Preprints
https://doi.org/10.5194/essd-2022-423
https://doi.org/10.5194/essd-2022-423
13 Dec 2022
 | 13 Dec 2022
Status: this discussion paper is a preprint. It has been under review for the journal Earth System Science Data (ESSD). The manuscript was not accepted for further review after discussion.

GlobalWheatYield4km: a global wheat yield dataset at 4-km resolution during 1982–2020 based on deep learning approach

Yuchuan Luo, Zhao Zhang, Juan Cao, Liangliang Zhang, Jing Zhang, Jichong Han, Huimin Zhuang, Fei Cheng, Jialu Xu, and Fulu Tao

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).

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Yuchuan Luo, Zhao Zhang, Juan Cao, Liangliang Zhang, Jing Zhang, Jichong Han, Huimin Zhuang, Fei Cheng, Jialu Xu, and Fulu Tao

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-423', Anonymous Referee #1, 07 Mar 2023
  • RC2: 'Comment on essd-2022-423', Anonymous Referee #2, 31 Mar 2023
  • RC3: 'Comment on essd-2022-423', Anonymous Referee #3, 28 Apr 2023

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-423', Anonymous Referee #1, 07 Mar 2023
  • RC2: 'Comment on essd-2022-423', Anonymous Referee #2, 31 Mar 2023
  • RC3: 'Comment on essd-2022-423', Anonymous Referee #3, 28 Apr 2023
Yuchuan Luo, Zhao Zhang, Juan Cao, Liangliang Zhang, Jing Zhang, Jichong Han, Huimin Zhuang, Fei Cheng, Jialu Xu, and Fulu Tao

Data sets

GlobalWheatYield4km: a global wheat yield dataset at 4-km resolution during 1982-2020 based on deep learning approaches Yuchuan Luo, Zhao Zhang, Juan Cao, Liangliang Zhang, Jing Zhang, Jichong Han, Huimin Zhuang, Fei Cheng, Jialu Xu, and Fulu Tao https://doi.org/10.6084/m9.figshare.10025006

Yuchuan Luo, Zhao Zhang, Juan Cao, Liangliang Zhang, Jing Zhang, Jichong Han, Huimin Zhuang, Fei Cheng, Jialu Xu, and Fulu Tao

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Short summary
We generated a 4-km dataset of global wheat yield (GlobalWheatYield4km) from 1982 to 2020 using a deep learning approach. The dataset was highly consistent with observed yields, which captured 82 % of yield variations with RMSE of 619.8 kg/ha across all subnational regions and years. Our GlobalWheatYield4km can be applied for many purposes, including large-scale agricultural system modeling and climate change impact assessments.
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