Comparison of fractional vegetation cover estimations using dimidiate pixel models and look- up table inversions of the PROSAIL model from Landsat 8 OLI data

文献类型： 外文期刊

第一作者： Ding, Yanling

作者： Ding, Yanling;Zhang, Hongyan;Li, Zhenwang;Xin, Xiaoping;Zheng, Xingming;Zhao, Kai;Zheng, Xingming;Zhao, Kai

作者机构：

关键词： fractional vegetation cover;dimidiate pixel model;look-up table;PROSAIL;in situ measurement

期刊名称：JOURNAL OF APPLIED REMOTE SENSING （ 影响因子：1.53； 五年影响因子：1.565 ）

ISSN： 1931-3195

年卷期： 2016 年 10 卷

页码：

收录情况： SCI

摘要： Fractional vegetation cover (FVC) is an important variable for describing the quality and changes of vegetation in terrestrial ecosystems. Dimidiate pixel models and physical models are widely used to estimate FVC. Six dimidiate pixel models based on different vegetation indices (VI) and four look-up table (LUT) methods were compared to estimate FVC from Landsat 8 OLI data. Comparisons with in situ FVC of steppe and corn showed that the model proposed by Baret et al., which is based on the normalized difference vegetation index (NDVI), predicted FVC most accurately followed by Carlson and Ripley's method. Gutman and Ignatov's method overestimated FVC. Modified soil adjusted vegetation index (MSAVI) and the mixture of NDVI and RVI showed potential to replace NDVI in Gutman and Ignatov's model, whereas the difference vegetation index (DVI) performed less well. At low vegetation cover, the LUT using reflectances to constrain the cost function performed better than LUTs using VI to constrain the cost function, whereas at high vegetation cover, the LUT based on NDVI estimated FVC most accurately. The applications of DVI and MSAVI to constrain the cost function also obtained improvement at high vegetation cover. Overall, the accuracies of LUT methods were a little lower than those of dimidiate pixel models. (C) 2016 Society of Photo-Optical Instrumentation Engineers (SPIE).

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