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

K. Obata,H. Yoshioka

Published 2011 in Remote Sensing

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.

PUBLICATION RECORD

  • Publication year

    2011

  • Venue

    Remote Sensing

  • Publication date

    2011-07-04

  • Fields of study

    Mathematics, Computer Science, Environmental Science

  • Identifiers
    DOI 10.3390/rs3071344
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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