Integrating Multi-Source Data for Rice Yield Prediction across China using Machine Learning and Deep Learning Approaches

doi:10.1016/j.agrformet.2020.108275

	Integrating Multi-Source Data for Rice Yield Prediction across China using Machine Learning and Deep Learning Approaches
	Cao, Juan 3; Zhang, Zhao 3; Tao, Fulu 1; Zhang, Liangliang 3; Luo, Yuchuan 3; Zhang, Jing 3; Han, Jichong 3; Xie, Jun 2
刊名	AGRICULTURAL AND FOREST METEOROLOGY
	2021-02-15
卷号	297 页码:15
关键词	Rice Google Earth Engine (GEE) Machine Learning (ML) Deep Learning (DL) Yield prediction Early warning system
ISSN号	0168-1923
DOI	10.1016/j.agrformet.2020.108275
通讯作者	Zhang, Zhao() ; Tao, Fulu()
英文摘要	Timely and reliable yield prediction at a large scale is imperative and prerequisite to prevent climate risk and ensure food security, especially with climate change and increasing extreme climate events. In this study, integrating the publicly available data (i.e., satellite vegetation indexes, meteorological indexes, and soil properties) within the Google Earth Engine (GEE) platform, we developed one Least Absolute Shrinkage and Selection Operator (LASSO) regression, one machine learning (Random Forest, RF), and one deep learning (Long Short-Term Memory Networks, LSTM) model to predict rice yield at county-level across China. For satellite data, we compared the contiguous solar-induced chlorophyll fluorescence (SIF), a newly emerging satellite retrieval, with a traditional vegetation index (enhanced vegetation index, EVI). The results showed that LSTM (with R-2 ranging from 0.77 to 0.87, RMSE from 298.11 to 724kg/ha) and RF (with R-2 ranging from 0.76 to 0.82, RMSE from 366 to 723.3 kg/ha) models outperformed LASSO (with R-2 ranging from 0.33 to 0.42, RMSE from 633.46 kg/ha to 1231.39 kg/ha) in yield prediction; and LSTM was better than RF. Besides, ESI (combining EVI and SIF together) could slightly improve the model performance compared with only using EVI or SIF as the single input, primarily due to the ability of satellite-based SIF in capturing extra information on drought and heat stress. Furthermore, we also explored the potential for timely rice yield prediction, and concluded that the optimal prediction could be achieved with approximately two/one-month leading-time before single/double rice maturity. Our findings demonstrated a scalable, simple and inexpensive methods for timely predicting rice yield over a large area with publicly available multi-source data, which can potentially be applied to areas with sparsely observed data and worldwide for estimating crop yields.
资助项目	National Science Foundation of China[31561143003] ; National Science Foundation of China[41977405] ; National Key Research and Development Program of China[2017YFA0604703] ; National Key Research and Development Program of China[2019YFA0607401] ; National Key Research and Development Program of China[2017YFD0300301]
WOS关键词	LEAF-AREA INDEX ; WHEAT YIELD ; PRIMARY PRODUCTIVITY ; TIME-SERIES ; SATELLITE ; LANDSAT ; STRESS ; MODIS ; MAIZE ; HEAT
WOS研究方向	Agriculture ; Forestry ; Meteorology & Atmospheric Sciences
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000608676000028
资助机构	National Science Foundation of China ; National Key Research and Development Program of China
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/136461]
专题	中国科学院地理科学与资源研究所
通讯作者	Zhang, Zhao; Tao, Fulu
作者单位	1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China 2.Univ Hull, Sch Environm Sci, Kingston Upon Hull, N Humberside, England 3.Beijing Normal Univ, Fac Geog Sci, Key Lab Environm Change & Nat Disaster MOE, State Key Lab Earth Surface Proc & Resource Ecol, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Cao, Juan,Zhang, Zhao,Tao, Fulu,et al. Integrating Multi-Source Data for Rice Yield Prediction across China using Machine Learning and Deep Learning Approaches[J]. AGRICULTURAL AND FOREST METEOROLOGY,2021,297:15.
APA	Cao, Juan.,Zhang, Zhao.,Tao, Fulu.,Zhang, Liangliang.,Luo, Yuchuan.,...&Xie, Jun.(2021).Integrating Multi-Source Data for Rice Yield Prediction across China using Machine Learning and Deep Learning Approaches.AGRICULTURAL AND FOREST METEOROLOGY,297,15.
MLA	Cao, Juan,et al."Integrating Multi-Source Data for Rice Yield Prediction across China using Machine Learning and Deep Learning Approaches".AGRICULTURAL AND FOREST METEOROLOGY 297(2021):15.