@article{pub.1153614644,
 abstract = {Accurate and spatially explicit information on global crop yield is paramount for guiding policy-making and ensuring food security. However, most public datasets are at coarse resolution in both space and time. Here, we used data-driven models to develop a 4-km dataset of global wheat yield (GlobalWheatYield4km) from 1982 to 2020. First, we proposed a phenology-based approach to map spatial distributions of spring and winter wheat. Then we determined the optimal grid-scale yield estimation model by comparing the performance of two data-driven models (i.e., Random Forest (RF) and Long Short-Term Memory (LSTM)), with publicly available data (i.e., satellite and climatic data from the Google Earth Engine (GEE) platform, soil properties, and subnational-level census data covering ~11000 political units). The results showed that GlobalWheatYield4km captured 82 % of yield variations with RMSE of 619.8 kg/ha across all subnational regions and years. In addition, our dataset had a higher accuracy (R2 ~0.71) as compared with Spatial Production Allocation Model (SPAM) (R2 ~ 0.49) across all subnational regions and three years. The GlobalWheatYield4km dataset might play important roles in modelling crop system and assessing climate impact over larger areas (DOI of the referenced dataset: https://doi.org/10.6084/m9.figshare.10025006; Luo et al., 2022b).},
 author = {Luo, Yuchuan and Zhang, Zhao and Cao, Juan and Zhang, Liangliang and Zhang, Jing and Han, Jichong and Zhuang, Huimin and Cheng, Fei and Xu, Jialu and Tao, Fulu},
 doi = {10.5194/essd-2022-423},
 journal = {EGUsphere},
 keywords = {},
 note = {https://essd.copernicus.org/preprints/essd-2022-423/essd-2022-423.pdf},
 number = {},
 pages = {1-21},
 title = {GlobalWheatYield4km: a global wheat yield dataset at 4-km resolution during 1982–2020 based on deep learning approach},
 url = {https://app.dimensions.ai/details/publication/pub.1153614644},
 volume = {2022},
 year = {2022}
}