Author:
Han Jichong,Zhang Zhao,Luo Yuchuan,Cao Juan,Zhang Liangliang,Zhang Jing,Li Ziyue
Abstract
Abstract. Large-scale, high-resolution maps of rapeseed (Brassica napus L.), a
major oilseed crop, are critical for predicting annual production and
ensuring global energy security, but such maps are still not freely
available for many areas. In this study, we developed a new pixel- and
phenology-based algorithm and produced a new data product for rapeseed
planting areas (2017–2019) in 33 countries at 10 m spatial resolution based
on multiple data. Our product is strongly consistent at the national level
with official statistics of the Food and Agricultural Organization of the
United Nations. Our rapeseed maps achieved F1 spatial consistency scores of
at least 0.81 when compared with the Cropland Data Layer in the United
States, the Annual Crop Inventory in Canada, the Crop Map of England, and
the Land Cover Map of France. Moreover, F1 scores based on independent
validation samples ranged from 0.84 to 0.91, implying a good consistency
with ground truth. In almost all countries covered in this study, the
rapeseed crop rotation interval was at least 2 years. Our derived maps
suggest, with reasonable accuracy, the robustness of the algorithm in
identifying rapeseed over large regions with various climates and
landscapes. Scientists and local growers can use the freely downloadable
derived rapeseed planting areas to help predict rapeseed production and
optimize planting structures. The product is publicly available at
https://doi.org/10.17632/ydf3m7pd4j.3 (Han et al., 2021).
Funder
National Key Research and Development Program of China
Subject
General Earth and Planetary Sciences
Cited by
36 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献