Explainable EO Data Preprocessing: Challenges and the Way Forward

Earth Observation (EO) data sit at the heart of tasks like climate tracking, farm management, and disaster relief. Still, optical sensors face a built in trade off: better spatial detail usually means poorer spectral detail, and vice versa. Deep learning based Spatial Super Resolution (SSR) and Spectral Super Resolution (SpSR) help break this trade off without new hardware. Yet these models often act like “black boxes,” raising worries about bias, image artifacts, and a lack of transparency—issues that matter even more for defense work and rule bound agencies. This chapter explores SSR and SpSR as explainable preprocessing steps for later analyses. It shows how saliency maps, Shapley values, and physics guided learning shine a light on what the models are actually doing. Clearer resolution boosts trust, cuts bias, and meets new rules that demand openness. The chapter also points out both standard and fresh evaluation metrics. By surveying top methods and domain specific tricks, we show that explainable workflows do more than sharpen images—they make sure the results stay true to the real world. In turn, they raise the reliability and overall value of EO analytics.