Master: The Modis/aster Airborne Simulator: Ames Research Center

MASTER: The MODIS/ASTER Airborne Simulator

Ames Research Center

Jeff Myers Nicholas Clinton Univ. Of California, Santa Cruz

Introduction

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Airborne Sensor Facility MASTER Instrument characteristics Data characteristics Applications

Airborne Sensor Facility Mission Statement: •Develop and Operate Prototype Remote Sensing Instruments •Collect Imagery for NASA Earth Science Research Programs •Provide Rapid Delivery of Calibrated Data

Component Labs For: •Sensor Engineering & Operations •Level-1B Data Processing and Archive •NIST-Traceable Sensor Calibration

Supported By: NASA Airborne Science Program

MASTER Instrument Characteristics

MASTER: MODIS/ASTER Airborne Simulator §Simulates the EOS Terra Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) and MODIS Sensors §Supports NASA Earth Science and Natural Hazards Research §50 Spectral bands in four spectral regions (visible through thermal infrared) Spectral Range (µm) 0.440 - 0.965 1.600 - 2.427

Number of Bands 11 14

Bandwidth ( µm) 0.040 0.050

Primary Mission 1.Collect high-resolution ASTER-like datasets to validate: • Terra satellite sensor performance • EOS retrieval algorithms • Spatial scaling studies 2. Support focus studies of geophysical processes and natural hazards phenomena 3. Provide high-temporal resolution data of

Principle MASTER Platforms WB-57F

ER-2

DC-8 D.o.E. B200

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Instrument Characteristics

ER-2 Sensor Coverage

65,000 ft

24 in. LENS 8 n. mi.

6.6 n. mi./min

12 in. LENS

IRIS II Panoramic Camera MAS, MASTER, AOCI, MAMS TMS

4 n. mi 8 n. mi.

6 in. LENS

20 n. mi. 21.4 n. mi. 16 n. mi

2 n. mi. at NADIR

16 n. mi

Spectrometer

Scanning optics

MASTER Optical Path

MASTER Scanhead in WB-57 Pallet

Data Characteristics

MASTER Bands

*Window and atmospheric bands

Airborne Sensor Calibration  The Requirement: A Science-Quality Image Data Product with Pixels Calibrated to At-Sensor-Radiance (“Level-1B Data”, Units: Watts/M2/µm/Steradian)

The Steps: VI. Radiometric Calibration VII. Spectral Characterization VIII.Data Integration (tare correction, radiance integration)

MASTER Calibration § MASTER Spectral Response Functions (SRFs) All 50 channels measured before and after every deployment with monochrometer and/or FTIR § NIST traceable radiometric source (Vis/SWIR) Pre and post deployment laboratory calibrations of bands 1-25 over 30 inch integrating sphere § Onboard and Laboratory Blackbodies (MWIR/TIR) § Environmental Simulation Chamber § Ground Truth Validation (field spect. & buoys)

Spectral Response Functions

Calibration Lab Support ASTL Spectral and Radiometric Calibration Facility for Airborne Sensors Spectral Range = 350nm – 14um Fully NIST-Traceable, with NASA EOS Program Oversight Currently supporting: AMS System MAS and MASTER SSFR (Solar Flux Radiometer) AATS-14 (Sun Photometer) CAR (Cloud Radiometer) Field Spectro-Radiometers

Transfer Radiometer Spectral Calibration Configuration

Ref. Paper: Radiometric Validation of NASA ARC Calibration Laboratory, S. Brown, C. Johnson, et al. Applied Optics/Vol.44, No. 30, Oct. 2005

MONOCHROMETER MAS SPECTROMETER LIGHT SOURCES

(Tungsten Lamp, Glowbar)

CHOPPER

Integrating Spheres

FILTER WHEEL

M3

Spectral Sources M2

M4

M1

Spectral Calibration Bench

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Level 1B HDF Data The Hierarchical Data Format (HDF) has been selected by the EOSDIS Project as the format of choice for standard product distribution. HDF consists of a directory structure and a collection of data objects or Scientific Data Sets (SDS). MASTER HDF image data is stored as integer but unpacked to real (floating point) data in radiance units. MASTER HDF currently consists of 37 Global attributes and 44 scientific data sets 15 SDS’s for calibration information 12 SDS’s for navigation information 27 SDS’s for engineering information Software is available to directly import MASTER HDF Unpacking code available to strip image data out of HDF format

http://www.hdfgroup.org

http://masterweb.jpl.nasa.gov/

Click!

Applications

MAS & MASTER Data Collections: TC4

TC4

DC-8 Contrail

Field-Generated Level-2 Data Products (with GSFC MODIS Cloud Team)

MAS IR Composite 30

30, 0.3, 0.5

Random Forest

MASTER – SRTM data merge Continental Divide

Southern California Post-fire Assessment Witch Fire Flight Plan

Witch Burn Area

Acquired November 13-14, 2007

Slide Fire

R 2.2um (22) G 0.87um (9) B 0.65um (5)

Arenal 44, 12, 3

Shaded Relief from LIDAR DEM (2.5 m) Temperatures from MASTER TIR (5 m)

10-14-2004 12:45 pm PST

>300 oC (highest temp = 330 oC) 200-300 oC 75-90 oC o 150-200 C 60-75 oC o 105-150 C 45-60 oC o 90-105 C 30-45 oC

2007 Bullion Fault

Simulated OCI Imagery Mayaguez Bay and Rio Grande de Anasco River Outfall, Puerto Rico

11/17/91

12/5/93

Airborne Ocean Color Imager (50 m. resolution, Natural Color. From ER-2 Aircraft at 65,000 ft) 44

Towards an Integrated Sensor Web for Earth Science Obsvervations

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Selected MASTER publications Peijun Li, Wooil M Moon, Hong-Yul Paik and Gi-Hyuk Choi. 2002. Land cover mapping in Kochang area of Korea using MASTER (MODIS/ASTER airborne simulator) data.

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Yang-Lang Chang, Chin-Chuan Han, Kuo-Chin Fan, K.S. Chen, and JengHorng Chang. 2002. A Modular Eigen Subspace Scheme for Highdimensional Data Classification with NASA MODIS/ASTER (MASTER) airborne simulator datasets of Pacrim II project.

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F. A. Kruse. 2002. Combined SWIR and LWIR Mineral Mapping Using MASTER/ASTER.

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Benoît Stoll and Patrick Capolsini. 2004. A Simple Class-Set Based Vegetation Classification of a South Pacific Volcanic Island (Moorea Island, French Polynesia) using Both AirSAR and MASTER Data.

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RG Vaughan, SJ Hook, WM Calvin, JV Taranik. 2005. Surface mineral mapping at Steamboat Springs, Nevada, USA, with multi-

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