Impact of Missing Views in Multi-view Model Predictions for Vegetation Applications

Earth observation (EO) applications involving complex and heterogeneous data sources are commonly approached with machine learning models. However, there is a common assumption that data sources will be persistently available. Different situations could affect the availability of EO sources, like noise, clouds, or satellite mission failures. In this work, we assess the impact of missing temporal andstatic EO sources in trained models across two datasets involving classification and regression tasks. We compare the predictive quality of different methods and find that some are naturally more robust to missing data. The Ensemble strategy, in particular, achieves a prediction robustness up to 99%. We evidence that missingscenarios are more challenging in regression than classification tasks. Finally, we find that the optical view is the most critical view when it is missing individually.