Winter wheat is one of the main crops in Canada. Near real-time forecasting of within-field variability of yield in winter wheat at the early stages is essential for precision farming. However, the crop yield modelling based on high spatial resolution satellite data is generally affected by the lack of continuous satellite observations, resulting in reducing the generalization ability of the models and increasing the difficulty of near real-time crop yield forecasting at the early stages. In this study, the correlations between Sentinel-2 data (vegetation indices and reflectance) and yield data collected by combine harvester were investigated and a generalized multivariate linear regression (MLR) model was built and tested with data acquired in different years. In addition, three simple unsupervised domain adaptation (DA) methods were adopted for improving the generalization ability of yield prediction. The winter wheat yield prediction using multiple vegetation indices showed higher accuracy than using single vegetation index. The optimum stage for winter wheat yield forecasting varied with different fields when using vegetation indices, while it was consistent when using multispectral reflectance and the optimum stage for winter wheat yield prediction was at the end of flowering stage. This study demonstrated that the simple mean matching (MM) performed better than other DA methods and it was found that “DA then MLR at the optimum stage” performed better than “MLR directly at the early stages” for winter wheat yield forecasting at the early stages. The results indicated that the DA had a great potential in near real-time crop yield forecasting at the early stages.

冬小麦是加拿大的主要农作物之一。在早期阶段近乎实时地预测冬小麦产量的田间变化对于精准农业至关重要。然而，基于高空间分辨率卫星数据的作物产量建模普遍受到缺乏连续卫星观测的影响，导致模型的泛化能力降低，增加了早期近实时作物产量预测的难度。在这项研究中，研究了 Sentinel-2 数据（植被指数和反射率）与联合收割机收集的产量数据之间的相关性，并建立了广义多元线性回归（MLR）模型并使用不同年份采集的数据进行了测试。此外，采用三种简单的无监督域自适应（DA）方法来提高产量预测的泛化能力。采用多种植被指数预测冬小麦产量比采用单一植被指数预测精度更高。植被指数预测冬小麦产量的最佳时期因田间不同而不同，多光谱反射率预测一致，冬小麦产量预测的最佳时期为花末期。本研究表明，简单均值匹配（MM）比其他 DA 方法表现更好，并且发现“DA 然后 MLR 在最佳阶段”在早期预测冬小麦产量方面优于“早期直接 MLR”阶段。