An automated sample generation method by integrating phenology domain optical-SAR features in rice cropping pattern mapping

• Published in: Remote sensing of environment 2024-12, Vol.314, p.114387, Article 114387
• Main Authors: Yang, Jingya, Hu, Qiong, Li, Wenjuan, Song, Qian, Cai, Zhiwen, Zhang, Xinyu, Wei, Haodong, Wu, Wenbin
• Format: Article
• Language: English
• Subjects: Automatic sample generation; Phenological feature; Rice cropping pattern mapping; Sentinel-1/2; Unsupervised clustering
• ISSN: 0034-4257
• DOI: 10.1016/j.rse.2024.114387

Accurate spatio-temporal information on rice cropping patterns is vital for predicting grain production, managing water resource and assessing greenhouse gas emissions. However, current automated mapping of rice cropping patterns at regional scale is heavily constrained by insufficient training samples and frequent cloudy weathers in major rice-producing areas. To tackle this challenge, we proposed a Phenology domain Optical-SAR feature inTegration method to Automatically generate single (SC-Rice) and double cropping Rice (DC-Rice) sample (POSTAR) for efficient and refined rice mapping. POSTAR includes three major steps: (1) generating a potential rice map using a phenology- and object-based classification method with optical data (Sentinel-2 MSI) to select candidate rice samples; (2) employing K-means to identify SC- and DC-Rice candidate samples according to unique SAR-based (Sentinel-1 SAR) phenological features; (3) implementing a two-step refinement strategy to filter high-confidence SC- and DC-Rice samples, maintaining a balance between intraclass phenological variance and sample purity. Test areas selected for validation include the Dongting Lake plain and Poyang Lake plain in South China, as well as Fujin county located in the Sanjiang plain of North China. POSTAR proved effective in producing reliable SC- and DC-Rice samples, achieving a high spectral correlation similarity (>0.85) and low dynamic time wrapping distance (