{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,3]],"date-time":"2026-01-03T21:09:45Z","timestamp":1767474585859,"version":"build-2065373602"},"reference-count":47,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2018,10,16]],"date-time":"2018-10-16T00:00:00Z","timestamp":1539648000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Kun Jia","award":["41671332","2016YFA0600103"],"award-info":[{"award-number":["41671332","2016YFA0600103"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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 (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.<\/jats:p>","DOI":"10.3390\/rs10101648","type":"journal-article","created":{"date-parts":[[2018,10,16]],"date-time":"2018-10-16T11:07:51Z","timestamp":1539688071000},"page":"1648","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Global Fractional Vegetation Cover Estimation Algorithm for VIIRS Reflectance Data Based on Machine Learning Methods"],"prefix":"10.3390","volume":"10","author":[{"given":"Duanyang","family":"Liu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100875, China"},{"name":"Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6646-0718","authenticated-orcid":false,"given":"Linqing","family":"Yang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100875, China"},{"name":"Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8586-4243","authenticated-orcid":false,"given":"Kun","family":"Jia","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100875, China"},{"name":"Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2708-9183","authenticated-orcid":false,"given":"Shunlin","family":"Liang","sequence":"additional","affiliation":[{"name":"Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiqiang","family":"Xiao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100875, China"},{"name":"Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9531-5336","authenticated-orcid":false,"given":"Xiangqin","family":"Wei","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Digital Earth, Chinese Academy of Science, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunjun","family":"Yao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100875, China"},{"name":"Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mu","family":"Xia","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100875, China"},{"name":"Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuwei","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100875, China"},{"name":"Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,10,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1016\/j.rse.2013.02.030","article-title":"GEOV1: LAI, FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part 2: Validation and intercomparison with reference products","volume":"137","author":"Camacho","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1016\/j.jag.2012.07.003","article-title":"Fractional vegetation cover estimation in arid and semi-arid environments using HJ-1 satellite hyperspectral data","volume":"21","author":"Zhang","year":"2013","journal-title":"Int. J. Appl. Earth Observ. Geoinform."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.rse.2016.02.019","article-title":"Fractional vegetation cover estimation algorithm for Chinese GF-1 wide field view data","volume":"177","author":"Jia","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Roujean, J.L., and Lacaze, R. (2002). Global mapping of vegetation parameters from POLDER multiangular measurements for studies of surface-atmosphere interactions: A pragmatic method and its validation. J. Geophys. Res. Atmos.","DOI":"10.1029\/2001JD000751"},{"key":"ref_5","unstructured":"Baret, F., Pavageau, K., B\u00e9al, D., Weiss, M., Barthelot, B., and Regner, P. (2006). Algorithm Theoretical Basis Document for MERIS Top of Atmosphere Land Products (TOAVEG), The European Space Agency. Report of ESA contract AO."},{"key":"ref_6","unstructured":"Garc\u00eda-Haro, F.J., Camacho-de Coca, F., and Miralles, J.M. (2008, January 22\u201326). Inter-comparison of SEVIRI\/MSG and MERIS\/ENVISAT biophysical products over Europe and Africa. Proceedings of the 2nd MERIS\/(A) ATSR User Workshop, Frascati, Italy."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/j.rse.2012.12.027","article-title":"GEOV1: LAI and FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part 1: Principles of development and production","volume":"137","author":"Baret","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_8","unstructured":"Guenther, B., de Luccia, F., McCarthy, J., Moeller, C., Xiong, X., and Murphy, R.E. (2018, May 25). Performance continuity of the A-Train MODIS observations: Welcome to the NPP VIIRS, Available online: https:\/\/www.star.nesdis.noaa.gov\/jpss\/documents\/meetings\/2011\/AMS_Seattle_2011\/Poster\/A-TRAIN%20%20Perf%20Cont%20%20MODIS%20Observa%20-%20Guenther%20-%20WPNB.pdf."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1109\/TGRS.2006.890567","article-title":"Multiyear on-orbit calibration and performance of Terra MODIS reflective solar bands","volume":"45","author":"Xiong","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.rse.2014.03.028","article-title":"Early evaluation of the VIIRS calibration, cloud mask and surface reflectance Earth data records","volume":"148","author":"Vermote","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.rse.2015.02.021","article-title":"Estimation of surface upward longwave radiation from MODIS and VIIRS clear-sky data in the Tibetan Plateau","volume":"162","author":"Jiao","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.rse.2015.03.005","article-title":"Spatio-temporal variability of Suomi-NPP VIIRS-derived aerosol optical thickness over China in 2013","volume":"163","author":"Meng","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.rse.2015.02.004","article-title":"Evaluation of the performance of Suomi NPP VIIRS top of canopy vegetation indices over AERONET sites","volume":"162","author":"Shabanov","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wang, D., Liang, S., He, T., and Yu, Y. (2013). Direct estimation of land surface albedo from VIIRS data: Algorithm improvement and preliminary validation. J. Geophys. Res. Atmos.","DOI":"10.1002\/2013JD020417"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Xiao, Z., Liang, S., Wang, T., and Jiang, B. (2016). Retrieval of leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FAPAR) from VIIRS time-series data. Remote Sens., 8.","DOI":"10.3390\/rs8040351"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.rse.2005.07.011","article-title":"A comparison of methods for estimating fractional green vegetation cover within a desert-to-upland transition zone in central New Mexico, USA","volume":"98","author":"Xiao","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1698","DOI":"10.1016\/j.agrformet.2011.07.004","article-title":"A comparison of methods for estimating fractional vegetation cover in arid regions","volume":"151","author":"Jiapaer","year":"2011","journal-title":"Agric. For. Meteorol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1109\/JSTARS.2012.2194696","article-title":"Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches","volume":"5","author":"Plaza","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"8098","DOI":"10.1029\/JB091iB08p08098","article-title":"Spectral mixture modeling: A new analysis of rock and soil types at the Viking Lander 1 site","volume":"91","author":"Adams","year":"1986","journal-title":"J. Geophys. Res. Solid Earth"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1109\/36.225538","article-title":"Estimating leaf area index from satellite data","volume":"31","author":"Price","year":"1993","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1533","DOI":"10.1080\/014311698215333","article-title":"The derivation of the green vegetation fraction from NOAA\/AVHRR data for use in numerical weather prediction models","volume":"19","author":"Gutman","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1772","DOI":"10.1109\/TGRS.2013.2255059","article-title":"A real-time MODIS vegetation product for land surface and numerical weather prediction models","volume":"52","author":"Case","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yang, L., Jia, K., Liang, S., Liu, J., and Wang, X. (2016). Comparison of four machine learning methods for generating the GLASS fractional vegetation cover product from MODIS data. Remote Sens., 8.","DOI":"10.3390\/rs8080682"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Yang, L., Jia, K., Liang, S., Wei, X., Yao, Y., and Zhang, X. (2017). A Robust Algorithm for Estimating Surface Fractional Vegetation Cover from Landsat Data. Remote Sens., 9.","DOI":"10.3390\/rs9080857"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.rse.2011.11.002","article-title":"Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and-3","volume":"118","author":"Verrelst","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.advwatres.2009.10.008","article-title":"Estimating soil moisture using remote sensing data: A machine learning approach","volume":"33","author":"Ahmad","year":"2010","journal-title":"Adv. Water Resour."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4787","DOI":"10.1109\/TGRS.2015.2409563","article-title":"Global land surface fractional vegetation cover estimation using general regression neural networks from MODIS surface reflectance","volume":"53","author":"Jia","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1109\/LGRS.2009.2039191","article-title":"Gaussian process regression for estimating chlorophyll concentration in subsurface waters from remote sensing data","volume":"7","author":"Pasolli","year":"2010","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1794","DOI":"10.1109\/TGRS.2006.876030","article-title":"Evaluation of the representativeness of networks of sites for the global validation and intercomparison of land biophysical products: Proposition of the CEOS-BELMANIP","volume":"44","author":"Baret","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"933","DOI":"10.1641\/0006-3568(2001)051[0933:TEOTWA]2.0.CO;2","article-title":"Terrestrial Ecoregions of the World: A New Map of Life on Earth: A new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity","volume":"51","author":"Olson","year":"2001","journal-title":"BioScience"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1080\/2150704X.2015.1101649","article-title":"Development and validation of the global surface type data product from S-NPP VIIRS","volume":"7","author":"Zhang","year":"2016","journal-title":"Remote Sens. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/S0034-4257(01)00289-9","article-title":"Novel algorithms for remote estimation of vegetation fraction","volume":"80","author":"Gitelson","year":"2002","journal-title":"Remote Sens.Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"9844","DOI":"10.3390\/rs70809844","article-title":"Reconstruction of satellite-retrieved land-surface reflectance based on temporally-continuous vegetation indices","volume":"7","author":"Xiao","year":"2015","journal-title":"Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1167","DOI":"10.1016\/j.csda.2009.09.020","article-title":"Robust smoothing of gridded data in one and higher dimensions with missing values","volume":"54","author":"Garcia","year":"2010","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Liang, S., Zhang, X., Xiao, Z., Cheng, J., Liu, Q., and Zhao, X. (2013). Global LAnd Surface Satellite (GLASS) Products: Algorithms, Validation and Analysis, Springer Science & Business Media.","DOI":"10.1007\/978-3-319-02588-9"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1080\/17538947.2013.805262","article-title":"A long-term Global LAnd Surface Satellite (GLASS) data-set for environmental studies","volume":"6","author":"Liang","year":"2013","journal-title":"Int. J. Dig. Earth"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1109\/JSTARS.2014.2342257","article-title":"Validating GEOV1 fractional vegetation cover derived from coarse-resolution remote sensing images over croplands","volume":"8","author":"Mu","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1111\/j.1654-1103.2011.01373.x","article-title":"A novel method for extracting green fractional vegetation cover from digital images","volume":"23","author":"Liu","year":"2012","journal-title":"J. Veg. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"10425","DOI":"10.3390\/rs70810425","article-title":"Extracting the green fractional vegetation cover from digital images using a shadow-resistant algorithm (SHAR-LABFVC)","volume":"7","author":"Song","year":"2015","journal-title":"Remote Sens."},{"key":"ref_40","first-page":"1","article-title":"30-m Global Land cover data product-Globe Land30","volume":"24","author":"Chen","year":"2017","journal-title":"Geomatics World"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"7442","DOI":"10.1109\/TGRS.2016.2604007","article-title":"Fractional Vegetation Cover Estimation Method Through Dynamic Bayesian Network Combining Radiative Transfer Model and Crop Growth Model","volume":"54","author":"Wang","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.rse.2007.02.018","article-title":"LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION: Part 1: Principles of the algorithm","volume":"110","author":"Baret","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"4907","DOI":"10.1080\/0143116031000114851","article-title":"The use of backpropagating artificial neural networks in land cover classification","volume":"24","author":"Kavzoglu","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1109\/72.97934","article-title":"A general regression neural network","volume":"2","author":"Specht","year":"1991","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_45","first-page":"1","article-title":"Multivariate adaptive regression splines","volume":"19","author":"Friedman","year":"1991","journal-title":"Ann. Stat."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1016\/j.eswa.2004.12.031","article-title":"A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines","volume":"28","author":"Lee","year":"2005","journal-title":"Expert Syst. Appl."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Rasmussen, C.E., and Williams, C.K. (2006). Gaussian Processes for Machine Learning, MIT Press.","DOI":"10.7551\/mitpress\/3206.001.0001"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/10\/1648\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:26:06Z","timestamp":1760196366000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/10\/1648"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,10,16]]},"references-count":47,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2018,10]]}},"alternative-id":["rs10101648"],"URL":"https:\/\/doi.org\/10.3390\/rs10101648","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2018,10,16]]}}}