High temporal resolution of leaf area data improves empirical estimation of grain yield

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1038/s41598-019-51715-7. This is version 3 of this Preprint.

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François Waldner, Heidi Horan, Yang Chen, Zvi Hochman


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.




Agriculture, Life Sciences


remote sensing, Leaf Area Index, APSIM, national-scale, temporal metrics, yield estimation


Published: 2019-05-30 22:44

Last Updated: 2019-11-11 08:22

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GNU Lesser General Public License (LGPL) 2.1