气候变化领域集成服务门户(CCPortal): Corn yield prediction and uncertainty analysis based on remotely sensed variables using a Bayesian neural network approach

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DOI	10.1016/j.rse.2021.112408
	Corn yield prediction and uncertainty analysis based on remotely sensed variables using a Bayesian neural network approach
	Ma Y.; Zhang Z.; Kang Y.; Özdoğan M.
发表日期	2021
ISSN	00344257
卷号	259
英文摘要	As the world's leading corn producer, the United States supplies more than 30% of the global corn production. Accurate and timely estimation of corn yield is therefore essential for commodity trading and global food security. Recently, several deep learning models have been explored for corn yield forecasting. Despite success, most existing models only provide yield estimations without quantifying the uncertainty associated with the predictions. Also, the traditional deep learning approaches typically require a large training set and are easily prone to overfitting when the number of samples in the training set is relatively small. To address these limitations, in this study, we developed a county-level corn yield prediction model based on Bayesian Neural Network (BNN) using multiple data sources that are publicly available, including time-series satellite products, sequential climate observations, soil property maps, and historical corn yield records. Using preceding years since 2001 for model training, the developed BNN model achieved an average coefficient of determination (R2) of 0.77 for late-season prediction across the U.S. Corn Belt in testing years 2010–2019, and outperformed five other state-of-the-art machine learning models. Detailed evaluation in three representative testing years demonstrated that the proposed BNN model could accurately estimate corn yield not only in normal years but also in abnormal years when extreme weather events happened. Moreover, the timeliness of the prediction was evaluated within the growing season with an R2~0.75 achieved by middle August, which is about 2 months before the harvest. We also assessed the predictive uncertainty, and more than 84% of the observed yield records were successfully enveloped in the 95% confidence interval of the predictive yield distribution. Our results also showed that the uncertainty level decreased steadily as time proceeded and stabilized around early August. Uncertainties in yield prediction were mainly induced by the observation noise and also related to the interannual and seasonal variabilities of environmental stress such as heat and water stress. This paper provides a robust framework for the within-season prediction of crop yield and highlights the need to obtain a deeper understanding of the effects of environmental stress on agricultural productivity and crop yield estimation. © 2021 Elsevier Inc.
英文关键词	Bayesian neural network; Corn yield prediction; Deep learning; Remote sensing; Uncertainty estimation
语种	英语
scopus关键词	Bayesian networks; Climate models; Crops; Deep neural networks; Financial markets; Food supply; Forecasting; Productivity; Uncertainty analysis; Yield stress; Bayesian neural networks; Corn yield prediction; Corn yields; Deep learning; Remote-sensing; Training sets; Uncertainty; Uncertainty estimation; Yield estimation; Yield prediction; Remote sensing; artificial neural network; Bayesian analysis; crop yield; estimation method; extreme event; food security; maize; numerical model; remote sensing; uncertainty analysis
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/178867
作者单位	Department of Biological Systems Engineering, University of Wisconsin-Madison, Madison, WI 53706, United States; Department of Geography, University of Wisconsin-Madison, Madison, WI 53706, United States; United States Department of Agriculture, Agricultural Research Service, Hydrology and Remote Sensing Laboratory, 10300 Baltimore Avenue, Beltsville, MD 20705, United States; Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI 53706, United States; Nelson Institute Center for Sustainability and the Global Environment, University of Wisconsin-Madison, Madison, WI 53726, United States
推荐引用方式 GB/T 7714	Ma Y.,Zhang Z.,Kang Y.,et al. Corn yield prediction and uncertainty analysis based on remotely sensed variables using a Bayesian neural network approach[J],2021,259.
APA	Ma Y.,Zhang Z.,Kang Y.,&Özdoğan M..(2021).Corn yield prediction and uncertainty analysis based on remotely sensed variables using a Bayesian neural network approach.Remote Sensing of Environment,259.
MLA	Ma Y.,et al."Corn yield prediction and uncertainty analysis based on remotely sensed variables using a Bayesian neural network approach".Remote Sensing of Environment 259(2021).