English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Climate change impacts could cause progressive decrease of crop quality andyield, up to harvest failures. In particular, heat waves and other climateextremes can lead to localized food shortages and even threaten food securityof communities worldwide. In this study, we apply a deep learning architecturefor 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 andimpact of heat waves. LAI models can be computed at 0.1 degree spatialresolution with an auto regressive component adjusted with weather conditions,validated with remote sensing measurements. However model actionability is poorin regions of varying terrain morphology at this scale (about 8 km at the Alpslatitude). Our deep learning model aims instead at forecasting LAI by trainingmultiscale multitemporal (MSMT) data from the Copernicus Global Land Service(CGLS) project for all Europe at 300m resolution and medium-resolutionhistorical weather data. Further, the deep learning model inputs integratehigh-resolution land surface features, known to improve forecasts ofagricultural productivity. The historical weather data are then replaced withforecast values to predict LAI values at 10 day horizon on Europe. We proposethe MSMT model to develop a high resolution crop-specific warning system formitigating damage due to heat waves and other extreme events.

High Resolution Forecasting of Heat Waves impacts on Leaf Area Index by Multiscale Multitemporal Deep Learning