Remote Sens. 2011, 3(7), 1344-1364; doi:10.3390/rs3071344
Article

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

Department of Information Science and Technology, Aichi Prefectural University, 1522-3 Kumabari, Nagakute, Aichi 480-1198, Japan
* Author to whom correspondence should be addressed.
Received: 13 May 2011; in revised form: 17 June 2011 / Accepted: 27 June 2011 / Published: 4 July 2011
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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.
Keywords: fraction of vegetation cover; linear mixture model; propagated error; vegetation index; optimum algorithm; asymmetric ellipse; noise robustness

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MDPI and ACS Style

Obata, K.; Yoshioka, H. Comparison of the Noise Robustness of FVC Retrieval Algorithms Based on Linear Mixture Models. Remote Sens. 2011, 3, 1344-1364.

AMA Style

Obata K, Yoshioka H. Comparison of the Noise Robustness of FVC Retrieval Algorithms Based on Linear Mixture Models. Remote Sensing. 2011; 3(7):1344-1364.

Chicago/Turabian Style

Obata, Kenta; Yoshioka, Hiroki. 2011. "Comparison of the Noise Robustness of FVC Retrieval Algorithms Based on Linear Mixture Models." Remote Sens. 3, no. 7: 1344-1364.

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