{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,1,5]],"date-time":"2025-01-05T23:10:15Z","timestamp":1736118615819,"version":"3.32.0"},"reference-count":61,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2019,8,1]],"date-time":"2019-08-01T00:00:00Z","timestamp":1564617600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&D Program","award":["2018YFD0300805"]},{"name":"Science and Technology Support Program of Jiangsu","award":["BE2016375"]},{"name":"the Priority Academic Program Development of Jiangsu Higher Education Institutions","award":["PAPD"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"The emergence of rice panicle substantially changes the spectral reflectance of rice canopy and, as a result, decreases the accuracy of leaf area index (LAI) that was derived from vegetation indices (VIs). From a four-year field experiment with using rice varieties, nitrogen (N) rates, and planting densities, the spectral reflectance characteristics of panicles and the changes in canopy reflectance after panicle removal were investigated. A rice \u201cpanicle line\u201d\u2014graphical relationship between red-edge and near-infrared bands was constructed by using the near-infrared and red-edge spectral reflectance of rice panicles. Subsequently, a panicle-adjusted renormalized difference vegetation index (PRDVI) that was based on the \u201cpanicle line\u201d and the renormalized difference vegetation index (RDVI) was developed to reduce the effects of rice panicles and background. The results showed that the effects of rice panicles on canopy reflectance were concentrated in the visible region and the near-infrared region. The red band (670 nm) was the most affected by panicles, while the red-edge bands (720\u2013740 nm) were less affected. In addition, a combination of near-infrared and red-edge bands was for the one that best predicted LAI, and the difference vegetation index (DI) (976, 733) performed the best, although it had relatively low estimation accuracy (R2 = 0.60, RMSE = 1.41 m2\/m2). From these findings, correcting the near-infrared band in the RDVI by the panicle adjustment factor (\u03b8) developed the PRDVI, which was obtained while using the \u201cpanicle line\u201d, and the less-affected red-edge band replaced the red band. Verification data from an unmanned aerial vehicle (UAV) showed that the PRDVI could minimize the panicle and background influence and was more sensitive to LAI (R2 = 0.77; RMSE = 1.01 m2\/m2) than other VIs during the post-heading stage. Moreover, of all the assessed VIs, the PRDVI yielded the highest R2 (0.71) over the entire growth period, with an RMSE of 1.31 (m2\/m2). These results suggest that the PRDVI is an efficient and suitable LAI estimation index.<\/jats:p>","DOI":"10.3390\/rs11151809","type":"journal-article","created":{"date-parts":[[2019,8,1]],"date-time":"2019-08-01T15:39:37Z","timestamp":1564673977000},"page":"1809","source":"Crossref","is-referenced-by-count":38,"title":["Estimating Leaf Area Index with a New Vegetation Index Considering the Influence of Rice Panicles"],"prefix":"10.3390","volume":"11","author":[{"given":"Jiaoyang","family":"He","sequence":"first","affiliation":[{"name":"National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China"},{"name":"Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture and Rural Affairs, Nanjing Agricultural University, Nanjing 210095, China"},{"name":"Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China"},{"name":"Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China"}]},{"given":"Ni","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China"},{"name":"Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture and Rural Affairs, Nanjing Agricultural University, Nanjing 210095, China"},{"name":"Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China"},{"name":"Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China"}]},{"given":"Xi","family":"Su","sequence":"additional","affiliation":[{"name":"National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China"},{"name":"Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture and Rural Affairs, Nanjing Agricultural University, Nanjing 210095, China"},{"name":"Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China"},{"name":"Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China"}]},{"given":"Jingshan","family":"Lu","sequence":"additional","affiliation":[{"name":"National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China"},{"name":"Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture and Rural Affairs, Nanjing Agricultural University, Nanjing 210095, China"},{"name":"Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China"},{"name":"Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China"}]},{"given":"Xia","family":"Yao","sequence":"additional","affiliation":[{"name":"National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China"},{"name":"Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture and Rural Affairs, Nanjing Agricultural University, Nanjing 210095, China"},{"name":"Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China"},{"name":"Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4184-0730","authenticated-orcid":false,"given":"Tao","family":"Cheng","sequence":"additional","affiliation":[{"name":"National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China"},{"name":"Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture and Rural Affairs, Nanjing Agricultural University, Nanjing 210095, China"},{"name":"Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China"},{"name":"Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1884-2404","authenticated-orcid":false,"given":"Yan","family":"Zhu","sequence":"additional","affiliation":[{"name":"National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China"},{"name":"Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture and Rural Affairs, Nanjing Agricultural University, Nanjing 210095, China"},{"name":"Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China"},{"name":"Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China"}]},{"given":"Weixing","family":"Cao","sequence":"additional","affiliation":[{"name":"National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China"},{"name":"Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture and Rural Affairs, Nanjing Agricultural University, Nanjing 210095, China"},{"name":"Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China"},{"name":"Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China"}]},{"given":"Yongchao","family":"Tian","sequence":"additional","affiliation":[{"name":"National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China"},{"name":"Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture and Rural Affairs, Nanjing Agricultural University, Nanjing 210095, China"},{"name":"Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China"},{"name":"Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,1]]},"reference":[{"key":"ref_1","first-page":"5783","article-title":"Improved spatial mapping of leaf area index using hyperspectral remote sensing for hydrological applications with a particular focus on canopy interception","volume":"6","author":"Bulcock","year":"2009","journal-title":"Hydrol. Earth Syst. Sci. Discuss."},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"2403","DOI":"10.1093\/jxb\/erg263","article-title":"Ground-based measurements of leaf area index: A review of methods, instruments and current controversies","volume":"54","year":"2003","journal-title":"J. Exp. Bot."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.rse.2014.01.004","article-title":"Relationships between gross primary production, green LAI, and canopy chlorophyll content in maize: Implications for remote sensing of primary production","volume":"144","author":"Gitelson","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1433","DOI":"10.1111\/gcb.13188","article-title":"Global patterns and predictors of stem CO2 efflux in forest ecosystems","volume":"22","author":"Yang","year":"2016","journal-title":"Glob. Chang. Boil."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.rse.2017.12.024","article-title":"Satellite-derived LAI products exhibit large discrepancies and can lead to substantial uncertainty in simulated carbon and water fluxes","volume":"206","author":"Liu","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_7","first-page":"350","article-title":"Brown and green LAI mapping through spectral indices","volume":"35","author":"Delegido","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/j.rse.2018.02.068","article-title":"Using negative soil adjustment factor in soil-adjusted vegetation index (SAVI) for aboveground living biomass estimation in arid grasslands","volume":"209","author":"Ren","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.fcr.2014.01.010","article-title":"Measuring leaf nitrogen concentration in winter wheat using double-peak spectral reflection remote sensing data","volume":"159","author":"Feng","year":"2014","journal-title":"Field Crop. Res."},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"5203","DOI":"10.3390\/rs70505203","article-title":"Exploring the Vertical Distribution of Structural Parameters and Light Radiation in Rice Canopies by the Coupling Model and Remote Sensing","volume":"7","author":"Guo","year":"2015","journal-title":"Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.ecolind.2013.03.025","article-title":"Developing and validating novel hyperspectral indices for leaf area index estimation: Effect of canopy vertical heterogeneity","volume":"32","author":"Li","year":"2013","journal-title":"Ecol. Indic."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/0034-4257(91)90009-U","article-title":"Potentials and limits of vegetation indices for LAI and APAR assessment","volume":"35","author":"Baret","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_14","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_15","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1080\/07038992.1996.10855178","article-title":"Evaluation of Vegetation Indices and a Modified Simple Ratio for Boreal Applications","volume":"22","author":"Chen","year":"1996","journal-title":"Can. J. Remote Sens."},{"key":"ref_16","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_17","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_18","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1109\/36.134076","article-title":"Atmospherically Resistant Vegetation Index (ARVI) for EOS-MODIS","volume":"30","author":"Kaufman","year":"1992","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1080\/07038992.2015.1025946","article-title":"SCAVI: A Sunlit Canopy Adjusted Vegetation Index","volume":"41","author":"Peddle","year":"2015","journal-title":"Can. J. Remote Sens."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","unstructured":"Kang, Y., \u00d6zdo\u011fan, M., Zipper, S.C., Rom\u00e1n, M.O., Walker, J., Hong, S.Y., Marshall, M., Magliulo, V., Moreno, J., and Alonso, L. (2016). How Universal Is the Relationship between Remotely Sensed Vegetation Indices and Crop Leaf Area Index? A Global Assessment. Remote Sens., 8.","DOI":"10.3390\/rs8070597"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Jin, X.L., Diao, W.Y., Xiao, C.H., Wang, F.Y., Chen, B., Wang, K.R., and Li, S.K. (2013). Estimation of Wheat Agronomic Parameters using New Spectral Indices. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0072736"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4589","DOI":"10.1109\/JSTARS.2014.2360069","article-title":"Newly combined spectral indices to improve estimation of total leaf chlorophyll content in cotton","volume":"7","author":"Jin","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Luo, J.H., Ma, R.H., Feng, H.H., and Li, X.C. (2016). Estimating the total nitrogen concentration of reed canopy with hyperspectral measurements considering a non-uniform vertical nitrogen distribution. Remote Sens., 8.","DOI":"10.3390\/rs8100789"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1080\/01431161.2014.999878","article-title":"Effect of leaf and spike morphological traits on the relationship between spectral reflectance indices and yield in wheat","volume":"36","author":"Gutierrez","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.rse.2015.08.021","article-title":"Variations in crop variables within wheat canopies and responses of canopy spectral characteristics and derived vegetation indices to different vertical leaf layers and spikes","volume":"169","author":"Li","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"016016","DOI":"10.1117\/1.JRS.12.016016","article-title":"Development and testing of an ear-leaf model for rice canopy reflectance","volume":"12","author":"Zhao","year":"2018","journal-title":"J. Appl. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Kawamura, K., Ikeura, H., Phongchanmaixay, S., and Khanthavong, P. (2018). Canopy Hyperspectral Sensing of Paddy Fields at the Booting Stage and PLS Regression can Assess Grain Yield. Remote Sens., 10.","DOI":"10.3390\/rs10081249"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5995","DOI":"10.3390\/rs6075995","article-title":"Potential of X-Band Images from High-Resolution Satellite SAR Sensors to Assess Growth and Yield in Paddy Rice","volume":"6","author":"Inoue","year":"2014","journal-title":"Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Asilo, S., Nelson, A., De Bie, K., Skidmore, A., Laborte, A., Maunahan, A., and Quilang, E.J.P. (2019). Relating X-band SAR Backscattering to Leaf Area Index of Rice in Different Phenological Phases. Remote Sens., 11.","DOI":"10.3390\/rs11121462"},{"key":"ref_31","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_32","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_33","doi-asserted-by":"crossref","first-page":"3468","DOI":"10.1016\/j.rse.2011.08.010","article-title":"Comparison of different vegetation indices for the remote assessment of green leaf area index of crops","volume":"115","author":"Gitelson","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_34","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_35","doi-asserted-by":"crossref","first-page":"2808","DOI":"10.3390\/rs70302808","article-title":"Estimation and Validation of RapidEye-Based Time-Series of Leaf Area Index for Winter Wheat in the Rur Catchment (Germany)","volume":"7","author":"Ali","year":"2015","journal-title":"Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"894","DOI":"10.1016\/j.isprsjprs.2011.09.013","article-title":"Mapping grassland leaf area index with airborne hyperspectral imagery: A comparison study of statistical approaches and inversion of radiative transfer models","volume":"66","author":"Darvishzadeh","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_37","first-page":"11","article-title":"Mapping forest leaf area index using reflectance and textural information derived from WorldView-2 imagery in a mixed natural forest area in Florida, US","volume":"42","author":"Pu","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_38","unstructured":"Rouse, J.W., Haas, R.H., Deering, D.W., Schell, J.A., and Harlan, J.C. (1974). Monitoring the Vernal Advancement and Retrogradation (Greenwave Effect) of Natural Vegetation. NASA\/GSFC Type III Final Report."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S0034-4257(96)00072-7","article-title":"Use of a green channel in remote sensing of global vegetation from EOS-MODIS","volume":"58","author":"Gitelson","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1078\/0176-1617-01176","article-title":"Wide Dynamic Range Vegetation Index for Remote Quantification of Biophysical Characteristics of Vegetation","volume":"161","author":"Gitelson","year":"2004","journal-title":"J. Plant Physiol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1267","DOI":"10.1016\/j.agrformet.2011.05.005","article-title":"Application of chlorophyll-related vegetation indices for remote estimation of maize productivity","volume":"151","author":"Peng","year":"2011","journal-title":"Agric. For. Meteorol."},{"key":"ref_42","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_43","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biopyhsical performance of the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_44","first-page":"52-1","article-title":"Remote estimation of leaf area index and green leaf biomass in maize canopies","volume":"30","author":"Rundquist","year":"2003","journal-title":"Geophys. Res. Lett."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/j.fcr.2010.01.010","article-title":"Measuring and predicting canopy nitrogen nutrition in wheat using a spectral index\u2014The canopy chlorophyll content index (CCCI)","volume":"116","author":"Fitzgerald","year":"2010","journal-title":"Field Crop. Res."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Xu, N., Tian, J., Tian, Q., Xu, K., and Tang, S. (2019). Analysis of Vegetation Red Edge with Different Illuminated\/Shaded Canopy Proportions and to Construct Normalized Difference Canopy Shadow Index. Remote Sens., 11.","DOI":"10.3390\/rs11101192"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"5329","DOI":"10.3390\/rs70505329","article-title":"Spectral index of quantifying leaf area index of winter wheat by field hyperspectral measurements: A case study in Gifu Prefectrue, Central Japan","volume":"7","author":"Tanaka","year":"2015","journal-title":"Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Hallik, L., Kuusk, A., Lang, M., and Kuusk, J. (2019). Reflectance Properties of Hemiboreal Mixed Forest Canopies with Focus on Red Edge and Near Infrared Spectral Regions. Remote Sens., 11.","DOI":"10.3390\/rs11141717"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1491","DOI":"10.1109\/36.934080","article-title":"Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data","volume":"39","author":"Miller","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.fcr.2013.06.012","article-title":"Optimizing nitrogen supply increases rice yield and nitrogen use efficiency by regulating yield formation factors","volume":"150","author":"Sui","year":"2013","journal-title":"Field Crop. Res."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/s11104-013-1937-0","article-title":"Comparison of different hyperspectral vegetation indices for canopy leaf nitrogen concentration estimation in rice","volume":"376","author":"Tian","year":"2014","journal-title":"Plant Soil"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"4014","DOI":"10.1109\/TGRS.2013.2278838","article-title":"Sensitivity analysis of vegetation reflectance to biochemical and biophysical variables at leaf, canopy, and regional scales","volume":"52","author":"Xiao","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.ecocom.2013.11.005","article-title":"The applicability of empirical vegetation indices for determining leaf chlorophyll content over different leaf and canopy structures","volume":"17","author":"Croft","year":"2014","journal-title":"Ecol. Complex."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"6279","DOI":"10.5194\/bg-10-6279-2013","article-title":"Remote sensing of LAI, chlorophyll and leaf nitrogen pools of crop- and grasslands in five European landscapes","volume":"10","author":"Boegh","year":"2013","journal-title":"Biogeosciences"},{"key":"ref_55","first-page":"140","article-title":"Estimating green LAI in four crops: Potential of determining optimal spectral bands for a universal algorithm","volume":"192","author":"Peng","year":"2014","journal-title":"Agric. For. Meteorol."},{"key":"ref_56","first-page":"63","article-title":"Estimating winter wheat biomass by assimilating leaf area index derived from fusion of Landsat-8 and MODIS data","volume":"49","author":"Dong","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.fcr.2013.04.029","article-title":"Tillering responses of rice to plant density and nitrogen rate in a subtropical environment of southern China","volume":"149","author":"Huang","year":"2013","journal-title":"Field Crop. Res."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fpls.2017.00820","article-title":"Evaluating Hyperspectral Vegetation Indices for Leaf Area Index Estimation of Oryza sativa L. at Diverse Phenological Stages","volume":"8","author":"Din","year":"2017","journal-title":"Front. Plant Sci."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"561","DOI":"10.3390\/rs4030561","article-title":"Optimal Exploitation of the Sentinel-2 Spectral Capabilities for Crop Leaf Area Index Mapping","volume":"4","author":"Richter","year":"2012","journal-title":"Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"6221","DOI":"10.3390\/rs6076221","article-title":"Exploring the Best Hyperspectral Features for LAI Estimation Using Partial Least Squares Regression","volume":"6","author":"Li","year":"2014","journal-title":"Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"3999","DOI":"10.1080\/01431160310001654923","article-title":"Narrow band vegetation indices overcome the saturation problem in biomass estimation","volume":"25","author":"Mutanga","year":"2004","journal-title":"Int. J. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/15\/1809\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,5]],"date-time":"2025-01-05T22:37:18Z","timestamp":1736116638000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/15\/1809"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,8,1]]},"references-count":61,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2019,8]]}},"alternative-id":["rs11151809"],"URL":"https:\/\/doi.org\/10.3390\/rs11151809","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2019,8,1]]}}}