Accurate prediction of crop yield is essential for agricultural trading, market risk management and food security. Although various statistical models and machine learning models have been developed to enhance prediction accuracy, spatial and temporal non-stationarity, an intrinsic attribute of many geographical processes, is still rarely considered in crop yield modeling. From a statistical point of view, this study respectively provided evidence for the existence of spatial non-stationarity and temporal non-stationarity in winter wheat yield prediction based on geographically weighted regression (GWR) and temporally weighted regression (TWR). Then, a geographically and temporally weighted neural network (GTWNN) model was proposed by integrating artificial neural network (ANN) into geographically and temporally weighted regression (GTWR) using publicly available data sources, including satellite imagery and climate data. For a more credible evaluation, the leave-one-year-out strategy was adopted to make out-of-sample prediction resulting in a total of 12 test years from 2008 to 2019. The experiment results showed that the proposed GTWNN outperformed ANN, GTWR and support vector regression (SVR) achieving the average coefficient of determination (R²) values of 0.766, 0.759 and 0.720 at the three prediction times of end of July, end of June and end of May. Moreover, an extended Moran's I was adopted to assess the degree of spatiotemporal autocorrelation of the prediction errors. The error aggregation of GTWNN was lower than other models, indicating that GTWNN is applicable to addressing spatial non-stationarity in modeling the relationship between predictors and yield response. The methodology proposed in this paper can be extended to handle spatiotemporal non-stationarity in other crop yield predictions and even other environmental phenomena.

准确预测作物产量对于农业贸易，市场风险管理和粮食安全至关重要。尽管已开发出各种统计模型和机器学习模型来提高预测准确性，但在作物产量建模中仍很少考虑时空非平稳性（许多地理过程的内在属性）。从统计学的角度来看，本研究分别为基于地理加权回归（GWR）和时间加权回归（TWR）的冬小麦产量预测中存在空间非平稳性和时间非平稳性提供了证据。然后，通过使用包括卫星图像和气候数据在内的公共数据源，将人工神经网络（ANN）集成到地理和时间加权回归（GTWR）中，提出了地理和时间加权神经网络（GTWNN）模型。为了进行更可靠的评估，从2008年到2019年，采用了“留一年制”策略进行样本外预测，总共进行了12个测试年。实验结果表明，拟议的GTWNN优于ANN，GTWR和支持向量回归（SVR）来实现平均确定系数（在7月底，6月底和5月底的三个预测时间，R ²）值分别为0.766、0.759和0.720。此外，采用扩展的Moran's I来评估预测误差的时空自相关程度。GTWNN的错误汇总低于其他模型，这表明GTWNN适用于在预测变量与产量响应之间的关系建模中解决空间非平稳性。本文提出的方法可以扩展到处理其他作物产量预测甚至其他环境现象中的时空非平稳性。