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https://doi.org/10.5194/essd-2022-297
https://doi.org/10.5194/essd-2022-297
02 Sep 2022
 | 02 Sep 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 approaches

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 crop yield over large areas is paramount for ensuring global food security and guiding policy-making. However, most public datasets are 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 distribution. 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 statistics covering ~11000 political units). The results showed that GlobalWheatYield4km captured 82 % of yield variations with RMSE of 619.8 kg/ha. In addition, our dataset had a higher accuracy (R2 ~0.73) as compared with Spatial Production Allocation Model (R2 ~ 0.49) across all regions and years. The GlobalWheatYield4km dataset will 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).

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-2022-297', Anonymous Referee #1, 07 Oct 2022
  • RC2: 'Comment on essd-2022-297', Anonymous Referee #2, 09 Oct 2022
  • RC3: 'Comment on essd-2022-297', Anonymous Referee #3, 28 Oct 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-297', Anonymous Referee #1, 07 Oct 2022
  • RC2: 'Comment on essd-2022-297', Anonymous Referee #2, 09 Oct 2022
  • RC3: 'Comment on essd-2022-297', Anonymous Referee #3, 28 Oct 2022
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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Latest update: 22 Feb 2024
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Short summary
We generated a 4-km dataset of global wheat yield (GlobalWheatYield4km) from 1982 to 2020 using data-driven models. Compared with subnational statistics covering ~11000 political units, GlobalWheatYield4km captured 82 % of yield variations with RMSE of 619.8 kg/ha. The dataset will play important roles in modelling crop system and assessing climate impact over larger areas
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