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).
Highlights
Fossil fuels are currently the main source of energy (Fang et al, 2016; Shafiee and Topal, 2009), their overexploitation is increasing various threats to human survival, such as greenhouse gas emission and environmental pollution (Fang et al, 2016; Höök and Tang, 2013)
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
As indicated by their accuracy based on confusion matrix values, our rapeseed maps were consistent at the pixel level with maps of the American Cropland Data Layer (CDL) in 2018 and 2019 and the Canadian Annual Crop Inventory (ACI), British Crop Map of England (CROME), and French Land Cover Map of France (LCMF) in 2018
Summary
Fossil fuels are currently the main source of energy (Fang et al, 2016; Shafiee and Topal, 2009), their overexploitation is increasing various threats to human survival, such as greenhouse gas emission and environmental pollution (Fang et al, 2016; Höök and Tang, 2013). Biofuel energy seems to be a promising alternative energy source (Hassan and Kalam, 2013). Rapeseed (Brassica napus L.) is an important source of biofuels, edible oil, animal feed, and plant protein powder (Firrisa et al, 2014; Malça and Freire, 2009; Sulik and Long, 2016). Global agricultural statistics on rapeseed in many regions are derived from field surveys, field sampling, and producer reports (Arata et al, 2020; Fuglie, 2010). Ground-based methods, which are time-consuming and labor-intensive, fail to provide detailed spatial information on rapeseed fields Remote sensing technology plays an important role in agricultural monitoring and yields accurate, objective spatial–temporal crop information (Dong et al, 2016; Salmon et al, 2015)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
