Relative Radiometric Normalization of Landsat Multispectral Scanner (MSS) Data Using an Automatic Scattergram—Controlled Regression

The scientific requirement for increased understanding of human impacts on terrestrial carbon stocks and biodiversity has created renewed interest in the use of Landsat Multispectral Scanner (MSS) data for the analysis of land-cover change. A series of five Landsat MSS sensors were used to acquire earth observations over a 21 year period (1972–1992), forming the longest available set of repetitive satellite observations of the earth’s surface.

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Publication Details

Published
2026-01-23
DOI
https://doi.org/10.1201/9781003761556-2
Citations
169
Primary Topic
Remote-Sensing Image Classification
Type
article
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article

Relative Radiometric Normalization of Landsat Multispectral Scanner (MSS) Data Using an Automatic Scattergram—Controlled Regression

Christopher D. Elvidge, Ross S. Lunetta, R. D. Weerackoon, Ding Yuan
169 citations
Remote-Sensing Image Classification
article

Relative Radiometric Normalization of Landsat Multispectral Scanner (MSS) Data Using an Automatic Scattergram—Controlled Regression

Christopher D. Elvidge, Ross S. Lunetta, R. D. Weerackoon, Ding Yuan
article en
169 citations

Abstract

The scientific requirement for increased understanding of human impacts on terrestrial carbon stocks and biodiversity has created renewed interest in the use of Landsat Multispectral Scanner (MSS) data for the analysis of land-cover change. A series of five Landsat MSS sensors were used to acquire earth observations over a 21 year period (1972–1992), forming the longest available set of repetitive satellite observations of the earth’s surface.

Vol. 61(10)
Remote-Sensing Image Classification
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