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Geostatistical estimation of signal-to-noise ratios for spectral vegetation indices

ISPRS Journal of Photogrammetry and Remote Sensing

By:
, , , , and
DOI: 10.1016/j.isprsjprs.2014.06.013

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Abstract

In the past 40 years, many spectral vegetation indices have been developed to quantify vegetation biophysical parameters. An ideal vegetation index should contain the maximum level of signal related to specific biophysical characteristics and the minimum level of noise such as background soil influences and atmospheric effects. However, accurate quantification of signal and noise in a vegetation index remains a challenge, because it requires a large number of field measurements or laboratory experiments. In this study, we applied a geostatistical method to estimate signal-to-noise ratio (S/N) for spectral vegetation indices. Based on the sample semivariogram of vegetation index images, we used the standardized noise to quantify the noise component of vegetation indices. In a case study in the grasslands and shrublands of the western United States, we demonstrated the geostatistical method for evaluating S/N for a series of soil-adjusted vegetation indices derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The soil-adjusted vegetation indices were found to have higher S/N values than the traditional normalized difference vegetation index (NDVI) and simple ratio (SR) in the sparsely vegetated areas. This study shows that the proposed geostatistical analysis can constitute an efficient technique for estimating signal and noise components in vegetation indices.

Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Geostatistical estimation of signal-to-noise ratios for spectral vegetation indices
Series title:
ISPRS Journal of Photogrammetry and Remote Sensing
DOI:
10.1016/j.isprsjprs.2014.06.013
Volume
96
Year Published:
2014
Language:
English
Publisher:
Elsevier
Contributing office(s):
Earth Resources Observation and Science (EROS) Center
Description:
8 p.
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
Larger Work Title:
ISPRS Journal of Photogrammetry and Remote Sensing
First page:
20
Last page:
27