High-resolution crop yield and water productivity dataset generated using random forest and remote sensing

文献类型： 外文期刊

第一作者： Cheng, Minghan

作者： Cheng, Minghan;Nie, Chenwei;Cheng, Minghan;Cheng, Minghan;Shi, Lei;Jin, Xiuliang;Jiao, Xiyun;Wu, Tianao;Penuelas, Josep;Penuelas, Josep;Kumar, Lalit;Liu, Kaihua;Wu, Wenbin;Jin, Xiuliang

作者机构：

期刊名称：SCIENTIFIC DATA （ 影响因子：8.501； 五年影响因子：11.211 ）

ISSN：

年卷期： 2022 年 9 卷 1 期

页码：

收录情况： SCI

摘要： Accurate and high-resolution crop yield and crop water productivity (CWP) datasets are required to understand and predict spatiotemporal variation in agricultural production capacity; however, datasets for maize and wheat, two key staple dryland crops in China, are currently lacking. In this study, we generated and evaluated a long-term data series, at 1-km resolution of crop yield and CWP for maize and wheat across China, based on the multiple remotely sensed indicators and random forest algorithm. Results showed that MOD16 products are an accurate alternative to eddy covariance flux tower data to describe crop evapotranspiration (maize and wheat RMSE: 4.42 and 3.81 mm/8d, respectively) and the proposed yield estimation model showed accuracy at local (maize and wheat rRMSE: 26.81 and 21.80%, respectively) and regional (maize and wheat rRMSE: 15.36 and 17.17%, respectively) scales. Our analyses, which showed spatiotemporal patterns of maize and wheat yields and CWP across China, can be used to optimize agricultural production strategies in the context of maintaining food security.

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