Near-surface geophysical imaging is critical to explore resource plays and characterize the subsurface sustainability. However, conventional subsurface imaging requires a long-term processing, including data acquisition, collection, processing, imaging, QC procedures and so on, which could take months. Distributed sensor networks (DSN) based Real-time and In-situ Subsurface Imaging (RISI) boosts up the near-surface characterization efficiency, which is essential to timely assess earth resources and potential hazards of geological structures. Dr. Fangyu Li has been actively working on creating a DSN based RISI system to study and monitor the near surface. The system is comprised of a self-sustainable sensor network of geophones that can autonomously perform in-network computing of the 3D shallow earth structure images in-situ and in real time. The proposed approach is general and can be implemented as a monitoring system in highly dynamic and complex environments for both natural and human activities, such as volcanoes, microearthquakes, underground infrastructure and so on.