Accurate production estimates, months before the harvest, are crucial for all parts of the food supply chain, from farmers to governments. While methods have been developed to use satellite data to monitor crop development and production, they typically rely on official crop statistics or ground-based data, limiting their application to the regions where they were calibrated. To address this issue, a new method called VeRsatile Crop Yield Estimator (VeRCYe) has been developed to estimate wheat yield at the pixel and field levels using satellite data and process-based crop models. The method uses the Leaf Area Index (LAI) as the linking variable between remotely sensed data and APSIM crop model simulations. In this process, the sowing dates of each field were detected (RMSE = 2.6 days) using PlanetScope imagery, with PlanetScope and Sentinel-2 data fused into a daily 3 m LAI dataset, enabling VeRCYe to overcome the traditional trade-off between satellite data that has either high temporal or high spatial resolution. The method was evaluated using 27 wheat fields across the Australian wheatbelt, covering a wide range of pedo-climatic conditions and farm management practices across three growing seasons. VeRCYe accurately estimated field-scale yield (R² = 0.88, RMSE = 757 kg/ha) and produced 3 m pixel size yield maps (R² = 0.32, RMSE = 1213 kg/ha). The method can potentially forecast the final yield (R² = 0.78–0.88) about 2 months before the harvest. Finally, the harvest dates of each field were detected from space (RMSE = 2.7 days), indicating when and where the estimated yield would be available to be traded in the market. VeRCYe can estimate yield without ground calibration, be applied to other crop types, and used with any remotely sensed LAI information. This model provides insights into yield variability from pixel to regional scales, enriching our understanding of agricultural productivity.

在收获前几个月进行准确的产量估算对于从农民到政府的食品供应链的各个部分都至关重要。虽然已经开发出使用卫星数据监测作物发育和生产的方法，但它们通常依赖于官方作物统计数据或地面数据，限制了其在校准区域的应用。为了解决这个问题，开发了一种名为 Versatile 作物产量估算器 (VeRCYe) 的新方法，利用卫星数据和基于过程的作物模型来估算像素和田间水平的小麦产量。该方法使用叶面积指数（LAI）作为遥感数据和 APSIM 作物模型模拟之间的链接变量。在此过程中，使用 PlanetScope 图像检测每个田地的播种日期（RMSE = 2.6 天），并将 PlanetScope 和 Sentinel-2 数据融合到每日 3 m LAI 数据集中，使 VeRCYe 能够克服卫星数据之间的传统权衡具有高时间或高空间分辨率。该方法使用澳大利亚小麦带的 27 个麦田进行了评估，涵盖了三个生长季节的广泛土壤气候条件和农场管理实践。 VeRCYe 准确估计了田间规模产量（R ² = 0.88，RMSE = 757 kg/ha）并生成了 3 m 像素大小的产量图（R ² = 0.32，RMSE = 1213 kg /哈）。该方法可以在收获前约 2 个月预测最终产量 (R ² = 0.78–0.88)。最后，从太空检测每块田地的收获日期（RMSE = 2.7 天），表明估计产量可在市场上交易的时间和地点。 VeRCYe 无需地面校准即可估算产量，适用于其他作物类型，并与任何遥感 LAI 信息一起使用。该模型提供了从像素到区域尺度的产量变异性的见解，丰富了我们对农业生产力的理解。