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.

This systematic literature review (SLR) examines the integration of circular economy (CE) principles into the agri-food supply chain over the past 20 years. The review aims to consolidate existing knowledge, identify research gaps, and provide actionable insights for future research. A comprehensive search across major databases yielded 1200 articles, which were screened, filtered, and assessed, resulting in a final selection of 100 high-quality studies. This review highlights the significant potential of the CE to enhance sustainability, reduce waste, and improve resource efficiency in the agri-food sector. These studies were categorized based on thematic issues, geographical regions, and supply chain stages. The findings highlight the predominant focus on waste management, resource efficiency, sustainable practices, and economic impacts, reflecting regional priorities and regulatory frameworks. The review also underscores the multiplier effect of CE implementation, which enhances sustainability and efficiency within the agri-food sector and generates significant economic and environmental benefits on a regional and global scale. Key challenges and areas for further research are identified, providing a comprehensive foundation for advancing CE practices in the agri-food supply chain.

Rewetting peatland is an ongoing effort in Indonesia to restore the hydrological cycle and carbon balance of the ecosystem. However, quantifying the impact of rewetting on mitigating fire remains a challenge. Here, we assess the impact of large-scale rewetting on fire risks and occurrences (duration, coverage area, and the number of events) in 2015–2021. The weather research and forecasting (WRF) model was integrated with a drought–fire model to spatially quantify fire hazards in Riau, Sumatra. The results show that rewetting has resulted in decreasing the frequency of extreme events in the study area (pre- and post-rewetting, respectively, were seven and four events). Although the area influenced by extreme events reduced following rewetting by 5%, the mean duration of extreme events increased. Our findings reveal that widespread prolonged extreme fire hazards only occurred during drying El Niño events in 2015 and 2019. The findings obtained in this case study provide quantitative evidence of the reduced fire hazard resulting from peat restoration in Indonesia. Further, the findings assist in assessing the success of peatland restoration programs and improve our knowledge of the ability to monitor and forecast fire risks in tropical peatlands.