A worldwide analysis of trend in crop yields and yield variability: Evidence from FAO data

The RapeseedMap10 database: annual maps of rapeseed at a spatial resolution of 10 m based on multi-source data

“…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 (J.…”

“…Five remote-sensing-based methods for rapeseed mapping have been developed in recent decades: (1) machine learning methods (Griffiths et al, 2019;Preidl et al, 2020;She et al, 2015;Tao et al, 2020), (2) classification based on time series data (Ashourloo et al, 2019), (3) threshold segmentation based on phenology (Tian et al, 2019), (4) multi-range spectral feature fitting (Pan et al, 2013), and (5) mapping based on HSV (hue, saturation, and value) transformation and spectral features (Wang et al, 2018). Most of these methods, however, only produce rapeseed maps for a small area using very limited imageries taken on rapeseed peak flowering dates (Ashourloo et al, 2019;She et al, 2015). Rapeseed peak flowering dates vary by area and cultivar because of differences in natural conditions and cultivation habits, especially over large regions (d'Andrimont et al, 2020;Ashourloo et al, 2019;McNairn et al, 2018).…”

“…Most of these methods, however, only produce rapeseed maps for a small area using very limited imageries taken on rapeseed peak flowering dates (Ashourloo et al, 2019;She et al, 2015). Rapeseed peak flowering dates vary by area and cultivar because of differences in natural conditions and cultivation habits, especially over large regions (d'Andrimont et al, 2020;Ashourloo et al, 2019;McNairn et al, 2018). Using the above methods to automatically map rapeseed areas with a finer resolution over a large region is thus still a huge challenge.…”

“…Taking into consideration the unique phenological characteristics of different crops, various researchers have developed potentially useful phenology-based methods for crop identification over large areas (Ashourloo et al, 2019;Dong et al, 2016;Zhang et al, 2020). These algorithms, which generate classification rules by analyzing the unique characteristics of the studied crop, have been successfully applied for mapping rice (Dong et al, 2016), soybean (Zhong et al, 2014), corn (Zhong et al, 2016), and sugarcane (J.…”

“…Wang et al, 2020) but have rarely been applied to rapeseed. Rapeseed has unique reflectance and scattering characteristics (Ashourloo et al, 2019;McNairn et al, 2018;Long, 2015, 2016) and has three canopy morphologies based on leaves, yellow petals, and pods/branches (Ashourloo et al, 2019;Rondanini et al, 2014). Each canopy shape strongly influences how solar radiation is intercepted (Sulik and Long, 2016).…”