Mapping Paddy Rice in Rice–Wetland Coexistence Zone by Integrating Sentinel-1 and Sentinel-2 Data

Abstract

Accurate mapping of vegetation in the coexisting area of paddy fields and wetlands plays a key role in the sustainable development of agriculture and ecology, which is critical for national food security and ecosystem balance. The phenology-based rice mapping algorithm uses unique flooding stages of paddy rice, and it has been widely used for rice mapping. However, wetlands with similar flooding signatures make rice extraction in rice–wetland coexistence challenging. In this study, we analyzed phenology differences between rice and wetlands based on the Sentinel-1/2 data and used the random forest algorithm to map vegetation in the Poyang Lake Basin, which is a typical rice–wetland coexistence zone in the south of China. The rice maps were validated with reference data, and the highest overall accuracy and Kappa coefficient was 0.94 and 0.93, respectively. First, monthly median composited and J-M distance methods were used to analyze radar and spectral data in key phenological periods, and it was found that the combination of the two approaches can effectively improve the confused signal between paddy rice and wetlands. Second, the VV and VH polarization characteristics of Sentinel-1 data enable better identification of wetlands and rice. Third, from 2018 to 2022, paddy rice in the Poyang Lake Basin showed the characteristics of planting structure around the Poyang Lake and its tributaries. The mudflats were mostly found in the middle and northeast of Poyang Lake, and the wetland vegetation was found surrounding the mudflats, forming a nibbling shape from the lake’s periphery to its center. Our study demonstrates the potential of mapping paddy rice in the rice–wetland coexistence zone using the combination of Sentinel-1 and Sentinel-2 imagery, which would be beneficial for balancing the changes between paddy rice and wetlands and improving the vulnerability of the local ecological environment.

Keywords

• paddy rice
• sentinel
• Google Earth Engine
• vegetation phenology
• machine learning algorithm