# Error Sources of Interferometric Synthetic Aperture Radar Satellites
## 1 Introduction  
The interferometric synthetic aperture radar (InSAR) technique has proven to be an excellent and reliable approach to measuring and map land surface topography since the early 1970s (e.g., [1, 2, 3, 4]). Spaceborne SAR systems possess the capability of obtaining radar images over wide areas of up to hundreds of kilometers under all-weather conditions at relatively low costs when compared to ground surveying or conventional aerial systems [5, 6]. Due to the merits of spaceborne SAR, mission programs in the 1990s, such as the European Remote Sensing Satellites series (ERS-1, ERS-2) or the Canadian Radarsatprogram, have initiated a large number of studies and methodological investigations into the application of topographic mapping [7; 8; 9; 10]. In 1989, the first demonstration of the potential of Differential InSAR (DInSAR) was proposed for surface deformation mapping at a centimeter or sub-centimeter accuracy [11; 12]. This opened the door for a variety of applications using the DInSAR technique, including seismic deformation [13; 14; 15], glacier motion [11; 16; 17; 18], volcanic eruption [19; 20; 21], landslides [22], and land subsidence [23; 24; 25; 26]. The principle of InSAR is based on the extraction of ground surface data to generate topography or deformation products through phase differencing between two acquisitions [27]. The workflow of both applications is very similar, including image coregistration, interferogram generation, phase filtering, phase unwrapping, phase to elevation/displacement, and lastly, geometric correction. The primary distinction between the two applications is that the topographic information is preserved during the interferogram generation phase for topographic mapping but is removed for deformation mapping. Early InSAR research extensively explored possibilities regarding the application of InSAR techniques, and it also revealed multiple error sources connected to imaging geometry, propagation path, and InSAR processing [28; 29; 30; 31; 32]. These error sources make the interpretation of interferograms difficult and thus might limit users' understanding of the resulting InSAR products without an in-depth knowledge of the InSAR technique. Early InSAR literature derived different error sources. The first effort was made by [PERSON] and [PERSON] [29] who presented an error model including signal-to-noise ratios (SNR), number of looks, pixel misregistration, and baseline decorrelation. Later on, [PERSON] and [PERSON] [30] provided a more extended view towards the topic and separated the errors into two types: (1) intrinsic height error and (2) location-induced error. Intrinsic height error occurs because of the errors in estimating the measured heights, while location-induced error refers to the fact that the measured heights are spatially mis-projected [30]. Later in the same year, [PERSON] and [PERSON] [32] published a paper concentrated on factors leading to InSAR decorrelation. These factors encompassed (1) thermal noise, causing thermal decorrelation; (2) lack of parallelism between radar flight tracks resulting in decorrelation due to target rotation concerning radar look location; (3) spatial baseline noise leading to baseline decorrelation; and (4) surficial changes contributing to both temporal and volume decorrelation. Subsequently, many research works discussed the impact of tropospheric delay as it was found to be one of the most significant error sources for InSAR measurements [7; 14; 33; 34]. It was not until 2001 that [PERSON] [28] provided an organized and more comprehensive view of the overall error budget and proposed several models to describe the influence of each contributor. [PERSON] created empirical and stochastic models to analyze the most important error sources for repeat-pass radar interferometry. Nevertheless, while the substance is excellent, it was a math-heavy research paper that is difficult for novices to understand.