Spectral Angle
Mapper Classification of Cuprite SWIR data.**

(Note: blue = kaolinite; red = alunite; light green = calcite; dark
green = alunite+kaolinite;

cyan = montmorillonite; purple = unaltered; yellow = silica or dickite)

When this map was compared with more detailed mineral classification
produced from AVIRIS data, the correspondence is excellent. The
resampled library spectra are shown in Figure 19 compared with ASTER
image spectra extracted from 3x3 pixel areas.

![](media/image19.png){width="5.996527777777778in"
height="3.115972222222222in"}

[]{#__RefHeading___Toc21151271 .anchor}**Figure 19: ASTER image spectra
(left) and library spectra (right) for minerals**

[]{#__RefHeading___Toc21151272 .anchor}**mapped at Cuprite.**

## 11.2 Lake Tahoe

## 

### 11.2.1 Objective

The objective of the Lake Tahoe CA/NV case study is to illustrate the
use of ASTER data for water-related studies.

### 11.2.2 Introduction

Lake Tahoe is a large lake situated in a granite graben near the crest
of the Sierra Nevada Mountains on the California - Nevada border, at 39°
N, 120° W. The lake level is approximately 1898 m above MSL. The lake is
roughly oval in shape with a N-S major axis (33 km long, 18 km wide),
and has a surface area of 500 km^2^ (Figure 20).

![](media/image20.png){width="2.6534722222222222in" height="4.5in"}

[]{#__RefHeading___Toc21151273 .anchor}**Figure 20: Outline map of Lake
Tahoe, CA/NV.**

The land portion of the watershed has an area of 800 km^2^. Lake Tahoe
is the 11^th^ deepest lake in the world, with an average depth of 330 m,
maximum depth of 499 m, and a total volume of 156 km^3^. The surface
layer of Lake Tahoe deepens during the fall and winter. Complete
vertical mixing only occurs every few years. Due to its large thermal
mass, Lake Tahoe does not freeze in winter. There are approximately 63
streams flowing into the lake and only one river flowing out of the
lake. Lake Tahoe is renowned for its high water clarity. However, the
water clarity has been steadily declining from a maximum secchi depth of
35 m in the sixties to its current value of \~20 m. Research by
University of California (UC), Davis has identified that the decline is
in part due to increased algal growth facilitated by an increase in the
amount of nitrogen and phosphorus entering the lake and, in part, due to
accumulation of small suspended inorganic particulates derived from
accelerated basin-wide erosion and atmospheric inputs.

### 11.2.3 Field Measurements

In order to validate the data from the MODIS and ASTER instruments, the
Jet Propulsion Laboratory (JPL) and UC Davis (UCD) are currently
maintaining four surface sampling stations on Lake Tahoe (Hook et al.
2002). The four stations (rafts/buoys) are referred to as TR1, TR2, TR3
and TR4 (Figure 20). Each raft/buoy has a single custom-built
self-calibrating radiometer for measuring the skin temperature and
several bulk temperature sensors. The radiometer is mounted on a pole
approximately 1m above the surface of the water that extends beyond the
raft (Figure 21).

![](media/image21.wmf){width="4.995833333333334in"
height="3.7472222222222222in"}

[]{#__RefHeading___Toc21151274 .anchor}**Figure 21: Raft Measurements.**

The radiometer is orientated such that it measures the skin temperature
of the water directly beneath it. The radiometer is contained in a
single box that is 13 cm wide, 43 cm long, and 23 cm high (Figure 21).
The sensor used in the radiometer is a thermopile detector with a
germanium lens embedded in a copper thermal reservoir. The sensor passes
radiation with wavelengths between 7.8 and 13.6 µm. The unit is
completely self-contained and has an on-board computer and memory and
operates autonomously. The unit can store data on-board for later
download or automatically transmit data to an external data logger. The
unit can be powered for short periods (several hours) with its internal
battery, or can be powered for longer periods with external power. In
this study the radiometer is powered externally and data are transferred
to an external data logger. The radiometer uses a cone blackbody in a
near-nulling mode for calibration and has an accuracy of ± 0.1 K. The
accuracy of the radiometers was confirmed in a recent cross- comparison
experiment with several other highly accurate radiometers in both a sea
trial and in laboratory comparisons. It should be noted the current
design of both the radiometers do not include a sky view and therefore
the correction for the reflected sky radiation is made using a radiative
transfer model (MODTRAN).

The bulk water temperature is measured with several temperature sensors
mounted on a float tethered behind the raft/buoy (Figure 21). The float
was built in the shape of a letter H and is 203 cm long and 70 cm wide.
At the end of each point of the letter H is a short leg at right angles
to the float and the temperature sensors are attached to the end of the
leg approximately 2cm beneath the surface. Multiple temperature sensors
are used to enable cross-verification and each float has up to 12
temperature sensors all at the same depth. The temperature sensors used
include the Optic Stowaway and Hobo Pro Temperature Loggers available
from Onset Corporation (<http://www.onsetcomp.com/>) and a TempLine
system available from Apprise Technologies
(<http://www.apprisetech.com/>). The Optic Stowaway Temperature Loggers
include both the sensor and data logger in a single sealed unit with a
manufacturer-specified *maximum* error of ± 0.25 °C. The Hobo Pro
Temp/External Temperature logger has an external temperature sensor at
the end of a short cable that returns data to a logger and a
manufacturer-specified *maximum* error of ± 0.2 °C. The TempLine system
consists of 4 temperature sensors embedded at different positions along
a cable that is attached to a data logger. The TempLine system has a
manufacturer specified error of ± 0.1° C. Note all sensors are placed at
the same depth ensuring both redundancy and cross-verification. The
calibration accuracy of the Onset temperature sensors was checked using
a NIST traceable water bath. NIST traceability was provided by use of a
NIST-certified reference thermometer. In all cases the sensors were
found to meet the manufacturer specified *typical* error of ± 0.12 C.

Data collected by the external data logger (radiometer and TempLine
system) can be downloaded automatically via cellular telephone.
Currently data from the external data logger are downloaded daily via
cellular telephone modem to JPL allowing near real-time monitoring. A
full set of measurements is made every 2 minutes. However, the units
attached to the external data logger can remotely be re-programmed if a
different sampling interval is desired. The initial rafts are currently
being replaced by buoys as pictured above which also include a
meteorological station providing wind speed, wind direction, air
temperature, relative humidity and net radiation (Figure 21).

Additional UCD atmospheric deposition collectors are located on TR2 and
TR3.

Both JPL and UCD maintain additional equipment at the US Coast Guard
station that provides atmospheric information (Figure 22). This includes
a full meteorological station (wind speed, wind direction, air
temperature, relative humidity), full radiation station (long and
shortwave radiation up and down), a shadow band radiometer and an all
sky camera. The shadow band radiometer provides information on total
water vapor and aerosol optical depth.

![](media/image22.jpeg){width="3.902083333333333in"
height="4.7444444444444445in"}

[]{#__RefHeading___Toc21151275 .anchor}**Figure 22: Field measurements
at the US Coast Guard.**

Measurements of algal growth rate using 14 C, nutrients (N, P),
chlorophyll, phytoplankton, zooplankton, light, temperature and secchi
disk transparency are also made tri-monthly at the Index station (Figure
10) and monthly samples for all constituents except algal growth and
light are made at the Mid-lake station (Figure 20). Many samples are
taken annually around the Tahoe Basin to examine stream chemistry and
snow and atmospheric deposition constituents.

### 11.2.4 Using ASTER to measure water clarity

Currently the decline in water clarity at Lake Tahoe is measured using a
secchi disk -- a white disk that is lowered into the water until it is
no longer visible. The UC Davis Tahoe Research Group have been making
secchi disk measurements since the mid 60's at two locations on the lake
(Midlake and Index -- see Figure 20). Such measurements have been used
to monitor the decline in clarity from a maximum of 35 m when
measurements began, to the current low of 20 m. These measurements are
crucial for monitoring temporal changes in clarity but provide little
information on spatial variations in clarity across and around the lake.
Knowledge of spatial variations in clarity could prove useful in
identifying areas of high nutrient or sediment input into the lake.

Examination of a color infrared composite image derived from ASTER for
Lake Tahoe (Figure 23) indicates that due to the high clarity, the
bottom of the lake is visible for some distance from the shore.

![](media/image23.png){width="5.875in" height="3.5944444444444446in"}

[]{#__RefHeading___Toc21151276 .anchor}**Figure 23: Color Infrared
Composite of ASTER bands 3, 2, 1 as R, G, B respectively.**

Red areas indicate vegetation, white areas are snow

Places where the bottom of the lake is visible appear dark blue, for
example the southern margin of the lake. The bottom can be seen for the
greatest distance from the shore in ASTER band 1 and this band can be
color-coded to show variations in the intensity of the bottom
reflectance (Figure 24).

![](media/image24.png){width="6.0in" height="3.6708333333333334in"}

[]{#__RefHeading___Toc21151277 .anchor}**Figure 24: ASTER band 1
(0.52-0.60 µm) color-coded to show variations in the intensity of the
near-shore bottom reflectance.**

In this image, areas where the bottom is visible are colored red and
green (greater bottom reflectance is shown in red). Where the lake is
blue the bottom cannot be seen. The depth to which the bottom is visible
varies depending on the clarity of the water. In order to investigate
this further, an accurate bathymetric map was registered to the ASTER
data. The accuracy of the bathymetric map is \~0.5 % of the water depth.
The bathymetric map is shown in Figure 25 color- coded with greater
depths shown in blue and shallower depths shown in red.

![](media/image25.png){width="6.0in" height="3.66875in"}

[]{#__RefHeading___Toc21151278 .anchor}**Figure 25: Bathymetric map of
Lake Tahoe CA/NV.**

Once the bathymetric map is registered to the ASTER image, the depth at
which the bottom is no longer visible can be determined and can be used
to produce a near shore clarity map shown in Figure 26.

![](media/image26.png){width="6.0in" height="3.66875in"}

[]{#__RefHeading___Toc21151279 .anchor}**Figure 26: Near-shore clarity
map derived from ASTER data and a bathymetric map.**

Examination of Figure 26 indicates some places where the lake is
exceptionally clear and other areas where it is less so. For example the
areas in the southwest and northeast are particularly clear whereas the
area in the southeast is less clear. There is little sediment input in
the southwest and northeast whereas the Upper Truckee River flows in
from the south and strongly affects the southeast. Further work is
underway to validate the accuracy of this map and look for seasonal
changes in clarity as well as changes over time.

### 11.2.5 Using ASTER to Measure Circulation

In addition to making measurements in the reflected infrared, the ASTER
instrument also measures the radiation emitted in the thermal infrared
part of the spectrum. These data can be used to measure the surface
temperature and produce maps of lake surface temperature. Such maps are
valuable in understanding a variety of lake processes, such as
wind-induced upwelling events and surface water transport patterns.

In order to derive the surface temperature it is necessary to correct
the data for atmospheric effects. Two approaches are commonly used to
correct the data. The most common approach is a split-window algorithm.
In the split-window algorithm the at-sensor radiances are regressed
against simultaneous ground measurements to derive a set of coefficients
that can then be used to correct other datasets without ground
measurements. Alternatively a physics-based approach can be used which
couples a surface temperature and emissivity model with a radiative
transfer model. The ASTER team has developed a physics based approach
for extracting temperature and emissivity and a user can order either a
surface temperature (AST_08) or surface emissivity (AST_05) product.

The image below (Figure 27) shows an at-sensor brightness temperature
image for Lake Tahoe from ASTER thermal data acquired at night on June
3^rd^ 2001. Examination of the image indicates a strong cold plume of
water originating in the west, traveling across the lake to the east
shore, then spreading north and south. This cold plume is the result of
a wind-induced upwelling event in the west. The upwelling is induced by
strong, persistent winds from the southwest which move the surface water
to the east allowing the deep cold water in the west to upwell. The cold
water is nutrient- rich compared with the warmer surface waters which
have been depleted of nutrients. The temperature images from ASTER can
be used to map these nutrient pathways which help explain the
distribution of organic matter and fine sediments around the lake.

![](media/image27.png){width="5.5055555555555555in" height="7.5in"}

[]{#__RefHeading___Toc21151280 .anchor}**Figure 27: ASTER Band-13
Brightness Temperature Image of Lake Tahoe from Thermal Data Acquired
June 3, 2001.**

# 12.0 Geo-Referencing ASTER Level-1B Data

## 12.1 Introduction

All image processing software packages employ distinctive procedures for
projecting and geo-referencing image data. Some software packages have
incorporated specific import routines that geo-reference ASTER data on
ingest, however, most have not. The purpose of this document is not to
provide step-by-step instructions for loading ASTER Level-1B data into
any particular software package, but rather to outline the various
components necessary to geo-reference the data in most application
software. All this information is also applicable to the [Level-2
On-Demand Products](http://edcdaac.usgs.gov/asterondemand/index.html)
derived from ASTER Level-1B data except for the ASTER Digital Elevation
Model (DEM) product which is generated from an ASTER Level-1A data set.

## 12.2 Accessing ASTER Level-1B Metadata

The information needed to geo-register ASTER Level-1B data is located
within the "embedded" metadata (i.e., the metadata contained within the
header of the ASTER image data). To access that metadata you will need
software capable of reading HDF-EOS-formatted data. A list of public
domain software that handle HDF-EOS is available from
<http://edcdaac.usgs.gov/dataformat.html>. Note that the ".met" file
accompanying the ASTER data file does NOT contain all the required
information to geo-reference ASTER data, and therefore, we suggest
utilizing the embedded hdf metadata.

Depending on your software, any or all of the following information may
be necessary to geo-reference your ASTER Level-1B image (Table 7):

+--------+--------------+------------------+--------------------------+
| **Cate | **Name as    | **HDF-EOS        | **Description**          |
| gory** | Referenced   | Subcategory**    |                          |
|        | in the HDF   |                  |                          |
|        | Metadata     |                  |                          |
|        | File**       |                  |                          |
+--------+--------------+------------------+--------------------------+
| Scene  | SCEN         | p                | This denotes the         |
| Corner | EFOURCORNERS | roductmetadata.0 | coordinates of the       |
| Coord  |              |                  | upper-left, upper-right, |
| inates |              |                  | lower-left and           |
|        |              |                  | lower-right corners of   |
|        |              |                  | the scene                |
|        |              |                  |                          |
|        |              |                  | (latitude and longitude) |
|        |              |                  | where:                   |
|        |              |                  |                          |
|        |              |                  | lat: geodetic latitude   |
|        |              |                  |                          |
|        |              |                  | long: geodetic longitude |
|        |              |                  |                          |
|        |              |                  | Unit: Degrees            |
+--------+--------------+------------------+--------------------------+
| Map    | MAPPR        | coremetadata.0   | The name of the mapping  |
| Proj   | OJECTIONNAME |                  | method for the data. The |
| ection |              | p                | available map            |
|        | Or           | roductmetadata.v | projections are:         |
|        |              |                  |                          |
|        | MPMETHOD     | p                | Equi-Rectangular         |
|        | (band#)      | roductmetadata.s |                          |
|        |              |                  | Lambert Conformal Conic  |
|        |              | p                |                          |
|        |              | roductmetadata.t | Polar Stereographic      |
|        |              |                  |                          |
|        |              |                  | Space Oblique Mercator,  |
|        |              |                  | & Universal Transverse   |
|        |              |                  | Mercator                 |
|        |              |                  |                          |
|        |              |                  | Map Projection Method:   |
|        |              |                  |                          |
|        |              |                  | 'EQRECT', 'LAMCC', 'PS', |
|        |              |                  |                          |
|        |              |                  | 'SOM', or 'UTM',         |
+--------+--------------+------------------+--------------------------+
| Datum  | Not          | Not specified in | WGS84 (for all ASTER     |
|        | specified in | the metadata     | data processed at GDS)   |
|        | the metadata |                  |                          |
+--------+--------------+------------------+--------------------------+
| Zone   | UTMZONECODE  | p                | For VNIR, SWIR and TIR:  |
| (UTM)  | (band#)      | roductmetadata.v |                          |
|        |              |                  | Zone code for UTM        |
|        |              | p                | projection (when mapping |
|        |              | roductmetadata.s | without UTM: 0 fixed).   |
|        |              |                  | If southern zone is      |
|        |              | p                | intended, use negative   |
|        |              | roductmetadata.t | values                   |
+--------+--------------+------------------+--------------------------+
| Number | IMAGEDAT     | p                | VNIR: 4980 pixels x 4200 |
| of     | AINFORMATION | roductmetadata.v | lines (1 BPP)\*          |
| Pixels |              |                  |                          |
| and    |              | p                | VNIR (3B): 4980 pixels x |
| Lines  |              | roductmetadata.s | 4600 lines (1 BPP)       |
|        |              |                  |                          |
|        |              | p                | SWIR: 2490 pixels x 2100 |
|        |              | roductmetadata.t | lines (1 BPP)            |
|        |              |                  |                          |
|        |              |                  | TIR: 830 pixels x 700    |
|        |              |                  | lines (2 BPP)            |
|        |              |                  |                          |
|        |              |                  | \*BPP: Bytes Per Pixels  |
+--------+--------------+------------------+--------------------------+
| Ro     | MAPORIE      | p                | This denotes the angle   |
| tation | NTATIONANGLE | roductmetadata.0 | between the              |
| Angle  |              |                  | path-oriented image and  |
|        |              |                  | the map-oriented image   |
|        |              |                  | within the range -180.0  |
|        |              |                  | to 180.0                 |
|        |              |                  |                          |
|        |              |                  | Unit: Degrees            |
+--------+--------------+------------------+--------------------------+
| Cell   | SPATI        | p                | The nominal spatial      |
| Size   | ALRESOLUTION | roductmetadata.0 | resolution (Unit:        |
|        |              |                  | Meters):                 |
|        |              |                  |                          |
|        |              |                  | VNIR: 15, SWIR: 30, TIR: |
|        |              |                  | 90                       |
+--------+--------------+------------------+--------------------------+
| Resa   | RESMETHOD    | p                | Resampling Method:       |
| mpling | (band#)      | roductmetadata.v |                          |
| Method |              |                  | CC: Cubic Convolution,   |
|        |              | p                | BL: Bilinear             |
|        |              | roductmetadata.s |                          |
|        |              |                  | NN: Nearest Neighbor     |
|        |              | p                |                          |
|        |              | roductmetadata.t |                          |
+--------+--------------+------------------+--------------------------+

[]{#__RefHeading___Toc21153359 .anchor}Table 7: Specific Metadata
Attributes Required for Geo-Referencing ASTER Level-1B Data.

## 12.3 ASTER Level-1B Geo-Referencing Methodology

How one geo-registers an ASTER Level-1B image will vary depending on
what image processing software package one uses. The following is a
generic description of the process independent of any particular image
processing system. All the required attributes for geo-registering an
ASTER Level-1B image are available from the embedded metadata in the hdf
file and include:

-   SCENEFOURCORNERS

-   MAPORIENTATIONANGLE

-   PROCESSINGPARAMETERS (Projection information)

    -   MPMETHOD

    -   UTMZONECODE

    -   RESMETHOD

SCENEFOURCORNERS represent the geodetic latitude and longitude
coordinates (UPPERLEFT, UPPERRIGHT, LOWERLEFT, LOWERRIGHT) of the ASTER
Level-1B scene.

The MAPORIENTATIONANGLE denotes the angle of rotation between the
path-oriented image and the transformed map-projected coordinates.
Ranging from -180° to +180°, it provides the amount by which the ASTER
Level-1B image is rotated from True North.

The PROCESSINGPARAMETERS object group lists a number of attributes among
which MPMETHOD lists the projection used and UTMZONECODE provides the
zone information. The PROCESSINGPARAMETERS object group is numbered 1
through 14 for each of the ASTER bands (VNIR, SWIR and TIR). The
component objects within this group are also numbered likewise.

You will have to edit the header information for the chosen set of ASTER
Level-1B bands that you need to geo-register. The header information for
hdf files are encapsulated within the main hdf file itself (this is
different from the external .met file). Your image processing system
(assuming it handles the hierarchical data format) will likely have a
mechanism to display the embedded header information and save it to an
ASCII file, and also edit their attributes.

The steps comprising the process of geo-registering your ASTER image
will vary with each application software system, and hence cannot be
generalized. But some of the broadly common requirements might include:

-   specifying the values for the corner column-row (pixel-line) image
    coordinates

-   specifying the correct pixel resolution for x and y

-   specifying the MAPORIENTATIONANGLE value

-   specifying the output projection parameters including the projection
    and its related information, datum etc. Presently, the datum
    information is not included in the metadata.

[]{.mark}

Committing the above changes and re-displaying your Level-1B should
ensure that the image is displayed in geographic latitude-longitude
coordinates. You can further verify the quality of geo-registration by
overlaying a reliable vector coverage layer of the same area that has
the same projection coordinates as your ASTER Level-1B image.

## 12.4 Unique Features of ASTER Level-1B Data

### 12.4.1 Pixel Reference Location

There is a fundamental difference between the alignment of the image
bands and how their pixels are referenced. Assuming that we have the
full complement of data from all the three sensors (VNIR, SWIR, and
TIR), the VNIR Band-2 is used as the reference band, and data for all
the three sensors are aligned by the upper-left **corners** of their
upper-left pixels. When there are only SWIR and TIR bands present, the
SWIR Band-6 is used as the reference band. When a Level-1B data set
contains only TIR data, the TIR Band-11 is used as the reference band.

The SceneFourCorners upper-left is calculated using Band-2 of the VNIR
(or Band-6 of SWIR or Band-11 of TIR as warranted by the data
acquisition), and represents the **center** of the upper left image
pixel. You may need to make necessary adjustments to the x and y values
representing the centers of any particular sensor's upper-left image
pixel depending on how your specific image processing software
references the pixel location (see Figure 28 below where "**x**" is the
SceneFourCorners upper-left).

*15 \| 30 \| 90m*

[]{#__RefHeading___Toc21151281 .anchor}**Figure 28: Upper-left pixel of
VNIR, SWIR and TIR bands in an ASTER Level-1B data set.**

### 12.4.2 Footprint of an ASTER Level-1B Image

The footprint of an ASTER Level-1B image is somewhat unique when viewed
in the context of the alignment of its SceneFourCorners. Only the
upper-left pixel of the SceneFourCorners lies within an ASTER Level-1B
(or Level-2) image extent. The other three corner coordinates represent
locations that are one pixel beyond the extent of the image (Figure 29).

[]{#__RefHeading___Toc21151282 .anchor}**Figure 29: ASTER L1B Footprint
in the Context of the SCENEFOURCORNERS Alignment.**

#### 

### 12.4.3 Path- or Satellite-Orientation of an ASTER Level-1B Image

The ASTER instrument aboard the Terra satellite platform orbits the
Earth with a 10:30 AM (GMT) equator-crossing time. This renders day-time
orbits to be descending passes while night-time orbits are ascending
passes. The MapOrientationAngle denotes the angle of rotation between
the path-oriented image and the transformed map-projected coordinates.
Ranging from -180° to +180°, it provides the amount by which an ASTER
Level-1B image is rotated to or from True North. It is therefore,
positive and clock-wise for descending orbits, and negative and counter
clock-wise for ascending orbits. This field is present in:

-   ASTER Level-1B data sets processed at the Ground Data System (GDS),
    Japan using version 4.0 (and higher versions) of the Level-1
    algorithm, and available from the LP-DAAC in Sioux Falls, SD.

-   ASTER Level-2 products produced at the LP-DAAC, in Sioux Falls, SD.

In the case of ASTER Level-1 data produced prior to the implementation
of algorithm version 4.0 (before May 2001), MapOrientationAngle is named
SceneOrientationAngle (in the hdf metadata), and is measured as the
angle from the path-oriented image to north-up*.* **If you are using an
ASTER Level-1B processed prior to May 2001, using an algorithm version
that is less than 4.0 (referred to as PGEVERSION in the hdf metadata),
it is important to bear in mind that the SceneOrientationAngle values
have reverse signs.**

There are two collection versions of the routinely produced ASTER
Level-1 data sets available from the LP-DAAC (Versions 002 and 003).
Typically, all data sets produced with algorithm version 4.0 (and higher
versions) are available in the version 003 collection while data sets
produced with algorithm versions lower than 4.0 are available in the
version 002 collection.

The upper-left for an ASTER scene is relative to the orbital path of the
Terra satellite (the diagram below (Figure 30) relates to Landsat-7
satellite but is also useful in visualizing the orbital paths of the
Terra satellite platform).

#### 

![](media/image29.png)

[]{#__RefHeading___Toc21151283 .anchor}**Figure 30: Ascending and
Descending Orbital Paths.**

Source: Landsat 7 Processing System (LPS) Output Files Data Format
Control Book,

510-3FCD/0195, July 1998

### 12.4.4 Geometric Correction Table

ASTER HDF metadata contains Geometric Correction Tables (GCTs) for each
telescope, including the VNIR 3B band. These GCTs are arrays of
pixel/line locations and their corresponding geographic coordinates. It
is important to remember that for ASTER Level-1A and Level-1B data, the
GCTs are in geocentric coordinates. For all ASTER Level-2 products, the
GCTs are in geodetic coordinates.

GCTs contain information that are internal to the swath data structures.
As part of the geometric correction, each scene is divided into block
units, and the processing of the scene is done block by block in both
the along-track and cross-track directions. The values for the lattice
points constitute coordinates for each lattice block located by their
center pixel and their corresponding latitudes and longitudes in
geocentric coordinates.

The stepping in each dimension is specified in the structural metadata
of the swath. The interval spacing for the GCT begins with

(0, 0), and increments by values contained in the HDF metadata fields
named ImageLine and ImagePixel.

Example of a VNIR Swath stepping:

VNIR Structural metadata is in a global attribute called
StructMetadata.0

OBJECT = DimensionMap_1

> GeoDimension = GeoTrack
>
> DataDimension = ImageLine
>
> Offset = 0
>
> Increment = 420
>
> END_OBJECT = DimensionMap_1
>
> OBJECT = DimensionMap_2
>
> GeoDimension = GeoXtrack
>
> DataDimension = ImagePixel
>
> Offset = 0
>
> Increment = 498

END_OBJECT = DimensionMap_2

> The stepping cross-track is 498 pixels and the along-track stepping is
> 420 pixels. Both Offsets are zero. The 11 x 11 arrays give the
> locations for the following pixels (using 0 for smallest index):
>
> (line, sample) = (0,0), (0, 498), \... (0, 4980)
>
> (420,0), (420, 498) \... (420, 4980)
>
> (840,0) (840, 498) \... (840, 4980)
>
> . .
>
> . .
>
> (3780,0), (3780,498) \... (3780,4980)
>
> (4200,0) \... (4200, 4980)

ASTER Level-1B and ASTER Level-2 GCT values, and the SceneFourCorners
values are both generated from the Level-1B. The SceneFourCorners
upper-left matches the GCT (0, 0) out to 6 decimal places. The GCT
coordinates are more precise, extending out to 12 or more decimal
places. There should be no significant difference between the positional
accuracy of the data using either the GCT values as ground control, or
the SceneFourCorners upper-left and MapOrientationAngle to orient either
the ASTER Level-1B data or Level-2 products.

The equation to convert the swath-based geocentric coordinates to
geodetic coordinates is:

#### *Geodetic = Arctan \[(tan (Latitude)) / 0.99330562\]* 

This equation is for the WGS 84 datum only. Geocentric longitudes do not
need to be converted to geodetic since they both are the same and they
also share the same reference meridian and axis.

### 

### 12.4.5 Geodetic versus Geocentric Coordinates

Geodetic coordinates specify a location on the Earth\'s oblate
(non-spherical) surface. Geodetic latitude is defined as the angle
between the equatorial plane and a line normal to the surface at that
location. Geodetic longitude is the angular distance between the
location\'s meridian and the Greenwich meridian.

Geocentric coordinates relate to a reference system where the origin is
the center of the Earth. Geocentric latitude is defined by the angle
between the equatorial plane and a line from the local position to the
intersection of the axis of rotation with the equatorial plane. Geodetic
longitude and geocentric longitude are the same because they share the
same reference meridian and axis.

# 13.0. Frequently-Asked Questions

This section provides a representative sample of frequently-asked
questions (FAQs) from users relating to various aspects of ASTER that
were received both at JPL and LP-DAAC since ASTER data were made
publicly available in November of 2000. These FAQs have been broadly
categorized in 10 groups:

  ----------------------------------- -----------------------------------
  13.1                                General ASTER

  13.2                                ASTER Instrument

  13.3                                ASTER Level-1 Data

  13.4                                Acquiring and Ordering ASTER Data

  13.5                                ASTER Metadata

  13.6                                ASTER Higher-Level Products

  13.7                                ASTER Expedited Data Sets

  13.8                                ASTER-Related Algorithms

  13.9                                ASTER Documentation

  13.10                               HDF-EOS Data Format
  ----------------------------------- -----------------------------------

## 13.1 General ASTER

**Could you give a brief description of ASTER and its sensor systems?**

The Advanced Spaceborne Thermal Emission and Reflection Radiometer
(ASTER) is one of 5 instruments aboard the Terra platform that was
launched in December, 1999. ASTER was built by a consortium of Japanese
government, industry, and research groups. It has three spectral bands
in the visible near-infrared (VNIR), six bands in the short-wave
infrared (SWIR), and five bands in the thermal infrared (TIR) regions,
with 15, 30, and 90 meters ground resolution respectively. This
combination of wide spectral coverage and high spatial resolution allows
ASTER to discriminate amongst a large variety of surface materials,
ideal for geological studies. The VNIR subsystem is specifically endowed
with a backward-viewing telescope for high-resolution stereoscopic
observation in the along-track direction. The ASTER instrument is unique
in two respects: one, it provides multispectral thermal infrared data of
high spatial resolution, and also the highest spatial resolution surface
spectral reflectance, temperature and emissivity data of all the Terra
instrument sensor systems; and two, because of its limited duty cycle,
data are acquired to fulfill on-demand data acquisition requests. The
ASTER data products include relative spectral reflectance and
emissivity, surface radiance, temperature, reflectance and emissivity,
brightness temperature-at-sensor, and digital elevation models. ASTER
data are expected to contribute to a wide array of application areas
including, geology and soils, vegetation and ecosystem dynamics, hazards
monitoring (volcanoes, wildfires, floods, landslides, and coastal
erosion), land surface climatology, hydrology, and land cover change.

Additional details regarding the instrument, data products etc. can be
accessed from:

[**http://asterweb.jpl.nasa.gov/default.htm**](http://asterweb.jpl.nasa.gov/default.htm)

**What are the sizes of ASTER pixels?**

ASTER provides data at 3 different spatial resolutions (pixel sizes).
The resolutions are 15 m, 30 m and 90 m for the visible-near-infrared,
shortwave infrared and thermal infrared respectively. For more
information check the ASTER web page (<http://asterweb.jpl.nasa.gov/>).

#### Is there an index map of all the ASTER images that are available?

There are some on-line tools that you can use to find the breadth of
ASTER coverage.\
\
The ASTER DAR (Data Acquisition Request) Tool at
[http://e0ins02u.ecs.nasa.gov:10400](http://e0ins02u.ecs.nasa.gov:10400/)/
allows one to run a search for an area of interest to see if images have
been collected. There is a map that will show the footprints of each
image and the outline of the area of interest. The DAR Tool also shows
the time frame in which scenes are scheduled to be acquired. Please read
the Getting Started and Limitations Documents before launching the
tool.\
\
Another tool you can use is available from the ASTER GDS (Ground Data
System) site at
<http://imsweb.aster.ersdac.or.jp/ims/cgi-bin/dprSearchMapByMenu.pl>\
Here, you will also need to enter your area of interest (or do a global
search) to find images that have been acquired. There is a map that
shows coverage, and there also are browse images.\
\
The most conclusive results can be derived by searching the EOS Data
Gateway (EDG) at <http://edcimswww.cr.usgs.gov/pub/imswelcome>/\
\
You can search for all the archived and available images from the EROS
Data Center's LP-DAAC archives. There is no end-to-end coverage map but
the EDG will return a footprint of your search results. Individual
browse images are available for most of the acquired scenes. Individual
footprints of selected granules are also returned upon querying. Please
read the tutorial for more information
(<http://edcdaac.usgs.gov/tutorial>/)

## 13.2 ASTER Instrument

## 

Where do I find information about the ASTER instrument characteristics,
e.g. the signal to noise?

The ASTER instrument characteristics including the signal to noise are
given on the ASTER [**webpage**](http://asterweb.jpl.nasa.gov/) under
instrument.

## 13.3 ASTER Level-1 Data

## 

**What is an ASTER Level-1A dataset?**

An ASTER Level-1A dataset contains reconstructed, unprocessed instrument
digital data derived from the telemetry streams of the 3 telescopes:
Visible Near Infrared (VNIR), Shortwave Infrared (SWIR), & Thermal
Infrared (TIR). Data derived from each of these telescopes or sensors
are at three different ground resolutions: VNIR at 15 m, SWIR at 30 m,
and TIR at 90 m. These depacketized, demultiplexed, and realigned
instrument image data have their geometric correction coefficients and
radiometric calibration coefficients calculated and appended but not
applied.

Level-1A datasets also include corrections for the SWIR parallax, and
intra- and inter-telescope registration information. ASTER Level-1A data
is produced at the Ground Data System (GDS) in Tokyo, Japan, and sent to
the EROS Data Center\'s (EDC) Distributed Active Archive Center (DAAC)
for archiving, distribution, and higher-level processing.

**Does ASTER Level-1A dataset have associated browse images?**

There are 3 separate browse files (one each for VNIR, SWIR and TIR)
along with an ASTER Level-1A dataset that can interactively be viewed
(through the View Image button) and/or ordered via the EOS Data Gateway
(EDG). On occasions there may only be data derived from one or two
sensor(s). At such times there will not be all three browse images. Each
browse file can be ordered via the EDG. The browse files can also be
downloaded as an UNIX tar file.

**Can you provide details (format, image dimensions) of the Level-1A
browse files?**

The ASTER Level-1 Browse Data Product contains 1-scene of image data
generated based on the Level-1A data with similar radiometric
corrections and mis-registration corrections applied to Level-1B data.
All image data (VNIR, SWIR, TIR) are 24-bit JPEG compressed images
stored in an HDF file in RIS24 objects. Refer to Table 2 in this
Handbook, which provides the main characteristics of the Level-1A browse
images.

**What is an ASTER Level-1B dataset?**

The ASTER Level-1B product contains radiometrically calibrated and
geometrically co-registered data for all the channels acquired
previously through the telemetry streams of the 3 different telescopes
in Level-1A. This product is created by applying the radiometric
calibration and geometric correction coefficients to the Level-1A data.
Both intra-telescope and inter-telescope registration corrections have
also been applied relative to the reference band of each sub-system. The
Level-1B product offers the same number of bands at the same resolution
as the Level-1A product. Level-1B data are required as the input for
generating higher level-2 geophysical products. On the EDG, the View
Image button allows the L1B granule to map back to the L1A's browse
(assuming the parent L1A granule exists in the inventory). These
Level-1B data are produced at the Ground Data System (GDS) in Tokyo,
Japan, and sent to the EROS Data Center\'s (EDC) Distributed Active
Archive Center (DAAC) for archiving, distribution, and higher-level
processing.

**Does ASTER Level-1B dataset have associated browse images?**

There are no browse images produced from ASTER Level-1B datasets. The
"View Image" button on the EDG for an ASTER L1B dataset (when active)
maps back to an ASTER Level-1A browse. The same caveats that apply to
Level-1A browse apply to Level-1B browse also.

**Why is the "View Image" button for my ASTER Level-1B dataset
inactive?**

There are a few occasions when we do not receive the parent Level-1A
granule or receive them late from GDS, Japan. Under such circumstances,
the Level-1B will not be mappable to its parent Level-1A granule, and
hence the inactive "View Image" button.

What are the average sizes of ASTER L1A and L1B data sets, and how are
the data quantized?

The ASTER Level-1A data set contains image and ancillary data from all 3
sensors (assuming data from all three sensors were collected). The
average sizes of these 14 channels or bands for L1A and L1B data sets
are:

  ----------------------- ----------------------- -----------------------
  **Sensor/Telescope**    **ASTER L1A**           **ASTER L1B**
                          (Megabytes)             (Megabytes)

  VNIR (Bands 1, 2, 3N)   51,660,000              62,748,000

  VNIR (Band 3B)          23,000,000              22,908,000

  SWIR (Bands 4 through   25,804,800              31,374,000
  9)                                              

  TIR (Bands 10 through   4,900,000               5,810,000
  14)                                             

  Total                   **107** Megabytes       **118** Megabytes
  ----------------------- ----------------------- -----------------------

VNIR and SWIR are 8-bit unsigned integer data, and TIR are 16-bit
unsigned integer data.

**What are the image dimensions of an ASTER L1A and L1B data set?**

The image dimensions for an ASTER L1A and L1B data set are:

  ----------------------- ----------------------- -----------------------
  **Sensor/Telescope**    **ASTER L1A**           **ASTER L1B**

  VNIR: (Bands 1, 2, 3N)  4200 rows x 4100        4200 rows x 4980
                          columns                 columns

  VNIR: (Band 3B)         4600 rows x 5400        4600 rows x 4980
                          columns                 columns

  SWIR: (Bands 4 through  2100 rows x 2048        2100 rows x 2490
  9)                      columns                 columns

  TIR: (Bands 10 through  700 rows x 700 columns  700 rows x 830 columns
  14)                                             
  ----------------------- ----------------------- -----------------------

**What are the areal dimensions of an ASTER image?**

The nominal size of an ASTER Level-1 scene is about 60 km by 60 km. This
is only slightly larger for the VNIR 3B image (which is used in
generating stereoscopic images for producing DEMs).

**Could you help me understand the file naming convention of an ASTER
Level-1 data set?**

The ASTER Level-1 File naming convention (from GDS) breaks down as
follows:

**pg-PR1B0000-2001021503_007_001**

**PR1B0000:** Processing level

**2001:** Production date (year)

**02:** Production date (month)

**15:** Production date (day)

**03:** Sequence number of production plan for that particular day

**007:** Processing strip number

**001:** Sequence number in the processing strip

The ASTER Level-1 file is distributed without a **.hdf** suffix. It is a
good idea to rename your file with it:
**pg-PR1B0000-2001021503_007_001.hdf.** This will enable your image
processing software to identify this particular format.

**How do I interpret ASTER Level-1 file names and match them with
granule/scene IDs?**

An ASTER Level-1 granule-ID on the EDG is specified:
SC:AST_L1A.002:2004799662

Following your order, an ASTER Level-1 granule is distributed with a
file name like:

pg-PR1A0000-2001111802_042_013 (This is the file naming convention from
GDS) along with an ECS-generated metadata file:
pg-PR1A0000-2001111802_042_013.met

The information used to match the file to the granule/scene ID is the
date and time found in the EDG-returned results and the embedded
metadata file. The embedded file exists as part of the header within the
image file. Some of the commercial and public domain software allow you
to open this information and save it as an ASCII file.

The date and time on the EDG-returned results for scene
SC:AST_L1A.002:2004799662 is 05 Nov 2001, 10:37:24, which is the
acquisition date and time. However, the values in the distributed file
name (pg-PR1A0000-2001111802_042_013) are the date and time that the
scene was produced.

To re-iterate from the earlier question, the details are to be
interpreted thus:

PR1A0000: Processing level

2001: Production date (year)

11: Production date (month)

18: Production date (day)

02: Sequence number of production plan for that particular day

042: Processing strip number

013: Sequence number in the processing strip

To match a file name with an image that is in our archive, you can use
the GDS ID found within the productmetadata.0 file (hidden file) under
the object attribute IDOFASTERGDSDATAGRANULE

For example: ASTL1A 0111051037240111180891 (from scene
SC:AST_L1A.002:2004799662/ file pg-PR1A0000-2001111802_042_013) can be
interpreted:

Format:

'ASTL1A YYMMDDHHMMSSyymmddNNNN'

where,

YYMMDD: observation date

HHMMSS: observation time

yymmdd: the data granule generation date

NNNN: the data granule sequential No. per day

So ASTL1A 0111051037240111180891 translates to:

011105: observation date

103724: observation time

011118: the data granule generation date

0891: the data granule sequential No. per day

**I searched and found a Level-1A dataset for my area of interest, but I
don't see its corresponding Level-1B dataset for the same area?**

There is always going to be more L1A than L1B data because not all L1A
data are processed to L1B at the GDS in Japan. Approximately one-third
of the data are processed from 1A to 1B based on cloud cover and
quality. Prior to May 2001, the cloud cover assessment algorithm at GDS
was over-estimating the cloud cover, resulting in fewer Level-1B
products being created and shipped to the LP-DAAC for archival and
higher-level processing. Requests for processing L1A to L1B can be
submitted to GDS directly at a cost of \$ 100.00 per scene. If you are
interested in submitting such a request to GDS, [**click
here**](http://www.gds.aster.ersdac.or.jp/gds_www2000/service_e/g.t.u.p_e/set_service_e3.html)
for more details.

**I searched and found a Level-1B dataset for my area of interest, but I
don't see its corresponding Level-1A dataset for the same area?**

There are occasions when we do not receive the L1A for the corresponding
L1B from GDS. This is mainly because we simply did not receive the
parent L1A granule for the L1B (from GDS, Japan) that you found. Do
write to us about it and we will investigate.

**How do I know that a certain L1B was derived from a particular L1A
granule?**

The indication that a L1B was derived from a particular L1A is the data
acquisition time (Start and Stop Time), which will be identical for both
of them. When the list of granules is returned on the EDG, the Start
Time and Stop Time columns display the data acquisition times. This
reports the hour, minutes, and seconds (example: **19:11:34**). If you
click on the Granule Attributes button for that granule, it will take
you to a series of metadata information. In the block entitled
Additional Attribute Information, there is a field called
**SingleDateTime**, which gives you the exact time of data acquisition,
including the milliseconds:

Example:

**SingleDateTime** **TimeofDay** = **19:11:34.773000**

**CalendarDate** = **2000-08-31**

**How do I order an ASTER Level-1 dataset?**

ASTER Level-1 (L1A and L1B) datasets can be ordered through the EOS Data
Gateway (EDG). There is a tutorial for new users who require more help
with understanding how the EDG works: <http://edcdaac.usgs.gov/tutorial>

**The browse image for a L1A data set that I searched for, displayed as
a black box. Why is that?**

It is likely that this granule was a night-time acquisition, and only
the Shortwave Infrared (SWIR) and Thermal Infrared (TIR) sensors are
likely to have acquired data during this time frame. The browse shows up
as a blank image because there is no reflected energy being sensed in
the SWIR bands unless there is a feature emitting energy at a high
level. Please click on the second browse icon that relates to the
thermal infrared (TIR) browse to consider whether these data would be of
use.

**I am interested in thermal images acquired at night. How do I search
for such acquisitions?**

Night-time acquisitions are, in theory, acquired between sunset and
sunrise. You can search for such acquisitions by defining a temporal
window for instance, 11:00 pm to 2:00 am in GMT. It is important to bear
in mind that these searches can only work for a 24-hour period.

The ASTER 1A data contain both geometric (e.g. band offsets) and
radiometric artifacts (e.g. striping). Is there any software I can get
to remove these?

ASTER 1A data are the raw data with the radiometric and geometric
correction coefficients appended but not applied. They are applied in
the 1B product. You should order the 1B product. The software for
processing the 1A to 1B data presently is used at the LP-DAAC only to
produce ASTER Expedited data.

**Are ASTER 1B images radiometrically calibrated?**

Yes, ASTER 1B images are radiometrically calibrated, however, in order
to keep image sizes small they are rescaled in the byte and halfword
ranges (the VNIR and SWIR are in byte) and the TIR are in halfword (2
byte). In order to scale the data to the correct range you need to apply
the coefficients listed earlier in this document.

#### Are ASTER 1B images always processed to the same version?

No - there have been multiple updates to the software and if you
downloaded the ASTER data over your site a few months ago there may now
be a new version of the same scene. You should always try and get the
latest version since each new version improves the data, e.g. the
registration between bands. This is because our understanding of how the
instrument behaves in space has improved over time.  You get the latest
version by ordering the data from the most recent collection for a given
product from the LP-DAAC archives. In some cases only an older version
may be available. Presently, there are two collection versions of the
routinely produced ASTER Level-1 data sets available from the LP-DAAC
(Versions 002 and 003). Typically, all data sets produced with algorithm
version 4.0 (and higher versions) are available in the version 003
collection while data sets produced with algorithm versions lower than
4.0 are available in the version 002 collection.

**Is the ASTER L1B product terrain-corrected?**

The ASTER L1B product is not terrain-corrected so there may be some
vertical scaling anomalies.

## 13.4 Acquiring and Ordering ASTER Data

## 

How do I find out if ASTER data are available over my study area and if
they are not how do I get them acquired?

In order to find out if ASTER data are available, search the ASTER
archives at the EROS Data Center's Land Processes DAAC through the
[**EDG**](http://edcimswww.cr.usgs.gov/pub/imswelcome/). All acquired
and processed ASTER data are archived and distributed from here
(<http://edcimswww.cr.usgs.gov/pub/imswelcome/>). You may enter as Guest
for search purposes. There will also be information for ordering data.
If your Area of Interest has not been collected, you may submit a Data
Acquisition Request proposal for evaluation. Once approved the user is
granted privileges for submitting a limited number of observation
requests. These requests go directly into the scheduling database. The
following URL will direct you to the DAR tool users request form.
<http://asterweb.jpl.nasa.gov/gettingdata/authorization/default.htm>.
Read through the short three page directions and proceed to enter your
proposal request. Further instructions will follow once the proposal has
been submitted for evaluation.

#### How much do ASTER data cost?

Currently ASTER Level-1A and 1B data are available from the LP-DAAC at
\$55 per scene. Higher-level data products are free.
(<http://edcimswww.cr.usgs.gov/pub/imswelcome/>).

**How do I find out when ASTER will be over my site?**

There is a web-based tool available at:
<http://earthobservatory.nasa.gov/MissionControl/overpass.html> that can
be used for overpass predictions. Using the tool: The Spacecraft
designation should be "TERRA". Enter the overpass criteria then execute
the prediction using the predict button. On the results page, look at
the Peak time (UTC) for the given date with Peak Elevation angles
greater than 81.5 deg. and with "Vis" field 'DDD' for Day scenes and '
NNN' for night scenes. If the Peak Elevation is less than 81.5 deg, the
target is NOT in the instruments field of view for that overpass on that
day. ASTER can acquire data from the VNIR telescope more frequently by
pointing.

#### Are ASTER data acquired over every location every 16 days?

No, although ASTER can acquire data over the same location every 16
days, its limited duty cycle prevents it from acquiring all locations
every 16 days. If you want data for a particular overpass then you
should get in touch with the ASTER team to find out how to submit a
request to make sure the instrument is turned on over your site on that
particular 16-day cycle. The ASTER Science Support Scheduling group
(SSSG) can be contacted via the ASTER web site:
http://asterweb.jpl.nasa.gov

**Does the EDC Catalogue contain all the ASTER scenes?**

Yes, the EDC catalogue contains all the ASTER scenes, searching on
Level-1A data searches all ASTER data acquired. It typically takes 4-6
weeks after acquisition for a scene to reach the LP-DAAC.

**Can I order a mosaic of several ASTER scenes?**

No ASTER data are only available as single scenes.

#### I submitted a proposal to have ASTER data acquired over my area. What happens next?

When a proposal is submitted, there is an automated confirmation
response sent from the server database. If you do not receive the email
notification you will need to resubmit the proposal again. After proper
acceptance into the database, it has been taking eight to twelve weeks
for complete evaluation and processing. We are actively working towards
reducing the cycle time response to less than six weeks. We do
appreciate everyone's patience during this transition*.*

I submitted a Data Acquisition Request (DAR) to have ASTER acquire data
over my site. Where do I go to find out whether the DAR was successful?

The DAR tool is capable of indicating acquisition status but does not
include any cloud cover ratings. If the status of a DAR is said to have
'failed' it generally means that there was no acquisition made before
the Lifetime of the DAR expired. Acquisitions are determined by very
complex scheduling algorithms. Delivery of the acquired data sets is
usually between three and four weeks after acquisition. On occasion, the
process can be completed within two weeks of an acquisition but may
require special operator handling.

## 13.5 ASTER Metadata

## 

**What is an HDF metadata file and how can I access it?**

The HDF metadata file is the information that is embedded within the
header of the Level-1 hdf file. We have chosen to call it "the embedded
HDF metadata" to discriminate it from the ".met" file produced
externally by ECS. The embedded HDF metadata are written in ODL (Object
Description Language), an ASCII-based "parameter = value" format, and
contains a number of attributes relating to the Level-1 dataset. How
these are viewed depends on the image-processing package being used to
open the Level-1 dataset. If you have the HDF libraries set up to use
the basic HDF tools available from the National Center for
Supercomputing Applications (NCSA), you can use the "ncdump" utility to
capture the header of the hdf file that contains the same information as
the HDF metadata. The HDF metadata file broadly contains 8 attribute
groups with a number of specific attributes in each of them. These
groups and the attributes are listed below: See further below for
details on each of them.

-   Attribute 1: HDFEOS Version

-   Attribute 2: StructMetadata.0

-   Attribute 3: ProductMetadata.0

-   Attribute 4: ProductMetadata.1

-   Attribute 5: ProductMetadata.v

-   Attribute 6: ProductMetadata.s

-   Attribute 7: ProductMetadata.t

-   Attribute 8: CoreMetadata.0

## 

**What is an ECS metadata (.met) file and how can I access it?**

The ECS metadata file is the external .met file that is delivered to the
user along with the Level-1 dataset. We have chosen to call it "ECS
metadata" only for convenience and to discriminate it from the embedded
HDF metadata file. It is written in ODL (Object Description Language),
an ASCII-based "parameter = value" format, and contains a number of
attributes relating to the Level-1 dataset. The .met file provides a
subset of the hdf metadata. It contains 3 broad groups of information:
INVENTORYMETADATA, COLLECTIONMETADATA, and ARCHIVEMETADATA.

**What is the difference between the HDF & ECS metadata files?**

There are both similarities and differences between the HDF & ECS
metadata files. The HDF metadata that is generated when the Level-1 data
is produced in GDS, Japan is written as part of the HDF file. It
contains 8 attribute groups with a number of specific attributes in each
of them. These groups and the attributes contained in them are broadly
summarized below. The reference to ***ASTER Level-1A*** and ***1B*** in
brackets indicate at which level of the data set's HDF metadata, the
information is present:

**Attribute \# 1: HDFEOS Version**

HDFEOS_V2.6 ***(ASTER Level-1A & 1B)***

**Attribute #2: StructMetadata.0**

SwathStructure

VNIR_Bands 1 through 3B ***(ASTER Level-1A)***

> VNIR_Swath ***(ASTER Level-1B)***
>
> SWIR Bands 4 through 9 ***(ASTER Level-1A)***

SWIR_Swath ***(ASTER Level-1B)***

TIR Bands 10 through 14 ***(ASTER Level-1A***

TIR_Swath ***(ASTER Level-1B)***

Dimension ***(ASTER Level-1A & 1B)***

GeoTrack

GeoXTrack

ImageLine

> ImagePixel

DimensionMap ***(ASTER Level-1A & 1B)***

Geo Dimension

Data Dimension

Offset

Increment

GeoField ***(ASTER Level-1A & 1B)***

Latitude

Longitude

DataField ***(ASTER Level-1A & 1B)***

**Attribute #3: ProductMetadata.0**

ASTERGenericMetadata ***(ASTER Level-1A & 1B)***

IDofASTERGDSDataGranule ***(ASTER Level-1A & 1B)***

ReceivingCenter

ProcessingCenter

PointingAngles ***(ASTER Level-1A & 1B)***

SensorName

PointingAngle

SettingTimeofPointing

GainInformation ***(ASTER Level-1A & 1B)***

GainSettings (Bands 1 -- 9)

CalibrationInformation ***(ASTER Level-1A & 1B)***

GeometricDbVersion

> RadiometricDbVersion
>
> CoarseDEMVersion

DataQuality ***(ASTER Level-1A & 1B)***

CloudCoverage

SceneCloudCoverage

QuadCloudCoverage

SourceDataProduct ***(ASTER Level-1A & 1B)***

ObservationMode ***(ASTER Level-1A & 1B)***

ProcessedBands ***(ASTER Level-1A & 1B)***

SceneInformation ***(ASTER Level-1A & 1B)***

ASTERSceneID

OrbitNumber

RecurrentCycleNumber

FlyingDirection

SolarDirection

SpatialResolution

SceneFourCorners ***(ASTER Level-1A & 1B)***

UpperLeft

UpperRight

LowerLeft

LowerRight

SceneCenter ***(ASTER Level-1A & 1B)***

SceneOrientationAngle ***(ASTER Level-1A & 1B)***

**Attribute #4: ProductMetadata.1**

GDSGenericMetadata ***(ASTER Level-1A & 1B)***

SensorShortName

IDofASTERGDSDataBrowse

**Attribute #5: ProductMetadata.v**

ProductSpecificMetadata-VNIR

ExtractionfromL01 ***(ASTER Level-1A)***

RelativeScanCount (First & Last)

ScanStartTime (First & Last)

PDSID (1 & 2)

ImageDataInformation for VNIR 1, 2, 3N, 3B ***(ASTER Level-1A & 1B)***

NumberofPixels

NumberofLines

BytesperPixel

GeometricCorrection ***(ASTER Level-1A)***

Number of Lattice Points in Along-Track Direction

Number of Lattice Points in Cross-Track Direction

Distance Between 2 Neighboring Lattice Points in Along-Track Direction

Distance Between 2 Neighboring Lattice Points in Cross-Track Direction

RadiometricCorrection ***(ASTER Level-1A)***

Number of Detectors

Number of Parameters

DataQuality

Number of Bad Pixels

Number of Bad Pixels due to Damaged Detectors

Number of Elements of List of Bad Pixels

UnitConversionCoefficients ***(ASTER Level-1B)***

Inclination

Offset

ConvertedUnit

DeStripeParameter ***(ASTER Level-1B)***

NumberofParameters

ImageStatistics ***(ASTER Level-1B)***

MinValue

MaxValue

MeanValue

StdDeviation

ProcessingParameters ***(ASTER Level-1B)***

Correction of Intertelescope Error (SWIR & TIR)

Correction of the SWIR Parallax Error

Resampling Method

Zone Code

Map Projection Method

Projection Parameters

**Attribute #6: ProductMetadata.s**

ProductSpecificMetadata-SWIR

ExtractionfromL01 ***(ASTER Level-1A)***

RelativeScanCount (First & Last)

ScanStartTime (First & Last)

PDSID (1 & 2)

ImageDataInformation for SWIR 4, 5, 6, 7, 8, 9 ***(ASTER Level-1A &
1B)***

NumberofPixels

NumberofLines

BytesperPixel

GeometricCorrection ***(ASTER Level-1A)***

Number of Lattice Points in Along-Track Direction

Number of Lattice Points in Cross-Track Direction

Distance Between 2 Neighboring Lattice Points in Along-Track Direction

Distance Between 2 Neighboring Lattice Points in Cross-Track Direction

RadiometricCorrection ***(ASTER Level-1A)***

Number of Detectors

Number of Parameters

DataQuality ***(ASTER Level-1A)***

Number of Missing Pixels

Number of Bad Pixels due to Damaged Detectors

Number of Elements of List of Bad Pixels

UnitConversionCoefficients ***(ASTER Level-1B)***

Inclination

Offset

ConvertedUnit

DeStripeParameter ***(ASTER Level-1B)***

NumberofParameters

ImageStatistics (Level-1B) ***(ASTER Level-1B)***

MinValue

MaxValue

MeanValue

StdDeviation

**Attribute #7: ProductMetadata.t**

ProductSpecificMetadata-TIR

ExtractionfromL01 ***(ASTER Level-1A)***

RelativeScanCount (First & Last)

ScanStartTime (First & Last)

PDSID (1 & 2)

ImageDataInformation for TIR 10, 11, 12, 13, 14 ***(ASTER Level-1A &
1B)***

NumberofPixels

NumberofLines

BytesperPixel

GeometricCorrection ***(ASTER Level-1A)***

Number of Lattice Points in Along-Track Direction

Number of Lattice Points in Cross-Track Direction

Distance Between 2 Neighboring Lattice Points in Along-Track Direction

Distance Between 2 Neighboring Lattice Points in Cross-Track Direction

RadiometricCorrection ***(ASTER Level-1A)***

Number of Detectors

Number of Parameters

NumberofBadPixels ***(ASTER Level-1A & L1B)***

> Number of Missing Pixels

Number of Bad Pixels due to Damaged Detectors

Number of Elements of List of Bad Pixels

UnitConversionCoefficients ***(ASTER Level-1A & L1B)***

Inclination

Offset

ConvertedUnit

DeStripeParameter ***(ASTER Level-1A & L1B)***

NumberofParameter

Level0TIRData ***(ASTER Level-1A)***

PDSID

First Packet Time

Last Packet Time

Packet Counts

L0DataType ***(ASTER Level-1A)***

L0DataQuality ***(ASTER Level-1A)***

Sensor Group Name

Number of Packets

Percent of Missing Packets

Percent of Corrected Packets

TIRRegistrationQuality ***(ASTER Level-1A & L1B)***

Number of Measurements

Measurement Point Number

Average Offset

Line Direction Average Offset

Pixel Direction Average Offset

Standard Deviation Offset

Line Direction Average Offset

Pixel Direction Average Offset

> Threshold
>
> Correlation Threshold
>
> Line Direction Offset Threshold
>
> Pixel Direction Offset Threshold
>
> Vector Offset Threshold

TIRShortTermCalibrationInformation ***(ASTER Level-1A)***

Blackbody Mean

Blackbody Standard Deviation

Blackbody Temperature Mean

Blackbody Temperature Standard Deviation

Chopper Mean

Chopper Standard Deviation

ImageStatistics ***(ASTER Level-1B)***

Minimum Value

Maximum Value

Mean Value

Standard Deviation Value

> Mode Value
>
> Median Value

ProcessingParameters ***(ASTER Level-1B)***

Correction of Intertelescope Error (SWIR & TIR)

Correction of the SWIR Parallax Error

Resampling Method

Zone Code

Map Projection Method

Projection Parameters

**Attribute #8: CoreMetadata.0**

InventoryMetadata ***(ASTER Level-1A & L1B)***

Short Name

Size of Data Granule (Megabytes)

Production Date & Time

Platform Short Name

Instrument Short Name

BoundingRectangle

West Bounding Coordinate

North Bounding Coordinate

East Bounding Coordinate

South Bounding Coordinate

SingleDateTime

Time of Day

Calendar Date

Review

Future Review Date

Science Review Date

QAStats

QA Percent Missing Data

QA Percent Out of Bounds Data

QA Percent Interpolated Data

ReProcessingActual

PGEVersion

ProcessingLevelID

MapProjectionName

**What is User-Friendly Metadata (UFM) and how can I use it to make my
metadata file friendlier to use?**

Since there is a large volume of metadata that is produced along with
ASTER Level-1 datasets, it is not always practical to print the entire
metadata file which can run into many pages. User Friendly Metadata
(UFM) is a utility that helps condense and reformat your metadata file
into a more usable size. It outputs the metadata to a hypertext markup
language (html) file that can be viewed using any web browser. It is
available for all flavors of UNIX and NT platforms. It is available free
of charge and can be downloaded from:

**<http://hdfeos.gsfc.nasa.gov/hdfeos/SoftwareDist.html>.**

On average, it condenses an ASTER L1 metadata file from 84,000 bytes to
25,000 bytes. UFM presently works only with the ECS-produced .met file.

**What are G-Rings and G-Polygons?**

A G-Ring (or Geometry Ring) is a closed sequence of ordered pairs of
coordinates of latitude and longitude in floating point that define the
boundary of the area. Latitude and longitude are specified in decimal
degrees with north latitudes positive and south negative, east longitude
positive and west negative. The first and last points in the set of
coordinates must be the same (Figure 31).

The outer G-Ring describes the outside edge of the G-Polygon (the full
coverage extent). Inner

G-Rings describe any holes that may occur in the G-Polygon (areas of
missing coverage within the outer ring). The GPolygon class contains the
G-Ring attribute for the exclusion ring flag, which is added to each
polygon definition to describe whether the polygon is an inner or outer
ring of coverage.

## 

## 

## 

## 

## 

## 

## 

## 

## 

## 

## 

## 

## 

[]{#__RefHeading___Toc21151284 .anchor}**Figure 31: G-Ring and
G-Polygon.**

**What is the difference between the GRing coordinates in the .met file
versus those specified for the SCENEFOURCORNERS in the embedded metadata
of the hdf file?**

The GRing coordinates are populated by ECS using the SCENEFOURCORNERS
information from the embedded metadata in the hdf file. The only
difference is that G-Ring coordinates are rounded till the fourth
decimal place while the SCENEFOURCORNERS have up to six decimal places.

**Where would I find the projection information within the metadata?**

This information is present under "Processing Parameters" in the product
metadata for each sensor within the embedded metadata of an ASTER
Level-1B granule. It includes the following attributes: Map projection,
Projection parameters, Zonecode, Resampling method, Correction of
inter-telescope error (for SWIR & TIR), and Correction of SWIR parallax
error.

**Where would I find the datum information within the metadata?**

Currently, the datum information is not carried by the metadata. It is
recommended that you use NAD1983 for the continental United States and
WGS1984 for the rest of the world.

**Are the pixel coordinates referenced by the upper left corner of the
pixel or the center of that pixel?**

The pixel coordinates are referenced by the center of that pixel.

**Do the SCENEFOURCORNERS reference the corners of the image or those of
the file?**

In the case of an ASTER L1A image, the location of the four corners
correspond to the area of the actual image, but in the case of an ASTER
L1B image, these locations correspond to the corners of the file (in
other words they include the fill or no-data area).

**In general, what are the differences between geodetic & geocentric
coordinates?**

Geodetic coordinates specify a location on the Earth\'s oblate
(non-spherical) surface. Geodetic latitude is defined as the angle
between the equatorial plane and a line normal to the surface at that
location. Geodetic longitude is the angular distance between the
location\'s [meridian](http://ssd.jpl.nasa.gov/#meridian) and the
Greenwich [meridian](http://ssd.jpl.nasa.gov/#meridian).

Geocentric coordinates relate to a reference system where the origin is
the center of the Earth. Geocentric latitude is defined by the angle
between the equatorial plane and a line from the local position to the
intersection of the axis of rotation with the equatorial plane.

Geodetic longitude and geocentric longitude are the same because they
share the same reference meridian and axis.

**Do ASTER Level-1 data conform to geodetic or geocentric coordinates?**

The coordinates within an interior ASTER swath are geocentric while all
the metadata are geodetic. So if you use the interior swath-based
coordinate information (from the embedded metadata in the hdf file), you
will need to convert it to geodetic coordinates. All the higher-level
products use geodetic coordinates, both in the metadata and in the
swaths.

**How do I convert my geocentric coordinates in the swath to geodetic
ones?**

The equation to convert the swath-based geocentric coordinates to
geodetic coordinates is:

*Geodetic = Arctan \[(tan (Latitude)) / 0.99330562\]*

This equation is for the WGS 84 datum only. Geocentric longitudes do not
need to be converted to geodetic since they both are the same and they
also share the same reference meridian and axis.

**What are the 11 x 12 (VNIR), 104 x 107 (SWIR), 11 x 12 (TIR) latitude
and longitude files for a Level-1A, and 11 x 11 (VNIR), 11 x 11 (SWIR),
11 x 11 (TIR) latitude and longitude files that come bundled with a
Level-1B granule?**

These files represent the Geometric Correction Table (GCT) and are
internal to the swath data structures. Also referred to as the swath
metadata, these geolocation files provide the values for the lattice
points for each of the sensors. As part of the geometric correction,
each scene is divided into block units, and the processing of the scene
is done block by block in both the along-track and cross-track
directions. The values for the lattice points constitute coordinates for
each lattice block located by their center pixel and their corresponding
latitudes and longitudes in geocentric coordinates. The lattice blocks
and their intervals for ASTER L1A are:

  ----------------------- ----------------------- -----------------------
  ***Sensor (Bands)***    ***Lattice Block        ***Number of Lattice
                          Size***                 Points***

  VNIR (1, 2, 3N)         498 pixels x 420 lines  11 x 13

  VNIR (3B)               498 pixels x 460 lines  11 x 13

  SWIR (4 through 9)      249 pixels x 210 lines  104 x 107

  TIR ( 10 through 14)    83 pixels x 70 lines    11 x 12
  ----------------------- ----------------------- -----------------------

The lattice blocks and their intervals for ASTER L1B are:

  ----------------------- ----------------------- -----------------------
  ***Sensor (Bands)***    ***Lattice Block        ***Number of Lattice
                          Size***                 Points***

  VNIR (1, 2, 3N)         498 pixels x 420 lines  11 x 11

  VNIR (3B)               498 pixels x 460 lines  11 x 11

  SWIR (4 through 9)      249 pixels x 210 lines  11 x 11

  TIR ( 10 through 14)    83 pixels x 70 lines    11 x 11
  ----------------------- ----------------------- -----------------------

**What are Product-Specific Attributes (PSAs) and how can I extend my
search criteria by employing the available PSAs on the EDG**

PSAs are additional metadata attributes by means of which you can refine
your search criteria to filter out a specifically defined data set.
Presently the available PSAs on the EOS Data Gateway include: Data Set,
Data Center, Parameter, Processing Level, Sensor, Source, Band
Availability, Cloud Amount, Database Identifier, Granule Details,
Observation Mode, and Resampling Method. You have to individually define
the attribute values for each PSA before executing your search.

**Where can I find the latitude/longitude of the corner points of my
ASTER scene?**

The corner points of a given ASTER scene are provided in the metadata
inside the HDF file. They are also given in the .met file, an ASCII text
file that comes with the HDF file.

**How do I find the latitude and longitude of an ASTER pixel?**

The 1B file contains latitude/longitude arrays for each of the 3
instruments (VNIR, SWIR, TIR), which can be expanded to obtain the
latitude/longitude of every pixel. Note the latitude values of the 1B
data are in geocentric coordinates whereas the longitude values are in
geodetic coordinates. You need to convert the geocentric to geodetic
coordinates. For further details see the section on geometric conversion
in this document.

**ASTER scenes are rotated (not N-S) where do I find the amount of
rotation?**

The amount of rotation is specified by the MapOrientationAngle attribute
in the HDF file. It should also be noted that the latitude in the
Level-1B product is in geocentric coordinates whereas the longitude is
in geodetic coordinates. In the case of ASTER Level-1 data produced
prior to the implementation of algorithm version 4.0 (before May 2001),
MapOrientationAngle is named SceneOrientationAngle (in the hdf
metadata), and is measured as the angle from the path-oriented image to
north-up*.* **If you are using an ASTER Level-1B processed prior to May
2001, using an algorithm version that is less than 4.0 (referred to as
PGEVERSION in the hdf metadata), it is important to bear in mind that
the SceneOrientationAngle values have reverse signs.**

## 13.6 ASTER Higher-Level Products

## 

**What higher-level products are produced from an ASTER L1A data set,
and where can I learn more about them?**

Presently, the only higher-level products produced from an ASTER L1A
data set are

-   an absolute Digital Elevation Model (DEM). This product requires a
    requestor to submit Ground Control Points (GCPs) for their area of
    interest

-   A relative DEM. This does not require a requestor to provide GCPs.

For more general information on the ASTER DEM product, check the
product's one-pager at: <http://edcdaac.usgs.gov/aster/ast14dem.html>

For more technical details on the ASTER DEM, check its Algorithm
Theoretical Basis Document (ATBD) at:

<http://eospso.gsfc.nasa.gov/ftp_ATBD/REVIEW/ASTER/ATBD-AST-08/atbd-ast-08.pdf>

**What higher-level products are produced from an ASTER L1B data set,
and where can I learn more about them?**

All the higher-level ASTER geophysical products (except an absolute DEM)
are produced using a L1B as its input. These products including their
Long and Short names are listed below:

\[There are 2 sources of information on each of these products that you
would find useful:

-   one, an ASTER Data Products Specification document that provides
    both algorithm and product descriptions besides sections on
    applications and constraints. These product specifications are also
    available in Appendix II of this Handbook.

-   two, there are product one-pagers for each of these products that
    are available from LP-DAAC
    (http://edcdaac.usgs.gov/aster/asterdataprod.html). These provide a
    brief product description, data set characteristics, image
    dimensions, file sizes, search and ordering information, and links
    to algorithm, documentation etc.\].

**Level-2 products** (the Short-Names also provide links to the LP-DAAC
Product One-Pagers):

**Routinely Produced at ECS-EDAAC**

> 1\. ASTER Level-2 Decorrelation Stretch Product (VNIR)
> [**AST_06V**](http://edcdaac.usgs.gov/aster/ast_06v.html)

2\. ASTER Level-2 Decorrelation Stretch Product (SWIR)
[**AST_06S**](http://edcdaac.usgs.gov/aster/ast_06s.html)

3\. ASTER Level-2 Decorrelation Stretch Product (TIR)
[**AST_06T**](http://edcdaac.usgs.gov/aster/ast_06t.html)

**Produced on-demand at ECS-EDAAC**

> 1\. ASTER On-Demand Level-2 Decorrelation Stretch Product (VNIR)
> [**AST_06VD**](http://edcdaac.usgs.gov/aster/ast_06vd.html)
>
> 2\. ASTER On-Demand Level-2 Decorrelation Stretch Product (SWIR)
> [**AST_06SD**](http://edcdaac.usgs.gov/aster/ast_06sd.html)
>
> 3\. ASTER On-Demand Level-2 Decorrelation Stretch Product (TIR)
> [**AST_06TD**](http://edcdaac.usgs.gov/aster/ast_06td.html)
>
> 4\. ASTER Level-2 Brightness Temperature at the Sensor
> [**AST_04**](http://edcdaac.usgs.gov/aster/ast_04.html)

5\. ASTER Level-2 Emissivity Product
[**AST_05**](http://edcdaac.usgs.gov/aster/ast_05.html)

> 6\. ASTER Level-2 Surface Reflectance Product (VNIR & SWIR)
> [**AST_07**](http://edcdaac.usgs.gov/aster/ast_07vd.html)
>
> 7\. ASTER Level-2 Surface Kinetic Temperature Product
> [**AST_08**](http://edcdaac.usgs.gov/aster/ast_08.html)
>
> 8\. ASTER Level-2 Surface Radiance Product (VNIR & SWIR)
> [**AST_09**](http://edcdaac.usgs.gov/aster/ast_09vd.html)
>
> 9\. ASTER Level-2 Surface Radiance Product (TIR)
> [**AST_09T**](http://edcdaac.usgs.gov/aster/ast_09td.html)
>
> 10\. ASTER Polar Surface & Cloud Classification
> [**AST13POL**](http://edcdaac.usgs.gov/aster/ast_13pol.html)

**Level-3 products**

**A. Produced on-demand at ECS-EDAAC**

1\. Digital Elevation Model Product
[**AST14DEM**](http://edcdaac.usgs.gov/aster/ast14dem.html)

You can order any of these products using the **ASTER On-Demand
Gateway**.

**How do I get a DEM generated from ASTER data for my area?**

ASTER DEM's can be produced as an on-demand product through the EDG
website. You must first search for Level-1A data, then follow the link
next to the granule ID. Please note that our production rate is 1-2
DEM's per day, so there is a queue of several months.

**Are the DEM's absolute or relative?**

Both absolute and relative DEM's can be ordered. For absolute DEM's you
need to provide a control point.

**What is the difference between a Relative DEM and an Absolute DEM?**

An Absolute ASTER DEM is generated by using Ground Control Points (GCP)
supplied by the requestor. The software uses the GCPs to tie the DEM to
known points on the ground and yields a product with real ground
elevations. The Absolute DEM is geo-coded using customer-supplied Ground
Control Points (GCPs).

A Relative ASTER DEM is generated by using only the satellite ephemeris
data and will yield a product with ground elevations that are quite near
those of an Absolute DEM. The Relative DEM is not nearly as accurate as
the Absolute DEM horizontally since the Relative DEM is geo-coded using
only satellite ephemeris data.

**Why are some ASTER DEMs approximately 12 Mb in size and some others
are approximately 25 Mb in size?**

There was a change in the ASTER DEM output format from a 16-bit to a
32-bit file in late-January, 2002. Hence, all ASTER DEMs processed on or
after 22 January 2002 will be approximately 25 Mb in size compared to
the DEMs produced prior to that date, which are approximately 12 Mb in
size.

**How can I determine if the ASTER image is a good candidate for DEM
production?**

You can execute your search with 2 Product Specific Attributes, "Cloud
Amount", and "Band Availability" to ensure you have a suitable image
with the requisite bands (3n and 3b) present. It is highly recommended
that you view the browse image to verify cloud areas. The best candidate
for DEM production would be cloud-free, and would have sufficient
contrast in it's image values. A DEM can be produced from an image with
clouds, however the clouds and their dark shadows on the image will be
shown as a "void area" or a "no data" area on the DEM. Both thick, and
high wispy clouds will cause void areas in a DEM and will also cause
data near the clouds to be inaccurate. Areas of constant tone, such as
large sand areas or snowfields, may also cause failure in the resulting
data.

**How can I tell the difference between an Absolute and a Relative DEM
on the EDG?**

While viewing a list of the returned results of DEM granules on the EDG,
one of the columns provides a link called "Granule Attributes." Click on
that link to view additional information on the DEM including whether it
is an absolute or a relative DEM.

**How do I get the surface reflectance from the ASTER 1B data?**

The ASTER team provides a reflectance product that can be ordered when
the 1B data are ordered. However, there are problems with crosstalk from
band 5 to band 9. The problem is under investigation.

**How do I get temperature and emissivity from ASTER 1B data?**

The ASTER team provides a temperature and emissivity product that can be
ordered when the 1B data are ordered.

## 13.7 ASTER Expedited Data Sets

## 

**What is an ASTER Level-1 Expedited Data Set (EDS)?**

The Expedited ASTER Level-1A data set is produced with the express
purpose of providing the ASTER Science Team members data of their
particular interest in quick turn-around time from the moment the data
are acquired. This is usually done to support on-going field calibration
and validation efforts or to support emergency response to natural
disasters when processed Level-1 data with minimum turn-around time
would prove beneficial in initial damage or impact assessments. This
data set is expected to be publicly available for a period of 30 days
after which time it will be removed from the archive. This is done
because the routinely processed (Production Data Set or PDS) version of
this data set will be available from Japan in due course for search and
order from the LP-DAAC archives. ASTER Expedited Data Sets (EDS) serve
the short-term requirements of a small group of scientists and fulfill
immediate imagery needs during times of natural disasters.

**How is an ASTER Level-1 Expedited Data Set different from the
Production Data Set (PDS)?**

The general product description details as described for the Production
Data Set version of the ASTER Level-1A Data Set -- Reconstructed,
Unprocessed Instrument Data apply to the expedited data set with a few
notable exceptions. These include:

-   Stereo data is not available from the Expedited Level - 1 data set
    since it does not contain the VNIR 3B (aft-viewing) Band.

-   TIR data quality is expected to be lower than that of the regular
    PDS data because short-term calibration for TIR is not available.
    Instead, the long-term calibration is used.

-   The Inter-telescope registration quality may be lower than that of
    PDS. This is because regular PDS data are surrounded by 13 adjacent
    scenes to ensure sufficient information for inter-telescope
    registration. However, these 13 adjacent scenes are not transmitted
    via the expedited pathway, and therefore are not available.

-   The geometry of L1BE scenes is slightly different than L1B because
    the expedited processing uses the raw spacecraft ephemeris data, and
    the L1B processing uses the refined (post-processed) ephemeris data.

**Why are there only a few EDS granules in that collection compared to
the PDSs?**

The expedited data sets are produced in response to particular
requirements such as on-going field calibration and validation
activities carried out by a core group of scientists. It is also
produced on occasion, to provide immediate imagery data that
characterize natural emergency situations. Both these requirements are
infrequent and satisfy certain immediate short-term needs. They are not
expected to be used for analytical science-driven application purposes.
That explains why these ad-hoc products are fewer in numbers. All
end-users seeking to apply ASTER data are encouraged to use the
Production Data Set (PDS) version of the Level-1 data available from the
LP-DAAC.

## 

## 

## 13.8 ASTER-Related Algorithms

## 

**Where do I find out more about the algorithms used to process ASTER
data?**

The algorithms used to process ASTER data are described in the ASTER
Theoretical Basis Documents (ATBD's). These can be downloaded from the
ASTER web site. Refer to the next section on ASTER Documentation.

## 13.9 ASTER Documentation

## 

The Product Specifications for ASTER Level-1:
[http://asterweb.jpl.nasa.gov/documents/ASTER%20Data%20Prodcut%20Specification.pdf](http://asterweb.jpl.nasa.gov/documents/ASTER Data Prodcut Specification.pdf)

Algorithm Theoretical Basis Documents (ATBDs) are detailed documents put
together by the Principal Investigator(s) for a particular product. They
provide an introduction, overview, theoretical description of the
algorithm, the mathematics, programming and implementation
considerations, constraints, limitations and assumptions. This is the
definite source of information for the serious researcher who requires a
lot of background information on theory, methodology and further
references.

The ATBDs for ASTER Level-1 and higher-level products can be accessed
from:

  -------------------- -----------------------------------------------------------------------------------------------------------------------------------------------------
  ASTER Level-1        [eospso.gsfc.nasa.gov/ftp_ATBD/REVIEW/ASTER/ATBD-AST-01/l1atbd.pdf](../eospso.gsfc.nasa.gov/ftp_ATBD/REVIEW/ASTER/ATBD-AST-01/l1atbd.pdf)

  Decorrelation        [eospso.gsfc.nasa.gov/ftp_ATBD/REVIEW/ASTER/ATBD-AST-06/atbd-ast-06.pdf](../eospso.gsfc.nasa.gov/ftp_ATBD/REVIEW/ASTER/ATBD-AST-06/atbd-ast-06.pdf)
  Stretch              

  Brightness Temp. at  [eospso.gsfc.nasa.gov/ftp_ATBD/REVIEW/ASTER/ATBD-AST-02/atbd-ast-02.pdf](../eospso.gsfc.nasa.gov/ftp_ATBD/REVIEW/ASTER/ATBD-AST-02/atbd-ast-02.pdf)
  Sensor               

  Surface              [eospso.gsfc.nasa.gov/ftp_ATBD/REVIEW/ASTER/ATBD-AST-03/atbd-ast-03.pdf](../eospso.gsfc.nasa.gov/ftp_ATBD/REVIEW/ASTER/ATBD-AST-03/atbd-ast-03.pdf)
  Temp./Emissivity     

  Surface Radiance &   [eospso.gsfc.nasa.gov/ftp_ATBD/REVIEW/ASTER/ATBD-AST-04/atbd-ast-04.pdf](../eospso.gsfc.nasa.gov/ftp_ATBD/REVIEW/ASTER/ATBD-AST-04/atbd-ast-04.pdf)
  Reflectance          

  Surface Radiance -   [eospso.gsfc.nasa.gov/ftp_ATBD/REVIEW/ASTER/ATBD-AST-05/atbd-ast-05.pdf](../eospso.gsfc.nasa.gov/ftp_ATBD/REVIEW/ASTER/ATBD-AST-05/atbd-ast-05.pdf)
  TIR                  

  Polar Surface        [eospso.gsfc.nasa.gov/ftp_ATBD/REVIEW/ASTER/ATBD-AST-07/atbd-ast-07.pdf](../eospso.gsfc.nasa.gov/ftp_ATBD/REVIEW/ASTER/ATBD-AST-07/atbd-ast-07.pdf)
  Classification       

  ASTER DEM            [eospso.gsfc.nasa.gov/ftp_ATBD/REVIEW/ASTER/ATBD-AST-08/atbd-ast-08.pdf](../eospso.gsfc.nasa.gov/ftp_ATBD/REVIEW/ASTER/ATBD-AST-08/atbd-ast-08.pdf)

  ASTER Validation     [eospso.gsfc.nasa.gov/ftp_ATBD/REVIEW/ASTER/VALIDATION/val-ast-full.pdf](../eospso.gsfc.nasa.gov/ftp_ATBD/REVIEW/ASTER/VALIDATION/val-ast-full.pdf)
  Plan                 

  ASTER Validation     [eospso.gsfc.nasa.gov/ftp_docs/ASTER_VAL_SUM_0901.pdf](../eospso.gsfc.nasa.gov/ftp_docs/ASTER_VAL_SUM_0901.pdf)
  Summaries            
  -------------------- -----------------------------------------------------------------------------------------------------------------------------------------------------

#### What on-line resources are available to learn more about ASTER?

[http://asterweb.jpl.nasa.gov](http://asterweb.jpl.nasa.gov/)

[http://www.gds.aster.ersdac.or.jp](http://www.gds.aster.ersdac.or.jp/)

<http://edcdaac.usgs.gov/dataproducts.html>

<http://edcdaac.usgs.gov/asterondemand/index.html>

[http://terra.nasa.gov](http://terra.nasa.gov/)

Can you give me a list of general overview articles/papers on ASTER that
have thus far been published?

A. KAHLE, F. PALLUCONI, S. HOOK, V.J. REALMUTO, and G. BOTHWELL, (1991)
The

Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER).
Int. J.Imaging System Tech., vol. 3, pp. 144-156.

FUJISADA, H., (1998) ASTER Level-1 Processing Algorithm.

IEEE Transactions on Geoscience and Remote Sensing, Vol. 36, pp. 1101 -
1112.

FUJISADA, H., (1994) Overview of ASTER Instruments on EOS-AM1 Platform.

Proceedings, SPIE, International Society for Optical Engineering, Vol.
2268, pp. 14 - 36.

YAMAGUCHI, Y., KAHLE, A., TSU, H., KAWAKAMI, T., and PNIEL, M. (1998)
Overview of ASTER. IEEE Transactions on Geoscience and Remote Sensing,
Vol. 36, pp. 1062 -- 1071

Y. YAMAGUCHI, H. TSU,, A. KAHLE, and D.A. NICHOLS, (1994) ASTER
instrument

design and science objectives. Amer. Inst. Aeron. Astron., vol. 7, paper
94-0597.

Y. YAMAGUCHI, H. TSU and H. FUJISADA, (1993) Scientific basis of ASTER
instrument

design. In Proc. SPIE, vol. 1939, pp. 150-160.

## 

## 13.10 HDF-EOS Data Format

**What data format is the ASTER Level-1 dataset in, and what software
can I use to access it?**

ASTER data is provided in HDF-EOS format. HDF stands for Hierarchical
Data Format, which was developed by the National Center for
Supercomputing Applications (NCSA) at the University of Illinois at
Urbana-Champaign. HDF was originally developed for handling massive
arrays to support supercomputing applications. HDF-EOS is a specific
implementation of HDF to handle swaths, grids, and points.

**Are there any tools available for converting ASTER data into other
formats, e.g. GeoTIFF so I can use them in other packages e.g. IDL/ENVI,
Arc/Info, ERDAS/Imagine?**

Among the commercial image processing software packages, ENVI version
3.5 (from RSI) allows you to ingest ASTER data. Other packages that
handle ASTER data include Imagine version 8.5 (from ERDAS), PCI version
8.2 (from PCI Geomatics), Matlab (from MathWorks), Noesys (from RSI),
and Idrisi (from Clark University). The ASTER team at JPL has developed
free tools for processing ASTER data. They are available from:
<http://winvicar.jpl.nasa.gov/>

Refer Appendix IV for more on available public domain software.

**Where can I get help on understanding more about HDF & HDF-EOS
formats?**

HDF, developed by NCSA, is a public domain software that is acknowledged
for its portability. HDF software includes I/O libraries and tools for
analyses, visualization, and data conversion. NASA decided to
incorporate a specific implementation of HDF for its EOS mission-derived
data sets called HDF-EOS. This includes specific data models for storage
and handling of swath, grid, and point primitives. There are a number of
on-line sources of information on HDF, and HDF-EOS:

[http://hdf.ncsa.uiuc.edu](http://hdf.ncsa.uiuc.edu/)

http://hdf.ncsa.uiuc.edu/hdfeos.html

<http://hdfeos.gsfc.nasa.gov/cfdocs/hdfeos/workshop.cfm>

<http://hdfeos.gsfc.nasa.gov/cfdocs/hdfeos/RESOURCE.html>

Appendix IV provides more information on public domain software that
handle HDF-EOS and the links to access and procure them.

# Appendix I: Dump of HDF Metadata in a ASTER L1B file

**coremetadata.0**

SHORTNAME= \"ASTL1B\"

SIZEMBDATAGRANULE= 119.185000

PRODUCTIONDATETIME= \"2001-04-14T07:24:17.000Z\"

PLATFORMSHORTNAME= \"AM-1\"

INSTRUMENTSHORTNAME= \"ASTER\"

WESTBOUNDINGCOORDINATE= 29.260599

NORTHBOUNDINGCOORDINATE= -4.082604

EASTBOUNDINGCOORDINATE= 30.006667

SOUTHBOUNDINGCOORDINATE= -4.741722

TIMEOFDAY= \"084727306000Z\"

CALENDARDATE= \"20000717\"

FUTUREREVIEWDATE= \"20001225\"

SCIENCEREVIEWDATE= \"20001109\"

QAPERCENTMISSINGDATA= 0.001309

QAPERCENTOUTOFBOUNDSDATA= 0.001309

QAPERCENTINTERPOLATEDDATA= 0.000000

REPROCESSINGACTUAL= \"not reprocessed\"

PGEVERSION= \"03.00R02\"

PROCESSINGLEVELID= \"1B\"

MAPPROJECTIONNAME= \"Universal Transverse Mercator\"

**productmetadata.0**

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RECEIVINGCENTER= \"EDOS\"

PROCESSINGCENTER= \"ASTER-GDS\"

SENSORNAME= \"VNIR\"

POINTINGANGLE= 8.578000

SETTINGTIMEOFPOINTING= \"2000-07-17T08:41:54Z\"

SENSORNAME= \"SWIR\"

POINTINGANGLE= 8.547000

SETTINGTIMEOFPOINTING= \"2000-07-17T08:41:53Z\"

SENSORNAME= \"TIR\"

POINTINGANGLE= 8.567000

SETTINGTIMEOFPOINTING= \"2000-07-17T08:41:53Z\"

GAIN= (\"01\", \"HGH\")

GAIN= (\"02\", \"HGH\")

GAIN= (\"3N\", \"NOR\")

GAIN= (\"3B\", \"NOR\")

GAIN= (\"04\", \"NOR\")

GAIN= (\"05\", \"NOR\")

GAIN= (\"06\", \"NOR\")

GAIN= (\"07\", \"NOR\")

GAIN= (\"08\", \"NOR\")

GAIN= (\"09\", \"NOR\")

GEOMETRICDBVERSION= (\"01.02\", \"2000-10-25\")

RADIOMETRICDBVERSION= (\"01.02\", \"2000-11-09\")

COARSEDEMVERSION= (\"1.00\", \"1997-09-03\", \"This data is generated
from GTOPO30\")

SCENECLOUDCOVERAGE= 7

QUADRANTCLOUDCOVERAGE= (4, 13, 1, 11)

SOURCEDATAPRODUCT= (\"ASTL1A 0007170847270103051199\",
\"2001-03-05T15:23:38.000Z\", \"PDS\")

ASTEROPERATIONMODE= \"OBSERVATION\"

ASTEROBSERVATIONMODE= (\"VNIR1\", \"ON\")

ASTEROBSERVATIONMODE= (\"VNIR2\", \"ON\")

ASTEROBSERVATIONMODE= (\"SWIR\", \"ON\")

ASTEROBSERVATIONMODE= (\"TIR\", \"ON\")

PROCESSEDBANDS= \"01023N3B0405060708091011121314\"

ASTERSCENEID= (173, 176, 7)

ORBITNUMBER= 3087

RECURRENTCYCLENUMBER= (14, 58)

FLYINGDIRECTION= \"DE\"

SOLARDIRECTION= (37.043010, 57.701316)

SPATIALRESOLUTION= (15, 30, 90)

UPPERLEFT= (-4.082604, 29.341137)

UPPERRIGHT= (-4.178226, 30.006667)

LOWERLEFT= (-4.646324, 29.260599)

LOWERRIGHT= (-4.741722, 29.926708)

SCENECENTER= (-4.412223, 29.633727)

SCENEORIENTATIONANGLE= -8.336200

**productmetadata.1**

SENSORSHORTNAME= (\"ASTER_VNIR\", \"ASTER_SWIR\", \"ASTER_TIR\")

IDOFASTERGDSDATABROWSE= \"ASTL1A 0007170847270103051199B\"

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MEANANDSTD1= (60.425335, 6.740928)

MODEANDMEDIAN1= (54, 148)

NUMBEROFBADPIXELS1= (0, 0)

CORINTEL1= \"N/A\"

CORPARA1= \"N/A\"

RESMETHOD1= \"CC\"

MPMETHOD1= \"UTM\"

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INCL1= 0.676000

OFFSET1= -0.676000

CONUNIT1= \"W/m2/sr/um\"

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MINANDMAX2= (25, 251)

MEANANDSTD2= (42.124893, 12.541837)

MODEANDMEDIAN2= (29, 138)

NUMBEROFBADPIXELS2= (0, 0)

CORINTEL2= \"N/A\"

CORPARA2= \"N/A\"

RESMETHOD2= \"CC\"

MPMETHOD2= \"UTM\"

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INCL2= 0.708000

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CONUNIT2= \"W/m2/sr/um\"

IMAGEDATAINFORMATION3N= (4980, 4200, 1)

MINANDMAX3N= (13, 209)

MEANANDSTD3N= (40.284557, 21.741985)

MODEANDMEDIAN3N= (17, 111)

NUMBEROFBADPIXELS3N= (0, 0)

CORINTEL3N= \"N/A\"

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INCL3N= 0.862000

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CONUNIT3N= \"W/m2/sr/um\"

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UTMZONECODE3B= 35

INCL3B= 0.862000

OFFSET3B= -0.862000

CONUNIT3B= \"W/m2/sr/um\"

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MINANDMAX4= (1, 255)

MEANANDSTD4= (35.554417, 25.348192)

MODEANDMEDIAN4= (9, 128)

NUMBEROFBADPIXELS4= (0, 0)

PROCESSINGFLAG4= 1

NUMBEROFMEASUREMENTS4= 266

MEASUREMENTPOINTNUMBER4= 224

AVERAGEOFFSET4= (-2.644275, 0.863551)

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PCTIMAGEMATCH4= 47

AVGCORRELCOEF4= 0.900000

CTHLD4= -0.020000

CORINTEL4= \"Corrected Intertelescope Error\"

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RESMETHOD4= \"CC\"

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IMAGEDATAINFORMATION5= (2490, 2100, 1)

MINANDMAX5= (1, 255)

MEANANDSTD5= (28.290176, 19.029987)

MODEANDMEDIAN5= (8, 128)

NUMBEROFBADPIXELS5= (0, 0)

PROCESSINGFLAG5= 1

NUMBEROFMEASUREMENTS5= 266

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PCTIMAGEMATCH5= 47

AVGCORRELCOEF5= 0.900000

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MINANDMAX6= (1, 255)

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CONUNIT9= \"W/m2/sr/um\"

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UTMZONECODE11= 35

INCL11= 0.006780

OFFSET11= -0.006780

CONUNIT11= \"W/m2/sr/um\"

IMAGEDATAINFORMATION12= (830, 700, 2)

MINANDMAX12= (1055, 2976)

MEANANDSTD12= (1428.515503, 117.746712)

MODEANDMEDIAN12= (1336, 2015)

NUMBEROFBADPIXELS12= (0, 0)

PROCESSINGFLAG12= 0

NUMBEROFMEASUREMENTS12= 500

MEASUREMENTPOINTNUMBER12= 22

AVERAGEOFFSET12= (-1.048756, -1.079842)

STANDARDDEVIATIONOFFSET12= (0.244948, 0.116378)

THRESHOLD12= (0.700000, 3.000000, 3.000000, 12.000000)

CORINTEL12= \"Corrected Intertelescope Error\"

CORPARA12= \"N/A\"

RESMETHOD12= \"CC\"

MPMETHOD12= \"UTM\"

PROJECTIONPARAMETERS12= (6378137.000000, 6356752.300000, 0.999600,
0.000000, 0.471239, 0.000000, 500000.000000, 0.000000, 0.000000,
0.000000, 0.000000, 0.000000, 0.000000)

UTMZONECODE12= 35

INCL12= 0.006590

OFFSET12= -0.006590

CONUNIT12= \"W/m2/sr/um\"

IMAGEDATAINFORMATION13= (830, 700, 2)

MINANDMAX13= (1245, 2893)

MEANANDSTD13= (1689.340332, 120.362442)

MODEANDMEDIAN13= (1600, 2069)

NUMBEROFBADPIXELS13= (0, 0)

PROCESSINGFLAG13= 0

NUMBEROFMEASUREMENTS13= 500

MEASUREMENTPOINTNUMBER13= 22

AVERAGEOFFSET13= (-1.048756, -1.079842)

STANDARDDEVIATIONOFFSET13= (0.244948, 0.116378)

THRESHOLD13= (0.700000, 3.000000, 3.000000, 12.000000)

CORINTEL13= \"Corrected Intertelescope Error\"

CORPARA13= \"N/A\"

RESMETHOD13= \"CC\"

MPMETHOD13= \"UTM\"

PROJECTIONPARAMETERS13= (6378137.000000, 6356752.300000, 0.999600,
0.000000, 0.471239, 0.000000, 500000.000000, 0.000000, 0.000000,
0.000000, 0.000000, 0.000000, 0.000000)

UTMZONECODE13= 35

INCL13= 0.005693

OFFSET13= -0.005693

CONUNIT13= \"W/m2/sr/um\"

IMAGEDATAINFORMATION14= (830, 700, 2)

MINANDMAX14= (1297, 2835)

MEANANDSTD14= (1765.809204, 115.020287)

MODEANDMEDIAN14= (1682, 2066)

NUMBEROFBADPIXELS14= (0, 0)

PROCESSINGFLAG14= 0

NUMBEROFMEASUREMENTS14= 500

MEASUREMENTPOINTNUMBER14= 22

AVERAGEOFFSET14= (-1.048756, -1.079842)

STANDARDDEVIATIONOFFSET14= (0.244948, 0.116378)

THRESHOLD14= (0.700000, 3.000000, 3.000000, 12.000000)

CORINTEL14= \"Corrected Intertelescope Error\"

CORPARA14= \"N/A\"

RESMETHOD14= \"CC\"

MPMETHOD14= \"UTM\"

PROJECTIONPARAMETERS14= (6378137.000000, 6356752.300000, 0.999600,
0.000000, 0.471239, 0.000000, 500000.000000, 0.000000, 0.000000,
0.000000, 0.000000, 0.000000, 0.000000)

UTMZONECODE14= 35

INCL14= 0.005225

OFFSET14= -0.005225

CONUNIT14= \"W/m2/sr/um\"

# Appendix II: ASTER Higher-Level Data Products 

**Decorrelation Stretch**

  ----------------------------------- -----------------------------------
  Lead Investigator:                  Ron Alley

  Short Name:                         AST_06

  Product Level:                      2

  Production Mode:                    Routine

  Absolute Accuracy:                  N/A

  Relative Accuracy:                  N/A

  Horizontal Resolution:              15, 30, and 90 m

  Units:                              None

  Product Size (Megabytes):           85 (VNIR), 22 (SWIR), 3 (TIR)
  ----------------------------------- -----------------------------------

#### Product Description

This product, which is available for each of ASTER's three telescopes,
is a decorrelation

stretched image of ASTER radiance data. The Decorrelation stretch is a
process to enhance (in

image processing parlance, "stretch") the color differences found in a
color image by a method

that includes the removal of the inter-channel correlation found in the
input pixels; hence, the

term "decorrelation stretch". The image is produced at pixel resolutions
of 15 m for VNIR, 30 m

for SWIR, and 90 m for TIR. Decorrelation-stretched images provide an
overview that enhances

spectral reflectance variations.

#### Algorithm Description

If one views the pixels in an ASTER scene as a set of 3-vectors, a
linear transformation can be

found which results in removing the correlation among the vectors in the
transformed space.

This is an eigenvector problem, and can be thought of as a rotation of
the coordinate system of

the original vector space. Within this rotated space, each component is
rescaled (contrast

stretched) by normalizing the variances of the eigenvectors. If
processing were to stop here, the

result would be a principal component image. To produce the
decorrelation stretched image, the

principal component image is modified by the linear transformation that
rotates the vectors back

into the original coordinate system.

In practice, the original transformation, the variance normalization
step, and the reverse transformation are combined into a single
algebraic step.

#### Applications

These images are used as a visual aid in reviewing the ASTER scene data
and making the selection of suitable scenes for further analysis and
research. In particular, a decorrelation-stretched image would show the
potential user which scenes have spectral variations large enough to be
useful for subsequent spectral analysis

#### Constraints

The decorrelation stretch algorithm is best suited to the case where the
input data of all three channels have a joint distribution that is
Gaussian (or near Gaussian) in form. Fortunately the algorithm is fairly
insensitive to substantial deviations from the ideal. One should be
aware, though, that if the distribution of the input pixels is strongly
bimodal (or multimodal), the effectiveness of the decorrelation stretch
is weakened, and there will be less diversity of color in this image
than in other images.

Additionally, the decorrelation stretch algorithm is a method of color
enhancement that exploits

whatever interchannel differences that may exist. Implicit in this
technique is the assumption that

the differences are real, and not noise or processing artifacts. The
algorithm single-mindedly

produces a color enhanced output; if noise is a major component of the
scene variation, the

algorithm will enhance those noise differences to produce an output
that, while colorful, will be

painfully noisy.

**Brightness Temperature at Sensor**

  ----------------------------------- -----------------------------------
  Lead Investigator:                  Ron Alley

  Short Name:                         AST_04

  Product Level:                      2

  Production Mode:                    On-Demand

  Absolute Accuracy:                  1-2 C

  Relative Accuracy:                  0.3 C

  Horizontal Resolution:              90 m

  Units:                              Degrees C

  Product Size (Megabytes):           \~7
  ----------------------------------- -----------------------------------

#### Product Description

The body of this product is the brightness temperature for ASTER\'s five
thermal-infrared bands (8-12 μm, bands 10-14). Brightness temperature is
the apparent observed temperature, assuming a surface emissivity of 1.0
(i.e., as if the object were a blackbody). The calculations are
performed starting with the radiance at sensor as input; no atmospheric
correction is included for this product.

#### Algorithm Description

The amount of radiance that an ASTER channel will observe when viewing a
source of a particular temperature is calculated in the following
manner. The spectral radiance at each wavelength (to a 0.01 μm
precision) is computed using the Planck function. This value is
multiplied by the normalized spectral response function at that
wavelength, and the results of this calculation are integrated over the
range of wavelengths that have a sensor response.

The above calculation was made for each of the five ASTER TIR channels
at all temperatures (to a 0.01 degree C precision) that the ASTER TIR
subsystem was designed to record (200 to 370 degrees Kelvin). The result
is a table of observed radiances as a function of temperature. This
table was used to construct a second table, which lists temperature as a
function of radiance. This second table is stored as a lookup table, to
be used to generate this product.

#### Applications

Brightness temperature has been used to observe volcanic ash clouds,
detect ice leads in the Arctic, and to identify anthropogenic and
natural fires, to name a few examples. The ASTER brightness temperature
will be used as an alternate to radiance in the temperature/emissivity
separation algorithm to report relative cloud-top temperature because
there will be no routinely available applicable atmospheric correction
to enable a calculation of exact cloud-top temperature. ASTER brightness
temperatures can be acquired during the day or night and over all
surface types (land, water, cloud, etc.).

#### Constraints

The algorithm is constrained only by the fact that it requires
unsaturated input radiance values. The algorithm should work on TIR data
acquired during the day or night and over land, clouds, water, or
anything else not hotter than about 120 degrees C or colder than about
--100 degrees C.

**Surface Reflectance**

+-----------------------------------+-----------------------------------+
| Lead Investigator:                | Kurt Thome                        |
+-----------------------------------+-----------------------------------+
| Short Name:                       | AST_07                            |
+-----------------------------------+-----------------------------------+
| Product Level:                    | 2                                 |
+-----------------------------------+-----------------------------------+
| Production Mode:                  | On-Demand                         |
+-----------------------------------+-----------------------------------+
| Absolute Accuracy:                | 0.01 for reflectance \< 0.15      |
|                                   |                                   |
|                                   | 7 % for reflectance \> 0.15       |
+-----------------------------------+-----------------------------------+
| Relative Accuracy:                | 0.005                             |
+-----------------------------------+-----------------------------------+
| Horizontal Resolution:            | 15, 30 m                          |
+-----------------------------------+-----------------------------------+
| Units:                            | None                              |
+-----------------------------------+-----------------------------------+
| Product Size (Megabytes):         | 189 (VNIR), 79 (SWIR)             |
+-----------------------------------+-----------------------------------+

#### Product Description

The Level-2 surface reflectance data set (AST07) contains surface
reflectance for each of the nine VNIR and SWIR bands at 15-m and 30-m
resolutions, respectively. The results are obtained by applying an
atmospheric correction to radiances reported by the ASTER sensor. The
atmospheric correction removes effects due to changes in satellite-sun
geometry and atmospheric conditions. The atmospheric correction
algorithm is applied to clear-sky pixels only and the results are
reported as a number between 0 and 1.

#### Algorithm Description

The atmospheric correction algorithm used to retrieve the surface
reflectance relies on a look-up table (LUT) approach. The LUT contains
forward radiative transfer calculations from a Gauss-Seidel iteration
code to compute at-satellite radiance for a set of assumed surface
reflectance values and a variety of atmospheric conditions. The
atmospheric correction is applied by using a set of input atmospheric
conditions relating to the ASTER scene of interest to select a portion
of the LUT. The output of the LUT search is a set of surface
reflectance/at-sensor radiance pairs. Using linear interpolation on
these pairs, a radiance reported by ASTER is converted to a surface
reflectance. The atmospheric conditions are defined by the aerosol size
distribution (or equivalently the aerosol type), the aerosol amount,
surface pressure, and the sun-satellite geometry, The aerosol
information is obtained from outside sources, for example MISR, MODIS,
or climatological means. The scattering phase functions of the aerosol
particles in the atmosphere are assumed to scatter as mie particles
using the aerosol size distribution information supplied by MISR or
MODIS. The results from this method will be in reflectance units (values
between 0 and 1) with an accuracy dependent upon the accuracy of input
atmospheric conditions and the surface slope. The model is expected to
lose accuracy in terrain with high relief due to the assumption of
horizontal homogeneity made in the radiative transfer code. Also because
of this assumption, the model will give less accurate results in regions
where the atmosphere or surface are not horizontally homogeneous on the
scale of several pixels.

#### Applications

Accurate atmospheric correction removes effects of changes in
satellite-sun geometry and atmospheric conditions and improves surface
type classification and estimates of the Earth\'s radiation budget, and
use of ASTER data for applications such as agricultural management
requires atmospheric correction.

#### Constraints

This description applies to the atmospheric correction method used for
the solar-reflective bands only for clear-sky pixels. This algorithm
requires a digital elevation model providing slope and elevation for
accurate modeling of surface reflectance. The model requires total and
component optical depths as input. The algorithm is computed only for
daytime image data for the VNIR - SWIR bands. The algorithm begins to
break down at large view angles (not applicable for ASTER) and large
solar zenith angles (\>75 degrees). The algorithm's accuracy also
degrades somewhat in regions around the backscatter direction due to
strong surface BRDF effects. Uncertainty in the results also increases
in regions of atmospheric heterogeneity.

#### Surface Radiance -- VNIR, SWIR

+-----------------------------------+-----------------------------------+
| Lead Investigator:                | Kurt Thome                        |
+-----------------------------------+-----------------------------------+
| Short Name:                       | AST_09                            |
+-----------------------------------+-----------------------------------+
| Product Level:                    | 2                                 |
+-----------------------------------+-----------------------------------+
| Production Mode:                  | On-Demand                         |
+-----------------------------------+-----------------------------------+
| Absolute Accuracy:                | 8% for reflectance \> 0.1         |
|                                   |                                   |
|                                   | 15% for reflectance \< 0.1        |
+-----------------------------------+-----------------------------------+
| Relative Accuracy:                | 1%                                |
+-----------------------------------+-----------------------------------+
| Horizontal Resolution:            | 15, 30 m                          |
+-----------------------------------+-----------------------------------+
| Units:                            | W m^-2^ sr^-1^ µm^-1^             |
+-----------------------------------+-----------------------------------+
| Product Size (Megabytes):         | 189 (VNIR), 79 (SWIR)             |
+-----------------------------------+-----------------------------------+

#### Product Description

The Level-2 surface radiance data set (AST09) contains surface radiance
for each of the nine VNIR and SWIR bands at 15-m and 30-m resolutions,
respectively. The results are obtained by applying an atmospheric
correction to radiances reported by the ASTER sensor. The atmospheric
correction removes effects due to changes in satellite-sun geometry and
atmospheric conditions. The atmospheric correction algorithm is applied
to clear-sky pixels only and the results are reported as a number
between 0 and 1.

#### Algorithm Description

The atmospheric correction algorithm used to retrieve the surface
radiance relies on a look-up table (LUT) approach. The LUT contains
forward radiative transfer calculations from a Gauss-Seidel iteration
code to compute at-satellite radiance for a set of assumed surface
reflectance values and a variety of atmospheric conditions. The
atmospheric correction is applied by using a set of input atmospheric
conditions relating to the ASTER scene of interest to select a portion
of the LUT. The output of the LUT search is a set of surface
radiance/at-sensor radiance pairs. Using linear interpolation on these
pairs, a radiance reported by ASTER is converted to a surface radiance.
The atmospheric conditions are defined by the aerosol size distribution
(or equivalently the aerosol type), the aerosol amount, surface
pressure, and the sun-satellite geometry, The aerosol information is
obtained from outside sources, for example MISR, MODIS, or
climatological means. The scattering phase functions of the aerosol
particles in the atmosphere are assumed to scatter as mie particles
using the aerosol size distribution information supplied by MISR or
MODIS. The accuracy of the results from this method are dependent upon
the accuracy of input atmospheric conditions and the surface slope. The
model is expected to lose accuracy in terrain with high relief due to
the assumption of horizontal homogeneity made in the radiative transfer
code. Also because of this assumption, the model will give less accurate
results in regions where the atmosphere or surface are not horizontally
homogeneous on the scale of several pixels.

#### Applications

Accurate atmospheric correction removes effects of changes in
satellite-sun geometry and atmospheric conditions and improves surface
type classification and estimates of the Earth\'s radiation budget, and
use of ASTER data for applications such as agricultural management
requires atmospheric correction.

#### Constraints

This description applies to the atmospheric correction method used for
the solar-reflective bands only for clear-sky pixels. This algorithm
requires a digital elevation model providing slope and elevation for
accurate modeling of surface reflectance. The model requires total and
component optical depths as input. The algorithm is computed only for
daytime image data for the VNIR - SWIR bands. The algorithm begins to
break down at large view angles (not applicable for ASTER) and large
solar zenith angles (\>75 degrees). The algorithm's accuracy also
degrades somewhat in regions around the backscatter direction due to
strong surface BRDF effects. Uncertainty in the results also increases
in regions of atmospheric heterogeneity

**Surface Radiance -- TIR**

  ----------------------------------- -----------------------------------
  Lead Investigator:                  Frank Palluconi

  Short Name:                         AST_09T

  Product Level:                      2

  Production Mode:                    On-Demand

  Absolute Accuracy:                  2%

  Relative Accuracy:                  1%

  Horizontal Resolution:              90 m

  Units:                              W m^-2^ sr^-1^ µm^-1^

  Product Size (Megabytes):           14
  ----------------------------------- -----------------------------------

#### Product Description

This product provides surface leaving radiance, in W m^-2^ sr^-1^
µm^-1^, for the five ASTER TIR channels at 90 m spatial resolution. In
addition, the down welling sky irradiance in W m^-2^ µm^-1^ for the five
ASTER TIR channels is also provided. Atmospheric correction has been
applied and the surface leaving radiance is valid for the clear sky
portion of scenes. This radiance includes both surface emitted and
surface reflected components. The surface radiance is only of known
accuracy for cloud-free pixels since insufficient information is
available about cloud properties for a valid correction of cloudy
pixels.

Accurate atmospheric correction is intended to remove the effect of the
atmosphere providing the opportunity to use these radiances in the
determination of surface spectral emissivity and surface kinetic
temperature. This atmospheric correction, along with similar corrections
for other Terra instruments, marks the first implementation of
operational atmospheric correction in environmental satellites. This
parameter is generated only upon request, and the data can be collected
during either the daytime or nighttime.

Algorithm Description

The radiance measured by the ASTER instrument includes emission,
absorption, and scattering by the constituents of the earth\'s
atmosphere. The purpose of atmospheric correction is to remove these
effects providing estimates of the radiation emitted and reflected at
the surface. Atmospheric correction is necessary to isolate those
features of the observation that are intrinsic to the surface from those
caused by the atmosphere.

The approach involves two fundamental elements: 1) the use of a
radiation transfer model capable of estimating the magnitude of
atmosphere emission, absorption, and scattering, and 2) the acquisition
of all the necessary atmospheric parameters (i.e. temperature, water
vapor, ozone, aerosols) at the time and location of the measurement to
be corrected. MODTRAN is the chosen radiation transfer model.

#### Applications

Surface leaving radiance is closely associated with the thermal
properties of the surface itself nearly independent of the overlying
atmosphere. If the spectral emissivity of the surface is known, the
surface kinetic temperature can be directly obtained given the
information provided with this product. Surface kinetic temperature can
be used in a number of applications ranging from derivations of sensible
heat flux to estimates of plant stress. Several methods of separating
surface leaving radiance into estimates of spectral emissivity and
surface kinetic temperature exist including the algorithm used for this
process by ASTER. Spectral emissivity can be used to estimate surface
composition, which has wide application in geology, environmental
assessment and urban planning.

#### Constraints

The surface leaving radiance is only of known accuracy for cloud-free
pixels. As this data product does not correct for the presence of water
or ice clouds it is of uncertain value when such clouds are present,
however, a cloud mask is included in the quality assurance \"QA plane\"
portion of the product, allowing the user to avoid cloudy pixels. In
addition, the cloud identity products from MODIS and MISR may be used if
the spatial resolution of these products is acceptable. This product is
used within the ASTER operational data product production framework as
an input to the generation of surface spectral emissivity for the five
ASTER TIR channels and the derivation of surface kinetic temperature.

#### Surface Emissivity

  ----------------------------------- -----------------------------------
  Lead Investigator:                  Alan Gillespie and S. Rokugawa

  Short Name:                         AST_05

  Product Level:                      2

  Production Mode:                    On-Demand

  Absolute Accuracy:                  0.05 - 0.1

  Relative Accuracy:                  0.005

  Horizontal Resolution:              90 m

  Units:                              None

  Product Size (Megabytes):           9
  ----------------------------------- -----------------------------------

#### Product Description

The Level-2 land surface emissivity product contains surface emissivity
at 90-m resolution generated only over the land from ASTER\'s five
thermal infrared channels. Surface emissivity is required to derive land
surface temperature (AST08) data, also at a resolution of 90 meters. The
emissivity product is critical for deriving accurate land surface
temperatures. It is therefore important in studies of surface energy and
water balance. The emissivity product is also useful for mapping
geologic and land-cover features.

Current sensors provide only limited information useful for deriving
surface emissivity and researchers are required to use emissivity
surrogates such as land-cover type or vegetation index in making rough
estimates of emissivity and hence land surface temperatures. The five
thermal infrared channels of the ASTER instrument enable direct surface
emissivity estimates. Mapping of thermal features from optical sensors
such as Landsat and AVHRR has been used for many developmental studies.
These instruments, however, lack the spectral coverage, resolution and
radiometric accuracy that will be provided by the ASTER instrument.

#### Algorithm Description

Read in the land-leaving radiance and down-welling sky irradiance
vectors for each pixel. Estimate the emissivity spectrum using the
Normalized Emissivity Method and iteratively compensate for reflected
skylight. Normalize the emissivity spectrum using the average emissivity
for each pixel. Calculate the min-max difference (MMD) of the normalized
spectrum and estimate the minimum emissivity using a regression that
relates the MMD and the minimum emissivity. Scale the normalized
emissivities using the minimum emissivity. Compensate for reflected
skylight using the refined emissivities. Use the emissivity value to
calculate a temperature using Planck\'s Law.

#### Applications

Emissivity is useful in identifying surface composition. Many minerals
\-- especially silicate minerals that make up the bulk of the Earth's
surface \-- have distinctive thermal infrared emissivity spectra, but
ambiguous or non-distinctive VNIR spectra. Quartz, feldspars,
amphiboles, and pyroxenes all are in this category. Carbonate rocks also
have distinctive spectra, although the diagnostic features are
unresolved by ASTER. Because other minerals \-- especially iron-bearing
and hydrated minerals \-- have distinctive VNIR and SWIR spectra,
surface composition mapping is best undertaken with the full range of
ASTER bands, not just the TIR bands alone.

Rock and soil emissivities also contrast with vegetation, snow and
water. Therefore, emissivity data are useful for mapping forest
clearings and snow coverage.

Atmospheric gases such as SO~2~, emitted from volcanoes, absorb
ground-emitted thermal radiation selectively. Therefore, emissivity maps
are useful in recognizing the presence of volcanic emissions, although
special processing is required to quantify them. The same comments apply
to industrial pollution.

#### Constraints

Currently there are no constraints, and the algorithm should work with
TIR data acquired during the day or night. The algorithm will return
incorrect values for clouds, however, because the atmospheric
corrections will have been inaccurate due to a lack of knowledge of
cloud height. Therefore, if a pixel is classified as \"cloud\" on the
basis of its spectral and temperature characteristics a notation to that
effect will be made in the QA plane. Because clouds radiate to the
ground, pixels not covered by clouds but in their vicinity will also
have inaccurate emissivities and spectra, and therefore these pixels are
also noted in the QA plane. For cold surfaces viewed through a warm or
humid atmosphere correction for reflected skylight can be inaccurate,
leading to inaccurate emissivity estimates.

#### Surface Kinetic Temperature

  ----------------------------------- -----------------------------------
  Lead Investigator:                  Alan Gillespie and S. Rokugawa

  Short Name:                         AST_08

  Product Level:                      2

  Production Mode:                    On-Demand

  Absolute Accuracy:                  1-4 K

  Relative Accuracy:                  0.3 K

  Horizontal Resolution:              90 m

  Units:                              degrees K

  Product Size (Megabytes):           3.5
  ----------------------------------- -----------------------------------

#### Product Description

The Level-2 land surface kinetic temperature product contains surface
temperatures at 90-m resolution generated only over the land from
ASTER\'s five thermal infrared channels. Land surface temperatures are
determined from Planck\'s Law, using the emissivities from AST05 to
scale the measured radiances after correction for atmospheric effects.
Surface temperatures are important in studies of surface energy and
water balance. They are also useful in studies of volcanism and thermal
pollution.

Current sensors provide only limited information useful for deriving
surface emissivity, and therefore land surface temperature estimates can
be inaccurate. The five thermal infrared channels of the ASTER
instrument enable direct surface emissivity estimates, and accurate
temperature estimation.

**Algorithm Description** See AST_05, Surface Emissivity.

#### 

#### Applications

The derived land surface temperature has applications in studies of
surface energy and water balance. Temperature data will be used in the
monitoring and analysis of volcanic processes, day and night temperature
data will be used to estimate thermal inertia, and thermal data will be
used for high-resolution mapping of fires as a complement to MODIS
global fire data. Thermal data are especially useful in fire studies
because they can "see through" smoke to the burning terrain below.

#### Constraints

See AST_05, Surface Emissivity. For cold surfaces viewed through a warm
or humid atmosphere correction for reflected skylight can be inaccurate,
leading to inaccurate emissivity estimates. The error increases as the
emissivity decreases. Therefore, even if some emissivities are
erroneous, surface temperatures may be accurate, provided some of the
emissivities are near unity.

**Polar Surface and Cloud Classification**

  ----------------------------------- -----------------------------------
  Lead Investigator:                  Ron Welch

  Short Name:                         AST13POL

  Product Level:                      2

  Production Mode:                    On-Demand

  Absolute Accuracy:                  3%

  Relative Accuracy:                  3%

  Horizontal Resolution:              30 m

  Units:                              None

  Product Size (Megabytes):           42
  ----------------------------------- -----------------------------------

#### Product Description

This Level-2 product is a polar classification map. The polar regions
are defined as all earth surfaces lying pole-ward from 60 N or 60 S. The
algorithm classifies each pixel of a scene into 8 classes: water cloud,
ice cloud, aerosol/dust, water, land, snow/ice, slush ice, and shadow.
This product is produced at 30-m spatial resolution and uses a
combination of visible, near-infrared and infrared channels. Both
daytime and nighttime classifications will be available, with the
daytime algorithm applied for solar zenith angles less then 85 degrees,
and the nighttime algorithm applied in all cases using only thermal
infrared channels. Only the daytime algorithm is available at this
time*.* This is an on-request product.

This data product builds on work over the past decade with imagery taken
from LANDSAT TM, AVIRIS, TIMS, and MAS satellite and aircraft sensors.
The improved spectral coverage, resolution, and radiometric accuracy
enables ASTER to provide remotely sensed polar data of unprecedented
accuracy.

#### Algorithm Description

The methodology implemented in this algorithm is a neural-network
classifier. The neural network technique is chosen because of its
advantages in accuracy and speed when compared to more conventional
techniques such as maximum likelihood. To classify a scene, additional
spectral features first are generated from the original channels.
Several of these additional features are nonlinear combinations of the
original channels, which are constructed to improve separability between
classes. Classification is then performed using a trained neural network
classifier with as input an optimal subset of all features.

#### Applications

Since greenhouse forcings are expected to be amplified in the polar
regions, the changes in these regions may act as early warning
indicators of global climate shifts. Cloud cover is expected to be
altered by greenhouse forcings, and cloud changes are expected to have a
significant effect on sea ice conditions and regional ice-albedo
feedbacks, especially to the polar heat balance which directly affects
surface melting. ASTER polar data will be used to monitor changes in
surface conditions, notably temperature, albedo, and sea ice breakup.

This ASTER polar data product also provides complementary validation to
the global-scale polar data and cloud property retrievals from MODIS. In
particular, this data set will be used to cross-validate the MODIS cloud
optical thickness and effective particle sizes which directly impact the
Earth\'s radiative budget. In addition, only ASTER has the spatial
resolution necessary to fully analyze the three-dimensional effects of
clouds.

#### Constraints

The daytime classification algorithm expects as input all the ASTER
channels. Furthermore, the algorithm expects the gain selections for
VNIR and SWIR channels to be both "LOW". The algorithm has not been
applied to data with other gain selections.

The nighttime algorithm utilizes only the thermal infrared channels as
input. Furthermore, the nighttime algorithm is developed by using only
thermal infrared channels of daytime images. This is due to the fact
that the human experts are less confident in classifying thermal
infrared images by visual inspection. Therefore, daytime images are used
to create training data sets for the classifier.

**Digital Elevation Model (DEM)**

  ----------------------------------- -----------------------------------
  **Lead Investigator:**              Mike Abrams, Roy Welch

  **Short Name:**                     AST14DEM

  **Product Level:**                  3

  **Production Mode:**                On-Demand

  **Absolute Accuracy:**              \>= 7 m

  **Relative Accuracy:**              \>= 10 m

  **Horizontal Resolution:**          30 m

  **Units:**                          meters

  **Product Size (Megabytes):**       25
  ----------------------------------- -----------------------------------

**Product Description**

This data set contains topographic information derived from the
along-track, 15 m ASTER optical stereo data acquired in near-infrared
bands 3N and 3B. It can be created as a Relative DEM (no ground control)
or an Absolute DEM (with ground control, which must be supplied by the
user). These high spatial resolution DEMs (up to 7 m absolute horizontal
and vertical accuracy with appropriate ground control, and up to 10 m
relative accuracy without ground control) can be used to derive absolute
slope and slope aspect good to 5 degrees over horizontal distances of
more than 100 m. ASTER DEMs should meet 1:50,000 to 1:250,000 map
accuracy standards.

This is an on-request product which will be generated by the Land
Processes DAAC at EROS Data Center at a rate of one 60 km X 60 km stereo
pair/day. Based on simulations of instrument operations, mission
planning, cloud cover and illumination, an ASTER digital stereo data set
with a base/height ratio of 0.6 should be acquired for all of the
Earth\'s land surface below 82 degrees latitude by the end of the 6 year
mission. ASTER stereo pairs also can be processed to DEMs by users
operating their own software.

Generation of elevation models from stereo photographic data, now a
routine adjunct to standard surveying methods, has been developed over
the past 60 years based on the principles of photogrammetry. Extensions
of these principles to the generation of DEMs from optical, digital
stereo satellite data has been implemented over the past two decades.
Examples of these satellite stereo systems include SPOT, JERS-1 OPS, and
MOMS. Currently, there are large areas of the globe for which no
consistent, high-resolution, widely available elevation models exist.
ASTER DEMs will help provide much needed coverage over many of these
areas.

**Algorithm Description**

An autocorrelation approach using commercial software at the LP-DAAC
will produce DEMs from Level-1A or 1B digital stereo pairs.

**Applications**

Topographic data as well as derived slope and slope aspect are basic to
all aspects of land surface research including; cartography, climate
modeling, biogeochemistry, biogeography, geophysics, geology,
geomorphology and soil science. Digital elevation data are also required
for atmospheric and radiometric correction of most satellite
observations of the land surface. Digital elevation data are also used
for practical engineering applications such as studies of drainage and
runoff, and site suitability studies for urban development, waste
containment, and recreation.

**Constraints**

This product will be produced using off the shelf commercial software.
Absolute accuracy depends on availability of user-provided ground
control points.

# 

# Appendix III: Metadata Cross Reference Table

If you know the name of a metadata attribute, but don\'t know which hdf
attribute contains it, use this table to find out. (Please ignore
case\--sometimes an item is represented in all upper case, sometimes in
mixed or lower case.)

**[Attribute Name]{.underline}** **[Location]{.underline}**

AdditionalAttributeName coremetadata.0

ASTEROBSERVATIONMODE formatted_product_summary

ASTERObservationMode productmetadata.0

ASTEROPERATIONMODE formatted_product_summary

ASTEROperationMode productmetadata.0

ASTERSCENEID formatted_product_summary

ASTERSceneID productmetadata.0

AUTOMATICQUALITYFLAG formatted_product_summary

AutomaticQualityFlag coremetadata.0

AutomaticQualityFlagExplanation coremetadata.0

AVERAGEOFFSET10 level_1_carryover

AVERAGEOFFSET11 level_1_carryover

AVERAGEOFFSET12 level_1_carryover

AVERAGEOFFSET13 level_1_carryover

AVERAGEOFFSET14 level_1_carryover

AVERAGEOFFSET4 level_1_carryover

AVERAGEOFFSET5 level_1_carryover

AVERAGEOFFSET6 level_1_carryover

AVERAGEOFFSET7 level_1_carryover

AVERAGEOFFSET8 level_1_carryover

AVERAGEOFFSET9 level_1_carryover

AVGCORRELCOEF4 level_1_carryover

AVGCORRELCOEF5 level_1_carryover

AVGCORRELCOEF6 level_1_carryover

AVGCORRELCOEF7 level_1_carryover

AVGCORRELCOEF8 level_1_carryover

AVGCORRELCOEF9 level_1_carryover

BadPixelSegments1 badpixelinformation

BadPixelSegments10 badpixelinformation

BadPixelSegments11 badpixelinformation

BadPixelSegments12 badpixelinformation

BadPixelSegments13 badpixelinformation

BadPixelSegments14 badpixelinformation

BadPixelSegments2 badpixelinformation

BadPixelSegments3B badpixelinformation

BadPixelSegments3N badpixelinformation

BadPixelSegments4 badpixelinformation

BadPixelSegments5 badpixelinformation

**[Attribute Name]{.underline}** **[Location]{.underline}**

BadPixelSegments6 badpixelinformation

BadPixelSegments7 badpixelinformation

BadPixelSegments8 badpixelinformation

BadPixelSegments9 badpixelinformation

BandScaleFactors productmetadata.0

BANDSCALEFACTORS formatted_product_summary

CALENDARDATE formatted_product_summary

CalendarDate coremetadata.0

COARSEDEMVERSION formatted_product_summary

CoarseDEMVersion productmetadata.0

CORINTEL1 level_1_carryover

CORINTEL10 level_1_carryover

CORINTEL11 level_1_carryover

CORINTEL12 level_1_carryover

CORINTEL13 level_1_carryover

CORINTEL14 level_1_carryover

CORINTEL2 level_1_carryover

CORINTEL3B level_1_carryover

CORINTEL3N level_1_carryover

CORINTEL4 level_1_carryover

CORINTEL5 level_1_carryover

CORINTEL6 level_1_carryover

CORINTEL7 level_1_carryover

CORINTEL8 level_1_carryover

CORINTEL9 level_1_carryover

CORPARA1 level_1_carryover

CORPARA10 level_1_carryover

CORPARA11 level_1_carryover

CORPARA12 level_1_carryover

CORPARA13 level_1_carryover

CORPARA14 level_1_carryover

CORPARA2 level_1_carryover

CORPARA3B level_1_carryover

CORPARA3N level_1_carryover

CORPARA4 level_1_carryover

CORPARA5 level_1_carryover

CORPARA6 level_1_carryover

CORPARA7 level_1_carryover

**[Attribute Name]{.underline}** **[Location]{.underline}**

CORPARA8 level_1_carryover

CORPARA9 level_1_carryover

CTHLD4 level_1_carryover

CTHLD5 level_1_carryover

CTHLD6 level_1_carryover

CTHLD7 level_1_carryover

CTHLD8 level_1_carryover

CTHLD9 level_1_carryover

DayNightFlag coremetadata.0

EASTBOUNDINGCOORDINATE formatted_product_summary

EastBoundingCoordinate coremetadata.0

ExclusionGRingFlag coremetadata.0

FlyingDirection productmetadata.0

FutureReviewDate coremetadata.0

GAIN formatted_product_summary

Gain productmetadata.0

GenerationDateandTime productmetadata.0

GENERATIONDATEANDTIME level_1_carryover

GEOMETRICDBVERSION formatted_product_summary

GeometricDBVersion productmetadata.0

GRingPointLatitude coremetadata.0

GRingPointLongitude coremetadata.0

GRingPointSequenceNo coremetadata.0

IDOFASTERGDSDATAGRANULE level_1_carryover

IDofASTERGDSDataGranule productmetadata.0

InputPointer coremetadata.0

INSTRUMENTSHORTNAME formatted_product_summary

InstrumentShortName coremetadata.0

LocalGranuleID coremetadata.0

LOWERLEFT formatted_product_summary

LowerLeft productmetadata.0

LowerRight productmetadata.0

LOWERRIGHT formatted_product_summary

MAPPROJECTIONNAME formatted_product_summary

MapProjectionName coremetadata.0

MEASUREMENTPOINTNUMBER10 level_1_carryover

MEASUREMENTPOINTNUMBER11 level_1_carryover

MEASUREMENTPOINTNUMBER12 level_1_carryover

**[Attribute Name]{.underline}** **[Location]{.underline}**

MEASUREMENTPOINTNUMBER13 level_1_carryover

MEASUREMENTPOINTNUMBER14 level_1_carryover

MEASUREMENTPOINTNUMBER4 level_1_carryover

MEASUREMENTPOINTNUMBER5 level_1_carryover

MEASUREMENTPOINTNUMBER6 level_1_carryover

MEASUREMENTPOINTNUMBER7 level_1_carryover

MEASUREMENTPOINTNUMBER8 level_1_carryover

MEASUREMENTPOINTNUMBER9 level_1_carryover

MPMETHOD1 level_1_carryover

MPMETHOD10 level_1_carryover

MPMETHOD11 level_1_carryover

MPMETHOD12 level_1_carryover

MPMETHOD13 level_1_carryover

MPMETHOD14 level_1_carryover

MPMETHOD2 level_1_carryover

MPMETHOD3B level_1_carryover

MPMETHOD3N level_1_carryover

MPMETHOD4 level_1_carryover

MPMETHOD5 level_1_carryover

MPMETHOD6 level_1_carryover

MPMETHOD7 level_1_carryover

MPMETHOD8 level_1_carryover

MPMETHOD9 level_1_carryover

NORTHBOUNDINGCOORDINATE formatted_product_summary

NorthBoundingCoordinate coremetadata.0

NUMBEROFBADPIXELS1 level_1_carryover

NUMBEROFBADPIXELS10 level_1_carryover

NUMBEROFBADPIXELS11 level_1_carryover

NUMBEROFBADPIXELS12 level_1_carryover

NUMBEROFBADPIXELS13 level_1_carryover

NUMBEROFBADPIXELS14 level_1_carryover

NUMBEROFBADPIXELS2 level_1_carryover

NUMBEROFBADPIXELS3B level_1_carryover

NUMBEROFBADPIXELS3N level_1_carryover

NUMBEROFBADPIXELS4 level_1_carryover

NUMBEROFBADPIXELS5 level_1_carryover

NUMBEROFBADPIXELS6 level_1_carryover

NUMBEROFBADPIXELS7 level_1_carryover

**[Attribute Name]{.underline}** **[Location]{.underline}**

NUMBEROFBADPIXELS8 level_1_carryover

NUMBEROFBADPIXELS9 level_1_carryover

NumberofElement1 badpixelinformation

NumberofElement10 badpixelinformation

NumberofElement11 badpixelinformation

NumberofElement12 badpixelinformation

NumberofElement13 badpixelinformation

NumberofElement14 badpixelinformation

NumberofElement2 badpixelinformation

NumberofElement3B badpixelinformation

NumberofElement3N badpixelinformation

NumberofElement4 badpixelinformation

NumberofElement5 badpixelinformation

NumberofElement6 badpixelinformation

NumberofElement7 badpixelinformation

NumberofElement8 badpixelinformation

NumberofElement9 badpixelinformation

NUMBEROFMEASUREMENTS10 level_1_carryover

NUMBEROFMEASUREMENTS11 level_1_carryover

NUMBEROFMEASUREMENTS12 level_1_carryover

NUMBEROFMEASUREMENTS13 level_1_carryover

NUMBEROFMEASUREMENTS14 level_1_carryover

NUMBEROFMEASUREMENTS4 level_1_carryover

NUMBEROFMEASUREMENTS5 level_1_carryover

NUMBEROFMEASUREMENTS6 level_1_carryover

NUMBEROFMEASUREMENTS7 level_1_carryover

NUMBEROFMEASUREMENTS8 level_1_carryover

NUMBEROFMEASUREMENTS9 level_1_carryover

OperationalQualityFlag coremetadata.0

OperationalQualityFlagExplanation coremetadata.0

OrbitNumber productmetadata.0

ParameterName coremetadata.0

ParameterValue coremetadata.0

PCTIMAGEMATCH4 level_1_carryover

PCTIMAGEMATCH5 level_1_carryover

PCTIMAGEMATCH6 level_1_carryover

PCTIMAGEMATCH7 level_1_carryover

PCTIMAGEMATCH8 level_1_carryover

**[Attribute Name]{.underline}** **[Location]{.underline}**

PCTIMAGEMATCH9 level_1_carryover

PGEVersion coremetadata.0

PGEVERSION formatted_product_summary

PlatformShortName coremetadata.0

PointingAngle productmetadata.0

POINTINGINFO formatted_product_summary

PROCESSEDBANDS level_1_carryover

ProcessedBands productmetadata.0

ProcessingCenter productmetadata.0

PROCESSINGCENTER formatted_product_summary

PROCESSINGCENTER level_1_carryover

PROCESSINGFLAG10 level_1_carryover

PROCESSINGFLAG11 level_1_carryover

PROCESSINGFLAG12 level_1_carryover

PROCESSINGFLAG13 level_1_carryover

PROCESSINGFLAG14 level_1_carryover

PROCESSINGFLAG4 level_1_carryover

PROCESSINGFLAG5 level_1_carryover

PROCESSINGFLAG6 level_1_carryover

PROCESSINGFLAG7 level_1_carryover

PROCESSINGFLAG8 level_1_carryover

PROCESSINGFLAG9 level_1_carryover

PROCESSLEVELID formatted_product_summary

ProcessLevelID coremetadata.0

PROJECTIONPARAMETERS1 level_1_carryover

PROJECTIONPARAMETERS10 level_1_carryover

PROJECTIONPARAMETERS11 level_1_carryover

PROJECTIONPARAMETERS12 level_1_carryover

PROJECTIONPARAMETERS13 level_1_carryover

PROJECTIONPARAMETERS14 level_1_carryover

PROJECTIONPARAMETERS2 level_1_carryover

PROJECTIONPARAMETERS3B level_1_carryover

PROJECTIONPARAMETERS3N level_1_carryover

PROJECTIONPARAMETERS4 level_1_carryover

PROJECTIONPARAMETERS5 level_1_carryover

PROJECTIONPARAMETERS6 level_1_carryover

PROJECTIONPARAMETERS7 level_1_carryover

PROJECTIONPARAMETERS8 level_1_carryover

**[Attribute Name]{.underline}** **[Location]{.underline}**

PROJECTIONPARAMETERS9 level_1_carryover

QAAlertEigenvalOOR QA_alert_table

QAAlertLUTExtrpReqd QA_alert_table

QAAlertNumAddlBadPixels QA_alert_table

QAAlertNumPixAllBndsOOR QA_alert_table

QAAlertNumTempImposs QA_alert_table

QAAlertNumTempOOR QA_alert_table

QAAlertOffDiagElemOOR QA_alert_table

QAAlertOffDiagInpLTLim QA_alert_table

QAAlertPctBadPixels QA_alert_table

QAAlertPctFailPixels QA_alert_table

QAAlertPctHiUncertPixels QA_alert_table

QAALERTSUMMARY formatted_product_summary

QACritAlertsCnt QA_alert_table

QANonCritAlertsCnt QA_alert_table

QAPercentCloudCover coremetadata.0

QAPERCENTINTERPOLATEDDATA formatted_product_summary

QAPercentInterpolatedData coremetadata.0

QAPercentMissingData coremetadata.0

QAPERCENTMISSINGDATA formatted_product_summary

QAPERCENTOUTOFBOUNDSDATA formatted_product_summary

QAPercentOutofBoundsData coremetadata.0

QAStatLUTExtrpReqd QA_alert_table

QAStatNumAddlBadPixels QA_alert_table

QAStatNumPixAllBndsOOR QA_alert_table

QAStatNumTempOOR QA_alert_table

QAStatPctBadPixels QA_alert_table

QAStatPctFailPixels QA_alert_table

QAStatPctHiUncertPixels QA_alert_table

QASummaryOfAlerts QA_alert_table

QuadrantCloudCoverage productmetadata.0

QUADRANTCLOUDCOVERAGE formatted_product_summary

QUADRANTCLOUDCOVERAGE level_1_carryover

RadiometricDBVersion productmetadata.0

RADIOMETRICDBVERSION formatted_product_summary

RECEIVINGCENTER formatted_product_summary

ReceivingCenter productmetadata.0

RecurrentCycleNumber productmetadata.0

**[Attribute Name]{.underline}** **[Location]{.underline}**

ReprocessingActual coremetadata.0

REPROCESSINGACTUAL formatted_product_summary

RESMETHOD1 level_1_carryover

RESMETHOD10 level_1_carryover

RESMETHOD11 level_1_carryover

RESMETHOD12 level_1_carryover

RESMETHOD13 level_1_carryover

RESMETHOD14 level_1_carryover

RESMETHOD2 level_1_carryover

RESMETHOD3B level_1_carryover

RESMETHOD3N level_1_carryover

RESMETHOD4 level_1_carryover

RESMETHOD5 level_1_carryover

RESMETHOD6 level_1_carryover

RESMETHOD7 level_1_carryover

RESMETHOD8 level_1_carryover

RESMETHOD9 level_1_carryover

SceneCenter productmetadata.0

SCENECENTER formatted_product_summary

SceneCloudCoverage productmetadata.0

SCENECLOUDCOVERAGE level_1_carryover

SCENECLOUDCOVERAGE formatted_product_summary

SceneOrientationAngle productmetadata.0

SCENEORIENTATIONANGLE formatted_product_summary

ScienceReviewDate coremetadata.0

SensorName productmetadata.0

SettingTimeofPointing productmetadata.0

ShortName coremetadata.0

SizeMBECSDataGranule coremetadata.0

SIZEMBECSDATAGRANULE formatted_product_summary

SolarDirection productmetadata.0

SOLARDIRECTION formatted_product_summary

SourceDataProduct productmetadata.0

SOURCEDATAPRODUCT formatted_product_summary

SOURCEDATAPRODUCT level_1_carryover

SouthBoundingCoordinate coremetadata.0

SOUTHBOUNDINGCOORDINATE formatted_product_summary

SpatialResolution productmetadata.0

**[Attribute Name]{.underline}** **[Location]{.underline}**

SPATIALRESOLUTION formatted_product_summary

STANDARDDEVIATIONOFFSET10 level_1_carryover

STANDARDDEVIATIONOFFSET11 level_1_carryover

STANDARDDEVIATIONOFFSET12 level_1_carryover

STANDARDDEVIATIONOFFSET13 level_1_carryover

STANDARDDEVIATIONOFFSET14 level_1_carryover

STANDARDDEVIATIONOFFSET4 level_1_carryover

STANDARDDEVIATIONOFFSET5 level_1_carryover

STANDARDDEVIATIONOFFSET6 level_1_carryover

STANDARDDEVIATIONOFFSET7 level_1_carryover

STANDARDDEVIATIONOFFSET8 level_1_carryover

STANDARDDEVIATIONOFFSET9 level_1_carryover

THRESHOLD10 level_1_carryover

THRESHOLD11 level_1_carryover

THRESHOLD12 level_1_carryover

THRESHOLD13 level_1_carryover

THRESHOLD14 level_1_carryover

THRESHOLD4 level_1_carryover

THRESHOLD5 level_1_carryover

THRESHOLD6 level_1_carryover

THRESHOLD7 level_1_carryover

THRESHOLD8 level_1_carryover

THRESHOLD9 level_1_carryover

TIMEOFDAY formatted_product_summary

TimeofDay coremetadata.0

UPPERLEFT formatted_product_summary

UpperLeft productmetadata.0

UPPERRIGHT formatted_product_summary

UpperRight productmetadata.0

UTMZONECODE1 level_1_carryover

UTMZONECODE10 level_1_carryover

UTMZONECODE11 level_1_carryover

UTMZONECODE12 level_1_carryover

UTMZONECODE13 level_1_carryover

UTMZONECODE14 level_1_carryover

UTMZONECODE2 level_1_carryover

UTMZONECODE3B level_1_carryover

UTMZONECODE3N level_1_carryover

**[Attribute Name]{.underline}** **[Location]{.underline}**

UTMZONECODE4 level_1_carryover

UTMZONECODE5 level_1_carryover

UTMZONECODE6 level_1_carryover

UTMZONECODE7 level_1_carryover

UTMZONECODE8 level_1_carryover

UTMZONECODE9 level_1_carryover

VersionID coremetadata.0

WESTBOUNDINGCOORDINATE formatted_product_summary

WestBoundingCoordinate coremetadata.0

# Appendix IV: Public Domain Software for Handling HDF-EOS Format

ASTER data products are stored in a specific implementation of HDF
called HDF-EOS. This format is very flexible and accommodates the wide
range of products available from the EOS instruments. Although HDF is a
flexible storage format, it can be a daunting task getting data out of
HDF and into a users image analysis and display package. Fortunately
several image analysis and display packages are able to ingest ASTER
data making it easy for users that have those packages to begin working
with the data. Users are cautioned that some of these packages do not
always ingest the files correctly and care should be taken to check the
file has been ingested correctly. There are also other programs that
allow users to search the metadata associated with a product. The EOS
project maintains a list of software packages that are able to work with
EOS data at:

<http://hdfeos.gsfc.nasa.gov/hdfeos/viewingHDFEOS.html>

There have been a number of public domain software packages to handle
HDF-EOS that have been developed over the last few years. Following is a
list of such software and URL addresses to procure them:

1\) **WINVICAR**: JPL has developed an image analysis package (WINVICAR)
that works under Windows NT, 2000 and XP. This package includes programs
for getting ASTER data out of HDF and into ASCII, BINARY and WINVICAR
format. There are also programs to dump the metadata from ASTER data
files. For more information:
[http://winvicar.jpl.nasa.gov](http://winvicar.jpl.nasa.gov/)

2\) **HEG** **(HDF-to-GeoTiff) Tool**: This tool was developed by the
ECS Project Office to fulfill the needs of the image processing and GIS
communities as they grappled with the HDF-EOS format. The tool converts
HDF-EOS swath and grid data to HDF-EOS Grid, GeoTIFF, or to a generic
binary format. The tool can be used to re-project data from its original
format to other standard projections, to subset data and to mosaic
adjacent files together. Swath data can also be converted to Grid data.
For more information:
<http://hdfeos.gsfc.nasa.gov/hdfeos/details.cfm?swID=4>

HEG's User Guide is available from:
<http://hdfeos.gsfc.nasa.gov/hdfeos/software/HEG/HEG_UsersGuide.pdf>

3\) **Java HDF Viewer (JHV)**: This is a visualization tool developed by
NCSA for browsing HDF4 files. It has a nested hierarchical structure
that allows easy navigation of the different component object files.
Since it is written in Java, it is machine-independent, and hence easily
portable across platforms. For more information:
<http://hdf.ncsa.uiuc.edu/java-hdf-html/jhv/>

JHV's User Guide is available from:
<ftp://ftp.ncsa.uiuc.edu/HDF/HDF/java/source/unpacked/docs/UsersGuide/JHV2.7/index.html>

4\) **MultiSpec**: This is a tool developed by Purdue University for
interactively analyzing multispectral and hyperspectral image data
serving especially the Macintosh and Windows platforms. For more
information: <http://www.ece.purdue.edu/~biehl/MultiSpec/>

5\) **Webwinds**: This is a java-based visualization tool developed by
Lee Elson and his team at JPL. It supports a range of different
functions and runs on a variety of platforms. For more information:
<http://www.sci-conservices.com/rel4/webpage/wwhome.html>

Documentation on Webwinds is available from:

<http://www.sci-conservices.com/rel4/webpage/webhelp.html>

There are yet more public domain software packages that are available
that include HDF and HDF-EOS utilities, format converters, and metadata
readers. If you are interested in any of them, a good starting point
would be the EOS/ECS project office's hdfeos page:

<http://hdfeos.gsfc.nasa.gov/hdfeos/softwarelist.cfm>

#  

# Appendix V: LP-DAAC Data Sets Available through EDC via the EDG

**DATA CENTER Defined as: EDC-ECS (ECS LAND PROCESSES DAAC)**\
EOSDIS Core System Data Sets:

ASTER L1A Reconstructed Unprocessed Instrument Data V002

ASTER L1B Registered Radiance at Sensor V002

ASTER L2 Decorrelation Stretch VNIR V002

ASTER L2 Decorrelation Stretch SWIR V002

ASTER L2 Decorrelation Stretch TIR V002

ASTER Digital Elevation Model V002

ASTER Expedited L1A Reconstructed Unprocessed Instrument Data V002

ASTER Expedited L1B Registered Radiance at Sensor V002

ASTER L1A Reconstructed Unprocessed Instrument Data V003

ASTER L1B Registered Radiance at Sensor V003

ASTER L2 Decorrelation Stretch VNIR V003

ASTER L2 Decorrelation Stretch SWIR V003

ASTER L2 Decorrelation Stretch TIR V003

ASTER Digital Elevation Model V003

ASTER Expedited L1A Reconstructed Unprocessed Instrument Data V003

ASTER Expedited L1B Registered Radiance at Sensor V003

MODIS/Terra Albedo 16-Day L3 Global 1km ISIN Grid V001

MODIS/Terra BRDF/Albedo Model-1 16-Day L3 Global 1km ISIN Grid V001

MODIS/Terra Geolocation Angles Daily L2G Global 1km ISIN Grid Day V001

MODIS/Terra Geolocation Angles Daily L2G Global 1km ISIN Grid Night V001

MODIS/Terra Land Cover Type 96-Day L3 Global 1km ISIN Grid V001

MODIS/Terra Land Surface Temperature/Emissivity 5-Min L2 Swath 1km V001

MODIS/Terra Land Surface Temperature/Emissivity 8-Day L3 Global 1km ISIN
Grid V001

MODIS/Terra Land Surface Temperature/Emissivity Daily L3 Global 1km ISIN
Grid V001

MODIS/Terra Leaf Area Index/FPAR 8-Day L4 Global 1km ISIN Grid V001

MODIS/Terra Nadir BRDF-Adjusted Reflectance 16-Day L3 Global 1km ISIN
Grid V001

MODIS/Terra Net Photosynthesis 8-Day L4 Global 1km ISIN Grid V001

MODIS/Terra Observation Pointers Daily L2G Global 1km ISIN Grid Day V001

MODIS/Terra Observation Pointers Daily L2G Global 1km ISIN Grid Night
V001

MODIS/Terra Observation Pointers Daily L2G Global 500m ISIN Grid V001

MODIS/Terra Surface Reflectance 8-Day L3 Global 500m ISIN Grid V001

MODIS/Terra Surface Reflectance Daily L2G Global 500m ISIN Grid V001

MODIS/Terra Surface Reflectance Quality Daily L2G Global 1km ISIN Grid
V001

MODIS/Terra Thermal Anomalies/Fire 5-Min L2 Swath 1km V001

MODIS/Terra Thermal Anomalies/Fire 8-Day L3 Global 1km ISIN Grid V001

MODIS/Terra Thermal Anomalies/Fire Daily L2G Global 1km ISIN Grid Day
V001

MODIS/Terra Thermal Anomalies/Fire Daily L2G Global 1km ISIN Grid Night
V001

MODIS/Terra Thermal Anomalies/Fire Daily L3 Global 1km ISIN Grid V001

MODIS/Terra Vegetation Indices 16-Day L3 Global 1km ISIN Grid V001

MODIS/Terra Vegetation Indices 16-Day L3 Global 500m ISIN Grid V001

MODIS/Terra Albedo 16-Day L3 Global 0.25Deg CMG V003

MODIS/Terra Albedo 16-Day L3 Global 1km ISIN Grid V003

MODIS/Terra BRDF/Albedo Model-1 16-Day L3 Global 1km ISIN Grid V003

MODIS/Terra BRDF/Albedo Parameters 16-Day L3 Global 0.25Deg CMG V003

MODIS/Terra Geolocation Angles Daily L2G Global 1km ISIN Grid Day V003

MODIS/Terra Geolocation Angles Daily L2G Global 1km ISIN Grid Night V003

MODIS/Terra Land Cover Type 96-Day L3 Global 1km ISIN Grid V003

MODIS/Terra Land Surface Temperature/Emissivity 5-Min L2 Swath 1km V003

MODIS/Terra Land Surface Temperature/Emissivity 8-Day L3 Global 1km ISIN
Grid V003

MODIS/Terra Land Surface Temperature/Emissivity Daily L3 Global 1km ISIN
Grid V003

MODIS/Terra Land Surface Temperature/Emissivity Daily L3 Global 5km ISIN
Grid V003

MODIS/Terra Leaf Area Index/FPAR 8-Day L4 Global 1km ISIN Grid V003

MODIS/Terra Nadir BRDF-Adjusted Reflectance 16-Day L3 Global 1km ISIN
Grid V003

MODIS/Terra Net Photosynthesis 8-Day L4 Global 1km ISIN Grid V003

MODIS/Terra Observation Pointers Daily L2G Global 1km ISIN Grid Day V003

MODIS/Terra Observation Pointers Daily L2G Global 1km ISIN Grid Night
V003

MODIS/Terra Observation Pointers Daily L2G Global 250m ISIN Grid V003

MODIS/Terra Observation Pointers Daily L2G Global 500m ISIN Grid V003

MODIS/Terra Surface Reflectance 8-Day L3 Global 250m ISIN Grid V003

MODIS/Terra Surface Reflectance 8-Day L3 Global 500m ISIN Grid V003

MODIS/Terra Surface Reflectance Daily L2G Global 250m ISIN Grid V003

MODIS/Terra Surface Reflectance Daily L2G Global 500m ISIN Grid V003

MODIS/Terra Surface Reflectance Quality Daily L2G Global 1km ISIN Grid
V003

MODIS/Terra Thermal Anomalies/Fire 5-Min L2 Swath 1km V003

MODIS/Terra Thermal Anomalies/Fire 8-Day L3 Global 1km ISIN Grid V003

MODIS/Terra Thermal Anomalies/Fire Daily L2G Global 1km ISIN Grid Day
V003

MODIS/Terra Thermal Anomalies/Fire Daily L2G Global 1km ISIN Grid Night
V003

MODIS/Terra Thermal Anomalies/Fire Daily L3 Global 1km ISIN Grid V003

MODIS/Terra Vegetation Indices 16-Day L3 Global 1km ISIN Grid V003

MODIS/Terra Vegetation Indices 16-Day L3 Global 250m ISIN Grid V003

MODIS/Terra Vegetation Indices 16-Day L3 Global 500m ISIN Grid V003

Landsat-7 Calibration Parameter File V002

Landsat-7 L0R Floating Scenes V002\
Landsat-7 Level-0R WRS-Scene V002

**DATA CENTER Defined as: EDC-L1 (L1 LAND PROCESSES DAAC)**

Landsat-7 Level-0R IGS Scene Metadata V002

Landsat-7 Level-1 Floating Scenes V002\
Landsat-7 Level-1 WRS-Scene V002

**DATA CENTER Defined as: EDC (LAND PROCESSES DAAC)**

Non-EOSDIS Core System Data Sets:

Advanced Solid-State Array Spectroradiometer Data Collection\
Aircraft Scanners\
AVHRR 1-KM Orbital Segments\
Global 30-Arc Second Elevation Data Set\
Global Land Cover Test Sites\
NASA Landsat Data Collection\
North American Landscape Characterization\
Spaceborne Imaging Radar-C Precision