CSIRO Agriculture & Food, 306 Carmody Road, St Lucia, Queensland, 4067, Australia.
CSIRO Data61, Underwood Avenue, Goods Shed North, 34 Village St, Victoria, 3008, Australia.
Sci Rep. 2019 Oct 31;9(1):15714. doi: 10.1038/s41598-019-51715-7.
Empirical yield estimation from satellite data has long lacked suitable combinations of spatial and temporal resolutions. Consequently, the selection of metrics, i.e., temporal descriptors that predict grain yield, has likely been driven by practicality and data availability rather than by systematic targetting of critically sensitive periods as suggested by knowledge of crop physiology. The current trend towards hyper-temporal data raises two questions: How does temporality affect the accuracy of empirical models? Which metrics achieve optimal performance? We followed an in silico approach based on crop modelling which can generate any observation frequency, explore a range of growing conditions and reduce the cost of measuring yields in situ. We simulated wheat crops across Australia and regressed six types of metrics derived from the resulting time series of Leaf Area Index (LAI) against wheat yields. Empirical models using advanced LAI metrics achieved national relevance and, contrary to simple metrics, did not benefit from the addition of weather information. This suggests that they already integrate most climatic effects on yield. Simple metrics remained the best choice when LAI data are sparse. As we progress into a data-rich era, our results support a shift towards metrics that truly harness the temporal dimension of LAI data.
从卫星数据中进行经验产量估计长期以来一直缺乏合适的时空分辨率组合。因此，选择指标（即预测谷物产量的时间描述符）可能是由实用性和数据可用性驱动的，而不是根据作物生理学的知识有针对性地选择关键敏感时期。当前向超时间数据发展的趋势提出了两个问题：时间性如何影响经验模型的准确性？哪些指标能达到最佳性能？我们采用了基于作物模型的模拟方法，该方法可以生成任何观测频率，探索一系列生长条件，并降低现场测量产量的成本。我们在澳大利亚模拟了小麦作物，并根据由此产生的叶面积指数 (LAI) 时间序列回归了六种类型的指标与小麦产量的关系。使用高级 LAI 指标的经验模型具有全国相关性，并且与简单指标不同，它们没有从添加天气信息中受益。这表明它们已经整合了对产量的大多数气候影响。当 LAI 数据稀疏时，简单指标仍然是最佳选择。随着我们进入一个数据丰富的时代，我们的结果支持向真正利用 LAI 数据时间维度的指标转变。
