{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:22:39Z","timestamp":1760149359916,"version":"build-2065373602"},"reference-count":115,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2023,8,7]],"date-time":"2023-08-07T00:00:00Z","timestamp":1691366400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Nitrogen is crucial for plant physiology due to the fact that plants consume a significant amount of nitrogen during the development period. Nitrogen supports the root, leaf, stem, branch, shoot and fruit development of plants. At the same time, it also increases flowering. To monitor the vegetation nitrogen concentration, one of the best indicators developed in the literature is the Normalized Difference Nitrogen Index (NDNI), which is based on the usage of the spectral bands of 1510 and 1680 nm from the Short-Wave Infrared (SWIR) region of the electromagnetic spectrum. However, the majority of remote sensing sensors, like cameras and\/or satellites, do not have an SWIR sensor due to high costs. Many vegetation indexes, like NDVI, EVI and MNLI, have also been developed in the VNIR region to monitor the greenness and health of the crops. However, these indexes are not very well correlated to the nitrogen content. Therefore, in this study, a novel method is developed which transforms the estimated VNIR band indexes to NDNI by using a regression method between a group of VNIR indexes and NDNI. Training is employed by using VNIR band indexes as the input and NDNI as the output, both of which are calculated from the same location. After training, an overall correlation of 0.93 was achieved. Therefore, by using only VNIR band sensors, it is possible to estimate the nitrogen content of the plant with high accuracy.<\/jats:p>","DOI":"10.3390\/rs15153898","type":"journal-article","created":{"date-parts":[[2023,8,7]],"date-time":"2023-08-07T06:38:48Z","timestamp":1691390328000},"page":"3898","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Estimating Plant Nitrogen by Developing an Accurate Correlation between VNIR-Only Vegetation Indexes and the Normalized Difference Nitrogen Index"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2980-9228","authenticated-orcid":false,"given":"Y\u00fccel","family":"\u00c7imtay","sequence":"first","affiliation":[{"name":"Computer Engineering, TED University, Ankara 06420, Turkey"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,7]]},"reference":[{"key":"ref_1","unstructured":"Givnish, T.J. (1986). On the Economy of Plant Form and Function, Cambridge University Press."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1007\/BF00377192","article-title":"Photosynthesis and nitrogen relationships in leaves of C3 plants","volume":"78","author":"Evans","year":"1989","journal-title":"Oecologia"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"948","DOI":"10.1046\/j.1365-2435.1998.00274.x","article-title":"Leaf structure (specific leaf area) modulates photosynthesis\u2013nitrogen relations: Evidence from within and across species and functional groups","volume":"12","author":"Reich","year":"1998","journal-title":"Funct. Ecol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1286","DOI":"10.1890\/1051-0761(2002)012[1286:DEOAFP]2.0.CO;2","article-title":"Direct estimation of aboveground forest productivity through hyperspectral remote sensing of canopy nitrogen","volume":"12","author":"Smith","year":"2002","journal-title":"Ecol. Appl."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/S0168-1923(02)00210-1","article-title":"Foliar morphology and canopy nitrogen as predictors of light-use efficiency in terrestrial vegetation","volume":"115","author":"Green","year":"2003","journal-title":"Agric. For. Meteorol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"760","DOI":"10.1007\/s10021-005-0079-5","article-title":"Net primary production and canopy nitrogen in a temperate forest landscape: An analysis using imaging spectroscopy, modeling and field data","volume":"8","author":"Ollinger","year":"2005","journal-title":"Ecosystems"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"19336","DOI":"10.1073\/pnas.0810021105","article-title":"Canopy nitrogen, carbon assimilation, and albedo in temperate and boreal forests: Functional relations and potential climate feedbacks","volume":"105","author":"Ollinger","year":"2008","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1029\/2000GB001250","article-title":"Forest canopy uptake of atmospheric nitrogen deposition at eastern US conifer sites: Carbon storage implications?","volume":"14","author":"Sievering","year":"2000","journal-title":"Glob. Biogeochem. Cycles"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2005JD005825","article-title":"Assessing future nitrogen deposition and carbon cycle feedback using a multimodel approach: Analysis of nitrogen deposition","volume":"110","author":"Lamarque","year":"2005","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/S0304-3800(00)00233-7","article-title":"Perspectives on combining ecological process models and remotely sensed data","volume":"129","author":"Plummer","year":"2000","journal-title":"Ecol. Model."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1007\/s40333-013-0146-2","article-title":"A spatial-explicit dynamic vegetation model that couples carbon, water, and nitrogen processes for arid and semiarid ecosystems","volume":"5","author":"Zhang","year":"2013","journal-title":"J. Arid Land"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1126\/science.1229931","article-title":"Essential biodiversity variables","volume":"339","author":"Pereira","year":"2013","journal-title":"Science"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1038\/523403a","article-title":"Environmental science: Agree on biodiversity metrics to track from space","volume":"523","author":"Skidmore","year":"2015","journal-title":"Nature"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1038\/nature02403","article-title":"The worldwide leaf economics spectrum","volume":"428","author":"Wright","year":"2004","journal-title":"Nature"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"S78","DOI":"10.1016\/j.rse.2008.10.018","article-title":"Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies","volume":"113","author":"Kokaly","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/0034-4257(89)90069-2","article-title":"Remote sensing of foliar chemistry","volume":"30","author":"Curran","year":"1989","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/S0034-4257(98)00084-4","article-title":"Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression","volume":"67","author":"Kokaly","year":"1999","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3511","DOI":"10.1016\/j.rse.2008.04.008","article-title":"A generalizable method for remote sensing of canopy nitrogen across a wide range of forest ecosystems","volume":"112","author":"Martin","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1283","DOI":"10.1080\/014311698215441","article-title":"On spectral estimates of fresh leaf biochemistry","volume":"19","author":"Fourty","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/S0034-4257(98)00014-5","article-title":"Biophysical and biochemical sources of variability in canopy reflectance","volume":"64","author":"Asner","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/0034-4257(95)00135-N","article-title":"Predicting nitrogen and chlorophyll content and concentrations from reflectance spectra (400\u20132500 nm) at leaf and canopy scales","volume":"53","author":"Yoder","year":"1995","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1338","DOI":"10.1109\/TGRS.2003.813135","article-title":"Prediction of eucalypt foliage nitrogen content from satellite-derived hyperspectral data","volume":"41","author":"Coops","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2004.06.008","article-title":"Estimating foliage nitrogen concentration from HYMAP data using continuum removal analysis","volume":"93","author":"Huang","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_25","first-page":"17","article-title":"Retrieval of chlorophyll and nitrogen in Norway spruce (Picea abies L. Karst.) using imaging spectroscopy","volume":"12","author":"Schlerf","year":"2010","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1016\/j.isprsjprs.2011.01.008","article-title":"Water-removed spectra increase the retrieval accuracy when estimating savanna grass nitrogen and phosphorus concentrations","volume":"66","author":"Ramoelo","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Riedl, A., Kainz, W., and Elmes, G.A. (2006). Progress in Spatial Data Handling, Springer.","DOI":"10.1007\/3-540-35589-8"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1890\/070152","article-title":"Airborne spectranomics: Mapping canopy chemical and taxonomic diversity in tropical forests","volume":"7","author":"Asner","year":"2008","journal-title":"Front. Ecol. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"9045","DOI":"10.3390\/rs70709045","article-title":"Prediction of macronutrients at the canopy level using spaceborne imaging spectroscopy and LiDAR data in a mixedwood boreal forest","volume":"7","author":"Thomas","year":"2015","journal-title":"Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2180","DOI":"10.1890\/14-2098.1","article-title":"Imaging spectroscopy algorithms for mapping canopy foliar chemical and morphological traits and their uncertainties","volume":"25","author":"Singh","year":"2015","journal-title":"Ecol. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1016\/j.rse.2003.11.001","article-title":"Predicting in situ pasture quality in the Kruger National Park, South Africa, using continuum-removed absorption features","volume":"89","author":"Mutanga","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.rse.2009.08.010","article-title":"Forage quality of savannas\u2014Simultaneously mapping foliar protein and polyphenols for trees and grass using hyperspectral imagery","volume":"114","author":"Skidmore","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.rse.2015.06.009","article-title":"Remote sensing of foliar nitrogen in cultivated grasslands of human dominated landscapes","volume":"167","author":"Pellissier","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.rse.2012.08.026","article-title":"Diagnostic mapping of canopy nitrogen content in rice based on hyperspectral measurements","volume":"126","author":"Inoue","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.fcr.2013.12.018","article-title":"Improving estimation of summer maize nitrogen status with red edge-based spectral vegetation indices","volume":"157","author":"Li","year":"2014","journal-title":"Field Crops Res."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1121073","DOI":"10.3389\/fpls.2023.1121073","article-title":"Nitrogen use efficiency\u2014A key to enhance crop productivity under a changing climate","volume":"14","author":"Govindasamy","year":"2023","journal-title":"Front. Plant Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1724","DOI":"10.1080\/01431161.2012.725958","article-title":"Hyperspectral analysis of mangrove foliar chemistry using PLSR and support vector regression","volume":"34","author":"Axelsson","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_38","unstructured":"(2023, July 02). Nitrogen in Plants. Available online: https:\/\/www.cropnutrition.com\/nutrient-management\/nitrogen\/."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ecocom.2013.06.003","article-title":"Review of optical-based remote sensing for plant trait mapping","volume":"15","author":"Homolova","year":"2013","journal-title":"Ecol. Complex."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3846","DOI":"10.1016\/j.rse.2008.06.005","article-title":"Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass","volume":"112","author":"Francois","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_41","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_42","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_43","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_44","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 da tabase and hyperspectral reflectance measurements","volume":"89","author":"Francois","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_45","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_46","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_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","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2005GL022688","article-title":"Remote estimation of canopy chlorophyll content in crops","volume":"32","author":"Gitelson","year":"2005","journal-title":"Geophys. Res. Lett."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1144930","DOI":"10.3389\/ffgc.2023.1144930","article-title":"Soil nitrogen dynamics in natural forest ecosystem: A review","volume":"6","author":"Fahad","year":"2023","journal-title":"Front. For. Glob. Chang."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"E185","DOI":"10.1073\/pnas.1210196109","article-title":"Hyperspectral remote sensing of foliar nitrogen content","volume":"110","author":"Knyazikhin","year":"2013","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"E2437","DOI":"10.1073\/pnas.1304176110","article-title":"Nitrogen cycling, forest canopy reflectance, and emergent properties of ecosystems","volume":"110","author":"Ollinger","year":"2013","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_52","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_53","doi-asserted-by":"crossref","first-page":"E1074","DOI":"10.1073\/pnas.1300952110","article-title":"Disentangling the contribution of biological and physical properties of leaves and canopies in imaging spectroscopy data","volume":"110","author":"Townsend","year":"2013","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Wang, Z., Wang, T., Darvishzadeh, R., Skidmore, A.K., Jones, S., Suarez, L., Woodgate, W., Heiden, U., Heurich, M., and Hearne, J. (2016). Vegetation Indices for Mapping Canopy Foliar Nitrogen in a Mixed Temperate Forest. Remote Sens., 8.","DOI":"10.3390\/rs8060491"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"398","DOI":"10.21273\/HORTTECH.16.3.0398","article-title":"Vegetable production best management practices to minimize nutrient loss","volume":"16","author":"Hartz","year":"2006","journal-title":"Horttechnology"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.agwat.2007.01.013","article-title":"Identification of irrigation and N management practices that contribute to nitrate leaching loss from an intensive vegetable production system by use of a comprehensive survey","volume":"89","author":"Thompson","year":"2007","journal-title":"Agric. Water Manag."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Tei, F., Nicola, S., and Nincasa, B.P. (2017). Advances in Research on Fertilization Management in Vegetable Crops, Springer.","DOI":"10.1007\/978-3-319-53626-2"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1007\/s10705-015-9704-7","article-title":"Nitrogen cycling and management in intensive horticultural systems","volume":"102","author":"Congreves","year":"2015","journal-title":"Nutr. Cycl. Agroecosyst."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1016\/j.scitotenv.2018.06.215","article-title":"Global trends in nitrate leaching research in the 1960\u20132017 period","volume":"643","author":"Padilla","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.1016\/j.agwat.2009.03.019","article-title":"Tomato nitrogen accumulation and fertilizer use efficiency on a sandy soil, as affected by nitrogen rate and irrigation scheduling","volume":"96","author":"Zotarelli","year":"2009","journal-title":"Agric. Water Manag."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.envpol.2005.11.005","article-title":"Nitrogen balance and groundwater nitrate contamination: Comparison among three intensive cropping systems on the North China Plain","volume":"143","author":"Ju","year":"2006","journal-title":"Environ. Pollut."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.agee.2014.10.022","article-title":"Consideration of total available N supply reduces N fertilizer requirement and potential for nitrate leaching loss in tomato production","volume":"200","author":"Soto","year":"2015","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.agee.2005.04.025","article-title":"Environmental implications of low nitrogen use efficiency in excessively fertilized hot pepper (Capsicum frutescens L.) cropping systems","volume":"111","author":"Zhu","year":"2005","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_64","first-page":"563","article-title":"Crop Nitrogen Requirement and Fertilization","volume":"49","author":"Meisinger","year":"2008","journal-title":"Nitrogen Agric. Syst."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Schepers, J.S., and Raun, W.R. (2008). Nitrogen in Agricultural Systems, Agronomy Monograph No. 49, American Society of Agronomy, Crop Science Society of America, Soil Science Society of America.","DOI":"10.2134\/agronmonogr49"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"3109","DOI":"10.3390\/s130303109","article-title":"Comparison and intercalibration of vegetation indices from different sensors for monitoring above-ground plant nitrogen uptake in winter wheat","volume":"13","author":"Yao","year":"2013","journal-title":"Sensors"},{"key":"ref_67","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_68","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_69","doi-asserted-by":"crossref","unstructured":"Padilla, F.M., Gallardo, M., Pe\u00f1a-Fleitas, M.T., de Souza, R., and Thompson, R.B. (2018). Proximal Optical Sensors for Nitrogen Management of Vegetable Crops: A Review. Sensors, 18.","DOI":"10.3390\/s18072083"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"800","DOI":"10.2134\/agronj2008.0162Rx","article-title":"Strategies to make use of plant sensors-based diagnostic information for nitrogen recommendations","volume":"101","author":"Samborski","year":"2009","journal-title":"Agron. J."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"2827","DOI":"10.3390\/rs6042827","article-title":"Assessing the robustness of vegetation indices to estimate wheat N in mediterranean environments","volume":"6","author":"Cammarano","year":"2014","journal-title":"Remote Sens."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1007\/s11119-015-9414-9","article-title":"Variable rate nitrogen fertilizer response in wheat using remote sensing","volume":"17","author":"Basso","year":"2015","journal-title":"Precis. Agric."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.eja.2016.12.007","article-title":"Determination of sufficiency values of canopy reflectance vegetation indices for maximum growth and yield of cucumber","volume":"84","author":"Padilla","year":"2017","journal-title":"Eur. J. Agron."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1093\/oxfordjournals.aob.a088118","article-title":"Growth rate and % N of field grown crops: Theory and experiments","volume":"67","author":"Greenwood","year":"1991","journal-title":"Ann. Bot."},{"key":"ref_75","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_76","doi-asserted-by":"crossref","unstructured":"Lemaire, G., and Gastal, F. (1997). Diagnosis of the Nitrogen Status in Crops, Springer.","DOI":"10.1007\/978-3-642-60684-7"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"S117","DOI":"10.2134\/agronj2006.0370c","article-title":"Application of spectral remote sensing for agronomic decisions","volume":"100","author":"Hatfield","year":"2008","journal-title":"Agron. J."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Liu, L., Lindsay, P.L., and Jackson, D. (2021). Next Generation Cereal Crop Yield Enhancement: From Knowledge of Inflorescence Development to Practical Engineering by Genome Editing. Int. J. Mol. Sci., 22.","DOI":"10.3390\/ijms22105167"},{"key":"ref_79","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-The canopy chlorophyll content index (CCCI)","volume":"116","author":"Fitzgerald","year":"2010","journal-title":"Field Crops Res."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.isprsjprs.2013.01.008","article-title":"Remotely detecting canopy nitrogen concentration and uptake of paddy rice in the Northeast China Plain","volume":"78","author":"Yu","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_81","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_82","doi-asserted-by":"crossref","unstructured":"Xu, S., Xu, X., Blacker, C., Gaulton, R., Zhu, Q., Yang, M., Yang, G., Zhang, J., Yang, Y., and Yang, M. (2023). Estimation of Leaf Nitrogen Content in Rice Using Vegetation Indices and Feature Variable Optimization with Information Fusion of Multiple-Sensor Images from UAV. Remote Sens., 15.","DOI":"10.3390\/rs15030854"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"de Souza, R., Pe\u00f1a-Fleitas, M.T., Thompson, R.B., Gallardo, M., and Padilla, F.M. (2020). Assessing Performance of Vegetation Indices to Estimate Nitrogen Nutrition Index in Pepper. Remote Sens., 12.","DOI":"10.3390\/rs12050763"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"7007","DOI":"10.3390\/rs70607007","article-title":"Estimating Cotton Nitrogen Nutrition Status Using Leaf Greenness and Ground Cover Information","volume":"7","author":"Muharam","year":"2015","journal-title":"Remote Sens."},{"key":"ref_85","first-page":"191","article-title":"Estimation of winter wheat nitrogen nutrition index using hyperspectral remote sensing","volume":"30","author":"Wang","year":"2014","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.rse.2012.08.019","article-title":"How Deep Does a Remote Sensor Sense? Expression of Chlorophyll Content in a Maize Canopy","volume":"126","author":"Ciganda","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"1814","DOI":"10.2135\/cropsci2000.4061814x","article-title":"Reflectance Indices with Precision and Accuracy in Predicting Cotton LeafNitrogen Concentration","volume":"40","author":"Tarpley","year":"2000","journal-title":"Crop Sci."},{"key":"ref_88","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_89","doi-asserted-by":"crossref","first-page":"106000","DOI":"10.1016\/j.compag.2021.106000","article-title":"Which Multispectral Indices Robustly Measure Canopy Nitrogen across Seasons: Lessons from an Irrigated Pasture Crop","volume":"182","author":"Patel","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1093\/jxb\/eraa432","article-title":"Unique Contributions of Chlorophyll and Nitrogen to Predict Crop Photosynthetic Capacity from Leaf Spectroscopy","volume":"72","author":"Wang","year":"2021","journal-title":"J. Exp. Bot."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Ayhan, B., Kwan, C., Budavari, B., Kwan, L., Lu, Y., Perez, D., Li, J., Skarlatos, D., and Vlachos, M. (2020). Vegetation Detection Using Deep Learning and Conventional Methods. Remote Sens., 12.","DOI":"10.3390\/rs12152502"},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Sun, Y., Lao, D., Ruan, Y., Huang, C., and Xin, Q. (2023). A Deep Learning-Based Approach to Predict Large-Scale Dynamics of Normalized Difference Vegetation Index for the Monitoring of Vegetation Activities and Stresses Using Meteorological Data. Sustainability, 15.","DOI":"10.3390\/su15086632"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.fcr.2012.10.013","article-title":"Assessing Leaf Nitrogen Content and Leaf Mass 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_94","doi-asserted-by":"crossref","first-page":"2634","DOI":"10.1080\/01431161.2015.1041176","article-title":"Estimating Winter Wheat (Triticum aestivum) LAI and Leaf Chlorophyll Content from Canopy Reflectance Data by Integrating Agronomic Prior Knowledge with the PROSAIL Model","volume":"36","author":"Li","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"14939","DOI":"10.3390\/rs71114939","article-title":"Evaluation of Six Algorithms to Monitor Wheat Leaf Nitrogen Concentration","volume":"7","author":"Yao","year":"2015","journal-title":"Remote Sens."},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Ma, L., Chen, X., Zhang, Q., Lin, J., Yin, C., Ma, Y., Yao, Q., Feng, L., Zhang, Z., and Lv, X. (2022). Estimation of Nitrogen Content Based on the Hyperspectral Vegetation Indexes of Interannual and Multi-Temporal in Cotton. Agronomy, 12.","DOI":"10.3390\/agronomy12061319"},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Wang, L., Chen, S., Li, D., Wang, C., Jiang, H., Zheng, Q., and Peng, Z. (2021). Estimation of Paddy Rice Nitrogen Content and Accumulation Both at Leaf and Plant Levels from UAV Hyperspectral Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13152956"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Xu, X., Fan, L., Li, Z., Meng, Y., Feng, H., Yang, H., and Xu, B. (2021). Estimating leaf nitrogen content in corn based on information fusion of multiple-sensor imagery from UAV. Remote Sens., 13.","DOI":"10.3390\/rs13030340"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"1012070","DOI":"10.3389\/fpls.2022.1012070","article-title":"Estimation of the nitrogen content of potato plants based on morphological parameters and visible light vegetation indices","volume":"13","author":"Fan","year":"2022","journal-title":"Front. Plant Sci."},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Haider, T., Farid, M.S., Mahmood, R., Ilyas, A., Khan, M.H., Haider, S.T.-A., Chaudhry, M.H., and Gul, M. (2021). A Computer-Vision-Based Approach for Nitrogen Content Estimation in Plant Leaves. Agriculture, 11.","DOI":"10.3390\/agriculture11080766"},{"key":"ref_101","unstructured":"(2023, June 22). EO-1 (Earth Observing-1). Available online: https:\/\/www.eoportal.org\/satellite-missions\/eo-1#eo-1-earth-observing-1."},{"key":"ref_102","unstructured":"(2023, June 24). QGIS Software. Available online: https:\/\/qgis.org\/en\/site\/."},{"key":"ref_103","unstructured":"(2023, June 26). Urfa Haber. Available online: https:\/\/www.medyaurfa.com\/gundem\/harran-ovasi-gap-ile-ihya-oldu-h81516.html."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.compag.2018.07.003","article-title":"A novel bilinear unmixing approach for reconsideration of subpixel classification of land cover","volume":"152","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"7676","DOI":"10.1016\/j.ijleo.2016.05.115","article-title":"Revised normalized difference nitrogen index (NDNI) for estimating canopy nitrogen concentration in wetlands","volume":"127","author":"Wang","year":"2016","journal-title":"Optik"},{"key":"ref_106","unstructured":"Rouse, J.W., Haas, R.H., Schell, J.A., and Deering, D.W. (1973). Monitoring Vegetation Systems in the Great Plains with ERTS, Third ERTS Symposium."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1016\/S0273-1177(97)01133-2","article-title":"Remote Sensing of Chlorophyll Concentration in Higher Plant Leaves","volume":"22","author":"Gitelson","year":"1998","journal-title":"Adv. Space Res."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the Radiometric and Biophysical Performance of the MODIS Vegetation Indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_109","unstructured":"Sripada, R.P. (2005). Determining In-Season Nitrogen Requirements for Corn Using Aerial Color-Infrared Photography. [Ph.D. Dissertation, North Carolina State University]."},{"key":"ref_110","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_111","doi-asserted-by":"crossref","first-page":"1563","DOI":"10.1080\/01431169308953986","article-title":"Red Edge Spectral Measurements from Sugar Maple Leaves","volume":"14","author":"Vogelmann","year":"1993","journal-title":"Int. J. Remote Sens."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1016\/S0034-4257(02)00011-1","article-title":"Remote Sensing of Nitrogen and Lignin in Mediterranean Vegetation from AVIRIS Data: Decomposing Biochemical from Structural Signals","volume":"81","author":"Serrano","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_113","unstructured":"(2023, June 28). Deep Learning Toolbox. Available online: https:\/\/www.mathworks.com\/products\/deep-learning.html."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1109\/72.329697","article-title":"Training feedforward networks with the Marquardt algorithm","volume":"5","author":"Hagan","year":"1994","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1109\/JAS.2016.7508796","article-title":"Control 5.0: From Newton to Merton in popper\u2019s cybersocial-physical spaces","volume":"3","author":"Wang","year":"2016","journal-title":"IEEE\/CAA J. Autom. Sin."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/15\/3898\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:27:05Z","timestamp":1760128025000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/15\/3898"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,7]]},"references-count":115,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["rs15153898"],"URL":"https:\/\/doi.org\/10.3390\/rs15153898","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,8,7]]}}}