{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T02:32:02Z","timestamp":1771036322025,"version":"3.50.1"},"reference-count":24,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2020,11,27]],"date-time":"2020-11-27T00:00:00Z","timestamp":1606435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Crop lodging, the tilting of stems from their natural upright position, usually occurs after a heavy storm event. Since lodging of a crop seriously affects its yield, rapid assessment of crop lodging is valuable for farmers, policymakers, agronomists, insurance companies, and relief workers. Synthetic Aperture Radar (SAR) sensors have been recognized as valuable data sources for mapping lodging extent because of their good penetrating power and high-resolution remote sensing ability. Compared to other sources, SAR\u2019s weather and illumination independence and large area coverage at fine spatial resolution (3 m to 20 m) support frequent and detailed observations. Because of these advantages, SAR has the potential in supporting near real-time monitoring of lodging in fields when combined with automated image processing. In this study, a method based on change detection using modified Hidden Markov Random Field (HMRF) and Sentinel-1A data were utilized to identify lodging and map its extent. Results obtained have shown that when lodging occurs, the VH polarization\u2019s backscatter (\u03c3VH) increases between the pre-lodging event image and the post-lodging event image. The increase in \u03c3VH is due to the increase in volume scattering and vegetation-soil double bounce scattering resulting from the structural changes in the crop canopy. Using Sentinel-1A images and applying our proposed approach across several fields in Iowa and Illinois, we mapped the extent of the 2020 Derecho (wind storm) lodging disaster. In addition, we separated lodged regions into severely and moderately lodged areas. We estimated that approximately 2.56 million acres of corn and 1.27 million acres of soybean were lodged. Further analysis also showed the separation between un-lodged (healthy) fields and lodged fields. The observations in this study can guide future use of SAR-based information for operational crop lodging assessment.<\/jats:p>","DOI":"10.3390\/rs12233885","type":"journal-article","created":{"date-parts":[[2020,11,27]],"date-time":"2020-11-27T09:16:49Z","timestamp":1606468609000},"page":"3885","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Landscape-Scale Crop Lodging Assessment across Iowa and Illinois Using Synthetic Aperture Radar (SAR) Images"],"prefix":"10.3390","volume":"12","author":[{"given":"Olaniyi A.","family":"Ajadi","sequence":"first","affiliation":[{"name":"Research &amp; Development Corteva Agriscience\u2122, 7000 NW 62nd Avenue, Johnston, IA 50131, USA"}]},{"given":"Heming","family":"Liao","sequence":"additional","affiliation":[{"name":"Research &amp; Development Corteva Agriscience\u2122, 7000 NW 62nd Avenue, Johnston, IA 50131, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4158-3439","authenticated-orcid":false,"given":"Jason","family":"Jaacks","sequence":"additional","affiliation":[{"name":"Research &amp; Development Corteva Agriscience\u2122, 7000 NW 62nd Avenue, Johnston, IA 50131, USA"}]},{"given":"Alfredo","family":"Delos Santos","sequence":"additional","affiliation":[{"name":"Research &amp; Development Corteva Agriscience\u2122, 7000 NW 62nd Avenue, Johnston, IA 50131, USA"}]},{"given":"Siva P.","family":"Kumpatla","sequence":"additional","affiliation":[{"name":"Research &amp; Development Corteva Agriscience\u2122, 7000 NW 62nd Avenue, Johnston, IA 50131, USA"}]},{"given":"Rinkal","family":"Patel","sequence":"additional","affiliation":[{"name":"Granular 8700 Crescent Chase, Johnston, IA 50131, USA"}]},{"given":"Anu","family":"Swatantran","sequence":"additional","affiliation":[{"name":"Research &amp; Development Corteva Agriscience\u2122, 7000 NW 62nd Avenue, Johnston, IA 50131, USA"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,27]]},"reference":[{"key":"ref_1","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":"Volume 25","author":"Pinthus","year":"1974","journal-title":"Advances in Agronomy"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"31890","DOI":"10.1038\/srep31890","article-title":"A new method for assessing plant lodging and the impact of management options on lodging in canola crop production","volume":"6","author":"Wu","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.fcr.2016.01.003","article-title":"Effects of light intensity within the canopy on maize lodging","volume":"188","author":"Xue","year":"2016","journal-title":"Field Crop. Res."},{"key":"ref_4","unstructured":"Nielsen, B., and Colville, D. (2020, October 30). Stalk Lodging in corn: Guidelines for Preventive Management. Available online: https:\/\/www.extension.purdue.edu\/extmedia\/ay\/ay-262.html."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.cj.2013.07.012","article-title":"Anatomical and chemical characteristics associated with lodging resistance in wheat","volume":"1","author":"Kong","year":"2013","journal-title":"Crop J."},{"key":"ref_6","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_7","first-page":"238871","article-title":"Effect of dose and timing of application of different plant growth regulators on lodging and grain yield of a scottish landrace of barley (bere) in orkney, scotland","volume":"2","author":"Shah","year":"2017","journal-title":"Int. J. Environ. Agric. Biotechnol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"111804","DOI":"10.1016\/j.rse.2020.111804","article-title":"Understanding wheat lodging using multi-temporal sentinel-1 and sentinel-2 data","volume":"243","author":"Chauhan","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Chauhan, S., Darvishzadeh, R., Lu, Y., Stroppiana, D., Boschetti, M., Pepe, M., and Nelson, A. (2019). Wheat lodging assessment using multispectral uav data. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., 235\u2013240.","DOI":"10.5194\/isprs-archives-XLII-2-W13-235-2019"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Han, D., Yang, H., Yang, G., and Qiu, C. (2017). 2017 SAR in Big Data Era: Models, Methods and Applications (BIGSARDATA). Monitoring Model of Corn Lodging Based on Sentinel-1 Radar Image, IEEE.","DOI":"10.1109\/BIGSARDATA.2017.8124928"},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.isprsjprs.2014.05.009","article-title":"Integrating sar and derived products into operational volcano monitoring and decision support systems","volume":"100","author":"Meyer","year":"2015","journal-title":"Isprs J. Photogramm. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1016\/j.asr.2019.09.034","article-title":"Monitoring of maize lodging using multi-temporal sentinel-1 sar data","volume":"65","author":"Shu","year":"2020","journal-title":"Adv. Space Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"111488","DOI":"10.1016\/j.rse.2019.111488","article-title":"Estimation of crop angle of inclination for lodged wheat using multi-sensor sar data","volume":"236","author":"Chauhan","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_15","first-page":"157","article-title":"Wheat lodging monitoring using polarimetric index from radarsat-2 data","volume":"34","author":"Yang","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Chen, J., Li, H., and Han, Y. (2016). Fifth International Conference on Agro-Geoinformatics (Agro-Geoinformatics). Potential of Radarsat-2 Data on Identifying Sugarcane Lodging Caused by Typhoon, IEEE.","DOI":"10.1109\/Agro-Geoinformatics.2016.7577665"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ajadi, O.A., Meyer, F.J., and Webley, P.W. (2016). Change detection in synthetic aperture radar images using a multiscale-driven approach. Remote Sens., 8.","DOI":"10.3390\/rs8060482"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"6469","DOI":"10.1109\/TGRS.2018.2839027","article-title":"A nonlocal insar filter for high-resolution dem generation from tandem-x interferograms","volume":"56","author":"Baier","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2021","DOI":"10.1109\/TGRS.2014.2352555","article-title":"Nl-sar: A unified nonlocal framework for resolution-preserving (pol)(in) sar denoising","volume":"53","author":"Deledalle","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1815","DOI":"10.1109\/TGRS.2002.802498","article-title":"An image change detection algorithm based on markov random field models","volume":"40","author":"Kasetkasem","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.patrec.2016.11.019","article-title":"A fuzzy clustering image segmentation algorithm based on hidden markov random field models and voronoi tessellation","volume":"85","author":"Zhao","year":"2017","journal-title":"Pattern Recognit. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"77","DOI":"10.2528\/PIERL19012502","article-title":"A novel method of ship detection in high-resolution sar images based on gan and hmrf models","volume":"83","author":"Yang","year":"2019","journal-title":"Prog. Electromagn. Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1080\/10106049.2011.562309","article-title":"Monitoring us agriculture: The us department of agriculture, national agricultural statistics service, cropland data layer program","volume":"26","author":"Boryan","year":"2011","journal-title":"Geocarto Int."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/42.906424","article-title":"Segmentation of brain mr images through a hidden markov random field model and the expectation-maximization algorithm","volume":"20","author":"Zhang","year":"2001","journal-title":"IEEE Trans. Med Imaging"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/23\/3885\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:38:15Z","timestamp":1760179095000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/23\/3885"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,27]]},"references-count":24,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["rs12233885"],"URL":"https:\/\/doi.org\/10.3390\/rs12233885","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,27]]}}}