@Article{Zhang2014,
author="Zhang, Ying
and Huang, Xiaodong
and Hao, Xiaohua
and Wang, Jie
and Wang, Wei
and Liang, Tiangang",
title="Fractional snow-cover mapping using an improved endmember extraction algorithm",
journal="Journal of Applied Remote Sensing",
year="2014",
volume="8",
number="1",
keywords="N-FINDR;orthogonal subspace projection;spectral unmixing algorithm;fully constrained least squares",
abstract="We describe and validate an improved endmember extraction method to improve the fractional snow-cover mapping based on the algorithm for fast autonomous spectral endmember determination (N-FINDR) maximizing volume iteration algorithm and orthogonal subspace projection theory. A spectral library time series is first established by choosing the expected spectra information using prior knowledge, and the fractional snow cover (FSC) is then retrieved by a fully constrained least squares linear spectral mixture analysis. The retrieved fractional snow-cover products are validated by the FSC derived from Landsat imagery. Our results indicate that the improved algorithm can obtain the endmember information accurately, and the retrieved FSC has better accuracy than the MODIS standard fractional snow-cover product (MOD10A1)."
}