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Multi-angle Imaging SpectroRadiometer (MISR)

Other Names:

MISR

Agency/Company Operating the Sensor

NASA

Description

The Multi-angle Imaging Spectroradiometer is a sensor onboard NASA’s Terra satellite launched in 1999. MISR is composed of 9 separate sensors, each with 4 spectral bands (blue, green, red, near infrared), that view the Earth at 9 different angles. As the satellite orbits, it takes 7 minutes for a 360 km wide area on the ground to be viewed by all 9 sensors. The main mission of the sensor is to observe the amount of solar radiation that is reflected and absorbed by the Earth. When solar radiation strikes aerosols (airborne solid particles), clouds, and surface features, the energy is partially absorbed and partially scattered (reflected) depending on their physical properties. Varying amounts of radiation can be scattered in any direction, therefore it requires many different views to observe all of the reflected radiation. When it comes to studying land, MISR can help us understand surface albedo which can be a major variable governing global climate. Albedo can also help us estimate biophysical processes such as photosynthesis, transpiration rates, and the amount of absorbed photosynthetically active radiation (PAR). Albedo can also be used to estimate structural characteristics such as leaf area index and percent of stem and branches.

A variety of preprocessed products are available free of charge including the parameters mentioned above. Images are available in two different spatial resolutions: 250-275 meters (Local mode) and 1.1 Km (Global mode). Also available are entire global scenes (Level 3) usually showing monthly averages of different parameters.

Level 1 Products: Processed to remove any instrument effects. Includes products with raw digital numbers, top of atmosphere radiance, and geolocated map-projections.
Level 2 Products: Stereoscopically-derived land elevation, leaf area index, fraction of photsynthetically active radiation, and normalized difference vegetation index.
Level 3 Products: Global or regional maps combining multiple orbits to create daily, monthly, quarterly, and yearly aggregated products. Includes leaf area index, fraction of photsynthetically active radiation, and normalized difference vegetation index.

A complete list and description of MISR data products can be found here: http://eosweb.larc.nasa.gov/GUIDE/campaign_documents/misr_ov.html

Similar Sensors

Sample Image


Image credit: NASA/GSFC/LaRC/JPL, MISR Team
These are nadir images show a grassland region of northern Queensland, Australia during and after a drought period. The top two images show the increase in green vegetation after the rains returned. The middle images show the directional-hemispherical reflectance (DHR) of the photosynthetically active region (PAR) of the visible spectrum (.4 -.7 µm). This is also known as albedo. These images show that less vegetation will lead to increased reflectance. The bottom images show the fraction of photosynthetically active radiation (FPAR) absorbed by vegetation. More vegetation will produce higher FPAR values.

Sensor Specifications

MISR views a spot on the earth at 9 different angles. Each of the sensors collects data in 4 spectral bands: blue, green, red, and near infrared. MISR can acquire images at two different levels of spatial resolution. While in local mode, each of the sensors acquires data at a 275 meter pixel size. When being used in global mode (default), each of the sensors gathers data at 1.1 km spatial resolution. The nadir sensor (An), regardless of mode will have a spatial resolution of 250 meters.




The diagram illustrates the roles each of the 9 cameras and 4 color bands play in MISR science. The colored boxes on the bottom show the purposes for each of the camera angles. The color boxes on the right show the purpose of each of the color bands.

Spectral Bands/Wavelengths

Band Resolution Wavelength µm Description
1 275m 0.425-0.467 Blue
2 275m 0.543-0.473 Green
3 275m 0.661-0.683 Red
4 275m 0.846-0.886 Near Infrared

Image footprint or swath width

The swath width varies from 376 to 414 km depending on the angle of the camera, but the swath overlap of all the cameras is 360 km. Images can be entire orbits in length or subsetted to any customized size.

The data in one orbit or path is broken into blocks for ease of location identification. Blocks measure 563.2 km (cross-track) x 140.8 km (along-track). There are 180 blocks along one path.

Local mode (LM) has scenes that are 360 km (cross-track) x 300 km (along-track)

Return Interval

The Terra satellite uses the WRS-2 orbiting system. It is in a sun-synchronous orbit passing the same latitude at the same time each day. It will return to the exact same location every 16 days but because of its wide swath width, will repeat coverage between 2 and 9 days depending on latitude.

Cost, Acquisition, Licensing

MISR images and products are archived and usually free of charge.

You can order and download most products from the MISR Order and Customization Tool. http://l0dup05.larc.nasa.gov/MISR/cgi-bin/MISR/main.cgi For Level 1 and 2 Products, one MISR data file is an entire orbit of data which is usually much too large for most applications. Fortunately, you can subset the data to only your region of interest. Level 3 products are entire global scenes.

Other Data Sources:


A tutorial on ordering MISR data products from these three sources in Microsoft Powerpoint. http://eosweb.larc.nasa.gov/PRODOCS/misr/docs/obtaining_misr_data.ppt

The MISR Browse tool will help users preview images and identify the path and specific block that covers their area of interest. http://l0dup05.larc.nasa.gov/MISR_BROWSE/

This tool will identify your path and block of interest based on latitude and longitude coordinates: http://eosweb.larc.nasa.gov/misr_loc/

Image format

MISR images generally come in one of two formats:

Stacked-block HDF-EOS format is a MISR specific extension to the HDF-EOS library that is only used by MISR and is not widely supported by third party data analysis tools. Tools which support HDF-EOS or HDF can usually import stacked-block data to a limited extent. Two problems commonly encountered are: 1) piecing together the MISR blocks so they are aligned correctly with respect to each other; and 2) geolocating the MISR data in a map context.

The conventional HDF-EOS format is an alternative format that can be imported into any tools that support generic HDF-EOS. The image processing software ENVI, will recognize this format. Still many users will find these formats cumbersome and may prefer formats such as GeoTiff.

Tools for working with and viewing MISR data products in both formats can be found at: http://eosweb.larc.nasa.gov/PRODOCS/misr/tools/misr_tools.html

Tools for carrying out data transformations into other formats and projections can be found at: https://lpdaac.usgs.gov/tools.

Examples of Rangeland Uses

  • Su et al. (2007) used MISR images from multiple angles to improve land classification in semi-arid grasslands.
  • Chopping et al. (2008) used MISR images to remotely sense woody shrub cover in desert grasslands
  • Knyazikhin et al. (1998) discusses how leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FPAR) are derived from MISR images.

Additional Information

References

  • Chopping, M, Su L., Rango A., Martonchik JV., Peters DPC., Laliberte A. (2008), Remote sensing of woody shrub cover in desert grasslands using MISR with a geometric-optical canopy reflectance model, Remote Sensing of the Environment, Vol. 112, Iss. 1, pg. 19-34.
  • Knyazikhin, Y., J. V. Martonchik, D. J. Diner, R. B. Myneni, M. Verstraete, B. Pinty, and N. Gobron (1998), Estimation of vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from atmosphere-corrected MISR data, Journal of Geophysical Research, Vol. 103, No. D24, pg. 32, 239-32,256.
  • Jenson, John R. (2007), Remote Sensing of the Environment: An Earth resource perspective, second edition, Prentice Hall series in geographic information science, Upper Saddle River, NJ.
  • Su, Lihong, Mark J. Chopping, Albert Rango, John V. Martonchik, and Debra P.C. Peters (2006), Support vector machines for recognition of semi-arid vegetation types using MISR multi-angle imagery, Remote Sensing of the Environment, Vol. 107, Iss. 1-2, pg. 299-311.

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remote_sensor_types/misr.txt · Last modified: 2013/02/11 11:10 by gtucker