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.
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
Received: 02 Dec 2022 – Discussion started: 16 Jan 2023
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.
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.
In the present study, we generated a 30m Chinese winter wheat yield from 2016 to 2021, called...
