Remote Sens. 2010, 2(12), 2680-2699; doi:10.3390/rs2122680
Article

Relationships Between Errors Propagated in Fraction of Vegetation Cover by Algorithms Based on a Two-Endmember Linear Mixture Model

Received: 3 November 2010; in revised form: 26 November 2010 / Accepted: 29 November 2010 / Published: 2 December 2010
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract: Remotely sensed reflectance spectra may be biased by several intervening factors, and the biases are propagated into estimations of the fraction of vegetation cover (FVC) by algorithms based on a linear mixture model (LMM). The errors propagated in FVCs depend on the retrieval algorithm used, due to differences in the assumptions of the model as well as constraints employed in the algorithm. These differences should be fully understood prior to algorithm selection for practical applications. Although numerous studies have investigated the relationships between errors propagated by different algorithms, these relationships have not been fully understood from a deterministic perspective. This study introduces a technique for deriving the analytical underpinnings of error propagation in FVC based on several LMM-based algorithms. The derivation assumes that measurement noise is band-correlated additive noise. The bias errors propagated in FVC depended on the endmember spectra assumed in the algorithm, the target spectrum, and the coefficients of the spectral vegetation index, which were employed as constraints, as well as magnitude of the input error. It was found that the relationships among the propagated errors assume asymmetric elliptical forms with coefficients that are determined by the input variables. These results suggest that the relationships depend heavily on the choice of endmember spectra as well as the spectrum of the target pixel and the vegetation index employed as a constraint. The present findings should assist in the selection of an optimum algorithm based on prior knowledge of the target field.
Keywords: fraction of vegetation cover; linear mixture model; propagated error; inter-algorithm relationship; vegetation index
PDF Full-text Download PDF Full-Text [222 KB, uploaded 19 June 2014 00:07 CEST]

Export to BibTeX |
EndNote


MDPI and ACS Style

Obata, K.; Yoshioka, H. Relationships Between Errors Propagated in Fraction of Vegetation Cover by Algorithms Based on a Two-Endmember Linear Mixture Model. Remote Sens. 2010, 2, 2680-2699.

AMA Style

Obata K, Yoshioka H. Relationships Between Errors Propagated in Fraction of Vegetation Cover by Algorithms Based on a Two-Endmember Linear Mixture Model. Remote Sensing. 2010; 2(12):2680-2699.

Chicago/Turabian Style

Obata, Kenta; Yoshioka, Hiroki. 2010. "Relationships Between Errors Propagated in Fraction of Vegetation Cover by Algorithms Based on a Two-Endmember Linear Mixture Model." Remote Sens. 2, no. 12: 2680-2699.

Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert