Recent breakthroughs in machine learning, notably deep learning, that facilitate massive amounts of data with data-driven AI models have led to an unprecedented potential for large-scale environmental monitoring through remote sensing. Despite the success of existing deep learning-based approaches in remote sensing for many applications, their shortcomings in jointly leveraging various aspects of Earth observation data prevent fully exploiting the potential of remote sensing for the environment. Namely, integrating multiple data modalities and remote sensing sensors, leveraging deep learning methods over multi-spatial/spectral resolution Earth observation data, and modeling space and temporality together offer remarkable opportunities for a comprehensive and accurate understanding of the environment. Throughout this session, we aim to gather the community to delve into the latest scientific advances that leverage these multi-dimensional approaches to tackle pressing environmental challenges.