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Comparison of the Noise Robustness of FVC Retrieval Algorithms Based on Linear Mixture Models

Authors :
Hiroki Yoshioka
Kenta Obata
Source :
Remote Sensing, Vol 3, Iss 7, Pp 1344-1364 (2011)
Publication Year :
2011
Publisher :
MDPI AG, 2011.

Abstract

The fraction of vegetation cover (FVC) is often estimated by unmixing a linear mixture model (LMM) to assess the horizontal spread of vegetation within a pixel based on a remotely sensed reflectance spectrum. The LMM-based algorithm produces results that can vary to a certain degree, depending on the model assumptions. For example, the robustness of the results depends on the presence of errors in the measured reflectance spectra. The objective of this study was to derive a factor that could be used to assess the robustness of LMM-based algorithms under a two-endmember assumption. The factor was derived from the analytical relationship between FVC values determined according to several previously described algorithms. The factor depended on the target spectra, endmember spectra, and choice of the spectral vegetation index. Numerical simulations were conducted to demonstrate the dependence and usefulness of the technique in terms of robustness against the measurement noise.

Details

Language :
English
ISSN :
20724292
Volume :
3
Issue :
7
Database :
Directory of Open Access Journals
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
edsdoj.076c32fa6782441483a99579386a1d70
Document Type :
article
Full Text :
https://doi.org/10.3390/rs3071344