SAR remote sensing is an invaluable tool for monitoring and responding to natural and human-induced hazards. Especially with the unprecedented spatio-temporal resolution and the rapid increase of SAR data collections from legacy SAR missions, we are allowed to exploit hazard-related signals from the SAR phase and amplitude imagery, characterize the associated spatio-temporal ground deformations and land alterations, and decipher the operating mechanism of the geosystems in geodetic timescales. Yet, optimally extracting surface displacements and disturbance from SAR imagery, synergizing cross-disciplinary big data, aggregating useful information by multimodal remote sensing fusion, and bridging the linking knowledge between observations and mechanisms of different hazardous events are still challenging. Therefore, in this session, we welcome contributions that focus on (1) new algorithms, including machine and deep learning approaches and multi-modal/platform integration, to retrieve critical products from SAR remote sensing big data in an accurate, automated, and efficient framework; (2) SAR applications for natural and human-induced hazards including such as flooding, landslides, earthquakes, volcanic eruptions, glacial movement, permafrost destroying, mining, oil/gas production, fluid injection/extraction, peatland damage, urban subsidence, sinkholes, oil spill, and land degradation; (3) multimodal remote sensing fusion to enhance information extraction related to hazards, agriculture, forestry, land management, and environmental monitoring; and (4) mathematical and physical modeling of the SAR products such as estimating displacement velocities and time series for a better understanding on the surface and subsurface processes.