Versatile crop yield estimator

作者：Yuval Sadeh, Xuan Zhu, David Dunkerley, Jeffrey P. Walker, Yang Chen, Karine Chenu · 发表于：Agronomy for Sustainable Development · 年份：2024 · DOI：10.1007/s13593-024-00974-4 · 被引用次数：11 · 研究领域：Rice Cultivation and Yield Improvement、Climate change impacts on agriculture、Irrigation Practices and Water Management

Abstract 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 (R 2 = 0.88, RMSE = 757 kg/ha) and produced 3 m pixel size yield maps (R 2 = 0.32, RMSE = 1213 kg/ha). The method can potentially forecast the final yield (R 2 = 0.78–0.88) about 2 months before the harvest. Finally, the har...