Published in the Journal of Remote Sensing on July 8, the research addresses the importance of precise rice mapping, which is crucial for effective agricultural management and food security, particularly in key rice-producing regions such as Guangdong, China. The challenge of mapping in tropical and subtropical areas arises from the lack of high-quality optical images and the varied planting times across these regions.

"There are two main difficulties in tropical and subtropical paddy rice mapping: the lack of high-quality optical images and differences in paddy rice planting times," explained Qian Shi, a professor at the School of Geography and Planning at Sun Yat-sen University and lead author of the study.

To tackle these issues, the research team employed a method known as phenology matching. This approach aligns rice plant growth stages with data from the Sentinel-1 and Sentinel-2 satellites to enhance mapping accuracy in Guangdong. The ability of Sentinel-1 to penetrate cloud cover enabled the researchers to identify specific rice growth periods. They then extracted radar and optical data from both satellites, which were analyzed using machine learning classifiers to improve mapping precision.

"By matching features derived from Sentinel-1 and Sentinel-2 to specific growth stages, our classifiers significantly enhanced mapping accuracy compared to conventional methods," Shi added.

The multi-source data strategy yielded accuracy improvements ranging from 6.44% to 16.10%. These results were corroborated by regression analysis, which showed a strong correlation between the mapped areas and statistical data.

Additionally, the study revealed key factors influencing rice growth and distribution in Guangdong, with thermal conditions, particularly cold severity during growth stages, identified as primary factors. The study also highlighted the role of both natural and human factors, including land slope and minimum temperature, in shaping the spatial distribution of rice paddies.

The researchers believe that applying this methodology to other tropical and subtropical regions will further refine mapping accuracy and expand understanding of global rice production patterns.

Research Report:Paddy Rice Mapping Based on Phenology Matching and Cultivation Pattern Analysis Combining Multi-Source Data in Guangdong, China

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