{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T08:55:12Z","timestamp":1776329712384,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2017,8,31]],"date-time":"2017-08-31T00:00:00Z","timestamp":1504137600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"High-Resolution Earth Observation Project of China","award":["30-Y20A29-9003-15\/17"],"award-info":[{"award-number":["30-Y20A29-9003-15\/17"]}]},{"name":"National Key Technologies of Research and Development Program","award":["2016YFD0300603-5"],"award-info":[{"award-number":["2016YFD0300603-5"]}]},{"name":"China Scholarship Council Project","award":["201509110050"],"award-info":[{"award-number":["201509110050"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Cotton (Gossypium hirsutum L.) is an economically important crop that is highly susceptible to cotton root rot. Remote sensing technology provides a useful and effective means for detecting and mapping cotton root rot infestations in cotton fields. This research assessed the potential of 10-m Sentinel-2A satellite imagery for cotton root rot detection and compared it with airborne multispectral imagery using unsupervised classification at both field and regional levels. Accuracy assessment showed that the classification maps from the Sentinel-2A imagery had an overall accuracy of 94.1% for field subset images and 91.2% for the whole image, compared with the airborne image classification results. However, some small cotton root rot areas were undetectable and some non-infested areas within large root rot areas were incorrectly classified as infested due to the images\u2019 coarse spatial resolution. Classification maps based on field subset Sentinel-2A images missed 16.6% of the infested areas and the classification map based on the whole Sentinel-2A image for the study area omitted 19.7% of the infested areas. These results demonstrate that freely-available Sentinel-2 imagery can be used as an alternative data source for identifying cotton root rot and creating prescription maps for site-specific management of the disease.<\/jats:p>","DOI":"10.3390\/rs9090906","type":"journal-article","created":{"date-parts":[[2017,8,31]],"date-time":"2017-08-31T10:54:44Z","timestamp":1504176884000},"page":"906","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Evaluation of Sentinel-2A Satellite Imagery for Mapping Cotton Root Rot"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0294-5705","authenticated-orcid":false,"given":"Xiaoyu","family":"Song","sequence":"first","affiliation":[{"name":"Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"},{"name":"National Engineering Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"},{"name":"United States Department of Agriculture, Agricultural Research Service, Aerial Application Technology Research Unit, College Station, TX 77845-4966, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenghai","family":"Yang","sequence":"additional","affiliation":[{"name":"United States Department of Agriculture, Agricultural Research Service, Aerial Application Technology Research Unit, College Station, TX 77845-4966, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingquan","family":"Wu","sequence":"additional","affiliation":[{"name":"United States Department of Agriculture, Agricultural Research Service, Aerial Application Technology Research Unit, College Station, TX 77845-4966, USA"},{"name":"The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, P.O. Box 9718, Datun Road, Chaoyang, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1448-5091","authenticated-orcid":false,"given":"Chunjiang","family":"Zhao","sequence":"additional","affiliation":[{"name":"Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"},{"name":"National Engineering Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guijun","family":"Yang","sequence":"additional","affiliation":[{"name":"Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"},{"name":"National Engineering Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wesley","family":"Hoffmann","sequence":"additional","affiliation":[{"name":"United States Department of Agriculture, Agricultural Research Service, Aerial Application Technology Research Unit, College Station, TX 77845-4966, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1710-8301","authenticated-orcid":false,"given":"Wenjiang","family":"Huang","sequence":"additional","affiliation":[{"name":"Laboratory of Digital Earth Sciences, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,8,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1094\/PD-67-981","article-title":"Potential range of Phymatotrichum omnivorum as determined by edaphic factors","volume":"67","author":"Percy","year":"1983","journal-title":"Plant Dis."},{"key":"ref_2","first-page":"50","article-title":"Root rot of cotton or \u201cCotton blight\u201d","volume":"4","author":"Pammel","year":"1888","journal-title":"Texas Agric. Exp. Stn. Ann. Bull."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"67","DOI":"10.56454\/ZNFG8484","article-title":"Site-specific relationships between cotton root rot and soil properties","volume":"20","author":"Cribben","year":"2016","journal-title":"J. Cotton Sci."},{"key":"ref_4","first-page":"843","article-title":"Cotton crop losses from Phymatotrichum root rot","volume":"49","author":"Ezekiel","year":"1934","journal-title":"J. Agric. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1619","DOI":"10.13031\/2013.19176","article-title":"Mapping Phymatotrichum root rot of cotton using airborne three-band digital imagery","volume":"48","author":"Yang","year":"2005","journal-title":"Trans. ASAE"},{"key":"ref_6","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_7","unstructured":"Isakeit, T., Minzenmayer, R.R., and Sansone, C.G. (2009, January 5\u20138). Flutriafol control of cotton root rot caused by Phymatotrichopsis omnivore. Proceedings of the Beltwide Cotton Conference, San Antonio, TX, USA."},{"key":"ref_8","unstructured":"Isakeit, T., Minzenmayer, R.R., Abrameit, A., Moore, G., and Scasta, J.D. (2010, January 4\u20137). Control of Phymatotrichopsis root rot of cotton with flutriafol. Proceedings of the Beltwide Cotton Conference, New Orleans, LA, USA."},{"key":"ref_9","unstructured":"Isakeit, T., Minzenmayer, R.R., Drake, D.R., Morgan, G.D., Mott, D.A., Fromme, D.D., Multer, W.L., Jungman, M., and Abrameit, A. (2012, January 3\u20136). Fungicide management of cotton root rot (Phymatotrichopsis omnivora): 2011 results. Proceedings of the Beltwide Cotton Conference, Orlando, FL, USA."},{"key":"ref_10","first-page":"1025","article-title":"Airplane photography in the study of cotton root rot","volume":"19","author":"Taubenhaus","year":"1929","journal-title":"Phytopathology"},{"key":"ref_11","unstructured":"Nixon, P.R., Lyda, S.D., Heilman, M.D., and Bowen, R.L. (1975). Incidence and Control of Cotton Root Rot Observed with Color Infrared Photography, Texas A&M Agricultural Experiment Station."},{"key":"ref_12","unstructured":"Nixon, P.R., Escobar, D.E., and Bowen, R.L. (May, January 27). A multispectral false-color video imaging system for remote sensing applications. Proceedings of the 11th Biennial Workshop on Color Aerial Photography and Videography in the Plant Sciences and Related Fields, Weslaco, TX, USA."},{"key":"ref_13","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_14","doi-asserted-by":"crossref","first-page":"096013","DOI":"10.1117\/1.JRS.9.096013","article-title":"Combining fuzzy set theory and nonlinear stretching enhancement for unsupervised classification of cotton root rot","volume":"9","author":"Song","year":"2015","journal-title":"J. Appl. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.compag.2016.02.026","article-title":"Change detection of cotton root rot infection over 10-year intervals using airborne multispectral imagery","volume":"123","author":"Yang","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","article-title":"Sentinel-2: ESA\u2019s optical high-resolution mission for GMES operational services","volume":"120","author":"Drusch","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Immitzer, M., Vuolo, F., and Atzberger, C. (2016). First experience with Sentinel-2 data for crop and tree species classifications in Central Europe. Remote Sens., 8.","DOI":"10.3390\/rs8030166"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Vuolo, F., \u017b\u00f3\u0142tak, M., Pipitone, C., Zappa, L., Wenng, H., Immitzer, M., Weiss, M., Baret, F., and Atzberger, C. (2016). Data service platform for Sentinel-2 surface reflectance and value-added products: System use and examples. Remote Sens., 8.","DOI":"10.3390\/rs8110938"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3640","DOI":"10.1016\/j.rse.2011.09.002","article-title":"Broadband red-edge information from satellites improves early stress detection in a New Mexico conifer woodland","volume":"115","author":"Eitel","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5583","DOI":"10.1080\/01431161.2012.666812","article-title":"Testing the red edge channel for improving land-use classifications based on high-resolution multi-spectral satellite data","volume":"33","author":"Schuster","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.rse.2011.11.002","article-title":"Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and-3","volume":"118","author":"Verrelst","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"7063","DOI":"10.3390\/s110707063","article-title":"Evaluation of Sentinel-2 red-edge bands for empirical estimation of green LAI and chlorophyll content","volume":"11","author":"Delegido","year":"2011","journal-title":"Sensors"},{"key":"ref_23","first-page":"344","article-title":"Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3","volume":"23","author":"Clevers","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.isprsjprs.2015.10.005","article-title":"Examining the potential of Sentinel-2 MSI spectral resolution in quantifying above ground biomass across different fertilizer treatments","volume":"110","author":"Sibanda","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.rse.2011.09.026","article-title":"Sentinels for science: Potential of Sentinel-1, -2, and -3 missions for scientific observations of ocean, cryosphere, and land","volume":"120","author":"Malenovsky","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Du, Y., Zhang, Y., Ling, F., Wang, Q., Li, W., and Li, X. (2016). Water bodies\u2019 mapping from Sentinel-2 imagery with modified normalized difference water index at 10-m spatial resolution produced by sharpening the SWIR band. Remote Sens., 8.","DOI":"10.3390\/rs8040354"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Congalton, R.G., and Green, K. (2008). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, Lewis Publishers.","DOI":"10.1201\/9781420055139"},{"key":"ref_28","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":"107","author":"Yang","year":"2010","journal-title":"Biosyst. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2194","DOI":"10.1016\/j.rse.2009.06.002","article-title":"Remote sensing of small and linear features: Quantifying the effects of patch size and length, grid position and detectability on land cover mapping","volume":"113","author":"Lechner","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/S0168-1699(02)00108-4","article-title":"Evaluating remotely sensed techniques for mapping riparian vegetation","volume":"37","author":"Congalton","year":"2002","journal-title":"Comput. Electron. Agric."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/S1470-160X(02)00053-5","article-title":"Applicability of landscape metrics for the monitoring of landscape change: Issues of scale, resolution and interpretability","volume":"2","author":"Lausch","year":"2002","journal-title":"Ecol. Indic."},{"key":"ref_32","first-page":"611","article-title":"Remote sensing of urban\/suburban infrastructure and socio-economic attributes","volume":"65","author":"Jensen","year":"1999","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.rse.2006.07.012","article-title":"Classification of ponds from high-spatial resolution remote sensing: Application to Rift Valley Fever epidemics in Senegal","volume":"106","author":"Lacaux","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1410","DOI":"10.1016\/j.rse.2008.05.023","article-title":"Spatial pattern analysis for monitoring protected areas","volume":"113","author":"Townsend","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Radoux, J., Chom\u00e9, G., Jacques, D.C., Waldner, F., Bellemans, N., Matton, N., Lamarche, C., Rapha\u00ebl, A., and Defourny, P. (2016). Sentinel-2\u2019s potential for sub-pixel landscape feature detection. Remote Sens., 8.","DOI":"10.3390\/rs8060488"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/S0924-2716(00)00004-6","article-title":"How well do we understand Earth observation electro-optical sensor parameters?","volume":"55","author":"Joseph","year":"2000","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_37","unstructured":"Schowengerdt, A.R. (2007). Remote Sensing: Models and Methods for Image Processing, Elsevier Inc."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2490","DOI":"10.1109\/36.964986","article-title":"Atmospheric Correction of Landsat ETM+ Land Surface Imagery\u2014Part I: Methods","volume":"39","author":"Liang","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","first-page":"4381","article-title":"Cotton crop discrimination using landsat-8 data","volume":"6","author":"Kharat","year":"2015","journal-title":"IJCSIT. Int. J. Comput. Sci. Inf. Technol."},{"key":"ref_40","first-page":"195","article-title":"Crop type classification using vegetation indices of RAPIDEYE imagery","volume":"XL-7","author":"Ustunera","year":"2014","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Wu, M., Yang, C., Song, X., Hoffmann, W.C., Huang, W., Niu, Z., Wang, C., and Li, W. (2017). Evaluation of orthomosics and digital surface models derived from aerial imagery for crop type mapping. Remote Sens., 9.","DOI":"10.3390\/rs9030239"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/9\/906\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:43:49Z","timestamp":1760208229000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/9\/906"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,8,31]]},"references-count":41,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2017,9]]}},"alternative-id":["rs9090906"],"URL":"https:\/\/doi.org\/10.3390\/rs9090906","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,8,31]]}}}