# Error Sources of Interferometric Synthetic Aperture Radar Satellites
## 4 Random Errors
### Temporal Decorrelation (\(\delta_{temp}\))  
Repeat pass SAR systems are susceptible to any changes between two acquisitions [133; 134]. That said, the time interval between the acquisitions is the primary factor that causes temporal decorrelation [32]. Everything (e.g., vegetation growth, weather) that can vary between acquisitions are secondary factors and will possibly induce temporal decorrelation. Temporal decorrelation would not occur without the existence of the time interval between two acquisitions. Therefore, it is a specific error cause for all repeat-pass InSAR systems. Once a time difference between two acquisitions exists, many conditions (e.g., changes in moisture, turbulence) will change at various degrees depending on how long the time interval is and the wavelength of the SAR sensor. The potential changes during the time interval include weather conditions, scatterer movements, surface movements, and surface type. Because the majority of the changes between time intervals are systematic, they do not contribute to temporal decorrelation but are regarded as systematic error causes [32]. For example, a change in weather condition could contribute to atmospheric phase shifts, scatterer motions may be systematic or random, and surface movements often generate systematic phase shifts. Surface type, on the other hand, is the element that reveals how much a specific land cover type varies over time, resulting in loss of coherence. As a result, temporal decorrelation is frequently expressed as the result of the combination of time interval and surface type [28]. Temporal decorrelation can be seen as a function of land cover types owing to different physical properties [32; 135]. Urban regions and forested areas are two of the most prevalent examples. Urban areas are less susceptible to temporal decorrelation because buildings are theoretically immobilized and remain stationary for long periods of time. In contrast, vegetated landscapes are more affected by temporal decorrelation due to the subtle movement of the leaves as well as the natural growth cycles of vegetation. Vegetated canopies are also subject to volumetric decorrelation, which makes it more difficult to inspect and quantify temporal decorrelation in vegetated areas [32; 136; 137; 138]. Consequently, it is often suggested to prevent selecting vegetated areas as study sites to simplify the error sources and to enhance the overall coherence. The triggers that activate these temporal changes on the land surface include wind, precipitation, seasons (phenology and cultivation cycles), anthropogenic activities (e.g., agriculture and construction), and natural hazards (e.g., landslides, eruption, floods). These triggers transform the landscape at varying scales [135]. Also, since their occurrences are stochastic and non-stationary, temporal decorrelation is the most challenging decorrelation component to be modeled and analyzed. Several studies have tried to model temporal decorrelation [32; 139; 140; 141; 137]. The significance of understanding, analyzing, and modeling temporal decorrelation is not only in separating different sources of decorrelation but also in benefiting change detection studies for natural hazard assessment [142; 143; 144]. Despite the complexity involved in quantifying temporal decorrelation, it is one of the most essential contributions for random noise as the coherence decreases rapidly when the time interval increases. This is especially true if the physical properties of the land cover type allow for the temporal triggers discussed above (e.g., natural surface). Therefore, a shorter time interval is necessary to limit the influence of temporal decorrelation. [PERSON] [57] demonstrated the influence of temporal decorrelation on Sentinel-1 imagery. The study has shown that an increase in temporal baseline from 6 to 18 days results in a mean coherence loss of 19.2%.