We introduce a simple yet effective early fusion method for crop yield
prediction that handles multiple input modalities with different temporal and
spatial resolutions. We use high-resolution crop yield maps as ground truth
data to train crop and machine learning model agnostic methods at the sub-field
level. We use Sentinel-2 satellite imagery as the primary modality for input
data with other complementary modalities, including weather, soil, and DEM
data. The proposed method uses input modalities available with global coverage,
making the framework globally scalable. We explicitly highlight the importance
of input modalities for crop yield prediction and emphasize that the
best-performing combination of input modalities depends on region, crop, and
chosen model.

通过使用多个具有不同时空分辨率的输入模态处理作物产量预测问题，我们提出了一种简单而有效的早期融合方法。我们使用高分辨率作物产量图作为训练数据，通过亚区域的农作物和机器学习模型不受限地处理。我们使用 Sentinel-2 卫星图像作为主要输入模态，结合其他补充模态，包括天气、土壤和 DEM 数据。所提出的方法使用全球覆盖的输入模态，使得该框架在全球范围内可扩展。我们明确强调输入模态在作物产量预测中的重要性，并强调最佳组合的输入模态取决于地区、作物和所选择的模型。