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

Linqing Yang,Jingcan Liu,Shunlin Liang,Xiaoxia Wang,Kun Jia

doi:10.3390/rs8080682

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.

Highlights

Fractional vegetation cover (FVC), generally defined as the fraction of green vegetation as seen from the nadir, is an important variable for describing land surface vegetation [1,2,3]
Direct validation results indicated that the performance of the multivariate adaptive regression splines (MARS) model (R2 = 0.836, root mean square error (RMSE) = 0.1488) was comparable to that of the general regression neural networks (GRNNs) method (R2 = 0.8353, RMSE = 0.1495), and the global land surface fractional vegetation cover (FVC) generated from the MARS model had good spatial and temporal consistency with that generated from the GRNNs method
The smoothing parameter for for the GRNNs method, the C and γ parameters for the support vector regression (SVR) method, and the maximum number of the GRNNs method, the C and γ parameters for the SVR method, and the maximum number of model terms for the MARS method are determined in the training process

Summary

Introduction

Fractional vegetation cover (FVC), generally defined as the fraction of green vegetation as seen from the nadir, is an important variable for describing land surface vegetation [1,2,3]. Empirical methods are limited temporally and spatially because their statistical relationships are constructed using data acquired at specific times in distinct regions They are typically applicable to specific research areas and vegetation types, they may become invalid when they are expanded to larger areas. The physical model-based methods for FVC estimation are based on the inversion of canopy radiative transfer models that simulate the physical relationships between vegetation canopy spectral reflectance and FVC. Such physical models have clear physical significance and can be adapted to a wide range of scenarios [17]. Because of the complexity of the physical models, direct inversion is generally complex, and artificial neural networks (ANNs) are usually used for indirect inversion of the physical model by training with a pre-computed reflectance database from the physical models to simplify the inversion [8,18]

Objectives

Results

Conclusion