Accurate evaluation of crop performance and yield prediction at a sub-field scale is essential for achieving high yields while minimizing environmental impacts. Two important approaches for improving agronomic management and predicting future crop yields are the spatial stability of historic crop yields and in-season remote sensing imagery. However, the relative accuracies of these approaches have not been well characterized. In this study, we aim to first, assess the accuracies of yield stability and in-season remote sensing for predicting yield patterns at a sub-field resolution across multiple fields, second, investigate the optimal satellite image date for yield prediction, and third, relate bi-weekly changes in GCVI through the season to yield levels. We hypothesize that historical yield stability zones provide high accuracies in identifying yield patterns compared to within-season remote sensing images.

To conduct this evaluation, we utilized biweekly Planet images with visible and near-infrared bands from June through September (2018–2020), along with observed historical yield maps from 115 maize fields located in Indiana, Iowa, Michigan, and Minnesota, USA. We compared the yield stability zones (YSZ) with the in-season remote sensing data, specifically focusing on the green chlorophyll vegetative index (GCVI). Our analysis revealed that yield stability maps provided more accurate estimates of yield within both high stable (HS) and low stable (LS) yield zones within fields compared to any single-image in-season remote sensing model.

For the in-season remote sensing predictions, we used linear models for a single image date, as well as multi-linear and random forest models incorporating multiple image dates. Results indicated that the optimal image date for yield prediction varied between and within fields, highlighting the instability of this approach. However, the multi-image models, incorporating multiple image dates, showed improved prediction accuracy, achieving R² values of 0.66 and 0.86 by September 1st for the multi-linear and random forest models, respectively. Our analysis revealed that most low or high GCVI values of a pixel were consistent across the season (77%), with the greatest instability observed at the beginning and end of the growing season. Interestingly, the historical yield stability zones provided better predictions of yield compared to the bi-weekly dynamics of GCVI. The historically high-yielding areas started with low GCVI early in the season but caught up, while the low-yielding areas with high initial GCVI faltered.

In conclusion, the historical yield stability zones in the US Midwest demonstrated robust predictive capacity for in-field heterogeneity in stable zones. Multi-image models showed promise for assessing unstable zones during the season, but it is crucial to link these two approaches to fully capture both stable and unstable zones of crop yield. This study provides opportunities to achieve better precision management and yield prediction by integrating historical crop yields and remote sensing techniques.

在子田范围内准确评估作物表现和产量预测对于实现高产同时最大限度地减少环境影响至关重要。改善农艺管理和预测未来作物产量的两个重要方法是历史作物产量的空间稳定性和当季遥感图像。然而，这些方法的相对准确性尚未得到很好的表征。在这项研究中，我们的目标是首先评估产量稳定性和季节遥感的准确性，以预测跨多个田地的子田分辨率的产量模式，其次，研究用于产量预测的最佳卫星图像数据，第三，将整个季节 GCVI 每两周的变化与产量水平联系起来。我们假设，与季节内遥感图像相比，历史产量稳定区在识别产量模式方面具有较高的准确性。

为了进行这项评估，我们利用了从 6 月到 9 月（2018-2020 年）每两周一次的可见光和近红外波段行星图像，以及来自美国印第安纳州、爱荷华州、密歇根州和明尼苏达州 115 个玉米田的历史产量图。我们将产量稳定区（YSZ）与季节遥感数据进行了比较，特别关注绿色叶绿素植被指数（GCVI）。我们的分析表明，与任何单图像季节遥感模型相比，产量稳定性图可以更准确地估计田地内高稳定（HS）和低稳定（LS）产量区域的产量。

对于季节性遥感预测，我们使用单个图像日期的线性模型，以及结合多个图像日期的多线性和随机森林模型。结果表明，产量预测的最佳图像数据在田间和田内各不相同，凸显了该方法的不稳定性。然而，包含多个图像日期的多图像模型显示出更高的预测精度，到 9 月 1 日，多线性模型和随机森林模型的R ^{2值分别达到 0.66 和 0.86。}我们的分析表明，像素的最低或最高 GCVI 值在整个季节中都是一致的 (77%)，在生长季节开始和结束时观察到的不稳定程度最大。有趣的是，与 GCVI 的双周动态相比，历史产量稳定区提供了更好的产量预测。历史高产区在产季初期 GCVI 较低，但后来赶上了，而初始 GCVI 高的低产区则步履蹒跚。

总之，美国中西部的历史产量稳定区表现出对稳定区田间异质性的强大预测能力。多图像模型显示出评估季节不稳定区域的前景，但将这两种方法联系起来以充分捕获作物产量的稳定和不稳定区域至关重要。这项研究提供了通过整合历史作物产量和遥感技术来实现更好的精确管理和产量预测的机会。