Mapping rapeseed in China during 2017-2021 using Sentinel data: an automated approach integrating rule-based sample generation and a one-class classifier (RSG-OC)
Creators: name: Zang, Yunze
name: Qiu, Yuean
name: Chen, Xuehong
name: Chen, Jin
name: Yang, Wei
name: Liu, Yifei
name: Peng, Longkang
name: Shen, Miaogen
name: Cao, Xin
Publisher: name: Taylor & Francis
Year: 2023
Subjects: subject: Space Science
subject: Biotechnology
subject: Environmental Sciences not elsewhere classified
subject: Ecology
subject: FOS: Biological sciences
subject: Biological Sciences not elsewhere classified
subject: Information Systems not elsewhere classified
subject: Mathematical Sciences not elsewhere classified
subject: Plant Biology

description: Rapeseed mapping is important for national food security and government regulation of land use. Various methods, including empirical index-based and machine learning-based methods, have been developed to identify rapeseed using remote sensing. Empirical index-based methods commonly employed empirically designed indices to enhance rapeseed’s bright yellow spectral feature during the flowering period, which is straightforward to implement. Unfortunately, the heavy cloud cover in the flowering period of China would lead to serious omission errors; and the required flowering period varies spatially and yearly, which often cannot be acquired accurately. Machine learning-based methods mitigate the reliance on clear observations during the flowering period by inputting all-season imagery to adaptively learn features. However, it is difficult to collect sufficient samples across all of China considering the large intraclass variation in both land cover types of rapeseed and non-rapeseed. This study proposed an automated rapeseed mapping approach integrating rule-based sample generation and a one-class classifier (RSG-OC) to overcome the shortcomings of the two types of methods. First, a set of sample selection rules based on empirical indices of rapeseed were developed to automatically generate samples in cloud-free pixels during the predicted flowering period throughout China. Second, all available features composited based on the rapeseed phenological calendar were used for classification to eliminate the phenology differences in different regions. Third, a specific sample augmentation that removes the observation in the flowering period was employed to improve the generalization to the pixels without cloud-free observation in the flowering period. Finally, to avoid the need for diverse samples of nonrapeseed classes, a typical one-class classifier, positive unlabeled learning implemented by random forest (PUL-RF) trained by the generated samples, was applied to map rapeseed. With the proposed method, China rapeseed was mapped at 20 m resolution during 2017–2021 based on the Google Earth Engine (GEE). Validation on six typical rapeseed planting areas demonstrates that RSG-OC achieves an average accuracy of 94.90%. In comparison, the average accuracy of the other methods ranged from 83.33% to 88.25%, all of which were poorer than the proposed method. Additional experiments show that the performance of RSG-OC was not sensitive to cloud contamination, inaccurate predicted flowering time and the threshold of sample selection rule. These results indicate that the rapeseed maps produced in China are overall reliable and that the proposed method is an effective and robust method for annual rapeseed mapping across China.