{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,18]],"date-time":"2026-10-18T03:16:36Z","timestamp":1792293396481,"version":"4.4.0"},"reference-count":51,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2016,12,27]],"date-time":"2016-12-27T00:00:00Z","timestamp":1482796800000},"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>The accurate estimation of crop biomass during the growing season is very important for crop growth monitoring and yield estimation. The objective of this paper was to explore the potential of hyperspectral and light detection and ranging (LiDAR) data for better estimation of the biomass of maize. First, we investigated the relationship between field-observed biomass with each metric, including vegetation indices (VIs) derived from hyperspectral data and LiDAR-derived metrics. Second, the partial least squares (PLS) regression was used to estimate the biomass of maize using VIs (only) and LiDAR-derived metrics (only), respectively. Third, the fusion of hyperspectral and LiDAR data was evaluated in estimating the biomass of maize. Finally, the biomass estimates were validated by a leave-one-out cross-validation (LOOCV) method. Results indicated that all VIs showed weak correlation with field-observed biomass and the highest correlation occurred when using the red edge-modified simple ratio index (ReMSR). Among all LiDAR-derived metrics, the strongest relationship was observed between coefficient of variation (H  C V of digital terrain model (DTM) normalized point elevations with field-observed biomass. The combination of VIs through PLS regression could not improve the biomass estimation accuracy of maize due to the high correlation between VIs. In contrast, the      H  C V       combined with      H  m e a n       performed better than one LiDAR-derived metric alone in biomass estimation (R2 = 0.835, RMSE = 374.655 g\/m2, RMSECV = 393.573 g\/m2). Additionally, our findings indicated that the fusion of hyperspectral and LiDAR data can provide better biomass estimates of maize (R2 = 0.883, RMSE = 321.092 g\/m2, RMSECV = 337.653 g\/m2) compared with LiDAR or hyperspectral data alone.<\/jats:p>","DOI":"10.3390\/rs9010011","type":"journal-article","created":{"date-parts":[[2016,12,28]],"date-time":"2016-12-28T11:22:14Z","timestamp":1482924134000},"page":"11","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":96,"title":["Estimating the Biomass of Maize with Hyperspectral and LiDAR Data"],"prefix":"10.3390","volume":"9","author":[{"given":"Cheng","family":"Wang","sequence":"first","affiliation":[{"name":"Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing 100094, China"},{"name":"Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sheng","family":"Nie","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6979-170X","authenticated-orcid":false,"given":"Xiaohuan","family":"Xi","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shezhou","family":"Luo","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaofeng","family":"Sun","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2016,12,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/S0176-1617(11)81633-0","article-title":"Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. Leaves. Spectral features and relation to chlorophyll estimation","volume":"143","author":"Gitelson","year":"1994","journal-title":"J. Plant Physiol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1167","DOI":"10.1016\/j.rse.2010.01.004","article-title":"Estimating crop stresses, aboveground dry biomass and yield of corn using multi-temporal optical data combined with a radiation use efficiency model","volume":"114","author":"Liu","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_3","first-page":"399","article-title":"Lidar remote sensing of vegetation biomass","volume":"399","author":"Chen","year":"2013","journal-title":"Remote Sens. Nat. Resour."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1786","DOI":"10.1109\/JSTARS.2013.2262767","article-title":"Coffee crop\u2019s biomass and carbon stock estimation with usage of high resolution satellites images","volume":"6","author":"Zullo","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.fcr.2014.01.008","article-title":"Lidar based biomass and crop nitrogen estimates for rapid, non-destructive assessment of wheat nitrogen status","volume":"159","author":"Eitel","year":"2014","journal-title":"Field Crop. Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"486","DOI":"10.1016\/j.ecolind.2015.04.016","article-title":"Airborne lidar technique for estimating biomass components of maize: A case study in Zhangye City, Northwest China","volume":"57","author":"Li","year":"2015","journal-title":"Ecol Indic."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.rse.2003.12.013","article-title":"Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture","volume":"90","author":"Haboudane","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Tilly, N., Hoffmeister, D., Cao, Q., Huang, S., Lenz-Wiedemann, V., Miao, Y., and Bareth, G. (2014). Multitemporal crop surface models: Accurate plant height measurement and biomass estimation with terrestrial laser scanning in paddy rice. J. Appl. Remote Sens., 8.","DOI":"10.1117\/1.JRS.8.083671"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1312","DOI":"10.1016\/j.rse.2010.01.010","article-title":"A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data","volume":"114","author":"Vermote","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_10","first-page":"1","article-title":"Estimating the leaf area index, height and biomass of maize using HJ-1 and RADARSAT-2","volume":"24","author":"Gao","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"638","DOI":"10.1016\/j.jenvman.2006.08.018","article-title":"LAI inversion algorithm based on directional reflectance kernels","volume":"85","author":"Tang","year":"2007","journal-title":"J. Environ. Manag."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/S0034-4257(01)00342-X","article-title":"Airborne multispectral data for quantifying leaf area index, nitrogen concentration, and photosynthetic efficiency in agriculture","volume":"81","author":"Boegh","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/S0034-4257(01)00332-7","article-title":"Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral reflectance data","volume":"81","author":"Broge","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_15","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_16","first-page":"140","article-title":"Estimating green LAI in four crops: Potential of determining optimal spectral bands for a universal algorithm","volume":"192\u2013193","author":"Peng","year":"2014","journal-title":"Agric. For. Meteorol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3586","DOI":"10.1109\/JSTARS.2014.2342291","article-title":"Leaf area index estimation using vegetation indices derived from airborne hyperspectral images in winter wheat","volume":"7","author":"Xie","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1355","DOI":"10.1109\/TGRS.2003.812910","article-title":"Estimation of forest leaf area index using vegetation indices derived from hyperion hyperspectral data","volume":"41","author":"Gong","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1016\/j.rse.2006.09.013","article-title":"Fusion of imaging spectrometer and lidar data over combined radiative transfer models for forest canopy characterization","volume":"106","author":"Koetz","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4048","DOI":"10.1016\/j.rse.2008.05.020","article-title":"Integrating multi-temporal spectral and structural information to map wetland vegetation in a lower connecticut river tidal marsh","volume":"112","author":"Gilmore","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_21","first-page":"165","article-title":"Estimating chlorophyll content of crops from hyperspectral data using a normalized area over reflectance curve (NAOC)","volume":"12","author":"Delegido","year":"2010","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/j.rse.2013.08.003","article-title":"Salt marsh elevation and habitat mapping using hyperspectral and LiDAR data","volume":"139","author":"Hladik","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.rse.2012.12.026","article-title":"Hyperspectral remote sensing of plant biochemistry using bayesian model averaging with variable and band selection","volume":"132","author":"Zhao","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/0034-4257(95)00195-6","article-title":"Retrieving leaf area index of boreal conifer forests using Landsat TM images","volume":"55","author":"Chen","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.isprsjprs.2014.01.001","article-title":"Above ground biomass estimation in an African tropical forest with LiDAR and hyperspectral data","volume":"89","author":"Chen","year":"2014","journal-title":"ISPRS J. Photogramm."},{"key":"ref_26","first-page":"1276","article-title":"Retrieving crop fractional cover and LAI based on airborne LiDAR data","volume":"15","author":"Cui","year":"2011","journal-title":"J. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1109\/LGRS.2013.2247022","article-title":"Radiometric correction of terrestrial LiDAR point cloud data for individual maize plant detection","volume":"11","author":"Hofle","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.rse.2008.09.009","article-title":"LiDAR remote sensing of forest biomass: A scale-invariant estimation approach using airborne lasers","volume":"113","author":"Zhao","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2931","DOI":"10.1016\/j.rse.2010.08.029","article-title":"Estimation of tropical rain forest aboveground biomass with small-footprint LiDAR and hyperspectral sensors","volume":"115","author":"Clark","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2917","DOI":"10.1016\/j.rse.2010.08.027","article-title":"Mapping biomass and stress in the Sierra Nevada using LiDAR and hyperspectral data fusion","volume":"115","author":"Swatantran","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1016\/j.ecolind.2014.09.024","article-title":"Estimation of wetland vegetation height and leaf area index using airborne laser scanning data","volume":"48","author":"Luo","year":"2015","journal-title":"Ecol. Indic."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1080\/2150704X.2015.1111536","article-title":"Estimating leaf area index of maize using airborne full-waveform lidar data","volume":"7","author":"Nie","year":"2016","journal-title":"Remote Sens. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"5106","DOI":"10.1364\/OE.22.005106","article-title":"Estimating FPAR of maize canopy using airborne discrete-return lidar data","volume":"22","author":"Luo","year":"2014","journal-title":"Opt. Express"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Asner, G.P., Knapp, D.E., Kennedy-Bowdoin, T., Jones, M.O., Martin, R.E., Boardman, J., and Field, C.B. (2007). Carnegie airborne observatory: In-flight fusion of hyperspectral imaging and waveform light detection and ranging for three-dimensional studies of ecosystems. J. Appl. Remote Sens., 1.","DOI":"10.1117\/1.2794018"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1856","DOI":"10.1016\/j.rse.2007.09.009","article-title":"Integrating waveform LiDAR with hyperspectral imagery for inventory of a northern temperate forest","volume":"112","author":"Anderson","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.rse.2012.01.015","article-title":"Forest structure modeling with combined airborne hyperspectral and LiDAR data","volume":"121","author":"Latifi","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.rse.2013.10.036","article-title":"Fusion of LiDAR and multispectral data to quantify salt marsh carbon stocks","volume":"154","author":"Kulawardhana","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1294","DOI":"10.3390\/rs6021294","article-title":"Segmentation-based filtering of airborne LiDAR point clouds by progressive densification of terrain segments","volume":"6","author":"Lin","year":"2014","journal-title":"Remote Sens."},{"key":"ref_39","first-page":"111","article-title":"DEM generation from laser scanner data using adaptive tin models","volume":"33","author":"Axelsson","year":"2000","journal-title":"Int. Arch. Photogramm. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1175\/BAMS-D-12-00154.1","article-title":"Heihe watershed allied telemetry experimental research (HiWATER): Scientific objectives and experimental design","volume":"94","author":"Li","year":"2013","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Matthew, M.W., Adler-Golden, S.M., Berk, A., Felde, G., Anderson, G.P., Gorodetzky, D., Paswaters, S., and Shippert, M. (2002, January 16\u201318). Atmospheric correction of spectral imagery: Evaluation of the flaash algorithm with AVIRIS data. Proceedings of the IEEE Applied Imagery Pattern Recognition Workshop, Washington, DC, USA.","DOI":"10.1117\/12.499604"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/S0034-4257(98)00045-5","article-title":"Modtran cloud and multiple scattering upgrades with application to AVIRIS","volume":"65","author":"Berk","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.agrformet.2006.04.008","article-title":"Spatial modelling of the fraction of photosynthetically active radiation absorbed by a boreal mixedwood forest using a LiDAR-hyperspectral approach","volume":"140","author":"Thomas","year":"2006","journal-title":"Agric. For. Meteorol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"663","DOI":"10.2307\/1936256","article-title":"Derivation of leaf-area index from quality of light on the forest floor","volume":"50","author":"Jordan","year":"1969","journal-title":"Ecology"},{"key":"ref_45","unstructured":"Rouse, J. (1974). Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/0034-4257(94)90134-1","article-title":"A modified soil adjusted vegetation index","volume":"48","author":"Qi","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Gitelson, A.A., Vi\u00f1a, A., Arkebauer, T.J., Rundquist, D.C., Keydan, G., and Leavitt, B. (2003). Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophys. Res. Lett., 30.","DOI":"10.1029\/2002GL016450"},{"key":"ref_48","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_49","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1016\/j.ecolind.2016.10.001","article-title":"Fusion of airborne LiDAR data and hyperspectral imagery for aboveground and belowground forest biomass estimation","volume":"73","author":"Luo","year":"2017","journal-title":"Ecol. Indic."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.isprsjprs.2008.01.006","article-title":"Accuracy assessment of high resolution satellite imagery orientation by leave-one-out method","volume":"63","author":"Brovelli","year":"2008","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1758","DOI":"10.3390\/rs4061758","article-title":"Combined use of airborne LiDAR and DBinSAR data to estimate LAI in temperate mixed forests","volume":"4","author":"Peduzzi","year":"2012","journal-title":"Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/1\/11\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:29:20Z","timestamp":1760210960000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/1\/11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,12,27]]},"references-count":51,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2017,1]]}},"alternative-id":["rs9010011"],"URL":"https:\/\/doi.org\/10.3390\/rs9010011","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,12,27]]}}}