Crop yield maps are valuable for many applications in precision agriculture, but are often inaccessible to growers and researchers wishing to better understand yield determinants and improve site-specific management strategies. A method for mapping sub-field crop yields from remote sensing imagery could increase the availability of crop yield maps. A variation of the scalable crop yield mapping approach (SCYM, Lobell et al. in Remote Sensing of Environment 164:324–333, 2015) was developed and tested for estimating sub-field maize ( Zea mays L.) yields at 10–30 m without the use of site-specific input data. The method was validated using harvester yield monitor records for 21 site-years for irrigated and rainfed fields in eastern Nebraska, USA. Prediction error ranged greatly across site-years, with relative RMSE scores of 10.8 to 38.5%, and R 2 values of 0.003 to 0.37. Significant proportional bias was detected in the predictions, but could be corrected with a small amount of ground truth data. Crop yield prediction accuracies without calibration were suitable for some precision applications such as mapping relative yields and delineating management zones, but model improvements or calibration datasets are needed for applications requiring absolute yield estimates.

中文翻译：

---

通过结合遥感图像、作物模拟模型和机器学习绘制美国内布拉斯加州的子田玉米产量图

作物产量图对于精准农业中的许多应用都很有价值，但对于希望更好地了解产量决定因素和改进特定地点管理策略的种植者和研究人员而言，它们往往无法获得。从遥感图像绘制子田作物产量的方法可以增加作物产量地图的可用性。开发并测试了可扩展的作物产量绘图方法的变体（SCYM、Lobell 等人在 Remote Sensing of Environment 164:324–333, 2015 中），用于估计 10–30 分田间玉米 (Zea mays L.) 的产量m 不使用特定于站点的输入数据。该方法使用美国内布拉斯加州东部灌溉和雨育田 21 个场地年的收割机产量监测记录进行了验证。预测误差在站点年间变化很大，相对 RMSE 分数为 10.8% 到 38.5%，R 2 值为0.003至0.37。在预测中检测到显着的比例偏差，但可以通过少量的地面实况数据进行纠正。无需校准的作物产量预测精度适用于某些精确应用，例如绘制相对产量和划定管理区域，但需要模型改进或校准数据集用于需要绝对产量估计的应用。