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Few studies have focused on the potential impacts of topography on regional crop simulation, which might constrain the development of crop models and lead to inaccurate estimations for food security. In this study, we used remote sensing data to calibrate a regional crop model (MCWLA-Rice) for yield simulation in a double-rice crop rotation system in counties of Hunan province dominated by three landforms (plain, hill, and mountain). The calibration scheme with coarse remote sensing data (Global LAnd Surface Satellite, GLASS) greatly improved model accuracy for the double-rice system and is a promising method for yield estimation in large areas. The average improvement in relative root mean square error (RRMSE) was at most 48.00% for early rice and 41.25% for late rice. The average improvement in coefficient of determination (R2) value was at most 0.54 for early rice and 0.19 for late rice. Estimation of yield in counties dominated by different landform types indicated that: (1) MCWLA-Rice tended to be unstable in areas of complex topography and resulted in unbalanced proportions of overestimations and underestimations. (2) Differences in yield simulation between early rice and late rice varied among counties; yield estimates were highest in predominantly hilly counties, followed by counties dominated by plains, and lowest in predominantly mountainous counties. The results indicated that the topography might harm the accuracy of crop model simulations. Integration of topographic factors into crop models may enable yield estimation with enhanced accuracy to promote social development.

Double-Rice System Simulation in a Topographically Diverse Region—A Remote-Sensing-Driven Case Study in Hunan Province of China