气候变化领域集成服务门户(CCPortal): Enhancing Maize Yield Simulations in Regional China Using Machine Learning and Multi-Data Resources

CCPortal

DOI	10.3390/rs16040701
	Enhancing Maize Yield Simulations in Regional China Using Machine Learning and Multi-Data Resources
	Zou, Yangfeng; Kattel, Giri Raj; Miao, Lijuan
发表日期	2024
EISSN	2072-4292
起始页码	16
结束页码	4
卷号	16 期号:4
英文摘要	Improved agricultural production systems, together with increased grain yield, are essential to feed the growing global population in the 21st century. Global gridded crop models (GGCMs) have been extensively used to assess crop production and yield simulation on a large geographical scale. However, GGCMs are less effective when they are used on a finer scale, significantly limiting the precision in capturing the yearly maize yield. To address this issue, we propose a relatively more advanced approach that downsizes GGCMs by combining machine learning and crop modeling to enhance the accuracy of maize yield simulations on a regional scale. In this study, we combined the random forest algorithm with multiple data sources, trained the algorithm on low-resolution maize yield simulations from GGCMs, and applied it to a finer spatial resolution on a regional scale in China. We evaluated the performance of the eight GGCMs by utilizing a total of 1046 county-level maize yield data available over a 30-year period (1980-2010). Our findings reveal that the downscaled models created for maize yield simulations exhibited a remarkable level of accuracy (R2 >= 0.9, MAE < 0.5 t/ha, RMSE < 0.75 t/ha). The original GGCMs performed poorly in simulating county-level maize yields in China, and the improved GGCMs in our study captured an additional 17% variability in the county-level maize yields in China. Additionally, by optimizing nitrogen management strategies, we identified an average maize yield gap at the county level in China ranging from 0.47 to 1.82 t/ha, with the south maize region exhibiting the highest yield gap. Our study demonstrates the high effectiveness of machine learning methods for the spatial downscaling of crop models, significantly improving GGCMs' performance in county-level maize yield simulations.
英文关键词	maize yield; global gridded crop models; random forest; multiple data sources; county-level
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:001176554900001
来源期刊	REMOTE SENSING
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/302380
作者单位	Nanjing University of Information Science & Technology; University of Melbourne
推荐引用方式 GB/T 7714	Zou, Yangfeng,Kattel, Giri Raj,Miao, Lijuan. Enhancing Maize Yield Simulations in Regional China Using Machine Learning and Multi-Data Resources[J],2024,16(4).
APA	Zou, Yangfeng,Kattel, Giri Raj,&Miao, Lijuan.(2024).Enhancing Maize Yield Simulations in Regional China Using Machine Learning and Multi-Data Resources.REMOTE SENSING,16(4).
MLA	Zou, Yangfeng,et al."Enhancing Maize Yield Simulations in Regional China Using Machine Learning and Multi-Data Resources".REMOTE SENSING 16.4(2024).