{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T08:24:26Z","timestamp":1785831866569,"version":"3.56.0"},"reference-count":83,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2018,11,30]],"date-time":"2018-11-30T00:00:00Z","timestamp":1543536000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["NO.41671332"],"award-info":[{"award-number":["NO.41671332"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["NO.2016YFB0501404"],"award-info":[{"award-number":["NO.2016YFB0501404"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["NO.2016YFA0600103"],"award-info":[{"award-number":["NO.2016YFA0600103"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Fractional vegetation cover (FVC) is an essential parameter for characterizing the land surface vegetation conditions and plays an important role in earth surface process simulations and global change studies. The Sentinel-2 missions carrying multi-spectral instrument (MSI) sensors with 13 multispectral bands are potentially useful for estimating FVC. However, the performance of these bands for FVC estimation is unclear. Therefore, the objective of this study was to assess the performance of Sentinel-2 MSI spectral band reflectances on FVC estimation. The samples, including the Sentinel-2 MSI canopy reflectances and corresponding FVC values, were simulated using the PROSPECT + SAIL radiative transfer model under different conditions, and random forest regression (RFR) method was then used to develop FVC estimation models and assess the performance of various band reflectances for FVC estimation. These models were finally evaluated using field survey data. The results indicate that the three most important bands of Sentinel-2 MSI data for FVC estimation are band 4 (Red), band 12 (SWIR2) and band 8a (NIR2). FVC estimation using these bands has a comparable accuracy (root mean square error (RMSE) = 0.085) with that using all bands (RMSE = 0.090). The results also demonstrate that band 12 had a better performance for FVC estimation than the green band (RMSE = 0.097). However, the newly added red-edge bands, with low scores in the RFR model, have little significance for improving FVC estimation accuracy compared with the Red, NIR2 and SWIR2 bands.<\/jats:p>","DOI":"10.3390\/rs10121927","type":"journal-article","created":{"date-parts":[[2018,11,30]],"date-time":"2018-11-30T12:13:17Z","timestamp":1543579997000},"page":"1927","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":90,"title":["Assessment of Sentinel-2 MSI Spectral Band Reflectances for Estimating Fractional Vegetation Cover"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2937-4757","authenticated-orcid":false,"given":"Bing","family":"Wang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, 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":[{"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, Faculty of Geographical Science, Beijing Normal University, 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":[{"vocabulary":"crossref","role":"author"}]},{"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":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianhong","family":"Xie","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, 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":[{"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\/0000-0002-0155-6735","authenticated-orcid":false,"given":"Xiang","family":"Zhao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, 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":[{"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, Faculty of Geographical Science, Beijing Normal University, 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":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaotong","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, 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":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,11,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3519","DOI":"10.1080\/014311698213795","article-title":"Relationships between percent vegetation cover and vegetation indices","volume":"19","author":"Purevdorj","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_2","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_3","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_4","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_5","doi-asserted-by":"crossref","first-page":"826","DOI":"10.1175\/1520-0450(2000)039<0826:DAEOGK>2.0.CO;2","article-title":"Derivation and evaluation of global 1-km fractional vegetation cover data for land modeling","volume":"39","author":"Zeng","year":"2000","journal-title":"J. Appl. Meteorol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"76","DOI":"10.2134\/jeq2008.0496","article-title":"A review of vegetated buffers and a meta-analysis of their mitigation efficacy in reducing nonpoint source pollution","volume":"39","author":"Zhang","year":"2010","journal-title":"J. Environ. Qual."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1016\/j.rse.2010.11.006","article-title":"Global estimates of evapotranspiration for climate studies using multi-sensor remote sensing data: Evaluation of three process-based approaches","volume":"115","author":"Vinukollu","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.rse.2011.12.004","article-title":"Landsat remote sensing approaches for monitoring long-term tree cover dynamics in semi-arid woodlands: Comparison of vegetation indices and spectral mixture analysis","volume":"119","author":"Yang","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.compag.2014.02.009","article-title":"Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from uav","volume":"103","year":"2014","journal-title":"Comput. Electron. Agric."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.catena.2012.11.007","article-title":"Vegetation cover reduces erosion and enhances soil organic carbon in a vineyard in the central Spain","volume":"104","author":"Bienes","year":"2013","journal-title":"Catena"},{"key":"ref_11","unstructured":"Liang, S., Li, X., and Wang, J. (2012). Advanced Remote Sensing: Terrestrial Information Extraction and Applications, Academic Press."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1335","DOI":"10.1080\/01431160210146668","article-title":"Derivation of wild vegetation cover density in semi-arid regions: Ers2\/sar evaluation","volume":"24","author":"Zribi","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1016\/S0034-4257(99)00112-1","article-title":"Hyperspectral mixture modeling for quantifying sparse vegetation cover in arid environments","volume":"72","author":"McGwire","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"036022","DOI":"10.1117\/1.JRS.10.036022","article-title":"Comparison of fractional vegetation cover estimations using dimidiate pixel models and look-up table inversions of the prosail model from landsat 8 oli data","volume":"10","author":"Ding","year":"2016","journal-title":"J. Appl. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Li, L., Yan, G., Mu, X., Liu, S., Chen, Y., Yan, K., Luo, J., and Song, W. (2017, January 23\u201328). Estimation of fractional vegetation cover using mean-based spectral unmixing method. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127672"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1080\/01431168308948546","article-title":"The red edge of plant leaf reflectance","volume":"4","author":"Horler","year":"1983","journal-title":"Int. J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3567","DOI":"10.1080\/01431169408954345","article-title":"Ndvi-derived land cover classifications at a global scale","volume":"15","author":"Defries","year":"1994","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A soil-adjusted vegetation index (savi)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_19","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_20","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1080\/02757259409532250","article-title":"Evaluation of the performance of various vegetation indices to retrieve vegetation cover from avhrr data","volume":"10","author":"Leprieur","year":"1994","journal-title":"Remote Sens. Rev."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1459","DOI":"10.1080\/01431169408954177","article-title":"The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status","volume":"15","author":"Filella","year":"1994","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","first-page":"344","article-title":"Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on sentinel-2 and -3","volume":"23","author":"Clevers","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1741","DOI":"10.1080\/01431169008955127","article-title":"Shape of the red edge as vitality indicator for plants","volume":"11","author":"Boochs","year":"1990","journal-title":"Int. J. Remote Sens."},{"key":"ref_24","first-page":"43","article-title":"Monitoring grass nutrients and biomass as indicators of rangeland quality and quantity using random forest modelling and worldview-2 data","volume":"43","author":"Ramoelo","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.rse.2005.12.011","article-title":"A new technique for extracting the red edge position from hyperspectral data: The linear extrapolation method","volume":"101","author":"Cho","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2241","DOI":"10.1080\/01431160701395328","article-title":"Towards red-edge positions less sensitive to canopy biophysical parameters for leaf chlorophyll estimation using properties optique spectrales des feuilles (prospect) and scattering by arbitrarily inclined leaves (sailh) simulated data","volume":"29","author":"Cho","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_27","first-page":"235","article-title":"Assessment of rapideye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops","volume":"34","author":"Kross","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_28","first-page":"80","article-title":"Assessing the potential of multi-seasonal worldview-2 imagery for mapping west african agroforestry tree species","volume":"50","author":"Karlson","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.rse.2015.11.030","article-title":"Investigating the capability of worldview-3 superspectral data for direct hydrocarbon detection","volume":"173","author":"Asadzadeh","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","article-title":"Sentinel-2: Esa\u2019s optical high-resolution mission for gmes operational services","volume":"120","author":"Drusch","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2636","DOI":"10.3390\/s7112636","article-title":"Sensitivity of the enhanced vegetation index (evi) and normalized difference vegetation index (ndvi) to topographic effects: A case study in high-density cypress forest","volume":"7","author":"Matsushita","year":"2007","journal-title":"Sensors"},{"key":"ref_32","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_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2017.04.031","article-title":"A snow-free vegetation index for improved monitoring of vegetation spring green-up date in deciduous ecosystems","volume":"196","author":"Wang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1198\/tast.2009.08199","article-title":"Variable importance assessment in regression: Linear regression versus random forest","volume":"63","year":"2009","journal-title":"Am. Stat."},{"key":"ref_35","first-page":"5","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"MLear"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.rse.2011.12.003","article-title":"Random forest classification of mediterranean land cover using multi-seasonal imagery and multi-seasonal texture","volume":"121","author":"Atkinson","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.cj.2016.01.008","article-title":"Estimation of biomass in wheat using random forest regression algorithm and remote sensing data","volume":"4","author":"Wang","year":"2016","journal-title":"Crop J."},{"key":"ref_38","first-page":"399","article-title":"High density biomass estimation for wetland vegetation using worldview-2 imagery and random forest regression algorithm","volume":"18","author":"Mutanga","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_39","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_40","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_41","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_42","doi-asserted-by":"crossref","unstructured":"Ding, Y., Zheng, X., and Jiang, T. (2016, January 10\u201315). Comparison of fractional vegetation cover estimating methods using in-situ measurements and the prosail model from landsat 8 oli data. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7730134"},{"key":"ref_43","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_44","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1051\/agro:2002008","article-title":"Improving canopy variables estimation from remote sensing data by exploiting ancillary information. Case study on sugar beet canopies","volume":"22","author":"Bruno","year":"2002","journal-title":"Agronomie"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.rse.2004.06.016","article-title":"Object-based retrieval of biophysical canopy variables using artificial neural nets and radiative transfer models","volume":"93","author":"Atzberger","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2592","DOI":"10.1016\/j.rse.2007.12.003","article-title":"Inversion of a radiative transfer model for estimating vegetation lai and chlorophyll in a heterogeneous grassland","volume":"112","author":"Darvishzadeh","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_47","first-page":"1","article-title":"Advances in remote sensing of vegetation function and traits","volume":"43","author":"Houborg","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"ACL","DOI":"10.1029\/2001JD000751","article-title":"Global mapping of vegetation parameters from polder multiangular measurements for studies of surface-atmosphere interactions: A pragmatic method and its validation","volume":"107","author":"Roujean","year":"2002","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/j.rse.2006.07.014","article-title":"Neural network estimation of lai, fapar, fcover and lai\u00d7cab, from top of canopy meris reflectance data: Principles and validation","volume":"105","author":"Bacour","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_50","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_51","doi-asserted-by":"crossref","first-page":"804","DOI":"10.1109\/LGRS.2011.2109934","article-title":"Multioutput support vector regression for remote sensing biophysical parameter estimation","volume":"8","author":"Tuia","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"3427","DOI":"10.3390\/rs6043427","article-title":"Estimating fractional shrub cover using simulated enmap data: A comparison of three machine learning regression techniques","volume":"6","author":"Schwieder","year":"2014","journal-title":"Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Campos-Taberner, M., Moreno-Mart\u00ednez, \u00c1., Garc\u00eda-Haro, J.F., Camps-Valls, G., Robinson, P.N., Kattge, J., and Running, W.S. (2018). Global estimation of biophysical variables from google earth engine platform. Remote Sens., 10.","DOI":"10.3390\/rs10081167"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Izquierdo-Verdiguier, E., and Zurita-Milla, R. (2018, January 22\u201327). Use of guided regularized random forest for biophysical parameter retrieval. Proceedings of the IGARSS 2018\u20142018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8517920"},{"key":"ref_55","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_56","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.agrformet.2015.02.001","article-title":"Improving winter wheat yield estimation by assimilation of the leaf area index from landsat tm and modis data into the wofost model","volume":"204","author":"Huang","year":"2015","journal-title":"Agric. For. Meteorol."},{"key":"ref_57","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_58","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_59","first-page":"1","article-title":"Sentinel-2 sen2cor: L2a processor for users","volume":"Volume SP-740","author":"Ouwehand","year":"2016","journal-title":"ESA Living Planet Symposium 2016"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.rse.2011.10.035","article-title":"Spatially constrained inversion of radiative transfer models for improved lai mapping from future sentinel-2 imagery","volume":"120","author":"Atzberger","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_61","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_62","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_63","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_64","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_65","doi-asserted-by":"crossref","unstructured":"Scurlock, J.M.O. (2002). Worldwide Historical Estimates of Leaf Area Index, 1932\u20132000, Oak Ridge National Laboratory.","DOI":"10.2172\/814100"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.agrformet.2003.08.001","article-title":"Review of methods for in situ leaf area index (lai) determination: Part ii. Estimation of lai, errors and sampling","volume":"121","author":"Weiss","year":"2004","journal-title":"Agric. For. Meteorol."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"3030","DOI":"10.1016\/j.rse.2008.02.012","article-title":"Prospect-4 and 5: Advances in the leaf optical properties model separating photosynthetic pigments","volume":"112","author":"Feret","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1109\/JSTARS.2010.2091492","article-title":"Evaluation of sentinel-2 spectral sampling for radiative transfer model based lai estimation of wheat, sugar beet, and maize","volume":"4","author":"Richter","year":"2011","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1393","DOI":"10.1016\/j.agrformet.2010.04.011","article-title":"Gai estimates of row crops from downward looking digital photos taken perpendicular to rows at 57.5\u00b0 zenith angle: Theoretical considerations based on 3d architecture models and application to wheat crops","volume":"150","author":"Baret","year":"2010","journal-title":"Agric. For. Meteorol."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1314","DOI":"10.2134\/agronj2003.1314","article-title":"Rapid characterization of organic resource quality for soil and livestock management in tropical agroecosystems using near-infrared spectroscopy","volume":"95","author":"Shepherd","year":"2003","journal-title":"Agron. J."},{"key":"ref_71","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_72","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_73","first-page":"483","article-title":"Accuracy improvement of spectral classification of crop using microwave backscatter data","volume":"31","author":"Jia","year":"2011","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_74","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_75","first-page":"12","article-title":"Inversion of the prosail model to estimate leaf area index of maize, potato, and sunflower fields from unmanned aerial vehicle hyperspectral data","volume":"26","author":"Duan","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"83","DOI":"10.3233\/IDA-150795","article-title":"Variable selection using support vector regression and random forests: A comparative study","volume":"20","year":"2016","journal-title":"Intell. Data Anal."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"2225","DOI":"10.1016\/j.patrec.2010.03.014","article-title":"Variable selection using random forests","volume":"31","author":"Genuer","year":"2010","journal-title":"Pattern Recognit. Lett."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/S0034-4257(70)80021-9","article-title":"Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation","volume":"1","author":"Knipling","year":"1970","journal-title":"Remote Sens. Environ."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/0034-4257(80)90096-6","article-title":"Remote sensing of leaf water content in the near infrared","volume":"10","author":"Tucker","year":"1980","journal-title":"Remote Sens. Environ."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/S0034-4257(00)00126-7","article-title":"A biogeophysical approach for automated swir unmixing of soils and vegetation","volume":"74","author":"Asner","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"5127","DOI":"10.1080\/01431160903283892","article-title":"Swir-based spectral indices for assessing nitrogen content in potato fields","volume":"31","author":"Herrmann","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_82","unstructured":"Breiman, L., Friedman, J., Stone, C.J., and Olshen, R.A. (1984). Classification and Regression Trees, CRC Press."},{"key":"ref_83","unstructured":"Baret, F., Guyot, G., and Major, D.J. (1989, January 10\u201314). Tsavi: A vegetation index which minimizes soil brightness effects on lai and apar estimation. Proceedings of the 12th Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium, Vancouver, BC, Canada."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/12\/1927\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:33:42Z","timestamp":1760196822000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/12\/1927"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,30]]},"references-count":83,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2018,12]]}},"alternative-id":["rs10121927"],"URL":"https:\/\/doi.org\/10.3390\/rs10121927","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,11,30]]}}}