A novel and robust method for large-scale single-season rice mapping based on phenology and statistical data

Accurate and detailed spatial information on rice cultivation is essential to developing agricultural policy and reducing the negative impacts of agriculture. However, the dependence of most traditional methods on samples severely limits the feasibility of large-scale rice cultivation mapping. This study proposes a robust large-scale sample-free monitoring method for single-season rice in northern China. A new rice phenology index, quantifying dynamic phenological features of rice (i.e., the occurrence of flooding during transplanting and the growth of rice after transplanting), was generated to highlight rice. Subsequently, a constrained cyclic threshold classification strategy was designed to obtain plausible rice maps using statistical data. Innovatively combining rice mapping with statistical data, the most detailed (10 m) single-season rice map in northern China to date was created. Compared with three other high-precision rice map products, the resulting rice map has high accuracy and good local details. The results indicate that the rice phenology index has excellent and robust performance in identifying rice cultivation locations in northern China. Moreover, the proposed mapping method exhibits clear advantages in the tracking of large-scale and historical rice cultivation. As a whole, this study provides a paradigm of using statistical data instead of samples for crop mapping.