A large part of the world population lives in deltas or in the vicinity of a river which provides many advantages, such as access to transportation, food and drinking water. Inundating floodplains are a natural and recurring phenomenon which is for instance a way to irrigate soils, but drawbacks occur when people or infrastructure are harmed by (extreme) inundations. Developing countries in Africa and Asia are the regions expected to experience the largest increase in impact of inundations in the coming decades. Conventional flood risk studies can effectively analyse (fluvial) inundations, but require considerable and sometimes complex data and software, which is not always available, in particular in the aforementioned regions in Africa and Asia. Additionally, these studies often rely on discharge data, which is not always available and becomes less accurate for extreme discharge when the river banks overflow. Furthermore, even when data is available, this might only be granted after long, bureaucratic procedures. Elevation data is available from publicly available models, but the horizontal and vertical resolution often does not suffice. Therefore, there is a need for an alternative method to analyse fluvial inundations that is more independent and uses open source data and software. Moreover this method needs to provide information on a high temporal resolution (daily) because of the dynamic behaviour of inundations. The spatial resolution should be as high as possible, given the limitations of temporal resolution. This research focuses on detecting, estimating and mapping inundations in the Ouémé delta (Benin, West-Africa) to retrieve information about extent and timing of inundations and analyse how inundations developed over time by means of satellite remote sensing imagery and open source data and software. Furthermore, it aims to investigate how upstream precipitation (causing fluvial inundations) and population in the Ouémé delta (subjected to the effects of inundations) developed over the past decades to make a first step towards analysing the impact of inundations. To answer the research questions an Inundation Extent Mapping Model (IEMM) is developed to detect inundations, estimate the inundation scale and subsequently allocate the estimated surface water fraction in an area of interest (the measurement cell). First an inundation detection method is developed and tested (CMC-ratio), which relies on scaling a measurement cell between a (dry) calibration cell and an additional (wet) calibration cell. Second, the inundation scale estimate of the CMC-ratio is validated with MODIS optical remote sensing imagery and improved by developing and testing various scale estimation methods. Third, the estimated scale is allocated by comparing different elevation maps (MERIT DEM and HAND) and mapping methods. The outcome of the IEMM is compared to discharge to obtain information about timing of inundations and confirms that the IEMM is increasingly sensitive to larger inundations whereas discharge measurements are reported to show the opposite. This shows that in terms of impact (i.e. affected people and deaths), the maximum extent of inundations likely has a larger effect than number of days of extreme inundations. The lead time is derived showing the speed at which inundations propagate through the Ouémé delta. Ka-band passive microwave imagery is available in the MEaSUREs dataset from 1978 - 2017, which enables multi-annual and multi-system comparisons to analyse inundation behaviour over time. In practice, however, variations in bandwidth, incidence angle, sensor sensitivity cause changes that need to be accounted for when conducting inter-system time series analysis. Precipitation analysis of daily rainfall of rainfall stations in the middle and upper Ouémé catchment show a decreasing trend in extreme rainfall, in line with the trend across West-Africa. Based on reported disasters and population growth a link is suggested between populated zones and impact in terms of affected people and deaths caused by fluvial inundations. In particular this sheds light on urbanized zones in the South of the Ouémé delta, such as Sô-Ava and Aguégués. This leads to the conclusion that with the current method it is indeed possible to deploy optical and passive microwave remote sensing to detect inundations, estimate the scale, which can subsequently be mapped to an elevation model to find an inundation extent. However, improved understanding of downscaling and re-gridding effects, vegetation and surface water content is required. For now, the Hybrid inundation scale method is therefore recommended, which uses an artificial wet calibration brightness temperature based on reverse engineering from optical imagery. Furthermore, recommendations to use additional validation methods should be included in further research. Population present in inundation prone areas has a larger effect on the impact of inundations than the mere occurrence of inundations. Considering the expected population growth in Benin, it is recommended to further investigate how this will develop in the future.