In the last three decades, Multibeam echo sounders (MBES) have become the tool of choice to study the seafloor. MBES collects two distinct types of data: bathymetry that provides topographic details of the seafloor and backscatter that has the potential to characterize the seafloor. While the uncertainty associated with MBE bathymetry has been well studied, the uncertainty in MBES backscatter measurement has received relatively little attention, hindering the improvements in quantitative analysis of backscatter data. Both acquisition and processing stages can introduce uncertainty in the final seafloor backscatter products. Application of well-established uncertainty quantification principles to seafloor backscatter data is challenging for several reasons: the uncertainty sources are not well known, they vary on a case-by-case basis, and standards do not exist for acquisition and processing. This dissertation focuses on assessing uncertainty in backscatter measurements and is comprised of four separate but related studiesthat identify and address the challenges of uncertainty quantification of backscatter measurements. The first study (Lucieer et al., 2018) which is presented as background, describes an end users’ survey identifying key uses and challenges of backscatter data acquisition and processing. The study identified that consistency and repeatability of backscatter measurements is a major constraint in the use and re-use of backscatter. The second study (Malik et al., 2018), identified the sources of uncertainty and categorized them as significant or insignificant based on various use cases. The most significant sources of uncertainty were found to be inherent statistical fluctuations in the backscatter measurement, calibration uncertainty, seafloor slope and water column absorption estimation. While calibration uncertainty remains the main issue in advancing the quantitative use of the backscatter, the other sources were also shown to cause large uncertainties. These include non-standardized methods used to account for xxiv seafloor slope and absorption, and data interpretation errors due to missing background information about the processing procedures. With a comprehensive list of uncertainty sources established, two uncertainty sources, seafloor slope and processing errors, were examined further in the third (Malik, 2019) and the fourth (Malik et al., submitted) study respectively. Seafloor slope corrections are important to correct for both the area insonified and the incidence angle. Both of these corrections are adversely affected if seafloor slope corrections are not applied. Even in cases where the seafloor slope is used, further uncertainty can occur if the highest resolution bathymetry is not used. The results from this study showed that for the purpose of accurate slope corrections, the spatial scale of backscatter data should be selected based on the best available bathymetry. The majority of end users depend on third-party software solutions to process the backscatter data. The fourth study evaluated the output of three commonly used software packages after inputting the same data set and found that there were significant differences in the outputs. This issue was addressed by working closely with software developers to explore options to make the processing chain more transparent. Two intermediate processing stages were proposed and implemented in three commonly used software tools. However, due to proprietary restrictions, it was not possible to know the full details of the software processing packages. Differing outputs likely result, in part, from the different approaches used by the various software packages to read the raw data. Quality assessment and uncertainty quantification of MBES backscatter measurements is still at an early stage and further work is required to develop data acquisition and processing standards to improve consistency in the backscatter acquisition and processing.