Accurate crop output forecasting is crucial for global food security and avoiding food crises. As highlighted by the Food and Agriculture Organization an urgent need exists for a robust expansion in global food output; a hike of nearly two-thirds is needed by mid-century to meet burgeoning sustenance requirements. Nevertheless, navigating persistent environmental hurdles and climatic fluctuations renders attaining these targets an arduous task. Reliable and precise agricultural production forecasting techniques are essential for informed decision-making by farmers, dealers, and policymakers. Rice is a significant crop worldwide, providing a staple food source for over four billion people. Accurate rice crop projection is vital for ensuring food security. Vietnam, one of the top rice producers globally, serves as a case study due to its heavy reliance on rice and vulnerability to climate change. Challenges in rice production in Vietnam include limited agricultural land availability and reduced yield growth rates due to climate change. Precise forecasting techniques are needed to enhance yields and reduce waste, benefiting farmers and decision-makers. Remote sensing data, such as Sentinel-1 radar data, can be utilized to predict rice yield. These data sources provide information on weather patterns, soil quality, and plant health, which are crucial for crop production estimation. Sentinel-1 radar data offers details on soil moisture, vegetation indices, and changes in land cover. Recent research has shown that incorporating Sentinel-1 radar data significantly improves the accuracy of rice yield forecasting. For example, studies conducted in Vietnam reported a 30% increase in yield forecasting accuracy when using these data sets.