Rice greenhouse gas (GHG) emissions in the Red River Delta, Vietnam, were mapped using multiscale satellite imagery and a processed-based biogeochemical model. Multiscale synthetic aperture radar (SAR) and optical imagery were fed into a random forest classifier using field observations and surveys as training data to map rice extent. Time series analysis then generated geospatial information on crop calendar, hydroperiod and cropping intensity to use as parameters for the denitrification–decomposition (DNDC) model to estimate emissions. Results show a 2015 rice extent of 583,470 ha with total harvested area of 1,078,783 ha and total methane emissions for the delta at 345.4 million kg CH4-C equivalent to 11.5 million tonnes CO2e (carbon dioxide equivalent). Satellite remote sensing was able to accurately map water management and improve model parameterization to understand the impacts of decisions such as irrigation practices, changes in GHG emissions, and mitigation initiatives. The approach described in this research provides a framework for using open-access SAR to derive maps of rice and landscape characteristics to drive process models like DNDC. These types of tools and approaches will support the next generation of monitoring, reporting, and verification (MRV) efforts to combat climate change and support robust and transparent policy decisions.