Abstract Crop yield maps estimated from satellite data increasingly are used to understand drivers of yield trends and variability, yet satellite-derived regional maps are rarely compared with location-specific yields due to the difficulty of acquiring sub-field ground truth data at scale. In commercial agricultural systems, combine harvesters with onboard yield monitors collect real-time yield data during harvest with high spatial resolution, generating a large ground dataset. Here, we leveraged a yield monitor dataset of over one million maize field observations across the United States Corn Belt from 2008 to 2018 to evaluate the Scalable Crop Yield Mapper (SCYM). SCYM uses region-specific crop model simulations and climate data to interpret vegetation indices from satellite observations, thus enabling efficient sub-field yield estimation across large regions and multiple years without reliance on ground data calibration. We used the ground dataset to compare alternative SCYM model implementations, define minimum required satellite observation criteria, and evaluate the sensitivity of satellite-based yield estimates to on-the-ground variation in management, soil, and annual weather. We found that smoothing annual time series data with harmonic regression increased 30 m pixel-level accuracy from r2 = 0.31 to 0.40 and reduced dependency on specific satellite observation timing, enabling robust yield estimation on 97% of annual maize area using only Landsat data. Agreement improved as the assessment was aggregated to field-level (r2 = 0.45) and county-level (r2 = 0.69) scales, demonstrating the need for fine-resolution ground truth data to better assess sub-field level accuracy in high resolution products. We found that SCYM and ground data showed a similar yield response to management and environmental variation, particularly capturing linear and nonlinear responses to sowing density, soil water holding capacity, and growing season precipitation. However, sensitivity to factors like soil quality and planting date was muted for SCYM estimates compared to ground-based yields. Random forest models were able to match SCYM performance when trained on at least 1000 ground observations, but performed poorly when tested on years and locations not represented in the training data. Our results demonstrate that satellite yield maps can provide much-needed information on multidecadal yield trends and inform yield gap analyses.

中文翻译：

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一百万个真相：从美国玉米带的广泛地面数据集深入了解可扩展的卫星玉米产量图和产量差距分析

摘要 从卫星数据估计的作物产量地图越来越多地用于了解产量趋势和变异性的驱动因素，但由于难以大规模获取子田地地面实况数据，因此很少将卫星衍生的区域地图与特定位置的产量进行比较。在商业农业系统中，联合收割机与机载产量监测器在收割期间以高空间分辨率收集实时产量数据，生成大型地面数据集。在这里，我们利用 2008 年至 2018 年美国玉米种植带超过 100 万个玉米田间观察的产量监测数据集来评估可扩展作物产量映射器 (SCYM)。SCYM 使用特定区域的作物模型模拟和气候数据来解释卫星观测中的植被指数，从而在不依赖地面数据校准的情况下实现跨大区域和多年的有效子场产量估计。我们使用地面数据集来比较替代 SCYM 模型实施，定义最低要求的卫星观测标准，并评估基于卫星的产量估计对管理、土壤和年度天气的地面变化的敏感性。我们发现，使用谐波回归平滑年度时间序列数据将 30 m 像素级精度从 r2 = 0.31 提高到 0.40，并减少对特定卫星观测时间的依赖，仅使用 Landsat 数据即可对 97% 的年度玉米面积进行可靠的产量估算。随着评估汇总到现场级 (r2 = 0.45) 和县级 (r2 = 0.69) 尺度，一致性得到改善，证明需要高分辨率地面实况数据以更好地评估高分辨率产品中的子场级精度。我们发现 SCYM 和地面数据显示出对管理和环境变化的相似产量响应，特别是捕捉对播种密度、土壤持水能力和生长季降水的线性和非线性响应。然而，与地面产量相比，SCYM 估计对土壤质量和种植日期等因素的敏感性较低。在对至少 1000 次地面观测进行训练时，随机森林模型能够匹配 SCYM 性能，但在对训练数据中未表示的年份和位置进行测试时表现不佳。我们的结果表明，卫星产量图可以提供有关多年产量趋势的急需信息，并为产量差距分析提供信息。