文献类型： 外文期刊
作者： Zhao, Yu 1 ; Han, Shaoyu 1 ; Zheng, Jie 1 ; Xue, Hanyu 1 ; Li, Zhenhai 1 ; Meng, Yang 1 ; Li, Xuguang 5 ; Yang, Xiaodong 1 ; Li, Zhenhong 6 ; Cai, Shuhong 5 ; Yang, Guijun 1 ;
作者机构： 1.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Key Lab Quantitat Remote Sensing Agr, Minist Agr & Rural Affairs, Beijing 100097, Peoples R China
2.Shanxi Agr Univ, Coll Agr, Taigu 030801, Shanxi, Peoples R China
3.Henan Acad Agr Sci, Inst Agr Econ & Informat, Zhengzhou 450002, Henan, Peoples R China
4.Shandong Univ Sci & Technol, Coll Geodesy & Geomat, Qingdao 266590, Peoples R China
5.Cultivated Land Monitoring & Protect Ctr Hebei, Shijiazhuang 050056, Peoples R China
6.Changan Univ, Sch Geol Engn & Geomat, Xian 710054, Peoples R China
期刊名称：EARTH SYSTEM SCIENCE DATA （ 影响因子：11.4； 五年影响因子：12.2 ）
ISSN： 1866-3508
年卷期： 2023 年 15 卷 9 期
页码：
收录情况： SCI
摘要： Generating spatial crop yield information is of great significance for academic research and guiding agricultural policy. Existing public yield datasets have a coarse spatial resolution, spanning from 1 to 43 km. Although these datasets are useful for analyzing large-scale temporal and spatial change in yield, they cannot deal with small-scale spatial heterogeneity, which happens to be the most significant characteristic of the Chinese 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 using in situ measurements and census statistics and indicated a stable performance of the HLM based on calibration datasets across China, with a correlation coefficient (r) of 0.81 and a relative root mean square error (rRMSE) of 12.59 %. With regards to validation, the ChinaWheatYield30m dataset was highly consistent with in situ measurement data and statistical data (p<0.01), indicated by an r (rRMSE) of 0.72** (15.34 %) and 0.69** (19.16 %). The ChinaWheatYield30m is a sophisticated dataset with both high spatial resolution and excellent accuracy; such a dataset will provide basic knowledge of detailed wheat yield distribution, which can be applied for many purposes including crop production modeling and regional climate evaluation. The ChinaWheatYield30m dataset generated from this study can be downloaded from 10.5281/zenodo.7360753 (Zhao et al., 2022b).
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