An Automatic Method for Rice Mapping Based on Phenological Features with Sentinel-1 Time-Series Images

作者：Guixiang Tian, Heping Li, Qi Jiang, Baojun Qiao, Ning Li, Zhengwei Guo, Jianhui Zhao, Huijin Yang · 发表于：Remote Sensing · 年份：2023 · DOI：10.3390/rs15112785 · 被引用次数：14 · 研究领域：Remote Sensing in Agriculture、Remote Sensing and Land Use、Remote-Sensing Image Classification

Rice is one of the most important staple foods in the world, feeding more than 50% of the global population. However, rice is also a significant emitter of greenhouse gases and plays a role in global climate change. As a result, quickly and accurately obtaining the rice mapping is crucial for ensuring global food security and mitigating global warming. In this study, we proposed an automated rice mapping method called automated rice mapping using V-shaped phenological features of rice (Auto-RMVPF) based on the time-series Sentinel-1A images, which are composed of four main steps. First, the dynamic threshold method automatically extracts abundant rice samples by flooding signals. Second, the second-order difference method automatically extracts the phenological period of rice based on the scattering feature of rice samples. Then, the key “V” feature of the VH backscatter time series, which rises before and after rice transplanting due to flooding, is used for rice mapping. Finally, the farmland mask is extracted to avoid interference from non-farmland features on the rice map, and the median filter is applied to remove noise from the rice map and obtain the final spatial distribution of rice. The results show that the Auto-RMVPF method not only can automatically obtain abundant rice samples but also can extract the accurate phenological period of rice. At the same time, the accuracy of rice mapping is also satisfactory, with an overall accuracy is more than 95% and an F1 scor...