Multi-sensor ML models for EO aim to enhance prediction accuracy by integrating data from various sources. However, the presence of missing data poses a significant challenge, particularly in non-persistent sensors that can be affected by external factors. Existing literature has explored strategies like temporal dropout and sensor-invariant models to address the generalization to missing data issues. Inspired by these works, we study two novel methods tailored for multi-sensor scenarios, namely Input Sensor Dropout (ISensD) and Ensemble Sensor Invariant (ESensI). Through experimentation on three multi-sensor temporal EO datasets, we demonstrate that these methods effectively increase the robustness of model predictions to missing sensors. Particularly, we focus on how the predictive performance of models drops when sensors are missing at different levels. We observe that ensemble multi-sensor models are the most robust to the lack of sensors. In addition, the sensor dropout component in ISensD shows promising robustness results.

翻译：用于地球观测的多传感器机器学习模型旨在通过整合来自不同来源的数据来提高预测准确性。然而，缺失数据的存在构成了重大挑战，尤其是在可能受外部因素影响的非持续性传感器中。现有文献已探索了诸如时间丢弃和传感器不变模型等策略，以解决模型对缺失数据的泛化问题。受这些工作的启发，我们研究了两种专为多传感器场景设计的新方法，即输入传感器丢弃（ISensD）和集成传感器不变（ESensI）。通过在三个多传感器时序地球观测数据集上的实验，我们证明了这些方法能有效提升模型预测对缺失传感器的鲁棒性。特别地，我们重点关注当传感器在不同程度上缺失时，模型的预测性能如何下降。我们观察到，集成多传感器模型对传感器缺失的鲁棒性最强。此外，ISensD中的传感器丢弃组件也显示出有前景的鲁棒性结果。