{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T18:06:58Z","timestamp":1789754818300,"version":"4.0.1"},"reference-count":22,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T00:00:00Z","timestamp":1789689600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>\n                    Rice (\n                    <jats:italic>Oryza sativa L.<\/jats:italic>\n                    ) is Bangladesh\u2019s staple crop, cultivated across three distinct seasons: Rabi (Boro), Kharif-1 (Aus) and Kharif-2 (Aman). This study develops an integrated, cloud-independent framework for mapping Aus and Aman rice areas using freely available Sentinel-1 C-band Synthetic Aperture Radar (SAR) imagery; to accomplish this, supervised machine-learning (ML) algorithms are used in the Google Earth Engine (GEE) platform. Time-series dual-polarization (VV and VH) backscatter data were analyzed for June\u2013November 2021 (Aman) and March\u2013August 2022 (Aus). Four ML models such as Classification and Regression Tree (CART), Random Forest (RF), k-Nearest Neighbour (k-NN), and Support Vector Machine (SVM) were evaluated to identify the most accurate mapping approach. Among all ML models, k-NN achieved the highest accuracy of 94.5% and 99.8% for Aman and Aus, respectively. Despite the higher accuracy, RF for Aman (92.7% accuracy) and CART for Aus (99.6%) were finally selected based on their lower deviation from area estimates reported by Bangladesh Bureau of Statistics (BBS). The estimated Aman area (5.72 Mha) was closely matched with the official figure from the Bangladesh Bureau of Statistics (BBS), while the Aus estimate (0.76 Mha) showed a 29% underestimation compared to BBS data (1.06 Mha). However, the results demonstrate that Sentinel-1 SAR combined with ML classifiers provides a reliable, scalable, and weather-resilient approach for multi-season rice mapping. The proposed methodology establishes a strong foundation for timely production forecasting and climate-resilient agricultural planning, particularly in cloud-prone tropical regions.\n                  <\/jats:p>","DOI":"10.1371\/journal.pone.0355502","type":"journal-article","created":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T17:22:06Z","timestamp":1789752126000},"page":"e0355502","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Estimating aman and aus rice area in Bangladesh using sentinel-1 imagery and machine learning algorithms"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-4579-9213","authenticated-orcid":true,"given":"H. M. Hamidur","family":"Rahman","sequence":"first","affiliation":[{"name":"Computer and GIS Unit, Bangladesh Agricultural Research Council","place":["Dhaka, Bangladesh"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hasan","family":"Mahmud","sequence":"additional","affiliation":[{"name":"Computer and GIS Unit, Bangladesh Agricultural Research Council","place":["Dhaka, Bangladesh"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-0193-8549","authenticated-orcid":true,"given":"Md Mahfuzul","family":"Hasan","sequence":"additional","affiliation":[{"name":"ACASA Project, Bangladesh Agricultural Research Council","place":["Dhaka, Bangladesh"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S. G.","family":"Hussain","sequence":"additional","affiliation":[{"name":"Center for Environmental and Geographic Information Services, Public Trust, Ministry of Water Resources, and Bangladesh Agricultural Research Council","place":["Dhaka, Bangladesh"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,9,18]]},"reference":[{"key":"pone.0355502.ref001","volume-title":"Yearbook of agricultural statistics-2024","author":"Bangladesh Bureau of Statistics","year":"2024"},{"key":"pone.0355502.ref002","doi-asserted-by":"crossref","first-page":"897","DOI":"10.5194\/isprs-archives-XLII-4-W18-897-2019","article-title":"Rice crop mapping using sentinel-1 time series images (Case study: Mazandaran, Iran)","author":"M Saadat","year":"2019","journal-title":"Int Arch Photogramm Remote Sens Spatial Inf Sci"},{"key":"pone.0355502.ref003","doi-asserted-by":"crossref","first-page":"433","DOI":"10.5194\/isprs-archives-XLII-4-433-2018","article-title":"Operational near real time rice area mapping using multi-temporal sentinel-1 SAR observations","author":"JD Mohite","year":"2018","journal-title":"Int Arch Photogramm Remote Sens Spatial Inf Sci"},{"issue":"1","key":"pone.0355502.ref004","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1038\/s41597-019-0036-3","article-title":"High resolution paddy rice maps in cloud-prone Bangladesh and Northeast India using Sentinel-1 data","volume":"6","author":"M Singha","year":"2019","journal-title":"Scientific Data"},{"issue":"14","key":"pone.0355502.ref005","doi-asserted-by":"crossref","first-page":"1666","DOI":"10.3390\/rs11141666","article-title":"Automated near-real-time mapping and monitoring of rice extent, cropping patterns, and growth stages in Southeast Asia using Sentinel-1 time series on a Google Earth Engine platform","volume":"11","author":"MB Rudiyanto","year":"2019","journal-title":"Remote Sensing"},{"key":"pone.0355502.ref006","doi-asserted-by":"crossref","unstructured":"Shen G, Nie C. Mapping rice area using sentinel-1 SAR data and deep learning. In: IGARSS 2023 - 2023 IEEE International geoscience and remote sensing symposium. 2023. 3402\u20135. https:\/\/doi.org\/10.1109\/igarss52108.2023.10282941","DOI":"10.1109\/IGARSS52108.2023.10282941"},{"issue":"21","key":"pone.0355502.ref007","doi-asserted-by":"crossref","first-page":"4400","DOI":"10.3390\/rs13214400","article-title":"Mapping a paddy rice area in a cloudy and rainy region using spatiotemporal data fusion and a phenology-based algorithm","volume":"13","author":"R Zhao","year":"2021","journal-title":"Remote Sensing"},{"key":"pone.0355502.ref008","doi-asserted-by":"crossref","first-page":"5018","DOI":"10.1109\/JSTARS.2025.3528124","article-title":"An automatic decision-level fusion rice mapping method of optical and SAR images based on cloud coverage","volume":"18","author":"X Jiang","year":"2025","journal-title":"IEEE J Sel Top Appl Earth Observ Remote Sensing"},{"issue":"17","key":"pone.0355502.ref009","doi-asserted-by":"crossref","first-page":"3180","DOI":"10.3390\/rs16173180","article-title":"A high-resolution distribution dataset of paddy rice in India based on satellite data","volume":"16","author":"X Chen","year":"2024","journal-title":"Remote Sensing"},{"key":"pone.0355502.ref010","doi-asserted-by":"crossref","unstructured":"Zhou Z, Zhao L, Shi H, Sun W, Shi L, Yang J. Early season mapping of rice using of time series sentinel-1 sar images. 2024;:4832\u20135. https:\/\/doi.org\/10.1109\/IGARSS53475.2024.10642214","DOI":"10.1109\/IGARSS53475.2024.10642214"},{"issue":"2","key":"pone.0355502.ref011","doi-asserted-by":"crossref","first-page":"119","DOI":"10.3390\/rs9020119","article-title":"Monitoring rice agriculture across myanmar using time series sentinel-1 assisted by landsat-8 and PALSAR-2","volume":"9","author":"N Torbick","year":"2017","journal-title":"Remote Sensing"},{"key":"pone.0355502.ref012","volume-title":"Grain and Feed Annual, Post Dhaka Report BG2025-0004","author":"USDA","year":"2025"},{"key":"pone.0355502.ref013","first-page":"177","article-title":"Unidirectional trends in rainfall and temperature of Bangladesh","volume":"363","author":"S Shahid","year":"2014","journal-title":"IAHS-Aish Proceed Rep"},{"issue":"1","key":"pone.0355502.ref014","doi-asserted-by":"crossref","first-page":"72","DOI":"10.3329\/bjagri.v50i1.82923","article-title":"Estimation of Boro rice area in Bangladesh using Sentinel-2 imagery and machine learning algorithms: Climate change and agricultural adaptation in Bangladesh","volume":"50","author":"HMH Rahman","year":"2025","journal-title":"Bangladesh J Agricul"},{"key":"pone.0355502.ref015","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: planetary-scale geospatial analysis for everyone","volume":"202","author":"N Gorelick","year":"2017","journal-title":"Remote Sens Environ"},{"issue":"2","key":"pone.0355502.ref016","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1007\/s12518-023-00501-2","article-title":"Use of time series Sentinel-1 and Sentinel-2 image for rice crop inventory in parts of Bangladesh","volume":"15","author":"MdA Aziz","year":"2023","journal-title":"Appl Geomat"},{"issue":"6","key":"pone.0355502.ref017","doi-asserted-by":"crossref","first-page":"1203","DOI":"10.3390\/land14061203","article-title":"Integrating remote sensing, landscape metrics, and random forest algorithm to analyze crop patterns, factors, diversity, and fragmentation in a kharif agricultural landscape","volume":"14","author":"S Banerjee","year":"2025","journal-title":"Land"},{"key":"pone.0355502.ref018","first-page":"141","article-title":"Rice area estimation using parameterized classification of sentinel 1A SAR data","volume":"42","author":"MG Raman","year":"2019","journal-title":"Int Arch Photogram Remote Sens Spatial Inform Sci"},{"issue":"01","key":"pone.0355502.ref019","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1117\/1.JRS.14.014518","article-title":"Monitoring rice growth status in the Mekong Delta, Vietnam using multitemporal Sentinel-1 data","volume":"14","author":"H-P Phung","year":"2020","journal-title":"J Appl Rem Sens"},{"issue":"2","key":"pone.0355502.ref020","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1080\/07038992.2019.1635877","article-title":"Utilizing sentinel-1a radar images for large-area land cover mapping with machine-learning methods","volume":"45","author":"M Imangholiloo","year":"2019","journal-title":"Canadian J Remote Sensing"},{"issue":"3","key":"pone.0355502.ref021","doi-asserted-by":"crossref","first-page":"557","DOI":"10.3390\/agronomy14030557","article-title":"An analysis of the rice-cultivation dynamics in the lower utcubamba river basin using SAR and optical imagery in google earth engine (GEE)","volume":"14","author":"AJ Medina Medina","year":"2024","journal-title":"Agronomy"},{"key":"pone.0355502.ref022","unstructured":"Bangladesh Agricultural Research Council. Administrative map of Bangladesh. 2015. https:\/\/apps.barc.gov.bd\/maps"}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0355502","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T17:22:12Z","timestamp":1789752132000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0355502"}},"subtitle":[],"editor":[{"given":"Bijeesh Kozhikkodan","family":"Veettil","sequence":"first","affiliation":[{"name":"Van Lang University: Truong Dai hoc Van Lang","place":["VIETNAM"]}],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,9,18]]},"references-count":22,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,9,18]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0355502","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,18]]}}}