{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T16:58:58Z","timestamp":1774630738991,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2020,6,15]],"date-time":"2020-06-15T00:00:00Z","timestamp":1592179200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Open Research Fund of National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University","award":["AE201901"],"award-info":[{"award-number":["AE201901"]}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development program","doi-asserted-by":"publisher","award":["2016YFB0500502"],"award-info":[{"award-number":["2016YFB0500502"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","award":["201806435005"],"award-info":[{"award-number":["201806435005"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The timely monitoring of crop disease development is very important for precision agriculture applications. Remote sensing-based vegetation indices (VIs) can be good indicators of crop disease severity, but current methods are mainly dependent on manual ground survey results. Based on VI normalization, an automated crop disease severity grading method without the use of ground surveys was proposed in this study. This technique was applied to two cotton fields infested with different levels of cotton root rot in south Texas in the United States, where airborne hyperspectral imagery was collected. Six typical VIs were calculated from the hyperspectral imagery and their histograms indicated that VI normalization could eliminate the influences of variable field conditions and the VI value range variations, allowing a potentially broader scope of application. According to the analysis of the obtained results from the spectral dimension, spatial dimension and descriptive statistics, the disease grading results were in general agreement with previous ground survey results, proving the validity of the disease severity grading method. Although satisfactory results could be achieved from different types of VI, there is still room for further improvement through the exploration of more VIs. With the advantages of independence of ground surveys and potential universal applicability, the newly proposed crop disease grading method will be of great significance for crop disease monitoring over large geographical areas.<\/jats:p>","DOI":"10.3390\/rs12121930","type":"journal-article","created":{"date-parts":[[2020,6,15]],"date-time":"2020-06-15T12:16:57Z","timestamp":1592223417000},"page":"1930","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["Automatic Estimation of Crop Disease Severity Levels Based on Vegetation Index Normalization"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0776-142X","authenticated-orcid":false,"given":"Hengqian","family":"Zhao","sequence":"first","affiliation":[{"name":"College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China"},{"name":"State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology, Beijing 100083, China"},{"name":"United States Department of Agriculture-Agricultural Research Service, Aerial Application Technology Research Unit, College Station, TX 77845, USA"},{"name":"Anhui Engineering Laboratory of Agro-Ecological Big Data, Anhui University, Hefei 230601, Anhui, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9898-628X","authenticated-orcid":false,"given":"Chenghai","family":"Yang","sequence":"additional","affiliation":[{"name":"United States Department of Agriculture-Agricultural Research Service, Aerial Application Technology Research Unit, College Station, TX 77845, USA"}]},{"given":"Wei","family":"Guo","sequence":"additional","affiliation":[{"name":"United States Department of Agriculture-Agricultural Research Service, Aerial Application Technology Research Unit, College Station, TX 77845, USA"},{"name":"College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, Henan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3533-9966","authenticated-orcid":false,"given":"Lifu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Oasis Eco Agriculture, Xinjiang Production and Construction Group, Shihezi University, Shihezi 832003, Xinjiang, China"},{"name":"Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Dongyan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Anhui Engineering Laboratory of Agro-Ecological Big Data, Anhui University, Hefei 230601, Anhui, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/S1672-6308(08)60047-5","article-title":"Characterizing and estimating fungal disease severity of rice brown spot with hyperspectral reflectance data","volume":"15","author":"Liu","year":"2008","journal-title":"Rice Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4723","DOI":"10.3390\/rs6064723","article-title":"Developing two spectral disease indices for detection of wheat leaf rust (Puccinia triticina)","volume":"6","author":"Ashourloo","year":"2014","journal-title":"Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2569","DOI":"10.1016\/j.ijleo.2012.07.026","article-title":"Hyperspectral identification of cotton verticillium disease severity","volume":"124","author":"Jin","year":"2013","journal-title":"Optik"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.rse.2013.07.031","article-title":"High-resolution airborne hyperspectral and thermal imagery for early detection of Verticillium wilt of olive using fluorescence, temperature and narrow-band spectral indices","volume":"139","author":"Lucena","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.compag.2013.11.001","article-title":"Comparison between wavelet spectral features and conventional spectral features in detecting yellow rust for winter wheat","volume":"100","author":"Zhang","year":"2014","journal-title":"Comput. Electron. Agric."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1016\/j.rse.2010.11.016","article-title":"Retrieving wheat Green Area Index during the growing season from optical time series measurements based on neural network radiative transfer inversion","volume":"115","author":"Duveiller","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.compag.2012.03.006","article-title":"Detecting powdery mildew of winter wheat using leaf level hyperspectral measurements","volume":"85","author":"Zhang","year":"2012","journal-title":"Comput. Electron. Agric."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.biosystemseng.2010.07.011","article-title":"Comparison of airborne multispectral and hyperspectral imagery for mapping cotton root rot","volume":"2","author":"Yang","year":"2010","journal-title":"Biosyst. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1007\/s11119-014-9370-9","article-title":"Evaluating unsupervised and supervised image classification methods for mapping cotton root rot","volume":"16","author":"Yang","year":"2015","journal-title":"Precis. Agric."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"85","DOI":"10.56454\/XTBP9985","article-title":"Monitoring cotton root rot progression within a growing season using airborne multispectral imagery","volume":"18","author":"Yang","year":"2014","journal-title":"J. Cotton Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"849","DOI":"10.13031\/trans.12563","article-title":"Site-Specific Management of Cotton Root Rot Using Airborne and High-Resolution Satellite Imagery and Variable-Rate Technology","volume":"61","author":"Yang","year":"2018","journal-title":"Trans. ASABE."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2516","DOI":"10.1109\/JSTARS.2013.2294961","article-title":"New optimized spectral indices for identifying and monitoring winter wheat diseases","volume":"7","author":"Huang","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1007\/s12524-012-0218-3","article-title":"Effect of different growing environments on population dynamics of sucking pests in relation to various spectral indices in cotton","volume":"41","author":"Kumar","year":"2013","journal-title":"J. Indian Soc. Remote"},{"key":"ref_14","first-page":"137","article-title":"Monitoring and evaluation of the diseases of and yield winter wheat from multi-temporal remotely-sensed data","volume":"25","author":"Liu","year":"2009","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.compag.2011.09.012","article-title":"Use of ground based hyperspectral remote sensing for detection of stress in cotton caused by leafhopper (Hemiptera: Cicadellidae)","volume":"79","author":"Prabhakar","year":"2011","journal-title":"Comput. Electron. Agric."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2706","DOI":"10.1080\/01431161.2011.619586","article-title":"Evaluating the severity level of cotton Verticillium using spectral signature analysis","volume":"33","author":"Chen","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2016","DOI":"10.1038\/s41598-018-20156-z","article-title":"Monitoring cotton root rot by synthetic Sentinel-2 NDVI time series using improved spatial and temporal data fusion","volume":"8","author":"Wu","year":"2018","journal-title":"Sci. Rep. UK"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2455","DOI":"10.1016\/j.biombioe.2011.02.028","article-title":"A review of remote sensing methods for biomass feedstock production","volume":"35","author":"Ahamed","year":"2011","journal-title":"Biomass Bioenergy"},{"key":"ref_19","first-page":"1","article-title":"Assessing cotton defoliation, regrowth control and root rot infection using remote sensing technology","volume":"4","author":"Yang","year":"2011","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2188","DOI":"10.1109\/LGRS.2015.2450218","article-title":"An analysis of shadow effects on spectral vegetation indexes using a ground-based imaging spectrometer","volume":"12","author":"Zhang","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_21","first-page":"113","article-title":"Predicting Thaumastocoris peregrinus damage using narrow band normalized indices and hyperspectral indices using field spectra resampled to the Hyperion sensor","volume":"21","author":"Oumar","year":"2013","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combinations for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_23","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_24","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_25","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A soil-adjusted vegetation index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2579","DOI":"10.1080\/01431169608949094","article-title":"Airborne multi-spectral monitoring of agricultural crop status: Effect of time of year, crop type and crop condition parameter","volume":"17","author":"Cloutis","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/S0176-1617(99)80314-9","article-title":"A new reflectance index for remote sensing of chlorophyll content in higher plants: Tests using Eucalyptus leaves","volume":"154","author":"Datt","year":"1999","journal-title":"J. Plant. Physiol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/0034-4257(93)90013-N","article-title":"The spectral image processing system (SIPS)\u2014interactive visualization and analysis of imaging spectrometer data","volume":"44","author":"Kruse","year":"1993","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1080\/10106049.2012.665498","article-title":"Spectral material mapping using hyperspectral imagery: A review of spectral matching and library search methods","volume":"28","author":"Vishnu","year":"2013","journal-title":"Geocarto Int."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Song, X., Yang, C., Wu, M., Zhao, C., Yang, G., Hoffmann, W., and Huang, W. (2017). Evaluation of Sentinel-2A Satellite Imagery for Mapping Cotton Root Rot. Remote Sens., 9.","DOI":"10.3390\/rs9090906"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1007\/s11119-012-9264-7","article-title":"Spectral vegetation indices selected for quantifying Russian wheat aphid (Diuraphis noxia) feeding damage in wheat (Triticum aestivum L.)","volume":"13","author":"Mirik","year":"2012","journal-title":"Precis. Agric."},{"key":"ref_32","first-page":"416","article-title":"Analysis of airborne hyperspectral image using vegetation indices, red edge position and continuum removal for detection of ganoderma disease in oil palm","volume":"30","author":"Izzuddin","year":"2018","journal-title":"J. Oil Palm Res."},{"key":"ref_33","first-page":"2709","article-title":"Spectrum characteristics of cotton single leaf infected by verticillium wilt and estimation on severity level of disease","volume":"40","author":"Chen","year":"2007","journal-title":"Sci. Agric. Sin."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5131","DOI":"10.1029\/2002JE001847","article-title":"Imaging spectroscopy: Earth and planetary remote sensing with the USGS Tetracorder and expert systems","volume":"108","author":"Clark","year":"2003","journal-title":"J. Geophys. Res."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.biosystemseng.2005.02.007","article-title":"Hyperspectral crop reflectance data for characterising and estimating fungal disease severity in wheat","volume":"91","author":"Muhammed","year":"2005","journal-title":"Biosyst. Eng."},{"key":"ref_36","first-page":"143","article-title":"A change detection experiment using vegetation indices","volume":"64","author":"Lyon","year":"1998","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"46028","DOI":"10.1117\/1.JRS.12.046028","article-title":"Mapping foliar N in miombo woodlands using sentinel-2 derived chlorophyll and structural indices","volume":"12","author":"Mutowo","year":"2018","journal-title":"J. Appl. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"329","DOI":"10.2135\/cropsci2006.05.0335","article-title":"Changes in spectral characteristics of rice canopy infested with brown planthopper and leaffolder","volume":"47","author":"Yang","year":"2007","journal-title":"Crop. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Ad\u00e3o, T., Hru\u0161ka, J., P\u00e1dua, L., Bessa, J., Peres, E., Morais, R., and Sousa, J.J. (2017). Hyperspectral imaging: A review on UAV-based sensors, data processing and applications for agriculture and forestry. Remote Sens., 9.","DOI":"10.3390\/rs9111110"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Raj, R., Kar, S., Nandan, R., and Jagarlapudi, A. (2020). Precision Agriculture and Unmanned Aerial Vehicles (UAVs). Unmanned Aerial Vehicle: Applications in Agriculture and Environment, Springer.","DOI":"10.1007\/978-3-030-27157-2_2"},{"key":"ref_41","first-page":"115","article-title":"Detection of rice sheath blight for in-season disease management using multispectral remote sensing","volume":"7","author":"Qin","year":"2005","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_42","first-page":"268","article-title":"Toward the discrimination of manganese, zinc, copper, and iron deficiency in \u2018Bragg\u2019soybean using spectral detection methods","volume":"92","author":"Adams","year":"2000","journal-title":"Agron. J."},{"key":"ref_43","first-page":"61","article-title":"Physiological change and hyperspectral character analysis of cotton leaves infested by Tetranychus turkestani","volume":"44","author":"Chen","year":"2007","journal-title":"Chin. Bull. Entomol."}],"updated-by":[{"DOI":"10.3390\/rs12223761","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2020,6,15]],"date-time":"2020-06-15T00:00:00Z","timestamp":1592179200000}}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/12\/1930\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,3]],"date-time":"2025-08-03T22:33:21Z","timestamp":1754260401000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/12\/1930"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,15]]},"references-count":43,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["rs12121930"],"URL":"https:\/\/doi.org\/10.3390\/rs12121930","relation":{"correction":[{"id-type":"doi","id":"10.3390\/rs12223761","asserted-by":"object"}]},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,15]]}}}