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Accurately expressing the spatial pattern of crop yield at a sizeable regional field scale is paramount for precision agriculture. However, the current methodologies consistently face numerous challenges, comprising inadequate yield samples and algorithmic tendencies to underestimate higher yield values and overestimate lower ones. Data-driven deep learning algorithms effectively transform remotely sensed data into high-dimensional feature representations. Developing a cost-efficient model that minimizes the impact of insufficient field samples while maximizing the representation of crop yield differences on plots is worthwhile. The study developed an ACNN (Attention-based One-dimensional Convolutional Neural Network) model to efficiently extract and optimize the spatiotemporal features of winter wheat yield from Sentinel-2 MSI Level-2A data while relatively reducing the model's reliance on training …