{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,20]],"date-time":"2025-12-20T22:25:24Z","timestamp":1766269524526,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,6]],"date-time":"2023-02-06T00:00:00Z","timestamp":1675641600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["2022YFD2001103","42171331"],"award-info":[{"award-number":["2022YFD2001103","42171331"]}]},{"name":"2115 Talent Development Program of China Agricultural University","award":["2022YFD2001103","42171331"],"award-info":[{"award-number":["2022YFD2001103","42171331"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Efficient, fast, and accurate crop lodging monitoring is urgent for farmers, agronomists, insurance loss adjusters, and policymakers. This study aims to explore the potential of Chinese GF-1 PMS high-spatial-resolution images for corn lodging monitoring and to find a robust and efficient way to identify corn lodging accurately and efficiently. Three groups of image features and five machine-learning approaches are used for classifying non-lodged, moderately lodged, and severely lodged areas. Our results reveal that (1) the combination of spectral bands, optimized vegetation indexes, and texture features classify corn lodging with an overall accuracy of 93.81% and a Kappa coefficient of 0.91. (2) The random forest is an efficient, robust, and easy classifier to identify corn lodging with the F1-score of 0.95, 0.92, and 0.95 for non-lodged, moderately lodged, and severely lodged areas, respectively. (3) The GF-1 PMS image has great potential for identifying corn lodging on a regional scale.<\/jats:p>","DOI":"10.3390\/rs15040894","type":"journal-article","created":{"date-parts":[[2023,2,6]],"date-time":"2023-02-06T06:35:05Z","timestamp":1675665305000},"page":"894","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Identifying Corn Lodging in the Mature Period Using Chinese GF-1 PMS Images"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2677-4210","authenticated-orcid":false,"given":"Xianda","family":"Huang","sequence":"first","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture, Beijing 100083, China"}]},{"given":"Fu","family":"Xuan","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture, Beijing 100083, China"}]},{"given":"Yi","family":"Dong","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8726-5858","authenticated-orcid":false,"given":"Wei","family":"Su","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture, Beijing 100083, China"}]},{"given":"Xinsheng","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0341-1983","authenticated-orcid":false,"given":"Jianxi","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6942-0746","authenticated-orcid":false,"given":"Xuecao","family":"Li","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4267-1841","authenticated-orcid":false,"given":"Yelu","family":"Zeng","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5308-1551","authenticated-orcid":false,"given":"Shuangxi","family":"Miao","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture, Beijing 100083, China"}]},{"given":"Jiayu","family":"Li","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture, Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.fcr.2006.12.002","article-title":"Lodging-related morphological traits of hybrid rice in a tropical irrigated ecosystem","volume":"101","author":"Islam","year":"2007","journal-title":"Field Crop. Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.isprsjprs.2020.04.012","article-title":"Discriminant analysis for lodging severity classification in wheat using RADARSAT-2 and Sentinel-1 data","volume":"164","author":"Chauhan","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/S0065-2113(08)60782-8","article-title":"Lodging in wheat, barley, and oats: The phenomenon, its causes, and preventive measures","volume":"25","author":"Pinthus","year":"1974","journal-title":"Adv. Agron."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.isprsjprs.2019.03.005","article-title":"Remote sensing-based crop lodging assessment: Current status and perspectives","volume":"151","author":"Chauhan","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_5","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_6","first-page":"103178","article-title":"Mapping crop type in Northeast China during 2013\u20132021 using automatic sampling and tile-based image classification","volume":"117","author":"Xuan","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_7","first-page":"83","article-title":"Microcomputer-assisted video image analysis of lodging in winter wheat","volume":"53","author":"Gerten","year":"1987","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/0034-4257(84)90036-1","article-title":"Polarization of light reflected from grain crops during the heading growth stage","volume":"15","author":"Fitch","year":"1984","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/0034-4257(90)90101-Q","article-title":"Ground-based X-band (3-cm wave) radar backscattering of agricultural crops. I. Sugar beet and potato; backscattering and crop growth","volume":"34","author":"Bouman","year":"1990","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"713","DOI":"10.14358\/PERS.76.6.713","article-title":"Detecting seasonal changes in crop community structure using day and night digital images","volume":"76","author":"Sakamoto","year":"2010","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_11","first-page":"207","article-title":"Area extraction of maize lodging based on remote sensing by small unmanned aerial vehicle","volume":"30","author":"Li","year":"2014","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Chu, T., Starek, M.J., Brewer, M.J., Murray, S.C., and Pruter, L.S. (2017). Assessing lodging severity over an experimental maize (Zea mays L.) field using UAS images. Remote Sens., 9.","DOI":"10.3390\/rs9090923"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wilke, N., Siegmann, B., Klingbeil, L., Burkart, A., Kraska, T., Muller, O., van Doorn, A., Heinemann, S., and Rascher, U. (2019). Quantifying lodging percentage and lodging severity using a UAV-based canopy height model combined with an objective threshold approach. Remote Sens., 11.","DOI":"10.3390\/rs11050515"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5704154","DOI":"10.34133\/2019\/5704154","article-title":"Monitoring maize lodging grades via unmanned aerial vehicle multispectral image","volume":"2019","author":"Sun","year":"2019","journal-title":"Plant Phenomics"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"106804","DOI":"10.1016\/j.compag.2022.106804","article-title":"An explainable XGBoost model improved by SMOTE-ENN technique for maize lodging detection based on multi-source unmanned aerial vehicle images","volume":"194","author":"Han","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.jag.2014.08.010","article-title":"Wheat lodging monitoring using polarimetric index from RADARSAT-2 data","volume":"34","author":"Yang","year":"2015","journal-title":"Int. J. Appl. Earth Observation Geoinf."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Chen, J., Li, H., and Han, Y. (2016, January 18\u201320). Potential of RADARSAT-2 data on identifying sugarcane lodging caused by typhoon. Proceedings of the 2016 Fifth International Conference on Agro-Geoinformatics (Agro-Geoinformatics), Tianjin, China.","DOI":"10.1109\/Agro-Geoinformatics.2016.7577665"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1080\/2150704X.2017.1312028","article-title":"Characterizing lodging damage in wheat and canola using Radarsat-2 polarimetric SAR data","volume":"8","author":"Zhao","year":"2017","journal-title":"Remote Sens. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Rajapaksa, S., Eramian, M., Duddu, H., Wang, M., Shirtliffe, S., Ryu, S., Josuttes, A., Zhang, T., Vail, S., and Pozniak, C. (2018, January 12\u201315). Classification of crop lodging with gray level co-occurrence matrix. Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA.","DOI":"10.1109\/WACV.2018.00034"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Gu, X., Sun, Q., Yang, G., Song, X., and Xu, X. (August, January 28). Monitoring Maize Lodging Disaster Via Multi-Temporal Remote Sensing Images. Proceedings of the IGARSS 2019\u20132019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8900560"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"8840","DOI":"10.1080\/01431161.2021.1942575","article-title":"Assessing rice lodging using UAV visible and multispectral image","volume":"42","author":"Tian","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"102992","DOI":"10.1016\/j.jag.2022.102992","article-title":"An improved approach to estimating crop lodging percentage with Sentinel-2 imagery using machine learning","volume":"113","author":"Guan","year":"2022","journal-title":"Int. J. Appl. Earth Observation Geoinf."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Liu, P., and Chen, X. (2019). Intercropping classification from GF-1 and GF-2 satellite imagery using a rotation forest based on an SVM. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8020086"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/S2095-3119(16)61479-X","article-title":"Perspective of Chinese GF-1 high-resolution satellite data in agricultural remote sensing monitoring","volume":"16","author":"Zhou","year":"2017","journal-title":"J. Integr. Agric."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"014514","DOI":"10.1117\/1.JRS.14.014514","article-title":"Remote sensing of regional-scale maize lodging using multitemporal GF-1 images","volume":"14","author":"Zhou","year":"2020","journal-title":"J. Appl. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Chen, Y., Sun, L., Pei, Z., Sun, J., Li, H., Jiao, W., and You, J. (2022). A Simple and Robust Spectral Index for Identifying Lodged Maize Using Gaofen1 Satellite Data. Sensors, 22.","DOI":"10.3390\/s22030989"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3800","DOI":"10.1109\/JSTARS.2022.3170345","article-title":"Monitoring Lodging Extents of Maize Crop Using Multitemporal GF-1 Images","volume":"15","author":"Qu","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Guan, H., Liu, H., Meng, X., Luo, C., Bao, Y., Ma, Y., Yu, Z., and Zhang, X. (2020). A quantitative monitoring method for determining Maize lodging in different growth stages. Remote Sens., 12.","DOI":"10.3390\/rs12193149"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1353691","DOI":"10.1155\/2017\/1353691","article-title":"Significant remote sensing vegetation indices: A review of developments and applications","volume":"2017","author":"Xue","year":"2017","journal-title":"J. Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1016\/j.rse.2004.12.009","article-title":"Mapping paddy rice agriculture in southern China using multi-temporal MODIS images","volume":"95","author":"Xiao","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1080\/02757259409532252","article-title":"Influences of canopy architecture on relationships between various vegetation indices and LAI and FPAR: A computer simulation","volume":"10","author":"Goel","year":"1994","journal-title":"Remote Sens. Rev."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An Introduction to Statistical Learning, Springer.","DOI":"10.1007\/978-1-4614-7138-7"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3105","DOI":"10.1080\/01431160701469016","article-title":"Textural and local spatial statistics for the object-oriented classification of urban areas using high resolution imagery","volume":"29","author":"Su","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_34","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_35","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/0034-4257(92)90011-8","article-title":"A comparison of spatial feature extraction algorithms for land-use classification with SPOT HRV data","volume":"40","author":"Gong","year":"1992","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/S0924-2716(98)00027-6","article-title":"Optimisation of building detection in satellite images by combining multispectral classification and texture filtering","volume":"54","author":"Zhang","year":"1999","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1023\/A:1009715923555","article-title":"A tutorial on support vector machines for pattern recognition","volume":"2","author":"Burges","year":"1998","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_38","first-page":"123","article-title":"Parameter optimizing for support vector machines classification","volume":"47","author":"Feng","year":"2011","journal-title":"Jisuanji Gongcheng Yu Yingyong (Comput. Eng. Appl.)"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_41","first-page":"331","article-title":"Automated high resolution mapping of coffee in Rwanda using an expert Bayesian network","volume":"33","author":"Mukashema","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_42","first-page":"1","article-title":"Xgboost: Extreme Gradient Boosting","volume":"1","author":"Chen","year":"2015","journal-title":"R Package Version 0.4-2"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"112316","DOI":"10.1016\/j.rse.2021.112316","article-title":"Improving satellite retrieval of oceanic particulate organic carbon concentrations using machine learning methods","volume":"256","author":"Liu","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1080\/01431160050505865","article-title":"Improvement of classification in urban areas by the use of textural features: The case study of Lucknow city, Uttar Pradesh","volume":"22","author":"Shaban","year":"2001","journal-title":"Int. J. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/4\/894\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:25:40Z","timestamp":1760120740000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/4\/894"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,6]]},"references-count":44,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["rs15040894"],"URL":"https:\/\/doi.org\/10.3390\/rs15040894","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,2,6]]}}}