# Error Sources of Interferometric Synthetic Aperture Radar Satellites
## 5 Conclusions  
In this paper, we have provided a comprehensive review of the error budget within the InSAR processing technique. Our aim was to address two critical issues: the lack of thorough discussion about each error source and their influence on InSAR-derived products; and the difficulty for novices in grasping the intricate mathematics and signal processing knowledge associated with InSAR workflows. We classified InSAR-related errors into two main categories, namely, intrinsic height errors and location-induced errors, with further subdivisions into systematic and random errors for intrinsic height errors. Our focus primarily revolved around systematic and random errors and their impact on InSAR-derived topographic and deformation products. Throughout the paper, we strived to make the content easily understandable by using plain language, fundamental mathematical concepts, and numerical and visual comparisons. We have also emphasized the influence of radar wavelengths by comparing different SAR bands. These include L-band, S-band, C-band, and X-band, using representative SAR platforms as examples (i.e., ERS-1, Sentinel-1, TerraSAR-X, and the upcoming NISAR mission).  
In conclusion, this review provides valuable insights into the error sources of InSAR processing and their implications for the accuracy of derived products. By offering an organized, comprehensive, and easily understandable discussion, we hope to bridge the gap between technical and non-technical audiences, thereby making InSAR more accessible to a wider range of readers and data users. Additionally, we acknowledge that there may be error sources not covered in this work, and we encourage further exploration in future research. Some minor error sources that were not included in this paper include approximation errors, some InSAR processing constants, as well as terms that are minimal and neglected (e.g., Faraday rotation). Moreover, the most important error which merits further discussion stems from phase unwrapping (PU), which results from phase noise. Compared to systematic errors, it is much more complicated to formulate how random errors hinder the PU process, lead to further PU errors, and jeopardize the overall accuracy of the InSAR-derived products. Further, PU is another topic that has been heavily discussed in the InSAR literature since the 1970s. The advances in different PU methods can help to solve the current error-prone PU problem and help track how phase noise introduces error into the final InSAR products.  
Overall, this paper contributes to the existing body of knowledge on InSAR processing by shedding light on the error budget and facilitating a deeper understanding of the challenges involved. We believe that our efforts will aid both researchers and InSAR practitioners in improving the accuracy of their InSAR-derived data products, ultimately advancing the applications and potential of this valuable remote sensing technique.  
**Author Contributions:** Conceptualization, Y.-Y.W.; methodology, Y.-Y.W.; software, Y.-Y.W.; validation, Y.-Y.W.; formal analysis, Y.-Y.W.; investigation, Y.-Y.W.; resources, Y.-Y.W.; data curation, Y.-Y.W.; writing--original draft preparation, Y.-Y.W.; writing--review and editing, A.M.; visualization, Y.-Y.W.; supervision, A.M.; project administration, A.M.; funding acquisition, A.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** There is no data involved in this review paper. The Python code that is used to create figures will not be shared.  
**Conflicts of Interest:** The authors declare no conflict of interest.