NDLI: Spectral Response Function Comparability Among 21 Satellite Sensors for Vegetation Monitoring

Content Provider	IEEE Xplore Digital Library
Author	Gonsamo, A. Chen, J.M.
Copyright Year	1980
Abstract	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.
Sponsorship	IEEE Geoscience and Remote Sensing Society IEEE URSI
Starting Page	1319
Ending Page	1335
Page Count	17
File Size	3248215
File Format	PDF
ISSN	01962892
Volume Number	51
Issue Number	3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vegetation mapping Sensors Satellites Atmospheric modeling Atmospheric measurements Monitoring spectral response function (SRF) Cross-sensor comparability Earth Observing System (EOS) land validation core sites normalized difference vegetation index (NDVI) continuity reflectance
Content Type	Text
Resource Type	Article
Subject	Earth and Planetary Sciences Electrical and Electronic Engineering

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