Identifying the spatiotemporal changes of annual harvesting areas for three staple crops in China by integrating multi-data sources

Reliable and continuous information on major crop harvesting areas is fundamental to investigate land surface dynamics and make policies affecting agricultural production, land use, and sustainable development. However, there is currently no spatially explicit and time-continuous crop harvesting area information with a high resolution for China. The spatiotemporal patterns of major crop harvesting areas at a national scale have rarely been investigated. In this study, we proposed a new crop phenology-based crop mapping approach to generate a 1 km harvesting area dataset for three staple crops (i.e. rice, wheat, and maize) in China from 2000 to 2015 based on GLASS leaf area index (LAI) products. First, we retrieved key phenological dates of the three staple crops by combining the inflexion- and threshold-based methods. Then, we determined the grids cultivated for a certain crop if its three key phenological dates could be simultaneously identified. Finally, we developed crop classification maps and a dataset of annual harvesting areas (ChinaCropArea1 km), comprehensively considering the characteristics of crop phenology and the references of drylands and paddy fields. Compared with the county-level agricultural statistical data, the crop classification had a high accuracy, with R-2 values consistently greater than 0.8. The spatiotemporal patterns of major crop harvesting areas during the period were further analyzed. The results showed that paddy rice harvesting areas had expanded aggressively in northeastern China but decreased in southern China. Maize harvesting areas expanded substantially in major maize cultivation areas across China. Wheat harvesting areas declined overall, although they increased notably in their major production areas. The spatiotemporal patterns could be ascribed to various anthropogenic, biophysical, and social-economic drivers, including urbanization, reduced cropping intensity in southern China, frequent disasters from climate change, and large areas of abandoned farmland in northern and southwestern China. The resultant dataset can be applied for many purposes, including land surface modeling, agro-ecosystem modeling, agricultural production and land use policy-making.