Comparison of Four Machine Learning Methods for Generating the GLASS Fractional Vegetation Cover Product from MODIS Data

Comparison of Four Machine Learning Methods for Generating the GLASS Fractional Vegetation Cover Product from MODIS Data

Long-term global land surface fractional vegetation cover (FVC) products are essential for various applications. Currently, several global FVC products have been generated from medium spatial resolution remote sensing data. However, validation results indicate that there are inconsistencies and spat...

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Bibliographic Details
Main Authors: Linqing Yang, Kun Jia, Shunlin Liang, Jingcan Liu, Xiaoxia Wang
Format: Article
Language:English
Published: MDPI AG 2016-08-01
Series:Remote Sensing
Subjects:
fractional vegetation cover (FVC)
GLASS FVC product
multivariate adaptive regression splines (MARS)
general regression neural networks (GRNNs)
MODIS
Online Access:http://www.mdpi.com/2072-4292/8/8/682

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http://www.mdpi.com/2072-4292/8/8/682

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