remote sensing Article 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 Rudiyanto 1,2,* , Budiman Minasny 3,* , Ramisah M. Shah 1,2, Norhidayah Che Soh 1,2, Chusnul Arif 4 and Budi Indra Setiawan 4,* 1 School of Food Science and Technology, Universiti Malaysia Terengganu, Kuala Nerus, Terengganu 21030, Malaysia 2 Faculty of Fishery and Food Sciences, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu, Malaysia 3 School of Life & Environmental Sciences, Sydney Institute of Agriculture, The University of Sydney, Sydney, NSW 2006, Australia 4 Department of Civil and Environmental Engineering, IPB University (Bogor Agricultural University), Bogor 16680, Indonesia * Correspondence:
[email protected] (R.);
[email protected] (B.M.);
[email protected] (B.I.S.) Received: 16 June 2019; Accepted: 10 July 2019; Published: 12 July 2019 Abstract: More than 50% of the world’s 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.