Volcanic hazards threaten millions of people in their vicinity worldwide. To mitigate the volcanic risk, we need to know which volcanoes are actively deforming and how much have they deformed. Ideally, ascending magma leads to surface uplift through elastic response, which can be precisely measured using the technique of interferometric synthetic aperture radar (InSAR) and inferred through geophysical inverse model, such as the point pressure source. In practice, the (de)pressurization process could have complex geometry in space and change nonlinearly in time, posing challenging for the deformation mapping and risk assessment afterwards. Here, I first develop algorithms to correct for phase unwrapping error in InSAR stack processing and merge them with other state-of-art algorithms to form a generic routine workflow, implement as the Miami INsar Time-series software in PYthon (MintPy). Then I demonstrate the power of this software by applying to the Kyushu Island in SW Japan using all available L-band SAR data from 1992 to 2019 and detect five out of eight actively deforming volcanoes in addition to subsidence due to anthropogenic activities. Next, I combine the radar imaging with geodetic modeling to study the shallow hydrothermal and magmatic systems in Kirishima volcanic complex during the recent unrest since 2008, covering the 2008-2010, 2011, 2017 and 2018 eruption at Shinmoe-dake and the 2018 eruption in Iwo-yama
