{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T19:01:20Z","timestamp":1775761280394,"version":"3.50.1"},"reference-count":56,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,1,20]],"date-time":"2022-01-20T00:00:00Z","timestamp":1642636800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["328017493\/GRK 2366"],"award-info":[{"award-number":["328017493\/GRK 2366"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Research project of Agricultural UAV system","award":["2021AC037"],"award-info":[{"award-number":["2021AC037"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>As an essential element, the effect of Phosphorus (P) on plant growth is very significant. In the early growth stage of maize, it has a high sensitivity to the deficiency of phosphorus. The main purpose of this paper is to monitor the maize status under two phosphorus levels in soil by a nondestructive testing method and identify different phosphorus treatments by spectral data. Here, the Analytical Spectral Devices (ASD) spectrometer was used to obtain canopy spectral data of 30 maize inbred lines in two P-level fields, whose reflectance differences were compared and the sensitive bands of P were discovered. Leaf Area Index (LAI) and yield under two P levels were quantitatively analyzed, and the responses of different varieties to P content in soil were observed. In addition, the correlations between 13 vegetation indexes and eight phenotypic parameters were compared under two P levels so as to find out the best vegetation index for maize characteristics estimation. A Back Propagation (BP) neural network was used to evaluate leaf area index and yield, and the corresponding prediction model was established. In order to classify different P levels of soil, the method of support vector machine (SVM) was applied. The results showed that the sensitive bands of P for maize canopy included 763 nm, 815 nm, and 900\u20131000 nm. P-stress had a significant effect on LAI and yield of most varieties, whose reduction rate reached 41% as a whole. In addition, it was found that the correlations between vegetation indexes and phenotypic parameters were weakened under low-P level. The regression coefficients of 0.75 and 0.5 for the prediction models of LAI and yield were found by combining the spectral data under two P levels. For the P-level identification in soil, the classification accuracy could reach above 86%. These abilities potentially allow for phenotypic parameters prediction of maize plants by spectral data and different phosphorus contents identification with unknown phosphorus fertilizer status.<\/jats:p>","DOI":"10.3390\/rs14030493","type":"journal-article","created":{"date-parts":[[2022,1,20]],"date-time":"2022-01-20T22:51:06Z","timestamp":1642719066000},"page":"493","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Maize Characteristics Estimation and Classification by Spectral Data under Two Soil Phosphorus Levels"],"prefix":"10.3390","volume":"14","author":[{"given":"Baiyu","family":"Qiao","sequence":"first","affiliation":[{"name":"College of Science, China Agricultural University, Beijing 100193, China"}]},{"given":"Xiongkui","family":"He","sequence":"additional","affiliation":[{"name":"College of Science, China Agricultural University, Beijing 100193, China"},{"name":"College of Agricultural Unmanned System, China Agricultural University, Beijing 100193, China"}]},{"given":"Yajia","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Science, China Agricultural University, Beijing 100193, China"},{"name":"College of Agricultural Unmanned System, China Agricultural University, Beijing 100193, China"}]},{"given":"Hao","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Science, China Agricultural University, Beijing 100193, China"}]},{"given":"Lanting","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Science, China Agricultural University, Beijing 100193, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1673-1831","authenticated-orcid":false,"given":"Limin","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Science, China Agricultural University, Beijing 100193, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8036-2738","authenticated-orcid":false,"given":"Alice-Jacqueline","family":"Reineke","sequence":"additional","affiliation":[{"name":"Department of Agricultural Engineering, University of Hohenheim, 70593 Stuttgart, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5733-1244","authenticated-orcid":false,"given":"Wenxin","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4623-5879","authenticated-orcid":false,"given":"Joachim","family":"M\u00fcller","sequence":"additional","affiliation":[{"name":"Department of Agricultural Engineering, University of Hohenheim, 70593 Stuttgart, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5989","DOI":"10.1038\/ncomms6989","article-title":"Climate variation explains a third of global crop yield variability","volume":"6","author":"Ray","year":"2015","journal-title":"Nat. Commun."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.rse.2015.08.021","article-title":"Variations in crop variables within wheat canopies and responses of canopy spectral characteristics and derived vegetation indices to different vertical leaf layers and spikes","volume":"169","author":"Li","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_3","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_4","doi-asserted-by":"crossref","first-page":"897","DOI":"10.1007\/s11119-020-09764-w","article-title":"An accurate method for predicting spatial variability of maize yield from UAV-based plant height estimation: A tool for monitoring agronomic field experiments","volume":"22","author":"Gilliot","year":"2021","journal-title":"Precis. Agric."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.cj.2019.06.005","article-title":"Deep neural network algorithm for estimating maize biomass based on simulated Sentinel 2A vegetation indices and leaf area index","volume":"8","author":"Jin","year":"2020","journal-title":"Crop J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e20180409","DOI":"10.1590\/1678-992x-2018-0409","article-title":"Estimation of leaf nutrient concentration from hyperspectral reflectance in Eucalyptus using partial least squares regression","volume":"77","author":"Oliveira","year":"2020","journal-title":"Sci. Agric."},{"key":"ref_7","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_8","doi-asserted-by":"crossref","first-page":"1843","DOI":"10.1016\/j.agrformet.2011.08.002","article-title":"Potential performances of remotely sensed LAI assimilation in WOFOST model based on an OSS Experiment","volume":"151","author":"Curnel","year":"2011","journal-title":"Agric. For. Meteorol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.rse.2010.08.009","article-title":"Real-time retrieval of Leaf Area Index from MODIS time series data","volume":"115","author":"Xiao","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1007\/s11119-019-09699-x","article-title":"Leaf Area Index evaluation in vineyards using 3D point clouds from UAV imagery","volume":"21","author":"Comba","year":"2019","journal-title":"Precis. Agric."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.biosystemseng.2017.08.013","article-title":"Linking thermal imaging and soil remote sensing to enhance irrigation management of sugar beet","volume":"165","author":"Quebrajo","year":"2018","journal-title":"Biosyst. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Khaliq, A., Comba, L., Biglia, A., Ricauda Aimonino, D., Chiaberge, M., and Gay, P. (2019). Comparison of Satellite and UAV-Based Multispectral Imagery for Vineyard Variability Assessment. Remote Sens., 11.","DOI":"10.3390\/rs11040436"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.eja.2015.07.004","article-title":"Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review","volume":"70","author":"Sankaran","year":"2015","journal-title":"Eur. J. Agron."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1007\/s11119-019-09659-5","article-title":"Assessment of maize yield and phenology by drone-mounted superspectral camera","volume":"21","author":"Herrmann","year":"2020","journal-title":"Precis. Agric."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.1890\/13-0025.1","article-title":"Synergism and context dependency of interactions between arbuscular mycorrhizal fungi and rhizobia with a prairie legume","volume":"95","author":"Larimer","year":"2014","journal-title":"Ecology"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Li, D., Wang, H., Wang, M., Li, G., Chen, Z., Leiser, W.L., Wei\u00df, T.M., Lu, X., Wang, M., and Chen, S. (2021). Genetic Dissection of Phosphorus Use Efficiency in a Maize Association Population under Two P Levels in the Field. Int. J. Mol. Sci., 22.","DOI":"10.3390\/ijms22179311"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"115261","DOI":"10.1016\/j.geoderma.2021.115261","article-title":"Soil phosphorus availability determines the preference for direct or mycorrhizal phosphorus uptake pathway in maize","volume":"403","author":"Zhang","year":"2021","journal-title":"Geoderma"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1007\/s11368-019-02418-z","article-title":"Prediction of macronutrients in plant leaves using chemometric analysis and wavelength selection","volume":"20","author":"Malmir","year":"2020","journal-title":"J. Soils Sediments"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3587","DOI":"10.1016\/j.rse.2011.08.020","article-title":"Spectroscopy of canopy chemicals in humid tropical forests","volume":"115","author":"Asner","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1016\/j.isprsjprs.2020.09.006","article-title":"Using hyperspectral plant traits linked to photosynthetic efficiency to assess N and P partition","volume":"169","author":"Watt","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1007\/s11119-019-09670-w","article-title":"Monitoring leaf potassium content using hyperspectral vegetation indices in rice leaves","volume":"21","author":"Lu","year":"2020","journal-title":"Precis. Agric."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1016\/j.jgg.2016.11.002","article-title":"Enhancing phosphorus uptake efficiency through QTL-based selection for root system architecture in maize","volume":"43","author":"Gu","year":"2016","journal-title":"J. Genet. Genom."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Sparks, D.L., Page, A.L., Helmke, P.A., Loppert, R.H., Soltanpour, P.N., Tabatabai, M.A., Johnston, C.T., and Summner, M.E. (1996). Methods of Soil Analysis: Chemical Methods, Part 3, ASA and SSSA.","DOI":"10.2136\/sssabookser5.3"},{"key":"ref_24","unstructured":"Olsen, S.R. (1954). Estimation of Available Phosphorus in Soils by Extraction with Sodium Bicarbonate."},{"key":"ref_25","unstructured":"Liu, L. (2014). Principle and Application of Vegetation Quantitative Remote Sensing, Science Press."},{"key":"ref_26","unstructured":"Zhao, C. (2016). Hyperspectral Remote Sensing Image Processing Method and Its Application, Electronic Industry Press."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Jin, X., Kumar, L., Li, Z., Xu, X., Yang, G., and Wang, J. (2016). Estimation of Winter Wheat Biomass and Yield by Combining the AquaCrop Model and Field Hyperspectral Data. Remote Sens., 8.","DOI":"10.3390\/rs8120972"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3468","DOI":"10.1016\/j.rse.2011.08.010","article-title":"Comparison of different vegetation indices for the remote assessment of green leaf area index of crops","volume":"115","author":"Gitelson","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Serrano-Calvo, R., Cutler, M.E.J., and Bengough, A.G. (2021). Spectral and Growth Characteristics of Willows and Maize in Soil Contaminated with a Layer of Crude or Refined Oil. Remote Sens., 13.","DOI":"10.3390\/rs13173376"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1127","DOI":"10.1007\/s11119-018-9570-9","article-title":"Spectral indices from aerial images and their relationship with properties of a corn crop","volume":"19","author":"Farrell","year":"2018","journal-title":"Precis. Agric."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"663","DOI":"10.2307\/1936256","article-title":"Derivation of Leaf-Area Index from Quality of Light on the Forest Floor","volume":"50","author":"Jordan","year":"1969","journal-title":"Ecology"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1090","DOI":"10.2134\/agronj2010.0395","article-title":"Remote Sensing Leaf Chlorophyll Content Using a Visible Band Index","volume":"103","author":"Hunt","year":"2011","journal-title":"Agron. J."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/0034-4257(94)90018-3","article-title":"Development of vegetation and soil indices for MODIS-EOS","volume":"49","author":"Huete","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Mourad, R., Jaafar, H., Anderson, M., and Gao, F. (2020). Assessment of Leaf Area Index Models Using Harmonized Landsat and Sentinel-2 Surface Reflectance Data over a Semi-Arid Irrigated Landscape. Remote Sens., 12.","DOI":"10.3390\/rs12193121"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/1011-1344(93)06963-4","article-title":"Quantitative estimation of chlorophyll-a using reflectance spectra: Experiments with autumn chestnut and maple leaves","volume":"22","author":"Gitelson","year":"1994","journal-title":"J. Photochem. Photobiol. B Biol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/0034-4257(95)00186-7","article-title":"Optimization of soil-adjusted vegetation indices","volume":"55","author":"Rondeaux","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_37","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_38","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S0034-4257(96)00072-7","article-title":"Use of a green channel in remote sensing of global vegetation from EOS-MODIS","volume":"58","author":"Gitelson","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/0034-4257(94)90134-1","article-title":"A modified soil adjusted vegetation index","volume":"48","author":"Qi","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Teke, M., Deveci, H.S., Halilo\u011flu, O., G\u00fcrb\u00fcz, S.Z., and Sakarya, U. (2013, January 12\u201314). A short survey of hyperspectral remote sensing applications in agriculture. Proceedings of the 2013 6th International Conference on Recent Advances in Space Technologies (RAST), Istanbul, Turkey.","DOI":"10.1109\/RAST.2013.6581194"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Osco, L.P., Ramos, A.P.M., Pinheiro, M.M.F., Moriya \u00c9rika, A.S., Imai, N.N., Estrabis, N., Ianczyk, F., De Ara\u00fajo, F.F., Liesenberg, V., and Jorge, L.A.D.C. (2020). A Machine Learning Framework to Predict Nutrient Content in Valencia-Orange Leaf Hyperspectral Measurements. Remote Sens., 12.","DOI":"10.3390\/rs12060906"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1215","DOI":"10.2134\/agronj2002.1215","article-title":"Detection of Phosphorus and Nitrogen Defificiencies in Corn Using Spectral Radiance Measurements","volume":"94","author":"Osbourne","year":"2002","journal-title":"Agron. J."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.asr.2004.09.008","article-title":"Changes in spectral reflectance of wheat leaves in response to specific macronutrient deficiency","volume":"35","author":"Beyl","year":"2005","journal-title":"Adv. Space Res."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1046\/j.1469-8137.2003.00695.x","article-title":"Phosphorus acquisition and use: Critical adaptations by plants for securing a nonrenewable resource","volume":"157","author":"Vance","year":"2003","journal-title":"New Phytol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"305","DOI":"10.3389\/fphys.2012.00305","article-title":"Phenotyping maize for adaptation to drought","volume":"3","author":"Araus","year":"2012","journal-title":"Front. Physiol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1741","DOI":"10.1080\/01431161.2019.1674461","article-title":"Improving leaf area index retrieval using spectral characteristic parameters and data splitting","volume":"41","author":"Lin","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1007\/s11277-020-07901-2","article-title":"Crop Nutrition and Computer Vision Technology","volume":"117","author":"Peng","year":"2021","journal-title":"Wirel. Pers. Commun."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"3011","DOI":"10.1080\/01431160701408386","article-title":"Corn-yield estimation through assimilation of remotely sensed data into the CSM-CERES-Maize model","volume":"29","author":"Fang","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.fcr.2011.12.016","article-title":"Prediction of grain yield using reflectance spectra of canopy and leaves in maize plants grown under different water regimes","volume":"128","author":"Weber","year":"2012","journal-title":"Field Crops Res."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1084","DOI":"10.3844\/jcssp.2014.1084.1093","article-title":"Image segmentation with artificial neural network for nutrient deficiency in cotton crop","volume":"10","author":"Sartin","year":"2014","journal-title":"J. Comput. Sci."},{"key":"ref_51","unstructured":"Dhawale, N.M., Adamchuk, V., Viscarra, R., Prasher, S., Whalen, J.K., and Ismail, A. (2013). Predicting Extractable Soil Phosphorus Using Visible\/Near-Infrared Hyperspectral. Soil Reflectance Measurements, The Canadian Society for Bioengineering. Paper No. CSBE13-047."},{"key":"ref_52","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_53","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.ecolmodel.2006.07.015","article-title":"Sensitivity of a crop growth simulation model to variation in LAI and canopy nitrogen used for run-time calibration","volume":"200","author":"Jongschaap","year":"2007","journal-title":"Ecol. Model."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.fcr.2010.12.001","article-title":"Exploring remotely sensed technologies for monitoring wheat potassium and phosphorus using field spectroscopy","volume":"121","author":"Pimstein","year":"2011","journal-title":"Field Crops Res."},{"key":"ref_55","first-page":"179","article-title":"Nutrient deficiency diagnosis method for rape leaves using color histogram on HSV space","volume":"32","author":"Zhang","year":"2016","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_56","first-page":"576","article-title":"Nutrient deficiency image diagnose of rapeseed based on color feature","volume":"37","author":"Xu","year":"2015","journal-title":"Chin. J. Oil Crop Sci."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/3\/493\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:04:58Z","timestamp":1760133898000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/3\/493"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,20]]},"references-count":56,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["rs14030493"],"URL":"https:\/\/doi.org\/10.3390\/rs14030493","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,20]]}}}