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Article: Global fractional vegetation cover estimation algorithm for VIIRS reflectance data based on machine learning methods

TitleGlobal fractional vegetation cover estimation algorithm for VIIRS reflectance data based on machine learning methods
Authors
KeywordsFractional vegetation cover
Global
Machine learning method
VIIRS surface reflectance
Issue Date2018
Citation
Remote Sensing, 2018, v. 10, n. 10, article no. 1648 How to Cite?
AbstractFractional 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 (R2 = 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.
Persistent Identifierhttp://hdl.handle.net/10722/321813
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLiu, Duanyang-
dc.contributor.authorYang, Linqing-
dc.contributor.authorJia, Kun-
dc.contributor.authorLiang, Shunlin-
dc.contributor.authorXiao, Zhiqiang-
dc.contributor.authorWei, Xiangqin-
dc.contributor.authorYao, Yunjun-
dc.contributor.authorXia, Mu-
dc.contributor.authorLi, Yuwei-
dc.date.accessioned2022-11-03T02:21:36Z-
dc.date.available2022-11-03T02:21:36Z-
dc.date.issued2018-
dc.identifier.citationRemote Sensing, 2018, v. 10, n. 10, article no. 1648-
dc.identifier.urihttp://hdl.handle.net/10722/321813-
dc.description.abstractFractional 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 (R2 = 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.-
dc.languageeng-
dc.relation.ispartofRemote Sensing-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectFractional vegetation cover-
dc.subjectGlobal-
dc.subjectMachine learning method-
dc.subjectVIIRS surface reflectance-
dc.titleGlobal fractional vegetation cover estimation algorithm for VIIRS reflectance data based on machine learning methods-
dc.typeArticle-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.3390/rs10101648-
dc.identifier.scopuseid_2-s2.0-85055453727-
dc.identifier.volume10-
dc.identifier.issue10-
dc.identifier.spagearticle no. 1648-
dc.identifier.epagearticle no. 1648-
dc.identifier.eissn2072-4292-
dc.identifier.isiWOS:000448555800148-

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