Towards regional grain yield forecasting with 1km-resolution EO biophysical products: Strengths and limitations at pan-European level

By Raúl López-Lozanoa, Gregory Duveillera, Lorenzo Seguinia, Michele Meronia, Sara García-Condadoa, Josh Hookera, Olivier Leoa, Bettina Barutha. 2015

102 citations recorded by the source corpus.

Abstract This study addresses the role of satellite Earth Observation (EO) indicators within an operational crop yield forecasting system for the European Union (EU) and neighbouring countries, by exploring the correlation between official yield statistics and indicators derived from fAPAR time-series at sub-national level for the period 1999–2012, and by identifying possible differences across agro-climatic conditions in Europe. A significant correlation between fAPAR and official yields (R2 > 0.6) was found in water-limited yield agro-climatic conditions (e.g. the Black Sea region and the Mediterranean basin) for all three crops studied. In regions where crops experience frequent water stress, most of the yield inter-annual variability is explained by substantial changes in leaf area from one year to another, and can be well captured by regional fAPAR time-series. By contrast, in regions characterized by high yields (e.g. northern Europe) – where water constraints are generally not frequent and, therefore, fAPAR inter-annual variability is low – the correlation between fAPAR and yield is weaker (R2 These results confirm the reliability of EO time-series for operational crop yield forecasting at regional level, but also suggest that additional meteorological variables (temperature, precipitation, evapotranspiration) need to be taken into account to interpret EO products meaningfully. Moreover, specific issues related to the spatial resolution of the EO-products, and the absence of dynamic crop masks, currently impede access to crop-specific time-series in the fragmented agricultural landscapes of Europe, and restrict the use of 1-km biophysical products to major crops.

Subfields: Ecology, Ecology, Evolution, Behavior and Systematics, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/55d0528fb41a41a8bcf1a80369560f76ad645321

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In Ecology

Allard de Wit

In Ecology, Evolution, Behavior and Systematics

In Global and Planetary Change

Citations in the corpus

How well do meteorological indicators represent agricultural and forest drought across Europe? (2018, background): “Several studies have reported a closer link between meteorological drought indicators and VIs or crop yield for climatically drier and warmer areas than for wet, energy limited ones (Karnieli et al 2006, López-Lozano et al 2015, Peled et al 2010, Quiring and Ganesh 2010, Vicente-Serrano 2007).”
Machine learning for large-scale crop yield forecasting (2020, methodology): “Lopez-Lozano et al. (2015) identified the optimal period for the correlation between fraction of absorbed photosynthetically active radiation (FAPAR) and yield statistics for three crops.”