{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T20:21:41Z","timestamp":1782937301388,"version":"3.54.5"},"reference-count":69,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2019,7,12]],"date-time":"2019-07-12T00:00:00Z","timestamp":1562889600000},"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>More than 50% of the world\u2019s population consumes rice. Accurate and up-to-date information on rice field extent is important to help manage food and water security. Currently, field surveys or MODIS satellite data are used to estimate rice growing areas. This study presents a cost-effective methodology for near-real-time mapping and monitoring of rice growth extent and cropping patterns over a large area. This novel method produces high-resolution monthly maps (10 m resolution) of rice growing areas, as well as rice growth stages. The method integrates temporal Sentinel-1 data and rice phenological parameters with the Google Earth Engine (GEE) cloud-based platform. It uses monthly median time series of Sentinel-1 at VH polarization from September 2016 to October 2018. The two study areas are the northern region of West Java, Indonesia (0.75 million ha), and the Kedah and Perlis states in Malaysia (over 1 million ha). K-means clustering, hierarchical cluster analysis (HCA), and a visual interpretation of VH polarization time series profiles are used to generate rice extent, cropping patterns, and spatiotemporal distribution of growth stages. To automate the process, four supervised classification methods (support vector machine (SVM), artificial neural networks (ANN), random forests, and C5.0 classification models) were independently trialled to identify cluster labels. The results from each classification method were compared. The method can also forecast rice extent for up to two months. The VH polarization data can identify four growth stages of rice\u2014T&amp;P: tillage and planting (30 days); V: vegetative-1 and 2 (60 days); R: reproductive (30 days); M: maturity (30 days). Compared to field survey data, this method measures overall rice extent with an accuracy of 96.5% and a kappa coefficient of 0.92. SVM and ANN show better performance than random forest and C5.0 models. This simple and robust method could be rolled out across Southeast Asia, and could be used as an alternative to time-consuming, expensive field surveys.<\/jats:p>","DOI":"10.3390\/rs11141666","type":"journal-article","created":{"date-parts":[[2019,7,12]],"date-time":"2019-07-12T11:49:38Z","timestamp":1562932178000},"page":"1666","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":93,"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"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4760-9783","authenticated-orcid":false,"family":"Rudiyanto","sequence":"first","affiliation":[{"name":"School of Food Science and Technology, Universiti Malaysia Terengganu, Kuala Nerus, Terengganu 21030, Malaysia"},{"name":"Faculty of Fishery and Food Sciences, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1182-2371","authenticated-orcid":false,"given":"Budiman","family":"Minasny","sequence":"additional","affiliation":[{"name":"School of Life &amp; Environmental Sciences, Sydney Institute of Agriculture, The University of Sydney, Sydney, NSW 2006, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ramisah","family":"Shah","sequence":"additional","affiliation":[{"name":"School of Food Science and Technology, Universiti Malaysia Terengganu, Kuala Nerus, Terengganu 21030, Malaysia"},{"name":"Faculty of Fishery and Food Sciences, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Norhidayah","family":"Che Soh","sequence":"additional","affiliation":[{"name":"School of Food Science and Technology, Universiti Malaysia Terengganu, Kuala Nerus, Terengganu 21030, Malaysia"},{"name":"Faculty of Fishery and Food Sciences, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chusnul","family":"Arif","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, IPB University (Bogor Agricultural University), Bogor 16680, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6011-9447","authenticated-orcid":false,"given":"Budi","family":"Indra Setiawan","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, IPB University (Bogor Agricultural University), Bogor 16680, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2101","DOI":"10.1080\/01431161.2012.738946","article-title":"Remote sensing of rice crop areas","volume":"34","author":"Kuenzer","year":"2013","journal-title":"Int. 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