  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.
