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Volume 23 Issue 1

Jan.  2006

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Article >  ADVANCES IN ATMOSPHERIC SCIENCES  > 2006 >  23(1): 69-79

An Overview of MODIS Radiometric Calibration and Characterization

  • 1.

    Earth Sciences Directorate, NASA/Goddard Space Flight Center, Greenbelt, MD 20771, USA,University of Maryland, Baltimore County, Baltimore, MD 21250, USA


doi: 10.1007/s00376-006-0008-3

  • The Moderate Resolution Imaging Spectroradiometer (MODIS) is one of the key instruments for NASA’s Earth Observing System (EOS), currently operating on both the Terra and Aqua satellites. The MODIS is a major advance over the previous generation of sensors in terms of its spectral, spatial, and temporal resolutions. It has 36 spectral bands: 20 reflective solar bands (RSB) with center wavelengths from 0.41 to 2.1 μm and 16 thermal emissive bands (TEB) with center wavelengths from 3.7 to 14.4 μm, making observations at three spatial resolutions: 250 m (bands 1–2), 500 m (bands 3–7), and 1km (bands 8-36). MODIS is a cross-track scanning radiometer with a wide field-of-view, providing a complete global coverage of the Earth in less than 2 days. Both Terra and Aqua MODIS went through extensive pre-launch calibration and characterization at various levels. In orbit, the calibration and characterization tasks are performed using its on-board calibrators (OBCs) that include a solar diffuser (SD) and a solar diffuser stability monitor (SDSM), a v-grooved flat panel blackbody (BB), and a spectro-radiometric calibration assembly (SRCA). In this paper, we present an overview of MODIS calibration and characterization activities, methodologies, and lessons learned from pre-launch characterization and in-orbit operation. Key issues discussed in this paper include in-orbit efforts of monitoring the noise characteristics of the detectors, tracking the solar diffuser and optics degradations, and updating the sensor’s response versus scan angle. The experiences and lessons learned through MODIS have played and will continue to play major roles in the design and characterization of future sensors.
  • [1] Xiangqian WU, Changyong CAO, 2006: Sensor Calibration in Support for NOAA’s Satellite Mission, ADVANCES IN ATMOSPHERIC SCIENCES, 23, 80-90.  doi: 10.1007/s00376-006-0009-2
    [2] WANG Hesong, JIA Gensuo, 2013: Regional Estimates of Evapotranspiration over Northern China Using a Remote-sensing-based Triangle Interpolation Method, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 1479-1490.  doi: 10.1007/s00376-013-2294-x
    [3] Ping YANG, Kuo-Nan LIOU, Lei BI, Chao LIU, Bingqi YI, Bryan A. BAUM, 2015: On the Radiative Properties of Ice Clouds: Light Scattering, Remote Sensing, and Radiation Parameterization, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 32-63.  doi: 10.1007/s00376-014-0011-z
    [4] QIU Jinhuan, CHEN Hongbin, 2004: Recent Progresses in Atmospheric Remote Sensing Research in China-- Chinese National Report on Atmospheric Remote Sensing Research in China during 1999-2003, ADVANCES IN ATMOSPHERIC SCIENCES, 21, 475-484.  doi: 10.1007/BF02915574
    [5] Zhao Gaoxiang, 1998: Analysis of the Ability of Infrared Water Vapor Channel for Moisture Remote Sensing in the Lower Atmosphere, ADVANCES IN ATMOSPHERIC SCIENCES, 15, 107-112.  doi: 10.1007/s00376-998-0022-8
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    • Manuscript History

    Manuscript received: 10 January 2006
    Manuscript revised: 10 January 2006
    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

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    An Overview of MODIS Radiometric Calibration and Characterization

    • 1. Earth Sciences Directorate, NASA/Goddard Space Flight Center, Greenbelt, MD 20771, USA,University of Maryland, Baltimore County, Baltimore, MD 21250, USA
    • Manuscript received: 2006-01-10
    • Manuscript revised: 2006-01-10

    Abstract: The Moderate Resolution Imaging Spectroradiometer (MODIS) is one of the key instruments for NASA’s Earth Observing System (EOS), currently operating on both the Terra and Aqua satellites. The MODIS is a major advance over the previous generation of sensors in terms of its spectral, spatial, and temporal resolutions. It has 36 spectral bands: 20 reflective solar bands (RSB) with center wavelengths from 0.41 to 2.1 μm and 16 thermal emissive bands (TEB) with center wavelengths from 3.7 to 14.4 μm, making observations at three spatial resolutions: 250 m (bands 1–2), 500 m (bands 3–7), and 1km (bands 8-36). MODIS is a cross-track scanning radiometer with a wide field-of-view, providing a complete global coverage of the Earth in less than 2 days. Both Terra and Aqua MODIS went through extensive pre-launch calibration and characterization at various levels. In orbit, the calibration and characterization tasks are performed using its on-board calibrators (OBCs) that include a solar diffuser (SD) and a solar diffuser stability monitor (SDSM), a v-grooved flat panel blackbody (BB), and a spectro-radiometric calibration assembly (SRCA). In this paper, we present an overview of MODIS calibration and characterization activities, methodologies, and lessons learned from pre-launch characterization and in-orbit operation. Key issues discussed in this paper include in-orbit efforts of monitoring the noise characteristics of the detectors, tracking the solar diffuser and optics degradations, and updating the sensor’s response versus scan angle. The experiences and lessons learned through MODIS have played and will continue to play major roles in the design and characterization of future sensors.

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