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A bibliometric study on mapping the rice cropping systems in VMD is crucial for understanding the trend of EO-based rice mapping and how remote sensing technologies are essential to address the food security issue in the region. This article presents an overview of Earth observation (EO)-based rice mapping strategies since 1979, prioritizing the scope of data, approaches, and techniques derived from 3700 research articles worldwide and contrasting them with the Vietnamese Mekong Delta (VMD). Various quantitative analyses were conducted through bibliometric analysis using the VOS viewer and Scopus database. Optical images, particularly the Landsat (~16%) and MODIS (~12%) time series datasets, were the most commonly utilized globally. MODIS data (~31%) had the highest share in the VMD context, followed by Landsat data (~19%), while Sentinel series (~13% for global and ~16% for VMD) data became more popular in recent years. Research on rice mapping using UAVs has been gradually creeping into rice mapping research globally, but a gap is yet to be filled in the VMD. The most widely used approaches for rice mapping globally were Random Forest, Support Vector Machine, and Principal Component Analysis. Spectral indices like EVI, NDVI, and RVI were commonly used for rice mapping and monitoring. The findings underscore the critical role of EO-based rice mapping studies in the VMD in addressing sustainability and food security challenges.
