LAUSR

Abstract Precise mapping and forecasting of rice cropping systems are essential for guaranteeing regional and even global food security. The central areas of China are the dominant Chinese rice-producing areas. However, due to limited land and a scarcity of skilled rural labourers in China, it has been difficult to meet the growing demand for rice production in recent years. Furthermore, the complex topography and climate in central China make it difficult to map paddy rice accurately. This paper maps the spatial pattern of rice in central China by using multiple data sources and a phenology-based time-series similarity approach, explores the driving forces for rice patterns through grey relational analysis (GRA), and presents a simulated future rice cropping system map by adding socio-economic factors to a cellular automata-Markov (CA-Markov) model. This study concluded that for 2015, the overall classification accuracy of rice was 88.12%, with a Kappa coefficient of 0.90. The planting acreage accuracies for single- and double-season rice in 2015 were 89.51% and 77.00%, respectively, and the corresponding R2 value for both were 0.80. From 2005 to 2015, the planting acreage of single-season rice significantly increased, while the planting acreage of double-season rice generally decreased. The planting acreage of total rice exhibited an overall increasing trend during 10 years. The cumulative temperature, slope and distance to water bodies were key influencing factors for the spatial distribution of rice. Compared with that for 2015, the simulated total planting area of rice for 2030 was slightly higher, and the spatial pattern of double-season rice exhibited an obvious southward trend. These findings show that the proposed rice mapping and forecasting method is effective. This method allows us to predict future rice patterns precisely under the influence of human activities in complex cropping conditions and various rice cropping systems.