A hierarchy framework for automatic mapping complex rice cropping patterns: National-scale annual datasets of ChinaCP-Rice10m during 2019–2024

Paddy rice cultivation can be implemented as single rice, double rice, and upland rice. Information on rice cropping patterns (CP) is crucial for enriching our understanding of sustainable intensification for a larger rice bowl. However, existing rice mapping efforts mainly focus on estimating planting area. It is challenging to produce annual rice CP datasets due to the difficulties of locating inundation signals from rice transplanting in complex-cropping regions. There is a lack of annual rice CP datasets in China in recent years, and there is an obvious omission error in existing rice algorithms in multiple-cropping regions. This study proposes a hierarchical Framework for the Automatic Mapping of complex Rice cropping patterns through the coupling of management practices and canopy structure. The challenge of the non-uniqueness of the "V" shaped feature in the Synthetic Aperture Radar (SAR) time series dataset was addressed by a phenology-assisted "V" shaped identification method using the Sentinel-1/2 datasets. The omission errors among multiple cropping regions were delivered using the hierarchy framework. This framework illustrated good performances among different rice cropping systems in China. This study developed the first 10-m annual rice Cropping Patterns (ChinaCP-Rice10m) maps over conterminous China from 2019 to 2024. An overall accuracy of over 90% was obtained when evaluated by ground-truth reference datasets. The upland rice cultivated areas have constantly increased, despite an overall declining trend of rice plantation areas since 2019. This study opens a new direction towards automatic complex rice cropping patterns by coupling management practices and canopy structure. • A hierarchy mapping framework for mapping complex rice cropping patterns (FAMR) was proposed. • The omission errors of existing rice algorithms in multiple-cropping regions were significantly reduced. • A phenology-assisted "V"-shaped identification method addressed the non-uniqueness of inundation signals. • The first annual ChinaCP-Rice10m datasets were produced in China during 2019–2024.