{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T12:09:31Z","timestamp":1770898171855,"version":"3.50.1"},"reference-count":78,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T00:00:00Z","timestamp":1641859200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Technologies of Research and Development Program of China","award":["2019YFE0125300"],"award-info":[{"award-number":["2019YFE0125300"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>A better understanding of wheat nitrogen status is important for improving N fertilizer management in precision farming. In this study, four different sensors were evaluated for their ability to estimate winter wheat nitrogen. A Gaussian process regression (GPR) method with the sequential backward feature removal (SBBR) routine was used to identify the best combinations of vegetation indices (VIs) sensitive to wheat N indicators for different sensors. Wheat leaf N concentration (LNC), plant N concentration (PNC), and the nutrition index (NNI) were estimated by the VIs through parametric regression (PR), multivariable linear regression (MLR), and Gaussian process regression (GPR). The study results reveal that the optical fluorescence sensor provides more accurate estimates of winter wheat N status at a low-canopy coverage condition. The Dualex Nitrogen Balance Index (NBI) is the best leaf-level indicator for wheat LNC, PNC and NNI at the early wheat growth stage. At the early growth stage, Multiplex indices are the best canopy-level indicators for LNC, PNC, and NNI. At the late growth stage, ASD VIs provide accurate estimates for wheat N indicators. This study also reveals that the GPR with SBBR analysis method provides more accurate estimates of winter wheat LNC, PNC, and NNI, with the best VI combinations for these sensors across the different winter wheat growth stages, compared with the MLR and PR methods.<\/jats:p>","DOI":"10.3390\/s22020549","type":"journal-article","created":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T20:33:04Z","timestamp":1641933184000},"page":"549","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Winter Wheat Nitrogen Estimation Based on Ground-Level and UAV-Mounted Sensors"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0294-5705","authenticated-orcid":false,"given":"Xiaoyu","family":"Song","sequence":"first","affiliation":[{"name":"Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"},{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing 100097, China"}]},{"given":"Guijun","family":"Yang","sequence":"additional","affiliation":[{"name":"Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"},{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing 100097, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8473-5631","authenticated-orcid":false,"given":"Xingang","family":"Xu","sequence":"additional","affiliation":[{"name":"Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"},{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing 100097, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3509-7482","authenticated-orcid":false,"given":"Dongyan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Anhui Engineering Laboratory of Agro-Ecological Big Data, Anhui University, Hefei 230601, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9898-628X","authenticated-orcid":false,"given":"Chenghai","family":"Yang","sequence":"additional","affiliation":[{"name":"Aerial Application Technology Research Unit, USDA-Agricultural Research Service, College Station, TX 77845, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3312-6200","authenticated-orcid":false,"given":"Haikuan","family":"Feng","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.eja.2008.05.007","article-title":"Estimating the nitrogen nutrition index using spectral canopy reflectance measurements","volume":"29","author":"Mistele","year":"2008","journal-title":"Eur. J. Agron."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1264","DOI":"10.2134\/agronj2008.0016","article-title":"Chlorophyll measurements and nitrogen nutrition index for the evaluation of corn nitrogen status","volume":"100","author":"Ziadi","year":"2008","journal-title":"Agron. J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.fcr.2013.08.005","article-title":"Nondestructive 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_4","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1007\/s13593-012-0111-z","article-title":"Precision nitrogen management of wheat: A review","volume":"33","author":"Diacono","year":"2013","journal-title":"Agron. Sustain. Dev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"S0034","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_6","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.fcr.2012.10.013","article-title":"Assessing leaf nitrogen content and leafmass per unit area of wheat in the field throughout plant cycle with a portable spectrometer","volume":"140","author":"Ecarnot","year":"2013","journal-title":"Field Crops Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1080\/01904160500416471","article-title":"Relationship between the normalized SPAD index and the nitrogen nutrition index: Application to durum wheat","volume":"29","author":"Debaeke","year":"2006","journal-title":"J. Plant Nutr."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1051\/agro:2007032","article-title":"Replacing the nitrogen nutrition index by the chlorophyll meter to assess wheat N status","volume":"27","author":"Prost","year":"2007","journal-title":"Agron. Sustain. Dev."},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1007\/s13593-011-0041-1","article-title":"Sensing crop nitrogen status with fluorescence indicators\u2014A review","volume":"32","author":"Tremblay","year":"2012","journal-title":"Agron. Sust. Dev."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.biosystemseng.2017.06.003","article-title":"Airborne and ground level sensors for monitoring nitrogen status in a maize crop","volume":"160","author":"Gabriel","year":"2017","journal-title":"Biosyst. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1663","DOI":"10.1046\/j.1365-3040.2002.00942.x","article-title":"The use of chlorophyll fluorescence excitation spectra for the non-destructive in situ assessment of UV absorbing compounds in leaves","volume":"25","author":"Cerovic","year":"2002","journal-title":"Plant Cell Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.fcr.2004.05.002","article-title":"Optically assessed contents of leaf polyphenolics and chlorophyll as indicators of nitrogen deficiency in wheat (Triticum aestivum L.)","volume":"91","author":"Cartelat","year":"2005","journal-title":"Field Crops Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3619","DOI":"10.1080\/01431160110114529","article-title":"Estimating leaf nitrogen concentration in ryegrass (Lolium spp.) pasture using the chlorophyll rededge: Theoretical modeling and experimental observations","volume":"23","author":"Lamb","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1080\/01431160500117907","article-title":"Measuring wheat nitrogen status from space and ground-based platform","volume":"27","author":"Reyniers","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.rse.2005.12.011","article-title":"A new technique for extracting the red edge position from hyperspectral data: The linear extrapolation method","volume":"101","author":"Cho","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"10040","DOI":"10.3390\/s101110040","article-title":"Non-destructive optical monitoring of grape maturation by proximal sensing","volume":"10","author":"Ben","year":"2010","journal-title":"Sensors"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Mukhamediev, R.I., Symagulov, A., Kuchin, Y., Zaitseva, E., Bekbotayeva, A., Yakunin, K., Assanov, I., Levashenko, V., Popova, Y., and Akzhalova, A. (2021). Review of Some Applications of Unmanned Aerial Vehicles Technology in the Resource-Rich Country. Appl. Sci., 11.","DOI":"10.3390\/app112110171"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"48572","DOI":"10.1109\/ACCESS.2019.2909530","article-title":"Unmanned aerial vehicles (UAVs): A survey on civil applications and key research challenges","volume":"7","author":"Shakhatreh","year":"2019","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"185","DOI":"10.5589\/m13-026","article-title":"Estimation of leaf chlorophyll content of rice using image color analysis","volume":"39","author":"Hu","year":"2013","journal-title":"Can. J. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"602647","DOI":"10.1155\/2014\/602647","article-title":"Use of a digital camera to monitor the growth and nitrogen status of cotton","volume":"2014","author":"Jia","year":"2014","journal-title":"Sci. World J."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"719","DOI":"10.2135\/cropsci1999.0011183X003900030019x","article-title":"Measuring wheat senescence with a digital camera","volume":"39","author":"Adamsen","year":"1999","journal-title":"Crop. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1080\/01904169909365631","article-title":"Estimating vegetation coverage in wheat using digital images","volume":"22","author":"Lukina","year":"1999","journal-title":"J. Plant Nutr."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1478","DOI":"10.3389\/fpls.2018.01478","article-title":"Passive Reflectance Sensing and Digital Image Analysis Allows for Assessing the Biomass and Nitrogen Status of Wheat in Early and Late Tillering Stages","volume":"9","author":"Elsayed","year":"2018","journal-title":"Front. Plant. Sci."},{"key":"ref_25","first-page":"369","article-title":"Research advancement on crop nitrogen nutrition diagnosis","volume":"37","author":"Song","year":"2006","journal-title":"Chin. J. Soil Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.eja.2016.10.009","article-title":"Remotely assessing leaf N uptake in winter wheat based on canopy hyperspectral red-edge absorption","volume":"82","author":"Guo","year":"2017","journal-title":"Eur. J. Agron."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/0034-4257(90)90100-Z","article-title":"PROSPECT: A Model of Leaf Optical Properties Spectra","volume":"34","author":"Jacquemoud","year":"1990","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1111\/j.1469-8137.2010.03536.x","article-title":"Sources of variability in canopy reflectance and the convergent properties of plants","volume":"189","author":"Ollinger","year":"2011","journal-title":"New Phytol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2866","DOI":"10.3390\/rs4092866","article-title":"Mapping vegetation density in aheterogeneous river floodplain ecosystem using pointable CHRIS\/PROBA data","volume":"4","author":"Verrelst","year":"2012","journal-title":"Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.isprsjprs.2015.05.005","article-title":"Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties\u2014A review","volume":"108","author":"Verrelst","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Manakos, I., and Braun, M. (2014). Beyond NDVI: Extraction of biophysical variables from remote sensing imagery. Land Use and Land Cover Mapping in Europe: Practices and Trends, Springer.","DOI":"10.1007\/978-94-007-7969-3"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2136","DOI":"10.3390\/s8042136","article-title":"Relationship between remotely-sensed vegetation indices, canopy attributes and plant physiological processes: What vegetation indices can and cannot tell us about the landscape","volume":"8","author":"Glenn","year":"2008","journal-title":"Sensors"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/S0004-3702(97)00063-5","article-title":"Selection of relevant features and examples in machine learning","volume":"97","author":"Blum","year":"1997","journal-title":"Artif. Intell."},{"key":"ref_34","first-page":"554","article-title":"Spectral band selection for vegetation properties retrieval using Gaussian processes regression","volume":"52","author":"Verrelst","year":"2016","journal-title":"Int. J. Appl. Earth Observ. Geoinf."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1651","DOI":"10.3390\/rs4061651","article-title":"Estimating canopy nitrogen concentration in sugarcane using field imaging spectroscopy","volume":"4","author":"Miphokasap","year":"2012","journal-title":"Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"867","DOI":"10.1109\/JSTARS.2012.2222356","article-title":"Gaussian process retrieval of chlorophyll content from imaging spectroscopy data","volume":"6","author":"Verrelst","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1832","DOI":"10.1109\/TGRS.2011.2168962","article-title":"Retrieval of vegetation biophysical parameters using gaussian process techniques","volume":"50","author":"Verrelst","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1007\/s10712-018-9478-y","article-title":"Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods","volume":"40","author":"Verrelst","year":"2018","journal-title":"Surv. Geophys."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Rasmussen, C.E., and Williams, C.K.I. (2006). Gaussian Processes for Machine Learning, The MIT Press. Available online: http:\/\/www.gaussianprocess.org\/gpml\/chapters\/RW.pdf.","DOI":"10.7551\/mitpress\/3206.001.0001"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1007\/s11119-010-9165-6","article-title":"Evaluating hyperspectral vegetation indices for estimating nitrogen concentration of winter wheat at different growth stages","volume":"11","author":"Li","year":"2010","journal-title":"Precis. Agric."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"6342","DOI":"10.1080\/01431161.2012.687473","article-title":"Assessment of plant nitrogen stress in wheat (Triticum aestivum L.) through hyperspectral indices","volume":"33","author":"Ranjan","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_42","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":"2013","journal-title":"Plant. Soil."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.isprsjprs.2016.10.002","article-title":"Examining view angle effects on leaf N estimation in wheat using field reflectance spectroscopy","volume":"122","author":"Song","year":"2016","journal-title":"ISPRS J. Photo. Remote Sens."},{"key":"ref_44","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_45","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/j.eja.2007.11.005","article-title":"Monitoring leaf nitrogen status with hyperspectral reflectance in wheat","volume":"28","author":"Feng","year":"2008","journal-title":"Eur. J. Agron."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.fcr.2008.11.004","article-title":"Plant nitrogen concentration in paddy rice from field canopy hyperspectral radiometry","volume":"111","author":"Daniela","year":"2009","journal-title":"Field Crops Res."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.fcr.2012.01.014","article-title":"Estimating leaf nitrogen concentration with three-band vegetation indices in rice and wheat","volume":"129","author":"Wang","year":"2012","journal-title":"Field Crops Res."},{"key":"ref_48","unstructured":"Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W., and Harlan, J.C. (2021, July 26). Monitoring the Vernal Advancement and Retrogradation of Natural Vegetation, Available online: https:\/\/ntrs.nasa.gov\/archive\/nasa\/casi.ntrs.nasa.gov\/19740004927.pdf."},{"key":"ref_49","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":"Reujean","year":"1995","journal-title":"Remote Sens Enviro."},{"key":"ref_50","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 Phys."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1887","DOI":"10.1080\/01431169308954010","article-title":"The reflectance at the 950\u2013970 nm region as an indicator of plant water status","volume":"14","author":"Penuelas","year":"1992","journal-title":"Int. J. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S0034-4257(96)00067-3","article-title":"NDWI\u2014A normalized difference water index for remote sensing of vegetation liquid water from space","volume":"58","author":"Gao","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_53","first-page":"77","article-title":"The influence of Soil Salinity, Growth Form, and Leaf Moisture on the Spectral Radiance of Spartina alterniflora Canopies","volume":"48","author":"Hardisky","year":"1983","journal-title":"Photo. Eng. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/S0034-4257(00)00113-9","article-title":"Estimating corn leaf chlorophyll content from leaf and canopy reflectance","volume":"74","author":"Daughtry","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_55","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. Feb."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/0034-4257(94)90134-1","article-title":"A Modified Soil Adjusted Vegetation Index (MSAVI)","volume":"48","author":"Qi","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_57","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_58","doi-asserted-by":"crossref","first-page":"2855","DOI":"10.1080\/01431160210163074","article-title":"Vegetation indices derived from high resolution airborne videography for precision crop management","volume":"24","author":"Metternicht","year":"2003","journal-title":"Int. J. Rem. Sens."},{"key":"ref_59","unstructured":"SpecTerra, S. (1999). Presentation and Analysis of Data, SpecTerra Services Pty Ltd.. Available online: http:\/\/www.specterra.com.au\/dmsv_data_frame.html."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/S1360-1385(96)80019-7","article-title":"The role of xanthophylls cycle carotenoids in the protection of photosynthesis","volume":"1","author":"Demmig","year":"1996","journal-title":"Trends Plant. Sci."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/S0034-4257(98)00007-8","article-title":"LIBERTY-Modeling the effects of leaf biochemical concentration on reflectance spectra","volume":"65","author":"Dawson","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1755","DOI":"10.1080\/01431169008955128","article-title":"Quantitative characterization of the vegetation red edge reflectance I. An inverted-Gaussian reflectance model","volume":"11","author":"Miller","year":"1990","journal-title":"Int. J. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Yue, J.B., Feng, H.K., Jin, X.L., Yuan, H.H., Li, Z.H., Zhou, C.Q., Yang, G.J., and Tian, Q.J. (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_64","doi-asserted-by":"crossref","first-page":"259","DOI":"10.13031\/2013.27838","article-title":"Color indices for weed identification under various soil, residue, and lighting conditions","volume":"38","author":"Woebbecke","year":"1995","journal-title":"Trans. ASAE"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.compag.2008.03.009","article-title":"Verification of color vegetation indices for automated crop imaging applications","volume":"63","author":"Meyer","year":"2008","journal-title":"Comput. Electron. Agric."},{"key":"ref_66","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_67","doi-asserted-by":"crossref","unstructured":"Bradstreet, R.B. (1965). The Kjeldahl Method for Organic Nitrogen, Academic Press Incorporated.","DOI":"10.1016\/B978-1-4832-3298-0.50005-9"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Zhao, H.T., Song, X.Y., Yang, G.J., Li, Z.H., Zhang, D.Y., and Feng, H.K. (2019). Monitoring of Nitrogen and Grain Protein Content in Winter Wheat Based on Sentinel-2A Data. Remote Sens., 11.","DOI":"10.3390\/rs11141724"},{"key":"ref_69","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_70","doi-asserted-by":"crossref","first-page":"523","DOI":"10.2134\/agronj2011.0258","article-title":"Critical nitrogen dilution curve for optimizing nitrogen management of winter wheat production in the North China Plain","volume":"104","author":"Yue","year":"2012","journal-title":"Agron. J."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"79","DOI":"10.3354\/cr030079","article-title":"Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance","volume":"30","author":"Willmott","year":"2005","journal-title":"Clim. Res."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/0304-3800(80)90042-3","article-title":"A model evaluation methodology applicable to environmental assessment models","volume":"8","author":"Schaeffer","year":"1980","journal-title":"Ecol. Model."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/0022-1694(70)90255-6","article-title":"River flow forecasting through conceptual models part I-A discussion of principles","volume":"10","author":"Nash","year":"1970","journal-title":"J. Hydrol."},{"key":"ref_74","first-page":"657","article-title":"A review of optical methods for assessing nitrogen contents during rice growth","volume":"30","author":"Saberioon","year":"2014","journal-title":"Appl. Eng. Agric."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1834","DOI":"10.3389\/fpls.2018.01834","article-title":"Potential of UAV-based active sensing for monitoring rice leaf nitrogen status","volume":"9","author":"Karim","year":"2018","journal-title":"Front. Plant Sci."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"1111","DOI":"10.3389\/fpls.2017.01111","article-title":"Unmanned aerial vehicle remote sensing for field-based crop phenotyping: Current status and perspectives","volume":"8","author":"Yang","year":"2017","journal-title":"Front. Plant Sci."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1007\/s10021-004-0144-5","article-title":"Spectral and structural measures of northwest forest vegetation at leaf to landscape scales","volume":"7","author":"Roberts","year":"2004","journal-title":"Ecosystems"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1016\/j.compag.2019.04.035","article-title":"Detection of nutrition deficiencies in plants using proximal images and machine learning: A review","volume":"162","author":"Jayme","year":"2019","journal-title":"Comput. Electron. 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