Preprints
https://doi.org/10.5194/essd-2022-417
https://doi.org/10.5194/essd-2022-417
 
16 Jan 2023
16 Jan 2023
Status: this preprint is currently under review for the journal ESSD.

ChinaWheatYield30m: A 30-m annual winter wheat yield dataset from 2016 to 2021 in China

Yu Zhao1,2,, Shaoyu Han1,3,, Jie Zheng1, Hanyu Xue1, Zhenhai Li1,4, Yang Meng1,2, XuGang Li5, Xiaodong Yang1, Zhenhong Li6, Shuhong Cai5, and Guijun Yang1,6 Yu Zhao et al.
  • 1Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
  • 2National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
  • 3College of Agronomy, Henan Agricultural University, Zhengzhou 450046, China
  • 4College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
  • 5Cultivated Land Monitoring and Protection Center of Hebei, Shijiazhuang, 050056, China
  • 6School of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, China
  • These authors contributed equally to this work.

Abstract. Generating spatial crop yield information is of great significance for academic research and guiding agricultural policy. Most existing public yield datasets have a coarse spatial resolution. Although these datasets are useful for analyzing regional temporal and spatial change, they cannot deal with spatial heterogeneity, which happens to be the most significant characteristic of the Chinese small-scale farmers' economy. Hence, we generated a 30-m Chinese winter wheat yield dataset (ChinaWheatYield30m) for major winter wheat-producing provinces in China for the period 2016–2021 with a semi-mechanistic model (hierarchical linear model, HLM). The yield prediction model was built by considering the wheat growth status and climatic factors. It can estimate wheat yield with excellent accuracy and low cost using a combination of satellite observations and regional meteorological information (i.e., Landsat 8, Sentinel-2 and ERA5 data from the Google Earth Engine (GEE) platform). The results were validated by using in situ measurements and census statistics and indicated a stable performance of the HLM model based on calibration datasets across China, with r of 0.81** and nRMSE of 12.59 %. With regards to validation, the ChinaWheatYield30m dataset was highly consistent with in situ measurement data and census data, indicated by r (nRMSE) of 0.72** (15.34 %) and 0.73** (19.41 %). With its high spatial resolution and accuracy, the ChinaWheatYield30m is a valuable dataset that can support numerous applications, including crop production modeling and regional climate evaluation.

Yu Zhao et al.

Status: open (until 13 Mar 2023)

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Yu Zhao et al.

Data sets

ChinaWheatYield30m: A 30-m annual winter wheat yield dataset from 2016 to 2021 in China Yu Zhao, Shaoyu Han, Jie Zheng, Hanyu Xue, Zhenhai Li, Yang Meng, Xuguang Li, Xiaodong Yang, Zhenhong Li, Shuhong Cai, Guijun Yang https://doi.org/10.5281/zenodo.7360753

Yu Zhao et al.

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Short summary
In the present study, we generated a 30m Chinese winter wheat yield from 2016 to 2021, called ChinaWheatYield30m. The dataset is with great accuracy in broad area. Also, it is the known highest resolution of yield dataset, such a dataset will provide basic knowledge of exquisite wheat yield distribution, which can be applied for many purposes including crop production modelling or regional climate evaluation.