刘端阳等：Global Fractional Vegetation Cover Estimation Algorithm for VIIRS Reflectance Data Based on Machine Learning Methods

Global Fractional Vegetation Cover Estimation Algorithm for VIIRS Reflectance Data Based on Machine Learning Methods

作者:Liu, DY (Liu, Duanyang)[ 1,2 ] ; Yang, LQ (Yang, Linqing)[ 1,2 ] ; Jia, K (Jia, Kun)[ 1,2 ] ; Liang, SL (Liang, Shunlin)[ 3 ] ; Xiao, ZQ (Xiao, Zhiqiang)[ 1,2 ] ; Wei, XQ (Wei, Xiangqin)[ 4 ] ; Yao, YJ (Yao, Yunjun)[ 1,2 ] ; Xia, M (Xia, Mu)[ 1,2 ] ; Li, YW (Li, Yuwei)[ 1,2 ]

REMOTE SENSING

卷: 10  期: 10

文献号: 1648

DOI: 10.3390/rs10101648

出版年: OCT 2018

摘要

Fractional vegetation cover (FVC) is an essential input parameter for many environmental and ecological models. Recently, several global FVC products have been generated using remote sensing data. The Global LAnd Surface Satellite (GLASS) FVC product, which is generated from Moderate Resolution Imaging Spectroradiometer (MODIS) data, has attained acceptable performance. However, the original MODIS operation design lifespan has been exceeded. The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-Orbiting Partnership (S-NPP) satellite was designed to be the MODIS successor. Therefore, developing an FVC estimation algorithm for VIIRS data is important for maintaining continuous FVC estimates in case of MODIS failure. In this study, a global FVC estimation algorithm for VIIRS surface reflectance data was proposed based on machine learning methods, which investigated the performances of back propagating neural networks (BPNNs), general regression networks (GRNNs), multivariate adaptive regression splines (MARS), and Gaussian process regression (GPR). The training samples were extracted from the GLASS FVC product and corresponding reconstructed VIIRS surface reflectance in 2013 over the global sampling locations. The VIIRS reflectances of red and near infrared (NIR) bands were the input variables for these machine learning methods. The theoretical performances and independent validation results indicated that the four machine learning methods could achieve similar and reliable FVC estimates. Regarding the FVC estimation accuracy, the GPR method achieved the best performance (R-2 = 0.9019, RMSE = 0.0887). The MARS method had the obvious advantage of computational efficiency. Furthermore, the FVC estimates achieved good spatial and temporal continuities. Therefore, the proposed FVC estimation algorithm for VIIRS data can potentially generate reliable global FVC data for related applications.

通讯作者地址: Jia, K (通讯作者)

Beijing Normal Univ & Inst Remote Sensing & Digit, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China.

通讯作者地址: Jia, K (通讯作者)

Beijing Normal Univ, Fac Geog Sci, Beijing Engn Res Ctr Global Land Remote Sensing P, Beijing 100875, Peoples R China.

地址:

[ 1 ] Beijing Normal Univ & Inst Remote Sensing & Digit, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China

[ 2 ] Beijing Normal Univ, Fac Geog Sci, Beijing Engn Res Ctr Global Land Remote Sensing P, Beijing 100875, Peoples R China

[ 3 ] Univ Maryland, Dept Geog Sci, College Pk, MD 20742 USA

[ 4 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100101, Peoples R China