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 and static EO sources in trained models across four datasets with 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 100%. We evidence that missing scenarios are significantly more challenging in regression than classification tasks. Finally, we find that the optical view is the most critical view when it is missing individually.

翻译：地球观测（EO）应用中涉及复杂异构数据源，通常采用机器学习模型进行处理。然而，现有方法普遍假设数据源具有持续可用性。实际情况中，噪声、云层覆盖或卫星任务故障等因素可能影响EO数据源的可用性。本研究基于四个包含分类与回归任务的数据集，评估训练模型中时间序列与静态EO数据源缺失的影响。通过比较不同方法的预测质量，我们发现某些方法对数据缺失具有天然鲁棒性。其中，集成策略的预测鲁棒性可达100%。研究证实，回归任务中数据缺失场景的挑战性显著高于分类任务。此外，我们发现光学视图在单独缺失时是最关键的观测视角。