Remote Sensing Applications and Decision Support

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

[+] Author Affiliations
Yanling Ding, Hongyan Zhang

Northeast Normal University, School of Geographical Sciences, Renmin Street, Changchun, Jilin 130024, China

Zhenwang Li, Xiaoping Xin

National Hulunber Grassland Ecosystem Observation and Research Station, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Zhongguancun Southern Street, Beijing 100081, China

Xingming Zheng, Kai Zhao

Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Shengbei Street, Changchun, Jilin 130102, China

Changchun Jingyuetan Remote Sensing Experiment Station, Chinese Academy of Sciences, Shengbei Street, Changchun, Jilin 130102, China

J. Appl. Remote Sens. 10(3), 036022 (Sep 12, 2016). doi:10.1117/1.JRS.10.036022
History: Received April 19, 2016; Accepted August 23, 2016
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Abstract.  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.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Yanling Ding ; Hongyan Zhang ; Zhenwang Li ; Xiaoping Xin ; Xingming Zheng, et al.
"Comparison of fractional vegetation cover estimations using dimidiate pixel models and look-up table inversions of the PROSAIL model from Landsat 8 OLI data", J. Appl. Remote Sens. 10(3), 036022 (Sep 12, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.036022


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