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. 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—T&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.
Highlights
Rice is a source of food for more than half of the global population [1]
The results show that rice fields in the study area have a double cropping system
At site 2, this study showed that rice fields in the northern and southern part of the MADA granary area are planted in October for the main season and April for the off-season
Summary
The past decade has seen a rapid increase in the use of satellite-based remote sensing data to map and monitor paddy rice fields. This growth can be attributed to at least three factors—the availability of open remote sensing data, advanced machine learning methods, and access to cloud computing platforms that can handle big data storage and processing. Rice fields have been mapped using different data sources, including optical products (e.g., MODIS, Landsat, Sentinel-2, and SPOT) and Synthetic Aperture Radar (SAR) or microwave data (e.g., RADARSAT, ALOS PALSAR, and Sentinel-1). SAR-based images have gained attention because they are not affected by clouds or illumination conditions [3]
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