Cart (Loading....) | Create Account
Close category search window
 

Spectral Response Function Comparability Among 21 Satellite Sensors for Vegetation Monitoring

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Gonsamo, A. ; Dept. of Geogr., Univ. of Toronto, Toronto, ON, Canada ; Chen, J.M.

Global and regional vegetation assessment strategies often rely on the combined use of multisensor satellite data. Variations in spectral response function (SRF) which characterizes the sensitivity of each spectral band have been recognized as one of the most important sources of uncertainty for the use of multisensor data. This paper presents the SRF differences among 21 Earth observation satellite sensors and their cross-sensor corrections for red, near infrared (NIR), and shortwave infrared (SWIR) reflectances, and normalized difference vegetation index (NDVI) aimed at global vegetation monitoring. The training data set to derive the SRF cross-sensor correction coefficients were generated from the state-of-the-art radiative transfer models. The results indicate that reflectances and NDVI from different satellite sensors cannot be regarded as directly equivalent. Our approach includes a polynomial regression and spectral curve information generated from a training data set representing a wide dynamics of vegetation distributions to minimize land cover specific SRF cross-sensor correction coefficient variations. The absolute mean SRF caused differences were reduced from 33.9% (20.1%) to 9.4 % (6%) for red, from 3.2 % (8.9%) to 1% (1.1% ) for NIR, from 2.9% (3.6 %) to 1.9% (1.6%) for SWIR, and from 7.1 % (9%) to 1.8% (1.7% ) for NDVI, after applying the SRF cross-sensor correction coefficients on independent top of canopy (top of atmosphere) data for all-embraced-sensor comparisons. Variations in processing strategies, non spectral differences, and algorithm preferences among sensor systems and data streams hinder cross-sensor spectra and NDVI comparability and continuity. The SRF cross-sensor correction approach provided here, however, can be used for studies aiming at large-scale vegetation monitoring with acceptable accuracy.

Published in:

Geoscience and Remote Sensing, IEEE Transactions on  (Volume:51 ,  Issue: 3 )

Date of Publication:

March 2013

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.