The combination of differential radar tomography with conventional tracer and/or hydraulic tests facili-tates high-resolution characterization of subsurface het-erogeneity and enables the identification of preferential flow paths. In dynamic imaging, each tomogram is typi-cally inverted independently, under the assumption that data sets are collected quickly relative to changes in the imaged property (e.g., attenuation or velocity); however, such "snapshot" tomograms may contain large errors if the imaged property changes significantly during data collection. Acquisition of less data over a shorter time interval could ameliorate the problem, but the resulting decrease in ray density and angular coverage could de-grade model resolution. To address these problems, we propose a new sequential approach for time-lapse tomo-graphic inversion. The method uses space-time parame-terization and regularization to combine data collected at multiple times and to account for temporal variation. The inverse algorithm minimizes the sum of weighted squared residuals and a measure of solution complex-ity based on an a priori space-time covariance function and a spatiotemporally variable mean. We demonstrate our approach using a synthetic 2-D time-lapse (x, z, t) data set based loosely on a field experiment in which difference-attenuation radar tomography was used to monitor the migration of a saline tracer in fractured rock. We quantitatively show the benefits of space-time inver-sion by comparing results for snapshot and time-lapse inversion schemes. Inversion over both space and time results in superior estimation error, model resolution, and data reproduction compared to conventional snap-shot inversion. Finally, we suggest strategies to improve time-lapse cross-hole inversions using ray-based inver-sion constraints and a modified survey design in which different sets of rays are collected in alternating time steps.