EGU26-11089, updated on 14 Mar 2026
https://doi.org/10.5194/egusphere-egu26-11089
EGU General Assembly 2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Oral | Thursday, 07 May, 14:40–14:50 (CEST)
 
Room 2.23
From rice planting area mapping to rice agricultural system mapping: A holistic remote sensing framework for understanding China's complex rice systems
Zizhang Zhao1,2, Jinwei Dong1,3, Jilin Yang4, Luo Liu5, Nanshan You1, Xiangming Xiao6, and Geli Zhang2
Zizhang Zhao et al.
  • 1Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
  • 2College of Land Science and Technology, China Agricultural University, Beijing 100193, China
  • 3University of Chinese Academy of Sciences, Beijing 100049, China
  • 4College of Grassland Science and Technology, China Agricultural University, Beijing 100193, China
  • 5Guangdong Province Key Laboratory for Land use and consolidation, South China Agricultural University, Guangzhou 510642, China
  • 6School of Biological Sciences, University of Oklahoma, Norman, OK 73019, USA

Information on the rice agricultural system, including not only planting area but also phenology and cropping intensity, is critical for advancing our understanding of food and water security, methane emissions, carbon and nitrogen cycles, and avian influenza transmission. However, existing efforts primarily focus on mapping planting area and lack a comprehensive picture of the rice agricultural system. To address this gap, we propose a remote sensing-based comprehensive framework for mapping the rice agricultural system in China: First, we identified valid growth cycles of crop by using 30-m Sentinel-2 and Landsat fused data; Second, we applied a well-established phenology-based algorithm to map rice planting areas, by extracting the flooding and rapid growth signals in the transplanting and rapid growth temporal windows; Third, the rice-specific phenology phases (i.e., transplanting, tillering, heading, and mature) were identified using a phenology extraction method tailored to the physiological characteristics of rice; Finally, rice cropping intensity was determined based on detailed phenological phases and planting area data. Due to the accurate identification of crop cycles and pixel-level temporal windows at the national scale, the generated rice planting area maps exhibit a high accuracy across China, with both overall accuracy and F1 scores exceeding 0.8, based on validation with over 13,000 field samples. Improvements in the extraction method have addressed the lag in phenology detection caused by rice's flooded environment, leading to more accurate phenological information. As a result, the phenological data shows reliable accuracy (R2 of 0.6–0.8 and RMSE of 8–15 days), facilitated by the mutual enhancement of rice planting area and phenology mapping. The resultant rice phenology and cropping intensity maps are the first of their kind with 30 m resolution. Together, the resultant rice planting area, rice phenology, and cropping intensity maps provide, for the first time, a comprehensive picture of China's rice agricultural system, better supporting multiple targets related to Sustainable Development Goals.

How to cite: Zhao, Z., Dong, J., Yang, J., Liu, L., You, N., Xiao, X., and Zhang, G.: From rice planting area mapping to rice agricultural system mapping: A holistic remote sensing framework for understanding China's complex rice systems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11089, https://doi.org/10.5194/egusphere-egu26-11089, 2026.