Desertification Forecasting in Semi-Arid Regions: Using Deep Learning to Probe the Observability Limits of Land Degradation Dynamics
Researchers used a Convolutional LSTM network trained on ten annual satellite data variables over Ceará, Brazil, to predict land degradation changes (IDV). The model successfully predicted regional trends in desertification rates but failed to accurately predict cell-level changes. This means EO data is reliable for forecasting broad regional trends rather than pinpointing specific future degradation locations.