{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T04:57:58Z","timestamp":1776833878254,"version":"3.51.2"},"reference-count":66,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2020,2,27]],"date-time":"2020-02-27T00:00:00Z","timestamp":1582761600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key Research and Development Program (2016YFD070030303), the Nation Science Foundation of China (41601346, 41871333, 41501481, 61661136003, 41771370, 41471285, 41471351)","award":["the National Key Research and Development Program (2016YFD070030303), the Nation Science Foundation of China (41601346, 41871333, 41501481, 61661136003, 41771370, 41471285, 41471351)"],"award-info":[{"award-number":["the National Key Research and Development Program (2016YFD070030303), the Nation Science Foundation of China (41601346, 41871333, 41501481, 61661136003, 41771370, 41471285, 41471351)"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Above-ground biomass (AGB) and the leaf area index (LAI) are important indicators for the assessment of crop growth, and are therefore important for agricultural management. Although improvements have been made in the monitoring of crop growth parameters using ground- and satellite-based sensors, the application of these technologies is limited by imaging difficulties, complex data processing, and low spatial resolution. Therefore, this study evaluated the use of hyperspectral indices, red-edge parameters, and their combination to estimate and map the distributions of AGB and LAI for various growth stages of winter wheat. A hyperspectral sensor mounted on an unmanned aerial vehicle was used to obtain vegetation indices and red-edge parameters, and stepwise regression (SWR) and partial least squares regression (PLSR) methods were used to accurately estimate the AGB and LAI based on these vegetation indices, red-edge parameters, and their combination. The results show that: (i) most of the studied vegetation indices and red-edge parameters are significantly highly correlated with AGB and LAI; (ii) overall, the correlations between vegetation indices and AGB and LAI, respectively, are stronger than those between red-edge parameters and AGB and LAI, respectively; (iii) Compared with the estimations using only vegetation indices or red-edge parameters, the estimation of AGB and LAI using a combination of vegetation indices and red-edge parameters is more accurate; and (iv) The estimations of AGB and LAI obtained using the PLSR method are superior to those obtained using the SWR method. Therefore, combining vegetation indices with red-edge parameters and using the PLSR method can improve the estimation of AGB and LAI.<\/jats:p>","DOI":"10.3390\/s20051296","type":"journal-article","created":{"date-parts":[[2020,2,28]],"date-time":"2020-02-28T09:30:36Z","timestamp":1582882236000},"page":"1296","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":152,"title":["Estimation of Crop Growth Parameters Using UAV-Based Hyperspectral Remote Sensing Data"],"prefix":"10.3390","volume":"20","author":[{"given":"Huilin","family":"Tao","sequence":"first","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture, Ministry of Agriculture and Rural Affairs, P. R. China, Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China"},{"name":"School of Geodesy and Geomatics, Anhui University of Science and Technology, Huainan 232001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haikuan","family":"Feng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture, Ministry of Agriculture and Rural Affairs, P. R. China, Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China"},{"name":"National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China"},{"name":"Beijing Engineering Research Center for Agriculture Internet of Things, Beijing 100097, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liangji","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Geodesy and Geomatics, Anhui University of Science and Technology, Huainan 232001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengke","family":"Miao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture, Ministry of Agriculture and Rural Affairs, P. R. China, Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China"},{"name":"National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huiling","family":"Long","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture, Ministry of Agriculture and Rural Affairs, P. R. China, Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China"},{"name":"National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China"},{"name":"Beijing Engineering Research Center for Agriculture Internet of Things, Beijing 100097, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9766-5313","authenticated-orcid":false,"given":"Jibo","family":"Yue","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture, Ministry of Agriculture and Rural Affairs, P. R. China, Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China"},{"name":"National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China"},{"name":"Beijing Engineering Research Center for Agriculture Internet of Things, Beijing 100097, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9878-3274","authenticated-orcid":false,"given":"Zhenhai","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture, Ministry of Agriculture and Rural Affairs, P. R. China, Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China"},{"name":"National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China"},{"name":"Beijing Engineering Research Center for Agriculture Internet of Things, Beijing 100097, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guijun","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture, Ministry of Agriculture and Rural Affairs, P. R. China, Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China"},{"name":"National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China"},{"name":"Beijing Engineering Research Center for Agriculture Internet of Things, Beijing 100097, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaodong","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture, Ministry of Agriculture and Rural Affairs, P. R. China, Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China"},{"name":"National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China"},{"name":"Beijing Engineering Research Center for Agriculture Internet of Things, Beijing 100097, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingling","family":"Fan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture, Ministry of Agriculture and Rural Affairs, P. R. China, Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China"},{"name":"National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,27]]},"reference":[{"key":"ref_1","first-page":"214","article-title":"Definition of crop condition and crop monitoring using remote sensing","volume":"15","author":"Yang","year":"1999","journal-title":"Nongye Gongcheng Xuebao Trans. Chin. Soc. Agric. Eng."},{"key":"ref_2","first-page":"104","article-title":"Quantifification winter wheat LAI with HJ-1CCD image features over multiple growing seasons","volume":"44","author":"Li","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1016\/j.agrformet.2017.08.018","article-title":"Modelling the effects of post-heading heat stress on biomass growth of winter wheat","volume":"247","author":"Liu","year":"2017","journal-title":"Agric. For. Meteorol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.agrformet.2015.10.013","article-title":"Assimilating a synthetic Kalman fifilter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation","volume":"216","author":"Huang","year":"2016","journal-title":"Agric. For. Meteorol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.agee.2005.06.005","article-title":"Assimilating remote sensing data into a crop model to improve predictive performance for spatial applications","volume":"111","author":"Launay","year":"2005","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Yue, J., Feng, H., Jin, X., Yuan, H., Li, Z., Zhou, C., Yang, G., and Tian, Q. (2018). A comparison of crop parameters estimation using images from UAV-mounted snapshot hyperspectral sensor and high-definition digital camera. Remote Sens., 10.","DOI":"10.3390\/rs10071138"},{"key":"ref_7","first-page":"19","article-title":"Comparative analysis of different retrieval methods for mapping grassland leaf area index using airborne imaging spectroscopy","volume":"43","author":"Atzberger","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1016\/j.rse.2004.11.012","article-title":"Discrimination of sugarcane varieties in Southeastern Brazil with EO-1 Hyperion data","volume":"94","author":"Formaggio","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/j.rse.2011.10.016","article-title":"Normalized difference chlorophyll index: A novel model for remote estimation of chlorophyll-a concentration in turbid productive waters","volume":"117","author":"Mishra","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2037","DOI":"10.1016\/j.rse.2009.05.003","article-title":"Remote sensing of solar-induced chlorophyll fluorescence: Review of methods and applications","volume":"113","author":"Meroni","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_11","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 reflflectance data: Principles and validation","volume":"105","author":"Bacour","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.isprsjprs.2017.10.011","article-title":"Unmanned Aerial System (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine","volume":"134","author":"Maimaitijiang","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1186\/s13007-015-0078-2","article-title":"Unmanned aerial platform-based multi-spectral imaging for field phenotyping of maize","volume":"11","author":"Vergara","year":"2015","journal-title":"Plant Methods."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.rse.2017.06.007","article-title":"Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery","volume":"198","author":"Jin","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_16","first-page":"1187","article-title":"The use of unmanned aerial vehicles (UAVs) for remote sensing and mapping","volume":"144","author":"Everaerts","year":"2008","journal-title":"Remote Sens. Spat. Inf. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.isprsjprs.2017.05.003","article-title":"Predicting grain yield in rice using multi-temporal vegetation indices from UAV-based multispectral and digital imagery","volume":"130","author":"Zhou","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"10335","DOI":"10.3390\/rs61110335","article-title":"Combined Spectral and Spatial Modeling of Corn Yield Based on Aerial Images and Crop Surface Models Acquired with an Unmanned Aircraft System","volume":"6","author":"Jakob","year":"2014","journal-title":"Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1007\/s11119-012-9274-5","article-title":"The application of small unmanned aerial systems for precision agriculture: A review","volume":"13","author":"Zhang","year":"2012","journal-title":"Precis. Agric."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.rse.2016.10.005","article-title":"Development of methods to improve soybean yield estimation and predict plant maturity with an unmanned aerial vehicle based platform","volume":"187","author":"Yu","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_21","first-page":"79","article-title":"Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley","volume":"39","author":"Bendig","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"10395","DOI":"10.3390\/rs61110395","article-title":"Estimating biomass of barley using crop surface models (CSMs) derived from UAV-based RGB imaging","volume":"6","author":"Bendig","year":"2014","journal-title":"Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Berger, K., Atzberger, C., Danner, M., D\u2019Urso, G., Mauser, W., Vuolo, F., and Hank, T. (2018). Evaluation of the PROSAIL model capabilities for future hyperspectral model environments: A review study. Remote Sens., 10.","DOI":"10.3390\/rs10010085"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"083621","DOI":"10.1117\/1.JRS.8.083621","article-title":"Derivation and approximation of soil isoline equations in the red\u2013near-infrared reflflectance subspace","volume":"8","author":"Taniguchi","year":"2014","journal-title":"J. Appl. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.rse.2004.11.017","article-title":"Use of coupled canopy structure dynamic and radiative transfer models to estimate biophysical canopy characteristics","volume":"95","author":"Koetz","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/S0034-4257(00)00197-8","article-title":"Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density","volume":"76","author":"Broge","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_27","first-page":"165","article-title":"Crop ecology management & quality-use of spectral radiance to estimate in-season biomass and grain yield in nitrogen- and water-stressed corn","volume":"42","author":"Osborne","year":"2002","journal-title":"Crop Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"723","DOI":"10.2135\/cropsci2000.403723x","article-title":"Remote sensing of biomass and yield of winter wheat under different nitrogen supplies","volume":"40","author":"Serrano","year":"2000","journal-title":"Crop Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/S0034-4257(99)00067-X","article-title":"Hyperspectral Vegetation Indices and Their Relationships with Agricultural Crop Characteristics","volume":"71","author":"Thenkabail","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1227","DOI":"10.2134\/agronj2001.1227","article-title":"Early prediction of soybean yield from canopy reflectance measurements","volume":"93","author":"Ma","year":"2001","journal-title":"Agron. J."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Yue, J., Feng, H., Yang, G., and Li, Z. (2018). A comparison of regression techniques for estimation of above-ground winter wheat biomass using near-surface spectroscopy. Remote Sens., 10.","DOI":"10.3390\/rs10010066"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.rse.2015.04.032","article-title":"Estimation of crop LAI using hyperspectral vegetation indices and a hybrid inversion method","volume":"165","author":"Liang","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1127\/1432-8364\/2012\/0117","article-title":"Multi-temporal hyperspectral and radar remote sensing for estimating winter wheat biomass in the north China plain","volume":"18","author":"Koppe","year":"2012","journal-title":"Photogramm. Fernerkund. Geoinf."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"13251","DOI":"10.3390\/rs71013251","article-title":"Combined Multi-temporal optical and radar parameters for estimating LAI and biomass in winter wheat using HJ and RADARSAR-2 data","volume":"7","author":"Jin","year":"2015","journal-title":"Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1127\/pfg\/2015\/0256","article-title":"Low-weight and UAV-based Hyperspectral Full-frame Cameras for Monitoring Crops: Spectral Comparison with Portable Spectroradiometer Measurements","volume":"1","author":"Bareth","year":"2015","journal-title":"Photogramm. Fernerkund. Geoinf."},{"key":"ref_36","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_37","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1002\/rob.21508","article-title":"HyperUAS-Imaging spectroscopy from a multirotor unmanned aircraft system","volume":"31","author":"Lucieer","year":"2014","journal-title":"J. Field Robot."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2738","DOI":"10.1109\/TGRS.2013.2265295","article-title":"Direct georeferencing of ultrahigh-resolution UAV imagery","volume":"52","author":"Turner","year":"2014","journal-title":"IEEE. Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1017\/S0021859607007514","article-title":"Estimation of leaf total chlorophyll and nitrogen concentrations using hyperspectral satellite imagery","volume":"146","author":"Garg","year":"2008","journal-title":"J. Agric. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/S0176-1617(99)80314-9","article-title":"A new reflectance index for remote sensing of chlorophyll content in higher plants: Tests using Eucalyptus leaves","volume":"154","author":"Datt","year":"1999","journal-title":"J. Plant Physiol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/S0034-4257(98)00059-5","article-title":"Quantifying chlorophylls and carotenoids at leaf and canopy scales: An evaluation of some hyperspectral approaches","volume":"66","author":"Blackburn","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/0034-4257(92)90089-3","article-title":"Ratio analysis of reflflectance spectra (RARS): An algorithm for the remote estimation of the concentrations of chlorophyll A, chlorophyll B, and carotenoids in soybean leaves","volume":"39","author":"Chappelle","year":"1992","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"415","DOI":"10.5344\/ajev.2006.57.4.415","article-title":"Relation of ground-sensor canopy reflflectance to biomass production and grape color in two merlot vineyards","volume":"57","author":"Stamatiadis","year":"2006","journal-title":"Am. J. Enol. Vitic."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1230","DOI":"10.1016\/j.agrformet.2008.03.005","article-title":"Estimating chlorophyll content from hyperspectral vegetation indices: Modeling and validation","volume":"148","author":"Wu","year":"2008","journal-title":"Agric. For. Meteorol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"198","DOI":"10.2135\/cropsci1997.0011183X003700010033x","article-title":"Visible and near-infrared reflectance assessment of salinity effects on barley","volume":"37","author":"Penuelas","year":"1997","journal-title":"Crop Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1355","DOI":"10.1109\/IGARSS.1989.576128","article-title":"TSAVI: A vegetation index which minimizes soil brightness effects on LAI and APAR estimation","volume":"3","author":"Baret","year":"1989","journal-title":"Symp. Remote Sens. Geosci. Remote Sens. Symp."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1034\/j.1399-3054.1999.106119.x","article-title":"Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening","volume":"106","author":"Merzlyak","year":"1999","journal-title":"Physiol. Plant."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/0034-4257(94)90136-8","article-title":"Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves","volume":"48","author":"Gamon","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.rse.2005.09.002","article-title":"Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflflectance simulation in a row-structured discontinuous canopy","volume":"99","author":"Miller","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/0034-4257(95)00186-7","article-title":"Optimization of soil-adjusted vegetation indices","volume":"55","author":"Rondeaux","year":"1996","journal-title":"Remote Sens Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/0034-4257(94)00114-3","article-title":"Estimating PAR absorbed by vegetation from bidirectional reflectance measurements","volume":"51","author":"Roujean","year":"1995","journal-title":"Remote Sens. Environ."},{"key":"ref_52","unstructured":"Merton, R., and Huntington, J. (1999, January 9\u201311). Early Simulation Results of the Aries-1 Satellite Sensor for Multi-Temporal Vegetation Research Derived from Aviris. Proceedings of the Eighth Annual JPL Airborne Earth Science Workshop, Pasadena, CA, USA."},{"key":"ref_53","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_54","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1016\/S0034-4257(02)00018-4","article-title":"Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture","volume":"81","author":"Haboudane","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_55","unstructured":"Vincini, M., and Frazzi, E. (2005). Angular dependence of maize and sugar beet VIs from directional CHRIS\/Proba data. Cuore., 5\u20139."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2133","DOI":"10.1080\/014311698214910","article-title":"Technical note a new technique for interpolating the reflectance red edge position","volume":"19","author":"Dawson","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_57","first-page":"194","article-title":"Monitoring nitrogen accumulation in wheat leaf with red edge characteristics parameters","volume":"25","author":"Feng","year":"2009","journal-title":"Nongye Gongcheng Xuebao\/Trans. Chin. Soc. Agric. Eng."},{"key":"ref_58","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_59","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1016\/j.isprsjprs.2008.01.001","article-title":"Lai and chlorophyll estimation for a heterogeneous grassland using hyperspectral measurements","volume":"63","author":"Darvishzadeh","year":"2008","journal-title":"Isprs J. Photogramm. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1336","DOI":"10.2134\/agronj2012.0065","article-title":"Green leaf area index estimation in maize and soybean: Combining vegetation indices to achieve maximal sensitivity","volume":"104","author":"Gitelson","year":"2012","journal-title":"Agron. J."},{"key":"ref_61","first-page":"113","article-title":"Retrieving winter wheat leaf area index based on unmanned aerial vehicle hyperspectral remote sensing","volume":"32","author":"Gao","year":"2016","journal-title":"Nongye Gongcheng Xuebao\/Trans. Chin. Soc. Agric. Eng."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/0034-4257(91)90066-F","article-title":"The effect of a red leaf pigment on the relationship between red edge and chlorophyll concentration","volume":"35","author":"Curran","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_63","first-page":"344","article-title":"Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentine l-2 and -3","volume":"23","author":"Clevers","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_64","first-page":"159","article-title":"A radiative transfer model-based method for the estimation of grassland aboveground biomass","volume":"54","author":"Quan","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Yue, J., Yang, G., Li, C.C., Li, Z.H., Wang, Y.J., Feng, H.K., and Xu, B. (2017). Estimation of winter wheat above-ground biomass using unmanned aerial vehicle-based snapshot hyperspectral sensor and crop height improved models. Remote Sens., 9.","DOI":"10.3390\/rs9070708"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Fan, L., Zhao, J., Xu, X., Liang, D., Yang, G., Feng, H., Yang, H., Wang, Y., Chen, g., and Wei, P. (2019). Hyperspectral-based estimation of leaf nitrogen content in corn using optimal selection of multiple spectral variables. Sensors, 19.","DOI":"10.3390\/s19132898"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/5\/1296\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:02:15Z","timestamp":1760173335000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/5\/1296"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,27]]},"references-count":66,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2020,3]]}},"alternative-id":["s20051296"],"URL":"https:\/\/doi.org\/10.3390\/s20051296","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,2,27]]}}}