Depending on one’s perspective, surface properties and processes can be either a source of noise or the target of remote sensing observations of the Earth. Interferometric Synthetic Aperture Radar (InSAR), a satellite-borne geodetic method that is used to constrain deformation of the Earth’s surface, is one such remote sensing technique that is impacted by dynamic surface states. In this dissertation, I view surface processes from both the perspective of a noise source to be mitigated in studies of ground deformation, and an independent measure of surface dynamics. In particular, I quantify the impact of mid-time series forest disturbance on InSAR estimates of secular and time variable ground displacement, particularly in regions that experience a significant degree of clearcutting, such as the American Pacific Northwest and Sumatra. I show how the contribution of forest disturbance can introduce either random error or systematic bias into time series estimation depending on satellite geometry, and propose a method for mitigating the contribution of forest disturbance using a priori knowledge of tree loss with independent, optically derived datasets. I then turn my focus to the impact of soil moisture variability on InSAR phase. First, I present a method to invert a dataset derived from a measure of InSAR data quality, known as coherence, that separates different causes of data quality variation over time and in hyper-arid regions where little to no vegetation is present. One product of this inversion is a relative soil moisture metric at each data point in time, and I compare this metric with a variety of other remote sensing and data assimilation soil moisture products over the southern Arabian Peninsula following two extreme rain events in 2018. I find that the coherence-derived metric agrees well with other datasets, but the timescale for which the moisture signal is present after a rain event is significantly longer than other datasets, suggesting that the coherence metric is sensitive to a different part of the soil moisture signal. Finally, I shift focus and analyze the impact of soil moisture on InSAR phase, with the goal of modeling and mitigating the increased amount of phase scatter caused by soil moisture variability. This dissertation presents important advances towards the collective aim to fully characterize how land cover and ground characteristics contribute to InSAR phase.