Performance Evaluation of Machine Learning Methods for Leaf Area Index Retrieval from Time …

Open AccessArticle Sensors 2017, 17(1), 81; doi:10.3390/s17010081 (registering DOI) State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China * Author to whom correspondence should be addressed. Received: 1 September 2016 / Accepted: 28 December 2016 / Published: 1 January 2017 Leaf area index (LAI) is an important biophysical parameter and the retrieval of LAI from remote sensing data is the only feasible method for generating LAI products at regional and global scales. However, most LAI retrieval methods use satellite observations at a specific time to retrieve LAI. Because of the impacts of clouds and aerosols, the LAI products generated by these methods are spatially incomplete and temporally discontinuous, and thus they cannot meet the needs of practical applications. To generate high-quality LAI products, four machine learning…


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