Spatial dynamics of crop yield provide useful information for improving the production. High sensitivity of crop growth models to uncertainties in input factors and parameters and relatively coarse parameterizations in conventional remote sensing (RS) approaches limited their applications over broad regions. In this study, a process-based and remote sensing driven crop yield model for maize (PRYM–Maize) was developed to estimate regional maize yield, and it was implemented using eight data-model coupling strategies (DMCSs) over the Northeast China Plain (NECP). Simulations under eight DMCSs were validated against the prefecture-level statistics (2010–2012) reported by National Bureau of Statistics of China, and inter-compared. The 3-year averaged result could give more robust estimate than the yearly simulation for maize yield over space. A 3-year averaged validation showed that prefecture-level estimates by PRYM–Maize under DMCS8, which coupled with the development stage (DVS)-based grain-filling algorithm and RS phenology information and leaf area index (LAI), had higher correlation (R, 0.61) and smaller root mean standard error (RMSE, 1.33 t ha–1) with the statistics than did PRYM–Maize under other DMCSs. The result also demonstrated that DVS-based grain-filling algorithm worked better for maize yield than did the harvest index (HI)-based method, and both RS phenology information and LAI worked for improving regional maize yield estimate. These results demonstrate that the developed PRYM–Maize under DMCS8 gives reasonable estimates for maize yield and provides scientific basis facilitating the understanding the spatial variations of maize yield over the NECP.