  In recent years, black-box machine learning approaches have become a dominant
modeling paradigm for knowledge extraction in Remote Sensing. Despite the
potential benefits of uncovering the inner workings of these models with
explainable AI, a comprehensive overview summarizing the used explainable AI
methods and their objectives, findings, and challenges in Remote Sensing
applications is still missing. In this paper, we address this issue by
performing a systematic review to identify the key trends of how explainable AI
is used in Remote Sensing and shed light on novel explainable AI approaches and
emerging directions that tackle specific Remote Sensing challenges. We also
reveal the common patterns of explanation interpretation, discuss the extracted
scientific insights in Remote Sensing, and reflect on the approaches used for
explainable AI methods evaluation. Our review provides a complete summary of
the state-of-the-art in the field. Further, we give a detailed outlook on the
challenges and promising research directions, representing a basis for novel
methodological development and a useful starting point for new researchers in
the field of explainable AI in Remote Sensing.
