[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122353-en":3,"doc-seo-122353-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},122353,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine learning-based and synthetic aperture radar time-series data for rice classification over Sentinel-1 imagery - Research focus on rice monitoring and SAR time-series classification","Rice extraction is vital to remote sensing, particularly for monitoring agricultural landscapes in Suphan Buri, Thailand. Using Sentinel-1 SAR time-series imagery and advanced machine learning, the research addresses classification difficulties caused by varied terrain and crop diversity. Multiple polarizations (VV, VH, and VV+VH) are tested to improve separability. Random Forest, XGBoost, and LightGBM are compared, with LightGBM achieving the best accuracy after hyperparameter tuning. VV+VH delivers superior performance by capturing richer object features, while rice classes show strong precision, recall, and F1-score. The study also highlights ongoing challenges for other land-use categories and motivates future multi-sensor and more sophisticated model integration.","Machine learning-based and synthetic aperture radar timeseries data for rice classification over Sentinel-1 imagery  \nAttawut Nardkulpat1, Wuttichai Boonpook1, Asamaporn Sitthi1, Yumin Tan2  \n1Department of Geography, Faculty of Social Sciences, Srinakharinwirot University, Bangkok, Thailand 2School of Transportation Science and Engineering, Beihang University, Beijing, China  \nArticle history:  \nReceived Nov 5, 2023 Revised Oct 21, 2024 Accepted Nov 19, 2024  \nKeywords:  \nMachine learning  \nRice classification Sentinel-1 time series data Suphan buri  \nSynthetic aperture radar  \nCorresponding Author:  \nRice extraction is critical in remote sensing, especially in Suphan Buri province, Thailand, using Sentinel-1 synthetic aperture radar (SAR) timeseries data and advanced machine learning algorithms. Given the challenges of varied terrains and diverse crop types, the research employs different polarization modes (vertical transmit and vertical receive (VV), vertical transmit and horizontal receive (VH), and VV+VH) to enhance classification accuracy. The study evaluates the performance of three machine learning algorithms: random forest, extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM) . The results demonstrate that combined VV+VH polarization outperforms VV and VH alone, providing better accuracy due to its ability to capture more detailed object features. LightGBM emerged as the most effective among the algorithms, particularly when dealing with large datasets. After hyperparameter tuning (n_estimators: 820, max_depth: 10, and learning_rate: 0.01), LightGBM achieved the highest accuracy. The rice class showed exceptional precision, recall, and F1-score, surpassing other land-use classes (agriculture/forest and urban areas) . However, these classes still pose challenges, highlighting the need for future studies to integrate multi-sensor data and explore more sophisticated machine-learning models. This research offers a promising approach to enhancing rice monitoring and management in diverse agricultural landscapes, contributing to more accurate and efficient farming practices.  \nThis is an open access article under the CC BY-SA license.  \nWuttichai Boonpook  \nDepartment of Geography, Faculty of Social Sciences, Srinakharinwirot University Bangkok, Thailand  \n[Email: wuttichaib@g.swu.ac.th](Email: wuttichaib@g.swu.ac.th)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nRice is one of the world ’s most important cereal crops and a staple food for a significant portion of the global population, particularly in Asia. In 2020–2021, Thailand ranked as the world ’s top rice producer and exporter, accounting for second in global output [1] . Thailand grows most of its rice in the northeastern, lower northern, and central regions, spanning 68.86 million hectares [2] . To cultivate and assess various aspects of rice production, remote sensing imagery serves as a valuable tool for rice identification and mapping, rice health assessment, yield estimation, monitoring growth stages, disease, and pest detection [3] .  \nRemote sensing technology plays a key role in rice monitoring and management. Ground surveysand remote sensing techniques source this invaluable information. The currency and precision of this data are pivotal for various land use applications. While ground surveys offer exceptional accuracy, they often require more time and financial resources [4] . In contrast, remote sensing methods provide up-to-date information  \nand extensive coverage, making them a practical choice for many applications. Satellite imagery is the primary data source for remote sensing. There are two primary types of sensors used for data collection: passive and active sensors. The Earth ’s surface emits or reflects electromagnetic radiation, which a passive sensor detects and records. This sensor provides multispectral bands that are suitable for rice classification. However, the sensors rely on available sunlight or natural r","cbCaiizcblXD6rkO","https://ap.wps.com/l/cbCaiizcblXD6rkO","pdf",1552665,1,13,"English","en",105,"# INTRODUCTION\n## Remote sensing for rice monitoring\n## Passive vs active sensors and SAR advantages\n## SAR and Sentinel-1 time-series for rice detection\n## Polarization and time-series backscatter behavior","[{\"question\":\"Why is remote sensing important for rice monitoring in Suphan Buri?\",\"answer\":\"Remote sensing supports rice identification, mapping, and crop health monitoring while reducing the time and cost required by ground surveys. It also enables up-to-date coverage for management decisions.\"},{\"question\":\"How does the study use Sentinel-1 SAR time-series data for classification?\",\"answer\":\"It analyzes temporal changes in SAR backscatter across the rice growing cycle. Backscatter patterns vary by flooding, tillering, and maturation stages, providing discriminative information for classification.\"},{\"question\":\"Which polarization and machine learning model performed best, and what are the reasons?\",\"answer\":\"VV+VH polarization outperformed using VV or VH alone because it captures more detailed object features. LightGBM produced the highest accuracy after hyperparameter tuning and performed especially well on large datasets.\"}]","Machine learning-based and synthetic aperture radar time-series data for rice classification over Sentinel-1 imagery - Research focus on rice monitoring and SAR time-series classification | PDF",1785810193,33,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-based-and-synthetic-aperture-radar-time-series-data-for-rice-classification-over-sentinel-1-imagery-research-focus-on-rice-monitoring-and-sar-time-series-classification","",{"@graph":36,"@context":85},[37,54,68],{"@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-and-synthetic-aperture-radar-time-series-data-for-rice-classification-over-sentinel-1-imagery-research-focus-on-rice-monitoring-and-sar-time-series-classification/122353/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is remote sensing important for rice monitoring in Suphan Buri?","Question",{"text":75,"@type":76},"Remote sensing supports rice identification, mapping, and crop health monitoring while reducing the time and cost required by ground surveys. It also enables up-to-date coverage for management decisions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the study use Sentinel-1 SAR time-series data for classification?",{"text":80,"@type":76},"It analyzes temporal changes in SAR backscatter across the rice growing cycle. Backscatter patterns vary by flooding, tillering, and maturation stages, providing discriminative information for classification.",{"name":82,"@type":73,"acceptedAnswer":83},"Which polarization and machine learning model performed best, and what are the reasons?",{"text":84,"@type":76},"VV+VH polarization outperformed using VV or VH alone because it captures more detailed object features. LightGBM produced the highest accuracy after hyperparameter tuning and performed especially well on large datasets.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]