{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T02:15:18Z","timestamp":1771035318856,"version":"3.50.1"},"reference-count":74,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,20]],"date-time":"2024-08-20T00:00:00Z","timestamp":1724112000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2020YFD1000400"],"award-info":[{"award-number":["2020YFD1000400"]}],"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":["CX (21) 2004"],"award-info":[{"award-number":["CX (21) 2004"]}],"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":["32302593"],"award-info":[{"award-number":["32302593"]}],"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":["BK20230996"],"award-info":[{"award-number":["BK20230996"]}],"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":["2022ZB339"],"award-info":[{"award-number":["2022ZB339"]}],"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":["2022M721638"],"award-info":[{"award-number":["2022M721638"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Jiangsu Agriculture Science and Technology Innovation Fund","award":["2020YFD1000400"],"award-info":[{"award-number":["2020YFD1000400"]}]},{"name":"Jiangsu Agriculture Science and Technology Innovation Fund","award":["CX (21) 2004"],"award-info":[{"award-number":["CX (21) 2004"]}]},{"name":"Jiangsu Agriculture Science and Technology Innovation Fund","award":["32302593"],"award-info":[{"award-number":["32302593"]}]},{"name":"Jiangsu Agriculture Science and Technology Innovation Fund","award":["BK20230996"],"award-info":[{"award-number":["BK20230996"]}]},{"name":"Jiangsu Agriculture Science and Technology Innovation Fund","award":["2022ZB339"],"award-info":[{"award-number":["2022ZB339"]}]},{"name":"Jiangsu Agriculture Science and Technology Innovation Fund","award":["2022M721638"],"award-info":[{"award-number":["2022M721638"]}]},{"name":"National Natural Science Foundation of China","award":["2020YFD1000400"],"award-info":[{"award-number":["2020YFD1000400"]}]},{"name":"National Natural Science Foundation of China","award":["CX (21) 2004"],"award-info":[{"award-number":["CX (21) 2004"]}]},{"name":"National Natural Science Foundation of China","award":["32302593"],"award-info":[{"award-number":["32302593"]}]},{"name":"National Natural Science Foundation of China","award":["BK20230996"],"award-info":[{"award-number":["BK20230996"]}]},{"name":"National Natural Science Foundation of China","award":["2022ZB339"],"award-info":[{"award-number":["2022ZB339"]}]},{"name":"National Natural Science Foundation of China","award":["2022M721638"],"award-info":[{"award-number":["2022M721638"]}]},{"name":"Natural Science Fund of Jiangsu Province","award":["2020YFD1000400"],"award-info":[{"award-number":["2020YFD1000400"]}]},{"name":"Natural Science Fund of Jiangsu Province","award":["CX (21) 2004"],"award-info":[{"award-number":["CX (21) 2004"]}]},{"name":"Natural Science Fund of Jiangsu Province","award":["32302593"],"award-info":[{"award-number":["32302593"]}]},{"name":"Natural Science Fund of Jiangsu Province","award":["BK20230996"],"award-info":[{"award-number":["BK20230996"]}]},{"name":"Natural Science Fund of Jiangsu Province","award":["2022ZB339"],"award-info":[{"award-number":["2022ZB339"]}]},{"name":"Natural Science Fund of Jiangsu Province","award":["2022M721638"],"award-info":[{"award-number":["2022M721638"]}]},{"name":"Jiangsu Funding Program for Excellent Postdoctoral Talent","award":["2020YFD1000400"],"award-info":[{"award-number":["2020YFD1000400"]}]},{"name":"Jiangsu Funding Program for Excellent Postdoctoral Talent","award":["CX (21) 2004"],"award-info":[{"award-number":["CX (21) 2004"]}]},{"name":"Jiangsu Funding Program for Excellent Postdoctoral Talent","award":["32302593"],"award-info":[{"award-number":["32302593"]}]},{"name":"Jiangsu Funding Program for Excellent Postdoctoral Talent","award":["BK20230996"],"award-info":[{"award-number":["BK20230996"]}]},{"name":"Jiangsu Funding Program for Excellent Postdoctoral Talent","award":["2022ZB339"],"award-info":[{"award-number":["2022ZB339"]}]},{"name":"Jiangsu Funding Program for Excellent Postdoctoral Talent","award":["2022M721638"],"award-info":[{"award-number":["2022M721638"]}]},{"name":"Fellowship of China Postdoctoral Science Foundation","award":["2020YFD1000400"],"award-info":[{"award-number":["2020YFD1000400"]}]},{"name":"Fellowship of China Postdoctoral Science Foundation","award":["CX (21) 2004"],"award-info":[{"award-number":["CX (21) 2004"]}]},{"name":"Fellowship of China Postdoctoral Science Foundation","award":["32302593"],"award-info":[{"award-number":["32302593"]}]},{"name":"Fellowship of China Postdoctoral Science Foundation","award":["BK20230996"],"award-info":[{"award-number":["BK20230996"]}]},{"name":"Fellowship of China Postdoctoral Science Foundation","award":["2022ZB339"],"award-info":[{"award-number":["2022ZB339"]}]},{"name":"Fellowship of China Postdoctoral Science Foundation","award":["2022M721638"],"award-info":[{"award-number":["2022M721638"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Precise nitrogen supply is crucial for ensuring the quality of cut chrysanthemums (Chrysanthemum morifolium Ramat.). The nitrogen nutrition index (NNI) serves as an important indicator for diagnosing crop nitrogen (N) nutrition. Hyperspectral remote sensing (HRS) technology has been widely used in monitoring crop N status due to its rapid, accurate, and non-destructive capabilities. However, its application in estimating the NNI of cut chrysanthemums has received limited attention. Therefore, this study aimed to use HRS to accurately determine the cut chrysanthemum NNI, thereby providing valuable guidance for managing N fertilization. During several key growth stages, a hyperspectral spectroradiometer was used to capture hyperspectral reflectance data (350\u20132500 nm) from three leaf layers. Subsequently, cut chrysanthemum canopies were sampled for aboveground biomass (AGB) and plant nitrogen concentration (PNC). The collected AGB and PNC data were then utilized to fit the critical N (Nc) dilution curve of cut chrysanthemums using a Bayesian hierarchical model, enabling the calculation of the NNI. Finally, spectral indices and partial least squares regression (PLSR) were used to establish the NNI estimation model for cut chrysanthemums. The results showed that the Nc dilution curve of the cut chrysanthemums was Nc = 5.401 \u00d7 AGB\u22120.468. The first leaf layer (L1) proved to be optimal for estimating cut chrysanthemum NNI. Additionally, a newly proposed two-band spectral index, DVI-L1 (R1105, R700), demonstrated moderate predictive capabilities for the NNI of cut chrysanthemums (R2 = 0.5309, RMSE = 0.3210). Compared with the spectral index-based NNI estimation model, PLSR-L1 showed the best performance in estimating the cut chrysanthemum NNI (R2 = 0.8177, RMSE = 0.2000). Our results highlight the rapid NNI prediction potential of HRS and its significance in facilitating precise N management in cut chrysanthemums.<\/jats:p>","DOI":"10.3390\/rs16163062","type":"journal-article","created":{"date-parts":[[2024,8,20]],"date-time":"2024-08-20T09:13:48Z","timestamp":1724145228000},"page":"3062","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Monitoring the Nitrogen Nutrition Index Using Leaf-Based Hyperspectral Reflectance in Cut Chrysanthemums"],"prefix":"10.3390","volume":"16","author":[{"given":"Yin","family":"Wu","sequence":"first","affiliation":[{"name":"Key Laboratory of Landscaping, Key Laboratory of Biology of Ornamental Plants in East China, Ministry of Agriculture and Rural Affairs, National Forestry and Grassland Administration, College of Horticulture, Nanjing Agricultural University, Nanjing 210095, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3858-2769","authenticated-orcid":false,"given":"Jingshan","family":"Lu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Landscaping, Key Laboratory of Biology of Ornamental Plants in East China, Ministry of Agriculture and Rural Affairs, National Forestry and Grassland Administration, College of Horticulture, Nanjing Agricultural University, Nanjing 210095, China"}]},{"given":"Huahao","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Landscaping, Key Laboratory of Biology of Ornamental Plants in East China, Ministry of Agriculture and Rural Affairs, National Forestry and Grassland Administration, College of Horticulture, Nanjing Agricultural University, Nanjing 210095, China"}]},{"given":"Tingyu","family":"Gou","sequence":"additional","affiliation":[{"name":"Key Laboratory of Landscaping, Key Laboratory of Biology of Ornamental Plants in East China, Ministry of Agriculture and Rural Affairs, National Forestry and Grassland Administration, College of Horticulture, Nanjing Agricultural University, Nanjing 210095, China"}]},{"given":"Fadi","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Landscaping, Key Laboratory of Biology of Ornamental Plants in East China, Ministry of Agriculture and Rural Affairs, National Forestry and Grassland Administration, College of Horticulture, Nanjing Agricultural University, Nanjing 210095, China"}]},{"given":"Weimin","family":"Fang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Landscaping, Key Laboratory of Biology of Ornamental Plants in East China, Ministry of Agriculture and Rural Affairs, National Forestry and Grassland Administration, College of Horticulture, Nanjing Agricultural University, Nanjing 210095, China"}]},{"given":"Sumei","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Landscaping, Key Laboratory of Biology of Ornamental Plants in East China, Ministry of Agriculture and Rural Affairs, National Forestry and Grassland Administration, College of Horticulture, Nanjing Agricultural University, Nanjing 210095, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2667-0788","authenticated-orcid":false,"given":"Shuang","family":"Zhao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Landscaping, Key Laboratory of Biology of Ornamental Plants in East China, Ministry of Agriculture and Rural Affairs, National Forestry and Grassland Administration, College of Horticulture, Nanjing Agricultural University, Nanjing 210095, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0078-8796","authenticated-orcid":false,"given":"Jiafu","family":"Jiang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Landscaping, Key Laboratory of Biology of Ornamental Plants in East China, Ministry of Agriculture and Rural Affairs, National Forestry and Grassland Administration, College of Horticulture, Nanjing Agricultural University, Nanjing 210095, China"}]},{"given":"Zhiyong","family":"Guan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Landscaping, Key Laboratory of Biology of Ornamental Plants in East China, Ministry of Agriculture and Rural Affairs, National Forestry and Grassland Administration, College of Horticulture, Nanjing Agricultural University, Nanjing 210095, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"126241","DOI":"10.1016\/j.eja.2021.126241","article-title":"An overview of crop nitrogen status assessment using hyperspectral remote sensing: Current status and perspectives","volume":"124","author":"Fu","year":"2021","journal-title":"Eur. J. Agron."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"113141","DOI":"10.1016\/j.rse.2022.113141","article-title":"Evaluating the role of solar-induced fluorescence (SIF) and plant physiological traits for leaf nitrogen assessment in almond using airborne hyperspectral imagery","volume":"279","author":"Wang","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"558","DOI":"10.1002\/gbc.20053","article-title":"Patterns and trends in nitrogen use and nitrogen recovery efficiency in world agriculture","volume":"27","author":"Conant","year":"2013","journal-title":"Glob. Biogeochem. Cycles"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"9199","DOI":"10.1073\/pnas.1322434111","article-title":"Global metaanalysis of the nonlinear response of soil nitrous oxide (N2O) emissions to fertilizer nitrogen","volume":"111","author":"Shcherbak","year":"2014","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Chandran, S., Unni, M.R., and Thomas, S. (2019). Chapter 2\u2014Fertilizer Management Strategies for Enhancing Nutrient Use Efficiency and Sustainable Wheat Production. Organic Farming, Woodhead Publishing.","DOI":"10.1007\/978-3-030-04657-6"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"614","DOI":"10.1016\/j.eja.2008.01.005","article-title":"Diagnosis tool for plant and crop N status in vegetative stage: Theory and practices for crop N management","volume":"28","author":"Lemaire","year":"2008","journal-title":"Eur. J. Agron."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"126046","DOI":"10.1016\/j.eja.2020.126046","article-title":"Sweet pepper and nitrogen supply in greenhouse production: Critical nitrogen curve, agronomic responses and risk of nitrogen loss","volume":"117","author":"Gallardo","year":"2020","journal-title":"Eur. J. Agron."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.scienta.2018.09.034","article-title":"Growth, N uptake and N critical dilution curve in broccoli cultivars grown under Mediterranean conditions","volume":"244","author":"Conversa","year":"2019","journal-title":"Sci. Hortic."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/s40003-018-0303-0","article-title":"Theoretical Determination of a Critical Nitrogen Dilution Curve Based on the Carrot Case Study","volume":"7","author":"Shlevin","year":"2018","journal-title":"Agric. Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"187","DOI":"10.17660\/ActaHortic.2003.627.24","article-title":"Critical nitrogen concentration in lettuce","volume":"627","author":"Tei","year":"2003","journal-title":"Acta Hortic."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"126076","DOI":"10.1016\/j.eja.2020.126076","article-title":"Analyzing uncertainty in critical nitrogen dilution curves","volume":"118","author":"Makowski","year":"2020","journal-title":"Eur. J. Agron."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"108605","DOI":"10.1016\/j.fcr.2022.108605","article-title":"Establishing a critical nitrogen dilution curve for estimating nitrogen nutrition index of potato crop in tropical environments","volume":"286","author":"Soratto","year":"2022","journal-title":"Field Crops Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"126615","DOI":"10.1016\/j.eja.2022.126615","article-title":"Establishing critical nitrogen dilution curves based on leaf area index and aboveground biomass for greenhouse cherry tomato: A Bayesian analysis","volume":"141","author":"Cheng","year":"2022","journal-title":"Eur. J. Agron."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"115459","DOI":"10.1016\/j.indcrop.2022.115459","article-title":"Estimation of nitrogen nutrition index in chrysanthemum using chlorophyll meter readings","volume":"187","author":"Lu","year":"2022","journal-title":"Ind. Crops Prod."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"106998","DOI":"10.1016\/j.compag.2022.106998","article-title":"Advances in the estimations and applications of critical nitrogen dilution curve and nitrogen nutrition index of major cereal crops. A review","volume":"197","author":"Li","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1007\/s11119-011-9244-3","article-title":"Quantifying spatial variability of indigenous nitrogen supply for precision nitrogen management in small scale farming","volume":"13","author":"Cao","year":"2012","journal-title":"Precis. Agric."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhao, B., Ata-Ui-Karim, S.T., Yao, X., Tian, Y., Cao, W., Zhu, Y., and Liu, X. (2016). A New Curve of Critical Nitrogen Concentration Based on Spike Dry Matter for Winter Wheat in Eastern China. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0164545"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"126287","DOI":"10.1016\/j.eja.2021.126287","article-title":"Simultaneous assessment of nitrogen and water status in winter wheat using hyperspectral and thermal sensors","volume":"127","author":"Pancorbo","year":"2021","journal-title":"Eur. J. Agron."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Xia, T., Miao, Y., Wu, D., Shao, H., Khosla, R., and Mi, G. (2016). Active Optical Sensing of Spring Maize for In-Season Diagnosis of Nitrogen Status Based on Nitrogen Nutrition Index. Remote Sens., 8.","DOI":"10.3390\/rs8070605"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2535","DOI":"10.1016\/S2095-3119(20)63379-2","article-title":"An entirely new approach based on remote sensing data to calculate the nitrogen nutrition index of winter wheat","volume":"20","author":"Zhao","year":"2021","journal-title":"J. Integr. Agric."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.fcr.2012.11.017","article-title":"Non-uniform vertical nitrogen distribution within plant canopy and its estimation by remote sensing: A review","volume":"142","author":"Li","year":"2013","journal-title":"Field Crops Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4159","DOI":"10.1080\/01431160600791650","article-title":"A linear model to predict with a multi-spectral radiometer the amount of nitrogen in winter wheat","volume":"27","author":"Reyniers","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"134926","DOI":"10.1016\/j.jclepro.2022.134926","article-title":"Improving active canopy sensor-based in-season rice nitrogen status diagnosis and recommendation using multi-source data fusion with machine learning","volume":"380","author":"Lu","year":"2022","journal-title":"J. Clean. Prod."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"126537","DOI":"10.1016\/j.eja.2022.126537","article-title":"Combining fixed-wing UAV multispectral imagery and machine learning to diagnose winter wheat nitrogen status at the farm scale","volume":"138","author":"Jiang","year":"2022","journal-title":"Eur. J. Agron."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"106421","DOI":"10.1016\/j.compag.2021.106421","article-title":"Estimation of nitrogen nutrition index in rice from UAV RGB images coupled with machine learning algorithms","volume":"189","author":"Qiu","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"112826","DOI":"10.1016\/j.rse.2021.112826","article-title":"Combining transfer learning and hyperspectral reflectance analysis to assess leaf nitrogen concentration across different plant species datasets","volume":"269","author":"Wan","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_27","first-page":"100598","article-title":"Monitoring of nitrogen accumulation in wheat plants based on hyperspectral data","volume":"23","author":"Song","year":"2021","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"104986","DOI":"10.1016\/j.chemolab.2023.104986","article-title":"Extension and significance testing of Variable Importance in Projection (VIP) indices in Partial Least Squares regression and Principal Components Analysis","volume":"242","author":"Mahieu","year":"2023","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1016\/S0034-4257(03)00131-7","article-title":"Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression","volume":"86","author":"Hansen","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_30","first-page":"89","article-title":"Exploring hyperspectral bands and estimation indices for leaf nitrogen accumulation in wheat","volume":"12","author":"Yao","year":"2010","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2727","DOI":"10.1080\/01431169508954588","article-title":"Reflectance Assessment of Mite Effects on Apple-Trees","volume":"16","author":"Penuelas","year":"1995","journal-title":"Int. J. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.compag.2011.04.008","article-title":"Hyperspectral image analysis for water stress detection of apple trees","volume":"77","author":"Kim","year":"2011","journal-title":"Comput. Electron. Agric."},{"key":"ref_33","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_34","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_35","doi-asserted-by":"crossref","first-page":"751","DOI":"10.1016\/j.isprsjprs.2011.08.001","article-title":"An investigation into robust spectral indices for leaf chlorophyll estimation","volume":"66","author":"Main","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/j.fcr.2010.11.002","article-title":"Assessing newly developed and published vegetation indices for estimating rice leaf nitrogen concentration with ground- and space-based hyperspectral reflectance","volume":"120","author":"Tian","year":"2011","journal-title":"Field Crops Res."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2003.09.004","article-title":"Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements","volume":"89","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/S0034-4257(02)00010-X","article-title":"Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages","volume":"81","author":"Sims","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_39","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_40","unstructured":"Barnes, E., Clarke, T.R., Richards, S.E., Colaizzi, P., Haberland, J., Kostrzewski, M., Waller, P., Choi, C., Riley, E., and Thompson, T.L. (2022, November 23). Coincident Detection of Crop Water Stress, Nitrogen Status, and Canopy Density Using Ground Based Multispectral Data. Available online: https:\/\/api.semanticscholar.org\/CorpusID:128773162."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"126414","DOI":"10.1016\/j.eja.2021.126414","article-title":"Development of critical nitrogen dilution curves for different leaf layers within the rice canopy","volume":"132","author":"He","year":"2022","journal-title":"Eur. J. Agron."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1007\/BF02198111","article-title":"Quantitative relationships for the dependence of growth rate of arable crops on their nitrogen content, dry weight and aerial environment","volume":"91","author":"Greenwood","year":"1986","journal-title":"Plant Soil"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1016\/S1002-0160(21)60086-3","article-title":"Key variable for simulating critical nitrogen dilution curve of wheat: Leaf area ratio-driven approach","volume":"32","author":"Zhang","year":"2022","journal-title":"Pedosphere"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1987","DOI":"10.1016\/j.rse.2010.04.006","article-title":"New spectral indicator assessing the efficiency of crop nitrogen treatment in corn and wheat","volume":"114","author":"Chen","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"6549","DOI":"10.3390\/rs6076549","article-title":"Nitrogen Status Assessment for Variable Rate Fertilization in Maize through Hyperspectral Imagery","volume":"6","author":"Cilia","year":"2014","journal-title":"Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"10646","DOI":"10.3390\/rs70810646","article-title":"Satellite Remote Sensing-Based In-Season Diagnosis of Rice Nitrogen Status in Northeast China","volume":"7","author":"Huang","year":"2015","journal-title":"Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.fcr.2017.08.023","article-title":"Use of a chlorophyll meter to assess nitrogen nutrition index during the growth cycle in winter wheat","volume":"214","author":"Ravier","year":"2017","journal-title":"Field Crops Res."},{"key":"ref_48","first-page":"88","article-title":"Fertilization effect on Chrysanthemum morifolium based on \u20183414\u2019 project","volume":"4","author":"Wang","year":"2013","journal-title":"Acta Agric. Jiangxi"},{"key":"ref_49","first-page":"84","article-title":"Effect of nitrogen mass concentration on dry matter and nutrient uptake of potted chrysanthemum \u2018Dong Li Qiu Xin\u2019","volume":"26","author":"Qiu","year":"2021","journal-title":"J. China Agric. Univ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.biosystemseng.2017.03.006","article-title":"Hyperspectral imaging of spinach canopy under combined water and nitrogen stress to estimate biomass, water, and nitrogen content","volume":"158","author":"Corti","year":"2017","journal-title":"Biosyst. Eng."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.fcr.2015.05.020","article-title":"Remote estimation of above ground nitrogen uptake during vegetative growth in winter wheat using hyperspectral red-edge ratio data","volume":"180","author":"Feng","year":"2015","journal-title":"Field Crops Res."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.indcrop.2018.02.051","article-title":"Assessing leaf nitrogen concentration of winter oilseed rape with canopy hyperspectral technique considering a non-uniform vertical nitrogen distribution","volume":"116","author":"Li","year":"2018","journal-title":"Ind. Crops Prod."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"113348","DOI":"10.1016\/j.rse.2022.113348","article-title":"Remote estimation of leaf nitrogen concentration in winter oilseed rape across growth stages and seasons by correcting for the canopy structural effect","volume":"284","author":"Liu","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"105590","DOI":"10.1016\/j.ecolind.2019.105590","article-title":"Estimating leaf nitrogen concentration considering unsynchronized maize growth stages with canopy hyperspectral technique","volume":"107","author":"Wen","year":"2019","journal-title":"Ecol. Indic."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"3472","DOI":"10.1080\/01431161.2011.604052","article-title":"Relationships of leaf nitrogen concentration and canopy nitrogen density with spectral features parameters and narrow-band spectral indices calculated from field winter wheat (Triticum aestivum L.) spectra","volume":"33","author":"Zhao","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.fcr.2012.01.007","article-title":"Assessing the vertical footprint of reflectance measurements to characterize nitrogen uptake and biomass distribution in maize canopies","volume":"129","author":"Winterhalter","year":"2012","journal-title":"Field Crops Res."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.compag.2013.07.014","article-title":"Determination of dry matter content of tea by near and middle infrared spectroscopy coupled with wavelet-based data mining algorithms","volume":"98","author":"Li","year":"2013","journal-title":"Comput. Electron. Agric."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.fcr.2013.08.005","article-title":"Non-destructive estimation of rice plant nitrogen status with Crop Circle multispectral active canopy sensor","volume":"154","author":"Cao","year":"2013","journal-title":"Field Crops Res."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.eja.2017.12.006","article-title":"Exploring new spectral bands and vegetation indices for estimating nitrogen nutrition index of summer maize","volume":"93","author":"Zhao","year":"2018","journal-title":"Eur. J. Agron."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Nigon, T.J., Yang, C., Dias Paiao, G., Mulla, D.J., Knight, J.F., and Fern\u00e1ndez, F.G. (2020). Prediction of Early Season Nitrogen Uptake in Maize Using High-Resolution Aerial Hyperspectral Imagery. Remote Sens., 12.","DOI":"10.3390\/rs12081234"},{"key":"ref_61","first-page":"47","article-title":"Remote estimation of nitrogen and chlorophyll contents in maize at leaf and canopy levels","volume":"25","author":"Schlemmer","year":"2013","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Yamashita, H., Sonobe, R., Hirono, Y., Morita, A., and Ikka, T. (2020). Dissection of hyperspectral reflectance to estimate nitrogen and chlorophyll contents in tea leaves based on machine learning algorithms. Sci. Rep., 10.","DOI":"10.1038\/s41598-020-73745-2"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"20347","DOI":"10.3390\/s141120347","article-title":"Estimation of Nitrogen Vertical Distribution by Bi-Directional Canopy Reflectance in Winter Wheat","volume":"14","author":"Huang","year":"2014","journal-title":"Sensors"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Luo, J., Ma, R., Feng, H., and Li, X. (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_65","doi-asserted-by":"crossref","first-page":"108490","DOI":"10.1016\/j.fcr.2022.108490","article-title":"Quantification and dynamic monitoring of nitrogen utilization efficiency in summer maize with hyperspectral technique considering a non-uniform vertical distribution at whole growth stage","volume":"281","author":"Li","year":"2022","journal-title":"Field Crops Res."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"112517","DOI":"10.1016\/j.scienta.2023.112517","article-title":"Estimation of plant water content in cut chrysanthemum using leaf-based hyperspectral reflectance","volume":"323","author":"Lu","year":"2024","journal-title":"Sci. Hortic."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1016\/j.biosystemseng.2016.10.003","article-title":"Crop reflectance monitoring as a tool for water stress detection in greenhouses: A review","volume":"151","author":"Katsoulas","year":"2016","journal-title":"Biosyst. Eng."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.aca.2017.06.001","article-title":"Improved variable reduction in partial least squares modelling by Global-Minimum Error Uninformative-Variable Elimination","volume":"982","author":"Andries","year":"2017","journal-title":"Anal. Chim. Acta"},{"key":"ref_69","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_70","doi-asserted-by":"crossref","unstructured":"Lu, Y., Saeys, W., Kim, M., Peng, Y., and Lu, R. (2020). Hyperspectral imaging technology for quality and safety evaluation of horticultural products: A review and celebration of the past 20-year progress. Postharvest Biol. Technol., 170.","DOI":"10.1016\/j.postharvbio.2020.111318"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"108859","DOI":"10.1016\/j.fcr.2023.108859","article-title":"Retrieving canopy nitrogen concentration and aboveground biomass with deep learning for ryegrass and barley: Comparing models and determining waveband contribution","volume":"294","author":"Patel","year":"2023","journal-title":"Field Crops Res."},{"key":"ref_72","first-page":"1","article-title":"Predicting leaf nitrogen content in olive trees using hyperspectral data for precision agriculture","volume":"22","year":"2020","journal-title":"Precis. Agric."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.biosystemseng.2021.12.008","article-title":"Exploring hyperspectral reflectance indices for the estimation of water and nitrogen status of spinach","volume":"214","author":"Rubo","year":"2022","journal-title":"Biosyst. Eng."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"6411","DOI":"10.3390\/s110606411","article-title":"Nitrogen Concentration Estimation in Tomato Leaves by VIS-NIR Non-Destructive Spectroscopy","volume":"11","author":"Ulissi","year":"2011","journal-title":"Sensors"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/16\/3062\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:39:46Z","timestamp":1760110786000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/16\/3062"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,20]]},"references-count":74,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["rs16163062"],"URL":"https:\/\/doi.org\/10.3390\/rs16163062","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,20]]}}}