Hyperspectral remote sensing is transforming our ability to monitor agricultural landscapes and environmental systems with unprecedented detail. With the rise of airborne and spaceborne hyperspectral missions (e.g., PRISMA, DESIS, EMIT, EnMAP, PACE), new opportunities have emerged to detect crop stress, soil properties, water quality, vegetation traits, and biogeochemical processes at scale. This session invites contributions that utilize hyperspectral data, either independently or integrated with other datasets, to advance fundamental science and real-world applications in agricultural and natural systems. We welcome research on algorithm development, functional trait estimation, data fusion, machine learning and AI integration, and the ecological interpretation of hyperspectral signals. Contributions addressing scaling, calibration, uncertainty quantification, and field validation are also encouraged. By bringing together diverse approaches and disciplines, this session aims to push the frontiers of hyperspectral remote sensing for sustainable land management, ecosystem monitoring, and climate adaptation.

• GC - Global Environmental Change
• H - Hydrology
• IN - Informatics