{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T18:48:06Z","timestamp":1776538086486,"version":"3.51.2"},"reference-count":41,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,2,6]],"date-time":"2022-02-06T00:00:00Z","timestamp":1644105600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["NO.42171394"],"award-info":[{"award-number":["NO.42171394"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["NO.41601467"],"award-info":[{"award-number":["NO.41601467"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>For the problem of multi-dimensional feature redundancy in remote sensing detection of wheat stripe rust using reflectance spectrum and solar-induced chlorophyll fluorescence (SIF), a feature selection and disease index (DI) monitoring model combining mRMR and XGBoost algorithm was proposed in this study. Firstly, characteristic wavelengths selected by successive projections algorithm (SPA) were combined with the vegetation indices, trilateral parameters, and canopy SIF parameters to constitute the initial feature set. Then, the max-relevance and min-redundancy (mRMR) algorithm and correlation coefficient (CC) analysis were used to reduce the dimensionality of the initial feature set, respectively. Features selected by mRMR and CC were input as independent variables into the extreme gradient boosting regression (XGBoost) and gradient boosting regression tree (GBRT) to monitor the severity of stripe rust. The experimental results show that, compared with CC analysis, the monitoring accuracy of the features selected by mRMR in the XGBoost and GBRT models increased by 12% and 17% on average, respectively. Meanwhile, the mRMR-XGBoost model achieved the best monitoring accuracy (R2 = 0.8894, RMSE = 0.1135). The R2 between the measured DI and predicted DI of mRMR-XGBoost was improved by an average of 5%, 12%, and 22% compared with mRMR-GBRT, CC-XGBoost, and CC-GBRT models. These results suggested that XGBoost is more suitable for the remote sensing monitoring of wheat stripe rust, and mRMR has more advantages than the commonly used CC analysis in feature selection. Field survey data validation results also confirm that the mRMR-XGBoost algorithm has excellent monitoring applicability and scalability. The proposed model could provide a reference for data dimensionality reduction and crop disease index monitoring based on hyperspectral data.<\/jats:p>","DOI":"10.3390\/rs14030756","type":"journal-article","created":{"date-parts":[[2022,2,6]],"date-time":"2022-02-06T20:38:40Z","timestamp":1644179920000},"page":"756","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["Remote Sensing Monitoring of Winter Wheat Stripe Rust Based on mRMR-XGBoost Algorithm"],"prefix":"10.3390","volume":"14","author":[{"given":"Xia","family":"Jing","sequence":"first","affiliation":[{"name":"College of Geometrics, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qin","family":"Zou","sequence":"additional","affiliation":[{"name":"College of Geometrics, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jumei","family":"Yan","sequence":"additional","affiliation":[{"name":"College of Geometrics, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"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"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bingyu","family":"Li","sequence":"additional","affiliation":[{"name":"College of Geometrics, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1111\/j.1472-765X.2007.02313.x","article-title":"Early molecular diagnosis and detection of puccinia striiformis f. sp. tritici in China","volume":"46","author":"Lihua","year":"2008","journal-title":"Lett. Appl. Microbiol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"104943","DOI":"10.1016\/j.compag.2019.104943","article-title":"Monitoring plant diseases and pests through remote sensing technology: A review","volume":"165","author":"Zhang","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_3","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_4","first-page":"275","article-title":"Identification of powdery mildew (Erysiphe graminis sp. tritici) and take-all disease (Gaeumannomyces graminis sp. tritici) in wheat (Triticum aestivum L.) by means of leaf reflectance measurements","volume":"1","author":"Graeff","year":"2006","journal-title":"Cent. Eur. J. Biol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.compag.2010.03.003","article-title":"Application of neural networks to discriminate fungal infection levels in rice panicles using hyperspectral reflectance and principal components analysis","volume":"72","author":"Liu","year":"2010","journal-title":"Comput. Electron. Agric."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Li, X., Yang, C., Huang, W., Tang, J., Tian, Y., and Zhang, Q. (2020). Identification of cotton root rot by multifeature selection from Sentinel-2 images using Random Forest. Remote Sens., 21.","DOI":"10.3390\/rs12213504"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Huang, L., Wu, Z., Huang, W., Ma, H., and Zhao, J. (2019). Identification of Fusarium Head Blight in winter wheat ears based on Fisher\u2019s Linear Discriminant Analysis and a Support Vector Machine. Appl. Sci., 18.","DOI":"10.3390\/app9183894"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"103518","DOI":"10.1016\/j.infrared.2020.103518","article-title":"Selecting key wavelengths of hyperspectral imagine for nondestructive classification of moldy peanuts using ensemble classifier","volume":"111","author":"Yuan","year":"2020","journal-title":"Infrared Phys. Technol."},{"key":"ref_9","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":"Davoud","year":"2014","journal-title":"Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.isprsjprs.2021.07.014","article-title":"Discriminating xylella fastidiosa from verticillium dahliae infections in olive trees using thermal- and hyperspectral-based plant traits","volume":"179","author":"Poblete","year":"2021","journal-title":"ISPRS J. Photogramm."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compag.2010.02.007","article-title":"A review of advanced techniques for detecting plant diseases","volume":"72","author":"Sankaran","year":"2010","journal-title":"Comput. Electron. Agric."},{"key":"ref_12","first-page":"253","article-title":"Feature Selection and Model Construction of Wheat Stripe Rust Based on GA and SVR Algorithm","volume":"51","author":"Jing","year":"2020","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Samat, A., Li, E., Wang, W., Liu, S., Lin, C., and Abuduwaili, J. (2020). Meta-XGBoost for hyperspectral image classification using extended MSER-guided morphological profiles. Remote Sens., 12.","DOI":"10.3390\/rs12121973"},{"key":"ref_14","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":"Wenjiang","year":"2007","journal-title":"Precis. Agric."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1109\/TIM.1975.4314448","article-title":"Fraunhofer line discriminator MK II\u2014airborne instrument for precise and standardized ecological luminescence measurement","volume":"24","author":"Plascyk","year":"1975","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_16","unstructured":"Mcdonald, M., Schepers, J., and Tartly, L. (2003). Sun-induced fluorescence: A new tool for precision farming. Digital Imaging and Spectral Techniques: Applications to Precision Agriculture and Crop Physiology, American Society of Agronomy Special Publication."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1882","DOI":"10.1016\/j.rse.2011.03.011","article-title":"Modeling the impact of spectral sensor configurations on the FLD retrieval accuracy of sun-induced chlorophyll fluorescence","volume":"115","author":"Damm","year":"2011","journal-title":"Remote Sen. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1109\/JSTARS.2010.2048200","article-title":"Detection of vegetation light-use efficiency based on solar-induced chlorophyll fluorescence separated from canopy radiance spectrum","volume":"3","author":"Liu","year":"2010","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/j.rse.2005.05.006","article-title":"Simple reflectance indices track heat and water stress-induced changes in steady-state chlorophyll fluorescence at the canopy scale","volume":"97","author":"Dobrowski","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/S0034-4257(02)00113-X","article-title":"Steady-state chlorophyll a fluorescence detection from canopy derivative reflectance and double-peak red-edge effects","volume":"84","author":"Pushnik","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_21","first-page":"1","article-title":"Research progress of crop diseases and pests monitoring based on remote sensing","volume":"28","author":"Zhang","year":"2012","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_22","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_23","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_24","first-page":"221","article-title":"Semi-empirical indices to assess carotenoids\/chlorophyll a ratio from leaf spectral reflectance","volume":"31","author":"Baret","year":"1995","journal-title":"Photosynthetica"},{"key":"ref_25","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_26","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_27","doi-asserted-by":"crossref","first-page":"2869","DOI":"10.1080\/014311697217396","article-title":"Estimation of plant water concentration by the reflectance water index wi (r900\/r970)","volume":"18","author":"Pinol","year":"1997","journal-title":"Int. J. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S0034-4257(96)00067-3","article-title":"NDWI\u2014A normalized difference water index for remote sensing of vegetation liquid water from space","volume":"58","author":"Gao","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/S0034-4257(00)00197-8","article-title":"Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density","volume":"76","author":"Broge","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/S0034-4257(96)00069-7","article-title":"Potential and limitations of information extraction on the terrestrial biosphere from satellite remote sensing","volume":"58","author":"Verstraete","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.rse.2004.06.002","article-title":"Use of hyperspectral derivative ratios in the red-edge region to identify plant stress responses to gas leaks","volume":"92","author":"Smith","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_32","first-page":"620","article-title":"Using hyperspectral derivative indices to diagnose severity of winter wheat stripe rust","volume":"4","author":"Jiang","year":"2007","journal-title":"Opt. Tech."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1402","DOI":"10.1007\/s11947-010-0492-4","article-title":"Determination of calcium content in powdered milk using near and mid-infrared spectroscopy with variable selection and chemometrics","volume":"5","author":"Wu","year":"2012","journal-title":"Food Bioprocess Technol."},{"key":"ref_34","first-page":"31507","article-title":"Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and min-redundancy","volume":"27","author":"Peng","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"115663","DOI":"10.1016\/j.envpol.2020.115663","article-title":"Prediction of sediment heavy metal at the Australian Bays using newly developed hybrid artificial intelligence models","volume":"268","author":"Bhagat","year":"2021","journal-title":"Environ. Pollut."},{"key":"ref_37","first-page":"E185","article-title":"Hyperspectral remote sensing of foliar nitrogen content","volume":"110","author":"Knyazikhin","year":"2012","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.rse.2015.06.002","article-title":"Global sensitivity analysis of the SCOPE model: What drives simulated canopy-leaving sun-induced fluorescence?","volume":"166","author":"Verrelst","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"4264","DOI":"10.1080\/01431161.2013.775533","article-title":"Assessing photosynthetic light-use efficiency using a solar-induced chlorophyll fluorescence and photochemical reflectance index","volume":"34","author":"Liu","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1007\/s10895-020-02561-8","article-title":"Application of fluorescence spectroscopy in wheat crop: Early disease detection and associated molecular changes","volume":"30","author":"Atta","year":"2020","journal-title":"J. Fluoresc."},{"key":"ref_41","first-page":"154","article-title":"Wheat stripe rust monitoring by random forest algorithm combined with SIF and reflectance spectrum","volume":"35","author":"Jing","year":"2019","journal-title":"Trans. Chin. Soc. Agric. Mach."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/3\/756\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:15:11Z","timestamp":1760134511000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/3\/756"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,6]]},"references-count":41,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["rs14030756"],"URL":"https:\/\/doi.org\/10.3390\/rs14030756","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,6]]}}}