Identifying the Contributions of Multi-Source Data for Winter Wheat Yield Prediction in China

doi:10.3390/rs12050750

	Identifying the Contributions of Multi-Source Data for Winter Wheat Yield Prediction in China
	Cao, Juan 1,2; Zhang, Zhao 1,2; Tao, Fulu 3,4; Zhang, Liangliang 1,2; Luo, Yuchuan 1,2; Han, Jichong 1,2; Li, Ziyue 1,2
刊名	REMOTE SENSING
	2020-03-01
卷号	12 期号:5 页码:22
关键词	machine learning (ML) multi-source data yield prediction winter wheat
DOI	10.3390/rs12050750
通讯作者	Zhang, Zhao(zhangzhao@bnu.edu.cn)
英文摘要	Wheat is a leading cereal grain throughout the world. Timely and reliable wheat yield prediction at a large scale is essential for the agricultural supply chain and global food security, especially in China as an important wheat producing and consuming country. The conventional approach using either climate or satellite data or both to build empirical and crop models has prevailed for decades. However, to what extent climate and satellite data can improve yield prediction is still unknown. In addition, socio-economic (SC) factors may also improve crop yield prediction, but their contributions need in-depth investigation, especially in regions with good irrigation conditions, sufficient fertilization, and pesticide application. Here, we performed the first attempt to predict wheat yield across China from 2001 to 2015 at the county-level by integrating multi-source data, including monthly climate data, satellite data (i.e., Vegetation indices (VIs)), and SC factors. The results show that incorporating all the datasets by using three machine learning methods (Ridge Regression (RR), Random Forest (RF), and Light Gradient Boosting (LightGBM)) can achieve the best performance in yield prediction (R-2: 0.68 similar to 0.75), with the most individual contributions from climate (similar to 0.53), followed by VIs (similar to 0.45), and SC factors (similar to 0.30). In addition, the combinations of VIs and climate data can capture inter-annual yield variability more effectively than other combinations (e.g., combinations of climate and SC, and combinations of VIs and SC), while combining SC with climate data can better capture spatial yield variability than others. Climate data can provide extra and unique information across the entire growing season, while the peak stage of VIs (Mar.similar to Apr.) do so. Furthermore, incorporating spatial information and soil proprieties into the benchmark models can improve wheat yield prediction by 0.06 and 0.12, respectively. The optimal wheat prediction can be achieved with approximately a two-month leading time before maturity. Our study develops timely and robust methods for winter wheat yield prediction at a large scale in China, which can be applied to other crops and regions.
资助项目	National Natural Science Foundation of China[41977405] ; National Natural Science Foundation of China[41621061] ; National Natural Science Foundation of China[31561143003] ; Academy of Finland[316172]
WOS关键词	LEAF-AREA INDEX ; RIDGE-REGRESSION ; LANDSAT TM ; CROP ; SATELLITE ; SUSCEPTIBILITY ; PERFORMANCE ; PATTERNS ; MODELS ; PHOTOSYNTHESIS
WOS研究方向	Remote Sensing
语种	英语
出版者	MDPI
WOS记录号	WOS:000531559300005
资助机构	National Natural Science Foundation of China ; Academy of Finland
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/159728]
专题	中国科学院地理科学与资源研究所
通讯作者	Zhang, Zhao
作者单位	1.Beijing Normal Univ Beijing, Fac Geog Sci, State Key Lab Earth Surface Proc & Resource Ecol, MEM, Beijing 100875, Peoples R China 2.Beijing Normal Univ Beijing, Fac Geog Sci, MoE Key Lab Environm Change & Nat Hazards, Beijing 100875, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China 4.Nat Resources Inst Finland Luke, FI-00790 Helsinki, Finland
推荐引用方式 GB/T 7714	Cao, Juan,Zhang, Zhao,Tao, Fulu,et al. Identifying the Contributions of Multi-Source Data for Winter Wheat Yield Prediction in China[J]. REMOTE SENSING,2020,12(5):22.
APA	Cao, Juan.,Zhang, Zhao.,Tao, Fulu.,Zhang, Liangliang.,Luo, Yuchuan.,...&Li, Ziyue.(2020).Identifying the Contributions of Multi-Source Data for Winter Wheat Yield Prediction in China.REMOTE SENSING,12(5),22.
MLA	Cao, Juan,et al."Identifying the Contributions of Multi-Source Data for Winter Wheat Yield Prediction in China".REMOTE SENSING 12.5(2020):22.