[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128144-en":3,"doc-seo-128144-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},128144,549768072016,"River Wang","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine Learning-Based Alfalfa Height Estimation Using Sentinel-2 Multispectral Imagery","Climate change threatens crop-yield sustainability as extreme weather events become more frequent, increasing the need for timely agricultural monitoring. Remote sensing enables continuous field observation, supporting decision-making when crop condition changes rapidly. This study estimates alfalfa crop height using Sentinel-2 satellite imagery and machine learning within the Google Earth Engine (GEE) Python API. Ground data collected over three years across four Canadian provinces were used to train and evaluate random forest, SVR, and extreme gradient boosting, using vegetation indices and feature selection.","4.2  \n8.6  \nArticle  \nMachine Learning-Based Alfalfa Height Estimation Using Sentinel-2 Multispectral Imagery  \nHazhir Bahrami, Karem Chokmani, Saeid Homayouni, Viacheslav I. Adamchuk, Rami Albasha, Md Saifuzzaman and Maxime Leduc  \nSpecial Issue  \nMachine Learning for Applications in Agriculture and Vegetation Using Remote Sensing  \nEdited by  \nDr. Christoph Jörges and Dr. Aaron Moody  \n[https://doi.org/10.3390/rs17101759](https://doi.org/10.3390/rs17101759)  \nArticle  \nMachine Learning-Based Alfalfa Height Estimation Using Sentinel-2 Multispectral Imagery  \nHazhir Bahrami 1, *, Karem Chokmani 1, Saeid Homayouni 1, Viacheslav I. Adamchuk 2, Rami Albasha 3,4, Md Saifuzzaman 2 and Maxime Leduc 3  \nAcademic Editors: Aaron Moody and Christoph Jörges  \nReceived: 9 April 2025  \nRevised: 10 May 2025  \nAccepted: 12 May 2025  \nPublished: 18 May 2025  \nCitation: Bahrami, H.; Chokmani, K.; Homayouni, S.; Adamchuk, V.I.; Albasha, R.; Saifuzzaman, M.; Leduc, M. Machine Learning-Based Alfalfa Height Estimation Using Sentinel-2 Multispectral Imagery. Remote Sens. 2025, 17, 1759. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/rs17101759](10.3390/rs17101759)  \nCopyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \n1 Water, Earth and Environment Centre, National Scienti􀀂c Research Institute (INRS), Quebec City, QC G1K 9A9, Canada; [karem.chokmani@inrs.ca](karem.chokmani@inrs.ca) (K.C.); [saeid.homayouni@inrs.ca](saeid.homayouni@inrs.ca) (S.H.)  \n2 Bioresource Engineering Department, McGill University, Macdonald Campus, Ste-Anne-de-Bellevue, QC H9X 3V9, Canada; [viacheslav.adamchuk@mcgill.ca](viacheslav.adamchuk@mcgill.ca) (V.I.A.); [md.saifuzzaman@mail.mcgill.ca](md.saifuzzaman@mail.mcgill.ca) (M.S.)  \n3 My Forage System, Montréal, QC H1W 3J5, Canada; [ralbasha@msfourrager.com](ralbasha@msfourrager.com) (R.A.); [mleduc@msfourrager.com](mleduc@msfourrager.com) (M.L.)  \n4 Department of Soil and Agri-Food Engineering, Laval University, Quebec City, QC G1V 0A6, Canada  \n* [Correspondence: hazhir.bahrami@inrs.ca](Correspondence: hazhir.bahrami@inrs.ca)  \nAbstract: Climate change is threatening the sustainability of crop yields due to an increasing frequency of extreme weather conditions, requiring timely agricultural monitoring. Remote sensing facilitates consistent and continuous monitoring of 􀀂eld crops. This study aimed to estimate alfalfa crop height through satellite images and machine learning methods within the Google Earth Engine (GEE) Python API. Ground measurements for this study were collected over three years in four Canadian provinces. We utilized Sentinel-2 data to obtain satellite imagery corresponding to the same timeframe and location as the ground measurements. Three machine learning algorithms were employed to estimate plant height from satellite images: random forest (RF), support vector regression (SVR), and extreme gradient boosting (XGB) . The ef􀀂cacy of these algorithms has been assessed and compared. Several widely used vegetation indices, for instance normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and normalized difference rededge (NDRE), were selected and assessed in this study. RF feature importance was utilized to determine the ranking of features from most to least signi􀀂cant. Several feature selection strategies were utilized and compared with the situation where all features are used. We demonstrated that RF and XGB surpassed SVR when assessing test data performance. Our 􀀂ndings showed that XGB and RF could predict alfalfa crop height with an R2 of 0.79 anda mean absolute error (MAE) of around 4 cm Our 􀀂ndings indicated that SVR exhibited the lowest accuracy among the three algorithms te","cbCaiisrHrR9lccg","https://ap.wps.com/l/cbCaiisrHrR9lccg","pdf",5372107,1,26,"English","en",105,"# Introduction\n## Background and motivation for remote monitoring\n# Materials and Methods\n## Study area and ground measurements\n## Sentinel-2 data and indices\n## Machine learning models and feature selection\n# Results and Discussion\n## Model comparison and predictive performance\n## Feature importance and key indices\n# Conclusions\n## Practical implications for decision support systems","[{\"question\":\"Why is remote sensing important for monitoring alfalfa under climate change?\",\"answer\":\"Extreme weather increases the need for timely crop status information. Remote sensing provides consistent and continuous field monitoring to support early warning and efficient resource management.\"},{\"question\":\"Which machine learning algorithms were used to estimate alfalfa height?\",\"answer\":\"Random forest (RF), support vector regression (SVR), and extreme gradient boosting (XGB) were used to estimate plant height from Sentinel-2 imagery.\"},{\"question\":\"What indices and features were found to be most important?\",\"answer\":\"Normalized difference red edge (NDRE) and normalized difference water index (NDWI) were identified as the most important variables for determining alfalfa crop height.\"}]","Machine Learning-Based Alfalfa Height Estimation Using Sentinel-2 Multispectral Imagery | PDF",1785945057,66,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learning-based-alfalfa-height-estimation-using-sentinel-2-multispectral-imagery","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-based-alfalfa-height-estimation-using-sentinel-2-multispectral-imagery/128144/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-26","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is remote sensing important for monitoring alfalfa under climate change?","Question",{"text":76,"@type":77},"Extreme weather increases the need for timely crop status information. Remote sensing provides consistent and continuous field monitoring to support early warning and efficient resource management.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning algorithms were used to estimate alfalfa height?",{"text":81,"@type":77},"Random forest (RF), support vector regression (SVR), and extreme gradient boosting (XGB) were used to estimate plant height from Sentinel-2 imagery.",{"name":83,"@type":74,"acceptedAnswer":84},"What indices and features were found to be most important?",{"text":85,"@type":77},"Normalized difference red edge (NDRE) and normalized difference water index (NDWI) were identified as the most important variables for determining alfalfa crop height.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]