Written by Jeffrey Gillan
Landsat 1 through Landsat 4
Agency/Company Operating the Sensor
Jointly managed by NASA and USGS
The Landsat program, started in 1972, is one of the mainstays of NASA’s earth-observation program. The Multispectral Scanner is one of the original Landsat imaging sensors and has flown on Landsat satellites 1 through 5. The MSS stopped acquiring images in 1992 because of improved data available through the Thematic Mapper. MSS data was acquired with a 6-bit system (64 brightness values while the newer sensors TM and ETM+ acquire data in an 8-bit system (256 brightness values). Converting the images to surface reflectance will eliminate the problems of comparing data with different levels of quantization. MSS also has courser spatial resolution than TM and ETM+ images. The Landsat satellites were designed to be of use to a variety of fields like forestry, agriculture, geology, and land-use planning, and the choice of spectral bands for the Landsat satellites was geared toward discriminating different types and amounts of vegetation.
Landsat’s strengths are generally seen to be its regular acquisition schedule (revisits each spot on the earth every 16 or 18 days), long-term data archive (image are available from 1972-1992), and relatively rich spectral information (not as rich as hyperspectral data, but more bands than most high-resolution satellites like IKONOS or Quickbird). Limitations of Landsat MSS data are that it is only a moderate-resolution image source (79m multispectral data), and the fixed acquisition schedule makes it sometimes difficult to acquire imagery for a particular place at a particular time (especially important if clouds or smoke are frequent).
Satellites carrying the MSS Sensor
|Landsat 1||July 23, 1972||January 6, 1978|
|Landsat 2||January 22, 1975||February 25, 1982|
|Landsat 3||March 5, 1978||March 31, 1983|
|Landsat 4||July 16, 1982||June 15, 2001|
|Landsat 5||March 1, 1984||MSS sensor failed in 1992, TM sensor still operational|
MSS on the first 3 Landsat satellites each had 4 spectral bands labeled 4, 5, 6, and 7. The bands were relabeled to 1, 2, 3, and 4 on Landsat 4 and 5 satellites. Two of the bands are in the visible range while 2 of them are in the reflective near-infrared. These bands have a spatial resolution of 79m x 79m. Band 8 (thermal infrared ) was only present on the Landsat 3 satellite and had a spatial resolution of 240m x 240m. The band sensitivities were selected to study specific resources on Earth. MSS band 1 can be used to detect green reflectance from healthy vegetation, and band 2 is designed for detecting chlorophyll absorption in vegetation. MSS bands 3 and 4 are ideal for recording near-infrared reflectance peaks in healthy green vegetation and for detecting water-land interfaces.
The radiometric resolution of the sensor is 6-bits, meaning it has a digital number range of 0-63.
Image footprint or swath width
Landsat MSS data is delivered in scenes that measure 115 miles (185km ) by 106 miles (170km).
Landsat 1-3 circled the earth in a near-polar orbit (WSR-1) and revisited the same spot on the earth every 18 days. Landsat 4 and 5 are on the WRS-2 orbit path and revisits the same spot on the earth every 16 days. The Landsat satellites are in a sun-synchronous orbit, meaning they cross over the same latitude at approximately the same time each day.
Cost, Acquisition, Licensing
As of October 2008, all Landsat MSS archived imagery is free. You can search for, order, and download Landsat TM data from a number of sources including: the USGS Global Visualization Viewer (GLOVIS), the USGS Earth Explorer, or Landsat.org. Images are available from 1972-1992.
Format and delivery options for Landsat MSS imagery varies with where you order and download the imagery. Images are commonly shipped as TIFF image files – one image for each band. Alternatively, data may come in USGS’ HDF format. Most images come as raw digital numbers and need to be transformed into radiance or reflectance for many applications.
Examples of Rangeland Uses
- Boyd (1986) estimated brush canopy cover using MSS vegetation indices.
- Yool et al (1997) explores techniques for detecting change in grassland landscapes.
- Pickup (1995) created a model for predicted herbage production in rangelands based on vegetation indices derived from MSS image.
Landsat imagery is one of the most ubiquitous satellite image types, and a lot of effort has gone into making it easy to access and use. For the most part, Landsat imagery can be used in ArcGIS or other GIS applications without any special processing. Most Landsat images obtained either through USGS or another provider are distributed in TIFF image format with one band per file. For the purposes of making it easier to handle, manipulate, and display the imagery, most people combine all of the separate image bands into a single, multi-band image file using an image-processing package like ENVI or ERDAS Imagine.
Landsat images are not terribly big or difficult to process by today’s computing standards. In a TIFF image format, the file for a single 30m band is approximately 53MB. As long as you are dealing with a study area that is contained within one or a few Landsat scenes, you should not need any special computer hardware to be able to use Landsat imagery.
- Earth Resources Observation and Science (EROS) Center http://eros.usgs.gov/#/Guides/landsat_mss
- NASA Landsat program http://landsat.gsfc.nasa.gov/
- Landsat calibration and other technical documents that can be used to convert digital numbers to radiance and reflectance http://landsat.usgs.gov/tools_project_documents.php
- Landsat image gallery http://landsat.usgs.gov/gallery.php
- Boyd, W.E. (1986), Correlation of rangelands brush canopy cover with Landsat MSS data, Journal of Rangeland Management, Vol. 39, No. 3, pp. 268-271.
- 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.
- Pickup, G. (1995), A simple model for predicting herbage production from rainfall in rangelands and its calibration using remotely-sensed data, Journal of Arid Environments, Vol. 30, Iss. 2, pp. 227-245.
- Yool, Stephen R., Mary Jane Makaio, and Joseph M. Watts (1997), Techniques for computer-assisted mapping of rangeland change, Journal of Rangeland Management, Vol. 50, No. 3, pp. 307-314.