  Climate change impacts could cause progressive decrease of crop quality and
yield, up to harvest failures. In particular, heat waves and other climate
extremes can lead to localized food shortages and even threaten food security
of communities worldwide. In this study, we apply a deep learning architecture
for high resolution forecasting (300 m, 10 days) of the Leaf Area Index (LAI),
whose dynamics has been widely used to model the growth phase of crops and
impact of heat waves. LAI models can be computed at 0.1 degree spatial
resolution with an auto regressive component adjusted with weather conditions,
validated with remote sensing measurements. However model actionability is poor
in regions of varying terrain morphology at this scale (about 8 km at the Alps
latitude). Our deep learning model aims instead at forecasting LAI by training
multiscale multitemporal (MSMT) data from the Copernicus Global Land Service
(CGLS) project for all Europe at 300m resolution and medium-resolution
historical weather data. Further, the deep learning model inputs integrate
high-resolution land surface features, known to improve forecasts of
agricultural productivity. The historical weather data are then replaced with
forecast values to predict LAI values at 10 day horizon on Europe. We propose
the MSMT model to develop a high resolution crop-specific warning system for
mitigating damage due to heat waves and other extreme events.
