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Forecasting the state of vegetation in response to climate and weather eventsis a major challenge. Its implementation will prove crucial in predicting cropyield, forest damage, or more generally the impact on ecosystems servicesrelevant for socio-economic functioning, which if absent can lead tohumanitarian disasters. Vegetation status depends on weather and environmentalconditions that modulate complex ecological processes taking place at severaltimescales. Interactions between vegetation and different environmental driversexpress responses at instantaneous but also time-lagged effects, often showingan emerging spatial context at landscape and regional scales. We formulate theland surface forecasting task as a strongly guided video prediction task wherethe objective is to forecast the vegetation developing at very fine resolutionusing topography and weather variables to guide the prediction. We use aConvolutional LSTM (ConvLSTM) architecture to address this task and predictchanges in the vegetation state in Africa using Sentinel-2 satellite NDVI,having ERA5 weather reanalysis, SMAP satellite measurements, and topography(DEM of SRTMv4.1) as variables to guide the prediction. Ours results highlighthow ConvLSTM models can not only forecast the seasonal evolution of NDVI athigh resolution, but also the differential impacts of weather anomalies overthe baselines. The model is able to predict different vegetation types, eventhose with very high NDVI variability during target length, which is promisingto support anticipatory actions in the context of drought-related disasters.

Learning to forecast vegetation greenness at fine resolution over Africa with ConvLSTMs