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Numerous studies on near-surface hyperspectral remote sensing at the leaf scale have achieved good results for disease monitoring. The next step is to monitor the disease at the field scale, which is of great significance for disease control. In our study, an unmanned aerial vehicle (UAV) equipped with a hyperspectral sensor was used to obtain hyperspectral images at the field scale. Vegetation indices (VIs) and texture features (TFs) extracted from the UAV-based hyperspectral images and their combination were used to establish partial least-squares regression (PLSR)-based disease monitoring models in different infection periods. In addition, we resampled the original images with 1.2 cm spatial resolution to images with different spatial resolutions (3 cm, 5 cm, 7 cm, 10 cm, 15 cm, and 20 cm) to evaluate the effect of spatial resolution on disease monitoring accuracy. The findings showed that the VI-based model had the highest monitoring accuracy (R2 = 0.75) in the mid-infection period. The TF-based model could be used to monitor yellow rust at the field scale and obtained the highest R2 in the mid- and late-infection periods (0.65 and 0.82, respectively). The VI-TF-based models had the highest accuracy in each infection period and outperformed the VI-based or TF-based models. The spatial resolution had a negligible influence on the VI-based monitoring accuracy, but significantly influenced the TF-based monitoring accuracy. Furthermore, the optimal spatial resolution for monitoring yellow rust using the VI-TF-based model in each infection period was 10 cm. The findings provide a reference for accurate disease monitoring using UAV hyperspectral images.<\/jats:p>","DOI":"10.3390\/rs13010123","type":"journal-article","created":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T22:35:48Z","timestamp":1609540548000},"page":"123","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":178,"title":["Wheat Yellow Rust Detection Using UAV-Based Hyperspectral Technology"],"prefix":"10.3390","volume":"13","author":[{"given":"Anting","family":"Guo","sequence":"first","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjiang","family":"Huang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Earth Observation, Sanya 572029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2865-5020","authenticated-orcid":false,"given":"Yingying","family":"Dong","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7836-497X","authenticated-orcid":false,"given":"Huichun","family":"Ye","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Key Laboratory of Earth Observation, Sanya 572029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5577-8632","authenticated-orcid":false,"given":"Huiqin","family":"Ma","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agriculture Sciences, No.2 West Yuanmingyuan Road, Beijing 100193, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenbin","family":"Wu","sequence":"additional","affiliation":[{"name":"Chinese Academy of Agriculture Sciences, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6042-396X","authenticated-orcid":false,"given":"Yu","family":"Ren","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3493-0401","authenticated-orcid":false,"given":"Chao","family":"Ruan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yun","family":"Geng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Han, J., Zhang, Z., Cao, J., Luo, Y., Zhang, L., Li, Z., and Zhang, J. (2020). Prediction of Winter Wheat Yield Based on Multi-Source Data and Machine Learning in China. Remote Sens., 12.","DOI":"10.3390\/rs12020236"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zheng, Q., Huang, W., Cui, X., Dong, Y., Shi, Y., Ma, H., and Liu, L. (2018). Identification of Wheat Yellow Rust Using Optimal Three-Band Spectral Indices in Different Growth Stages. Sensors, 19.","DOI":"10.3390\/s19010035"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ma, H., Huang, W., Jing, Y., Pignatti, S., Laneve, G., Dong, Y., Ye, H., Liu, L., Guo, A., and Jiang, J. (2020). Identification of Fusarium Head Blight in Winter Wheat Ears Using Continuous Wavelet Analysis. Sensors, 20.","DOI":"10.3390\/s20010020"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.compag.2004.04.003","article-title":"Automatic detection of \u2018yellow rust\u2019 in wheat using reflectance measurements and neural networks","volume":"44","author":"Moshou","year":"2004","journal-title":"Comput. Electron. Agric."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1080\/07060660509507230","article-title":"Epidemiology and control of stripe rust [Puccinia striiformis f. sp. tritici] on wheat","volume":"27","author":"Chen","year":"2005","journal-title":"Can. J. Plant. Pathol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zheng, Q., Huang, W., Cui, X., Shi, Y., and Liu, L. (2018). New spectral index for detecting wheat yellow rust using sentinel-2 multispectral imagery. Sensors, 18.","DOI":"10.3390\/s18030868"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1195","DOI":"10.3389\/fpls.2018.01195","article-title":"Hyperspectral Canopy Sensing of Wheat Septoria Tritici Blotch Disease","volume":"9","author":"Yu","year":"2018","journal-title":"Front. Plant. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"He, L., Qi, S.-L., Duan, J.-Z., Guo, T.-C., Feng, W., and He, D.-X. (2020). Monitoring of Wheat Powdery Mildew Disease Severity Using Multiangle Hyperspectral Remote Sensing. ITGRS.","DOI":"10.1109\/TGRS.2020.3000992"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"105094","DOI":"10.1016\/j.compag.2019.105094","article-title":"Assessment of sudden death syndrome in soybean through multispectral broadband remote sensing aboard small unmanned aerial systems","volume":"167","author":"Hatton","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Chivasa, W., Onisimo, M., and Biradar, C.M. (2020). UAV-Based Multispectral Phenotyping for Disease Resistance to Accelerate Crop Improvement under Changing Climate Conditions. Remote Sens., 12.","DOI":"10.3390\/rs12152445"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1016\/j.compag.2018.12.018","article-title":"A remote sensing technique for detecting laurel wilt disease in avocado in presence of other biotic and abiotic stresses","volume":"156","author":"Abdulridha","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.biosystemseng.2020.07.001","article-title":"Detecting powdery mildew disease in squash at different stages using UAV-based hyperspectral imaging and artificial intelligence","volume":"197","author":"Abdulridha","year":"2020","journal-title":"Biosyst. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1395","DOI":"10.1007\/s11676-017-0528-y","article-title":"Forest mapping: A comparison between hyperspectral and multispectral images and technologies","volume":"29","author":"Awad","year":"2017","journal-title":"J. For. Res."},{"key":"ref_14","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_15","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1016\/j.rse.2013.08.002","article-title":"Hyperspectral versus multispectral crop-productivity modeling and type discrimination for the HyspIRI mission","volume":"139","author":"Mariotto","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yao, Z., Lei, Y., and He, D. (2019). Early Visual Detection of Wheat Stripe Rust Using Visible\/Near-Infrared Hyperspectral Imaging. Sensors, 19.","DOI":"10.3390\/s19040952"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Shi, Y., Huang, W., Gonz\u00e1lez-Moreno, P., Luke, B., Dong, Y., Zheng, Q., Ma, H., and Liu, L. (2018). Wavelet-Based Rust Spectral Feature Set (WRSFs): A Novel Spectral Feature Set Based on Continuous Wavelet Transformation for Tracking Progressive Host\u2013Pathogen Interaction of Yellow Rust on Wheat. Remote Sens., 10.","DOI":"10.3390\/rs10040525"},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1016\/j.compag.2018.12.036","article-title":"Detection of peanut leaf spots disease using canopy hyperspectral reflectance","volume":"156","author":"Chen","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"105066","DOI":"10.1016\/j.compag.2019.105066","article-title":"Early detection of tomato spotted wilt virus infection in tobacco using the hyperspectral imaging technique and machine learning algorithms","volume":"167","author":"Gu","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1007\/s11119-009-9122-4","article-title":"Assessment of the severity of bacterial leaf blight in rice using canopy hyperspectral reflectance","volume":"11","author":"Yang","year":"2010","journal-title":"Precis. Agric."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.rse.2012.09.019","article-title":"Development of spectral indices for detecting and identifying plant diseases","volume":"128","author":"Mahlein","year":"2013","journal-title":"Remote Sens. Environ"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"52181","DOI":"10.1109\/ACCESS.2020.2980310","article-title":"A Disease Index for Efficiently Detecting Wheat Fusarium Head Blight Using Sentinel-2 Multispectral Imagery","volume":"8","author":"Liu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.compag.2006.01.004","article-title":"Identification of citrus disease using color texture features and discriminant analysis","volume":"52","author":"Pydipati","year":"2006","journal-title":"Comput. Electron. Agric."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Guo, A., Huang, W., Ye, H., Dong, Y., Ma, H., Ren, Y., and Ruan, C. (2020). Identification of Wheat Yellow Rust Using Spectral and Texture Features of Hyperspectral Images. Remote Sens., 12.","DOI":"10.3390\/rs12091419"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Al-Saddik, H., Laybros, A., Billiot, B., and Cointault, F. (2018). Using image texture and spectral reflectance analysis to detect Yellowness and Esca in grapevines at leaf-level. Remote Sens, 10.","DOI":"10.3390\/rs10040618"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"105035","DOI":"10.1016\/j.compag.2019.105035","article-title":"Spatio-temporal monitoring of wheat yellow rust using UAV multispectral imagery","volume":"167","author":"Su","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.compag.2018.10.017","article-title":"Wheat yellow rust monitoring by learning from multispectral UAV aerial imagery","volume":"155","author":"Su","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.biosystemseng.2020.02.016","article-title":"The use of UAVs in monitoring yellow sigatoka in banana","volume":"193","author":"Calou","year":"2020","journal-title":"Biosyst. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ye, H., Huang, W., Huang, S., Cui, B., Dong, Y., Guo, A., Ren, Y., and Jin, Y. (2020). Recognition of Banana Fusarium Wilt Based on UAV Remote Sensing. Remote Sens., 12.","DOI":"10.3390\/rs12060938"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Franceschini, M.H.D., Bartholomeus, H.M., Van Apeldoorn, D.F., Suomalainen, J., and Kooistra, L. (2019). Feasibility of Unmanned Aerial Vehicle Optical Imagery for Early Detection and Severity Assessment of Late Blight in Potato. Remote Sens., 11.","DOI":"10.3390\/rs11030224"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1094\/PDIS-03-15-0340-FE","article-title":"Plant Disease Detection by Imaging Sensors\u2014Parallels and Specific Demands for Precision Agriculture and Plant Phenotyping","volume":"100","author":"Mahlein","year":"2016","journal-title":"Plant Dis."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"955","DOI":"10.1007\/s11119-019-09703-4","article-title":"Detection of target spot and bacterial spot diseases in tomato using UAV-based and benchtop-based hyperspectral imaging techniques","volume":"21","author":"Abdulridha","year":"2019","journal-title":"Precis. Agric."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Abdulridha, J., Batuman, O., and Ampatzidis, Y. (2019). UAV-based remote sensing technique to detect citrus canker disease utilizing hyperspectral imaging and machine learning. Remote Sens., 11.","DOI":"10.3390\/rs11111373"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Deng, X., Zhu, Z., Yang, J., Zheng, Z., Huang, Z., Yin, X., Wei, S., and Lan, Y. (2020). Detection of Citrus Huanglongbing Based on Multi-Input Neural Network Model of UAV Hyperspectral Remote Sensing. Remote Sens., 12.","DOI":"10.3390\/rs12172678"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.rse.2015.09.011","article-title":"Optimum spectral and geometric parameters for early detection of laurel wilt disease in avocado","volume":"171","author":"Ehsani","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_37","first-page":"88","article-title":"Impact of the spatial resolution on the energy balance components on an open-canopy olive orchard","volume":"74","author":"Allen","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.rse.2015.10.004","article-title":"The impact of spatial resolution on the classification of plant species and functional types within imaging spectrometer data","volume":"171","author":"Roth","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_39","first-page":"100325","article-title":"Assessing the effect of spatial resolution on the delineation of management zones for smallholder farming in southern Brazil","volume":"19","author":"Breunig","year":"2020","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"964","DOI":"10.3389\/fpls.2018.00964","article-title":"Assessing the impact of spatial resolution on the estimation of leaf nitrogen concentration over the full season of paddy rice using near-surface imaging spectroscopy data","volume":"9","author":"Zhou","year":"2018","journal-title":"Front. Plant Sci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2017.07.007","article-title":"Assessing very high resolution UAV imagery for monitoring forest health during a simulated disease outbreak","volume":"131","author":"Dash","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zhang, J., Wang, C., Yang, C., Xie, T., Jiang, Z., Hu, T., Luo, Z., Zhou, G., and Xie, J. (2020). Assessing the Effect of Real Spatial Resolution of In Situ UAV Multispectral Images on Seedling Rapeseed Growth Monitoring. Remote Sens., 12.","DOI":"10.3390\/rs12071207"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Liu, M., Yu, T., Gu, X., Sun, Z., Yang, J., Zhang, Z., Mi, X., Cao, W., and Li, J. (2020). The Impact of Spatial Resolution on the Classification of Vegetation Types in Highly Fragmented Planting Areas Based on Unmanned Aerial Vehicle Hyperspectral Images. Remote Sens., 12.","DOI":"10.3390\/rs12010146"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1007\/s11119-007-9038-9","article-title":"Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging","volume":"8","author":"Huang","year":"2007","journal-title":"Precis. Agric."},{"key":"ref_45","unstructured":"(2020, October 15). Cubert-GmbH Hyperspectral Firefleye S185 SE. Available online: http:\/\/cubert-gmbh.de\/."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.isprsjprs.2019.02.022","article-title":"Estimate of winter-wheat above-ground biomass based on UAV ultrahigh-ground-resolution image textures and vegetation indices","volume":"150","author":"Yue","year":"2019","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"ref_47","unstructured":"Rouse, J.W., Haas, R.H., Schell, J.A., and Deering, D.W. (1973, January 10\u201314). Monitoring vegetation systems in the great plains with ERTS. Proceedings of the Third Earth Resources Technology Satellite-1 Symposium, Washington, DC, USA."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Zhang, X., Han, L., Dong, Y., Shi, Y., Huang, W., Han, L., Gonz\u00e1lez-Moreno, P., Ma, H., Ye, H., and Sobeih, T. (2019). A Deep Learning-Based Approach for Automated Yellow Rust Disease Detection from High-Resolution Hyperspectral UAV Images. Remote Sens., 11.","DOI":"10.3390\/rs11131554"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"4723","DOI":"10.3390\/rs6064723","article-title":"Developing two spectral disease indices for detection of wheat leaf rust (Pucciniatriticina)","volume":"6","author":"Ashourloo","year":"2014","journal-title":"Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1007\/s11119-008-9100-2","article-title":"Evaluating ten spectral vegetation indices for identifying rust infection in individual wheat leaves","volume":"10","author":"Devadas","year":"2008","journal-title":"Precis. Agric."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.isprsjprs.2020.02.013","article-title":"Above-ground biomass estimation and yield prediction in potato by using UAV-based RGB and hyperspectral imaging","volume":"162","author":"Li","year":"2020","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(92)90059-S","article-title":"A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency","volume":"41","author":"Gamon","year":"1992","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.rse.2005.09.002","article-title":"Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a row-structured discontinuous canopy","volume":"99","author":"Miller","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_54","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_55","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/S0034-4257(00)00113-9","article-title":"Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance","volume":"74","author":"Daughtry","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1562\/0031-8655(2001)074<0038:OPANEO>2.0.CO;2","article-title":"Optical properties and nondestructive estimation of anthocyanin content in plant leaves","volume":"74","author":"Gitelson","year":"2001","journal-title":"Photochem. Photobiol."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1034\/j.1399-3054.1999.106119.x","article-title":"Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening","volume":"106","author":"Merzlyak","year":"1999","journal-title":"Physiol. Plant."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1400","DOI":"10.2135\/cropsci1995.0011183X003500050023x","article-title":"Evaluating wheat nitrogen status with canopy reflectance indices and discriminant analysis","volume":"35","author":"Filella","year":"1995","journal-title":"Crop. Sci."},{"key":"ref_59","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_60","doi-asserted-by":"crossref","first-page":"125938","DOI":"10.1016\/j.eja.2019.125938","article-title":"Super-resolution enhancement of Sentinel-2 image for retrieving LAI and chlorophyll content of summer corn","volume":"111","author":"Zhang","year":"2019","journal-title":"Eur. J. Agron."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1016\/j.rse.2012.05.015","article-title":"Combining object-based texture measures with a neural network for vegetation mapping in the Everglades from hyperspectral imagery","volume":"124","author":"Zhang","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Fu, Y., Zhao, C., Wang, J., Jia, X., Yang, G., Song, X., and Feng, H. (2017). An Improved Combination of Spectral and Spatial Features for Vegetation Classification in Hyperspectral Images. Remote Sens., 9.","DOI":"10.3390\/rs9030261"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.isprsjprs.2019.01.019","article-title":"Tree species classification in tropical forests using visible to shortwave infrared WorldView-3 images and texture analysis","volume":"149","author":"Ferreira","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Li, S., Yuan, F., Ata-Ul-Karim, S.T., Zheng, H., Cheng, T., Liu, X., Tian, Y., Zhu, Y., Cao, W., and Cao, Q. (2019). Combining Color Indices and Textures of UAV-Based Digital Imagery for Rice LAI Estimation. Remote. Sens., 11.","DOI":"10.3390\/rs11151763"},{"key":"ref_65","first-page":"204","article-title":"Analyzing fine-scale wetland composition using high resolution imagery and texture features","volume":"23","author":"Szantoi","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"112175","DOI":"10.1016\/j.rse.2020.112175","article-title":"Satellite image texture captures vegetation heterogeneity and explains patterns of bird richness","volume":"253","author":"Farwell","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural Features for Image Classification","volume":"6","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"108194","DOI":"10.1016\/j.meatsci.2020.108194","article-title":"Combination of spectra and texture data of hyperspectral imaging for prediction and visualization of palmitic acid and oleic acid contents in lamb meat","volume":"169","author":"Wang","year":"2020","journal-title":"Meat Sci."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1016\/j.lwt.2014.10.021","article-title":"Combination of spectra and texture data of hyperspectral imaging for differentiating between free-range and broiler chicken meats","volume":"60","author":"Xiong","year":"2015","journal-title":"LWT"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1016\/j.foodchem.2014.03.096","article-title":"Combination of spectra and texture data of hyperspectral imaging for prediction of pH in salted meat","volume":"160","author":"Liu","year":"2014","journal-title":"Food Chem."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1016\/j.postharvbio.2015.09.027","article-title":"Nondestructive detection of chilling injury in cucumber fruit using hyperspectral imaging with feature selection and supervised classification","volume":"111","author":"Cen","year":"2016","journal-title":"Postharvest Biol. Technol."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1007\/s11119-017-9524-7","article-title":"Using hyperspectral imaging to discriminate yellow leaf curl disease in tomato leaves","volume":"19","author":"Lu","year":"2018","journal-title":"Precis. Agric."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Astor, T., Dayananda, S., Nidamanuri, R.R., Nautiyal, S., Hanumaiah, N., Gebauer, J., and Wachendorf, M. (2018). Estimation of Vegetable Crop Parameter by Multi-temporal UAV-Borne Images. Remote Sens., 10.","DOI":"10.3390\/rs10050805"},{"key":"ref_74","first-page":"104","article-title":"Quantification winter wheat LAI with HJ-1CCD image features over multiple growing seasons","volume":"44","author":"Li","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.isprsjprs.2019.03.003","article-title":"Vegetation Index Weighted Canopy Volume Model (CVMVI) for soybean biomass estimation from Unmanned Aerial System-based RGB imagery","volume":"151","author":"Maimaitijiang","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.rse.2017.06.008","article-title":"Estimating leaf chlorophyll content in sugar beet canopies using millimeter- to centimeter-scale reflectance imagery","volume":"198","author":"Jay","year":"2017","journal-title":"Remote. Sens. Environ."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.compag.2018.03.035","article-title":"Identification of purple spot disease on asparagus crops across spatial and spectral scales","volume":"148","author":"Navrozidis","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.pbi.2019.06.007","article-title":"Quantitative and qualitative phenotyping of disease resistance of crops by hyperspectral sensors: Seamless interlocking of phytopathology, sensors, and machine learning is needed!","volume":"50","author":"Mahlein","year":"2019","journal-title":"Curr. Opin. Plant. Biol."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1111\/j.1469-8137.1995.tb03064.x","article-title":"Assessment of photosynthetic radiation-use efficiency with spectral reflectance","volume":"131","author":"Penuelas","year":"1995","journal-title":"New Phytol."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.rse.2011.12.003","article-title":"Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture","volume":"121","author":"Atkinson","year":"2012","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/1\/123\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:06:05Z","timestamp":1760159165000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/1\/123"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,1]]},"references-count":80,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,1]]}},"alternative-id":["rs13010123"],"URL":"https:\/\/doi.org\/10.3390\/rs13010123","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,1]]}}}