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Remote Sens. 2016, 8(8), 682; doi:10.3390/rs8080682

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

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
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Abstract

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 products. Therefore, the Global LAnd Surface Satellite (GLASS) FVC product algorithm using general regression neural networks (GRNNs), which achieves an FVC estimation accuracy comparable to that of the GEOV1 FVC product with much improved spatial and temporal continuities, was developed. However, the computational efficiency of the GRNNs method is low and unsatisfactory for generating the long-term GLASS FVC product. Therefore, the objective of this study was to discover an alternative algorithm for generating the GLASS FVC product that has both an accuracy comparable to that of the GRNNs method and adequate computational efficiency. Four commonly used machine learning methods, back-propagation neural networks (BPNNs), GRNNs, support vector regression (SVR), and multivariate adaptive regression splines (MARS), were evaluated. After comparing its performance of training accuracy and computational efficiency with the other three methods, the MARS model was preliminarily selected as the most suitable algorithm for generating the GLASS FVC product. Direct validation results indicated that the performance of the MARS model (R2 = 0.836, RMSE = 0.1488) was comparable to that of the GRNNs method (R2 = 0.8353, RMSE = 0.1495), and the global land surface FVC generated from the MARS model had good spatial and temporal consistency with that generated from the GRNNs method. Furthermore, the computational efficiency of MARS was much higher than that of the GRNNs method. Therefore, the MARS model is a suitable algorithm for generating the GLASS FVC product from Moderate Resolution Imaging Spectroradiometer (MODIS) data. View Full-Text
Keywords: fractional vegetation cover (FVC); GLASS FVC product; multivariate adaptive regression splines (MARS); general regression neural networks (GRNNs); MODIS fractional vegetation cover (FVC); GLASS FVC product; multivariate adaptive regression splines (MARS); general regression neural networks (GRNNs); MODIS
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Yang, L.; Jia, K.; Liang, S.; Liu, J.; Wang, X. Comparison of Four Machine Learning Methods for Generating the GLASS Fractional Vegetation Cover Product from MODIS Data. Remote Sens. 2016, 8, 682.

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