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Sensors 2017, 17(1), 81; doi:10.3390/s17010081

Performance Evaluation of Machine Learning Methods for Leaf Area Index Retrieval from Time-Series MODIS Reflectance Data

State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China
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Author to whom correspondence should be addressed.
Received: 1 September 2016 / Accepted: 28 December 2016 / Published: 1 January 2017
(This article belongs to the Section Remote Sensors)
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Abstract

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 algorithms, including back-propagation neutral network (BPNN), radial basis function networks (RBFNs), general regression neutral networks (GRNNs), and multi-output support vector regression (MSVR) are proposed to retrieve LAI from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data in this study and performance of these machine learning algorithms is evaluated. The results demonstrated that GRNNs, RBFNs, and MSVR exhibited low sensitivity to training sample size, whereas BPNN had high sensitivity. The four algorithms performed slightly better with red, near infrared (NIR), and short wave infrared (SWIR) bands than red and NIR bands, and the results were significantly better than those obtained using single band reflectance data (red or NIR). Regardless of band composition, GRNNs performed better than the other three methods. Among the four algorithms, BPNN required the least training time, whereas MSVR needed the most for any sample size. View Full-Text
Keywords: BPNN; GRNNs; leaf area index; RBFNs; MSVR; retrieval BPNN; GRNNs; leaf area index; RBFNs; MSVR; retrieval
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Wang, T.; Xiao, Z.; Liu, Z. Performance Evaluation of Machine Learning Methods for Leaf Area Index Retrieval from Time-Series MODIS Reflectance Data. Sensors 2017, 17, 81.

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