Comparison of Four Machine Learning Methods for Generating the GLASS Fractional Vegetation …
Open AccessThis article is freely available re-usable Remote Sens. 2016, 8(8), 682; doi:10.3390/rs8080682 (registering DOI) Article 1 State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University, Beijing 100875, China 2 Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA * Author to whom correspondence should be addressed. Academic Editors: Jose Moreno, Clement Atzberger and Prasad S. Thenkabail Received: 18 May 2016 / Revised: 15 August 2016 / Accepted: 17 August 2016 / Published: 20 August 2016 No 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 spatial and temporal discontinuities in the current FVC…
Link to Full Article: Comparison of Four Machine Learning Methods for Generating the GLASS Fractional Vegetation …
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