{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T20:41:15Z","timestamp":1781901675321,"version":"3.54.5"},"reference-count":63,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2017,8,19]],"date-time":"2017-08-19T00:00:00Z","timestamp":1503100800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Fractional vegetation cover (FVC) is an essential land surface parameter for Earth surface process simulations and global change studies. The currently existing FVC products are mostly obtained from low or medium resolution remotely sensed data, while many applications require the fine spatial resolution FVC product. The availability of well-calibrated coverage of Landsat imagery over large areas offers an opportunity for the production of FVC at fine spatial resolution. Therefore, the objective of this study is to develop a general and reliable land surface FVC estimation algorithm for Landsat surface reflectance data under various land surface conditions. Two machine learning methods multivariate adaptive regression splines (MARS) model and back-propagation neural networks (BPNNs) were trained using samples from PROSPECT leaf optical properties model and the scattering by arbitrarily inclined leaves (SAIL) model simulations, which included Landsat reflectance and corresponding FVC values, and evaluated to choose the method which had better performance. Thereafter, the MARS model, which had better performance in the independent validation, was evaluated using ground FVC measurements from two case study areas. The direct validation of the FVC estimated using the proposed algorithm (Heihe: R2 = 0.8825, RMSE = 0.097; Chengde using Landsat 7 ETM+: R2 = 0.8571, RMSE = 0.078, Chengde using Landsat 8 OLI: R2 = 0.8598, RMSE = 0.078) showed the proposed method had good performance. Spatial-temporal assessment of the estimated FVC from Landsat 7 ETM+ and Landsat 8 OLI data confirmed the robustness and consistency of the proposed method. All these results indicated that the proposed algorithm could obtain satisfactory accuracy and had the potential for the production of high-quality FVC estimates from Landsat surface reflectance data.<\/jats:p>","DOI":"10.3390\/rs9080857","type":"journal-article","created":{"date-parts":[[2017,8,21]],"date-time":"2017-08-21T11:10:51Z","timestamp":1503313851000},"page":"857","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["A Robust Algorithm for Estimating Surface Fractional Vegetation Cover from Landsat Data"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6646-0718","authenticated-orcid":false,"given":"Linqing","family":"Yang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"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, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2708-9183","authenticated-orcid":false,"given":"Shunlin","family":"Liang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"},{"name":"Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"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 Sciences, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3803-8170","authenticated-orcid":false,"given":"Yunjun","family":"Yao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaotong","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,8,19]]},"reference":[{"key":"ref_1","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. Part1: Principles of development and production","volume":"137","author":"Baret","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_2","first-page":"506","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 Obs. Geoinf."},{"key":"ref_3","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_4","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1175\/JCLI3254.1","article-title":"The Effects of satellite-derived vegetation cover variability on simulated land\u2013atmosphere interactions in the NAMS","volume":"18","author":"Matsui","year":"2005","journal-title":"J. Clim."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.catena.2010.06.012","article-title":"Identification of priority areas for controlling soil erosion","volume":"83","author":"Zhang","year":"2010","journal-title":"Catena"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1046\/j.1365-2486.2003.00569.x","article-title":"Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model","volume":"9","author":"Sitch","year":"2003","journal-title":"Glob. Chang. Biol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1002\/(SICI)1096-9837(199806)23:6<527::AID-ESP868>3.0.CO;2-5","article-title":"The European Soil Erosion Model (EUROSEM): A dynamic approach for predicting sediment transport from fields and small catchments","volume":"23","author":"Morgan","year":"1998","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_8","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_9","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 Obs. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"928","DOI":"10.1016\/j.rse.2009.01.006","article-title":"Estimating fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil in the Australian tropical savanna region upscaling the EO-1 Hyperion and MODIS sensors","volume":"113","author":"Guerschman","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/S0034-4257(97)00104-1","article-title":"On the relation between NDVI, fractional vegetation cover, and leaf area index","volume":"62","author":"Carlson","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_13","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_14","doi-asserted-by":"crossref","first-page":"3833","DOI":"10.1016\/j.rse.2008.06.006","article-title":"Development of a two-band enhanced vegetation index without a blue band","volume":"112","author":"Jiang","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1201","DOI":"10.1080\/01431168808954929","article-title":"The assessment and monitoring of sparsely vegetated rangelands using calibrated Landsat data","volume":"9","author":"Graetz","year":"1988","journal-title":"Int. J. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"768","DOI":"10.3390\/s90200768","article-title":"Comparison between fractional vegetation cover retrievals from vegetation indices and spectral mixture analysis: Case study of PROBA\/CHRIS data over an agricultural area","volume":"9","author":"Sobrino","year":"2009","journal-title":"Sensors"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2619","DOI":"10.3390\/rs4092619","article-title":"Remote sensing of fractional green vegetation cover using spatially-interpolated endmembers","volume":"4","author":"Johnson","year":"2012","journal-title":"Remote Sens."},{"key":"ref_18","unstructured":"Wu, B., Li, M., Yan, C., Zhou, W., and Yan, C. (2004, January 20\u201324). Developing method of vegetation fraction estimation by remote sensing for soil loss equation: A case in the upper basin of miyun reservoir. Proceedings of the 2004 IEEE International Geoscience and Remote Sensing Symposium, Anchorage, AK, USA."},{"key":"ref_19","unstructured":"Jackson, T.J., Chen, J.M., Gong, P., Liang, S., Zhan, Y., Meng, Q., Wang, C., Li, J., Zhou, K., and Li, D. (2014, January 13). Fractional vegetation cover estimation over large regions using GF-1 satellite data. Proceedings of the Land Surface Remote Sensing II, Beijing, China."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/S0168-1923(00)00195-7","article-title":"Spatial and temporal dynamics of vegetation in the San Pedro River basin area","volume":"105","author":"Qi","year":"2000","journal-title":"Agric. For. Meteorol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1080\/02757250009532396","article-title":"Inversion methods for physically-based models","volume":"18","author":"Kimes","year":"2000","journal-title":"Remote Sens. Rev."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Roujean, J.-L. (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., 107.","DOI":"10.1029\/2001JD000751"},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.rse.2010.09.012","article-title":"Optimal modalities for radiative transfer-neural network estimation of canopy biophysical characteristics: Evaluation over an agricultural area with CHRIS\/PROBA observations","volume":"115","author":"Verger","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_25","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_26","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. Digit. Earth"},{"key":"ref_27","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","volume":"110","author":"Baret","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_28","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_29","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_30","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_31","doi-asserted-by":"crossref","first-page":"15494","DOI":"10.3390\/rs71115494","article-title":"A Generic algorithm to estimate LAI, FAPAR and FCOVER variables from SPOT4_HRVIR and Landsat sensors: Evaluation of the consistency and comparison with ground measurements","volume":"7","author":"Li","year":"2015","journal-title":"Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.14358\/PERS.72.10.1171","article-title":"Landsat","volume":"72","author":"Williams","year":"2006","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.14358\/PERS.72.10.1137","article-title":"Landsat-7 long-term acquisition plan","volume":"72","author":"Arvidson","year":"2006","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1196","DOI":"10.1016\/j.rse.2007.08.011","article-title":"The availability of cloud-free Landsat ETM+ data over the conterminous United States and globally","volume":"112","author":"Ju","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.rse.2015.08.004","article-title":"Mapping rice paddy extent and intensification in the Vietnamese Mekong River Delta with dense time stacks of Landsat data","volume":"169","author":"Kontgis","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"148","DOI":"10.3390\/land3010148","article-title":"Mapping urban transitions using multi-temporal Landsat and DMSP-OLS night-time lights imagery of the Red River Delta in Vietnam","volume":"3","author":"Castrence","year":"2014","journal-title":"Land"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1988","DOI":"10.1016\/j.rse.2009.05.011","article-title":"Generation of dense time series synthetic Landsat data through data blending with MODIS using a spatial and temporal adaptive reflectance fusion model","volume":"113","author":"Hilker","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.rse.2009.08.017","article-title":"An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks","volume":"114","author":"Huang","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1016\/j.rse.2012.06.006","article-title":"Monitoring land cover change in urban and peri-urban areas using dense time stacks of Landsat satellite data and a data mining approach","volume":"124","author":"Schneider","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.rse.2014.12.014","article-title":"Improvement and expansion of the Fmask algorithm: Cloud, cloud shadow, and snow detection for Landsats 4\u20137, 8, and Sentinel 2 images","volume":"159","author":"Zhu","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.rse.2011.10.028","article-title":"Object-based cloud and cloud shadow detection in Landsat imagery","volume":"118","author":"Zhu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/LGRS.2005.857030","article-title":"A Landsat surface reflectance dataset for north America, 1990\u20132000","volume":"3","author":"Masek","year":"2006","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2013.04.007","article-title":"Characterizing LEDAPS surface reflectance products by comparisons with AERONET, field spectrometer, and MODIS data","volume":"136","author":"Maiersperger","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1109\/LGRS.2011.2173290","article-title":"A Modified neighborhood similar pixel interpolator approach for removing thick clouds in Landsat images","volume":"9","author":"Zhu","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1016\/j.rse.2010.12.010","article-title":"A simple and effective method for filling gaps in Landsat ETM+ SLC-off images","volume":"115","author":"Chen","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Campos-Taberner, M., Garcia-Haro, F.J., Moreno, A., Gilabert, M.A., Martinez, B., Sanchez-Ruiz, S., and Camps-Valls, G. (2015, January 26\u201331). Development of an earth observation processing chain for crop bio-physical parameters at local scale. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium, Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7325689"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"S56","DOI":"10.1016\/j.rse.2008.01.026","article-title":"PROSPECT+SAIL models: A review of use for vegetation characterization","volume":"113","author":"Jacquemoud","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/0034-4257(84)90057-9","article-title":"Light scattering by leaf layers with application to canopy reflectance modeling: The SAIL model","volume":"16","author":"Verhoef","year":"1984","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/0034-4257(90)90100-Z","article-title":"PROSPECT: A model of leaf optical properties spectra","volume":"34","author":"Jacquemoud","year":"1990","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/S0034-4257(00)00139-5","article-title":"Comparison of Four Radiative Transfer models to simulate plant canopies reflectance direct and inverse mode","volume":"74","author":"Jacquemoud","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1376","DOI":"10.1364\/JOSA.59.001376","article-title":"Interaction of isotropic light with a compact plant leaf","volume":"59","author":"Allen","year":"1969","journal-title":"J. Opt. Soc. Am."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/0002-1571(71)90092-6","article-title":"A theoretical analysis of the frequency of gaps in plant stands","volume":"8","author":"Nilson","year":"1971","journal-title":"Agric. Meteorol."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Shepherd, K.D., Palm, C.A., Gachengo, C.N., and Vanlauwe, B. (2003). Rapid Characterization of organic resource quality for soil and livestock management in tropical agroecosystems using near-infrared spectroscopy. Agron. J., 95.","DOI":"10.2134\/agronj2003.1314"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/j.rse.2004.07.013","article-title":"A comparison of error metrics and constraints for multiple endmember spectral mixture analysis and spectral angle mapper","volume":"93","author":"Dennison","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"775","DOI":"10.1109\/JSTARS.2014.2349007","article-title":"Fractional forest cover changes in northeast China from 1982 to 2011 and its relationship with climatic variations","volume":"8","author":"Jia","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1214\/aos\/1176347964","article-title":"Multivariate adaptive regression splines","volume":"19","author":"Barron","year":"1991","journal-title":"Ann. Stat."},{"key":"ref_57","first-page":"1","article-title":"Multivariate adaptive regression splines (with discussion)","volume":"19","author":"Friedman","year":"1991","journal-title":"Ann. Stat."},{"key":"ref_58","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 Obs. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Zhao, J., Xie, D., Mu, X., Liu, Y., and Yan, G. (2012, January 22\u201327). Accuracy evaluation of the ground-based fractional vegetation cover measurement by using simulated images. Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Munich, Germany.","DOI":"10.1109\/IGARSS.2012.6350587"},{"key":"ref_60","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_61","first-page":"32","article-title":"Forest cover classification using Landsat ETM+ data and time series MODIS NDVI data","volume":"33","author":"Jia","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/MSP.2013.2249294","article-title":"Analyzing local structure in kernel-based learning: Explanation, complexity, and reliability assessment","volume":"30","author":"Montavon","year":"2013","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.rse.2016.10.009","article-title":"Multitemporal and multiresolution leaf area index retrieval for operational local rice crop monitoring","volume":"187","author":"Nutini","year":"2016","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/8\/857\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:42:48Z","timestamp":1760208168000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/8\/857"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,8,19]]},"references-count":63,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2017,8]]}},"alternative-id":["rs9080857"],"URL":"https:\/\/doi.org\/10.3390\/rs9080857","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,8,19]]}}}