Among the human activities altering the natural circulation of water on the Earth’s surface, irrigation is the most impactful one. In the near future, water exploitation aimed at improving food production through irrigation practices is expected to further increase to face population growth and rising living standards under a climate change scenario. Nevertheless, a detailed knowledge of irrigation dynamics (i.e., extents, timing, and amounts) is generally lacking worldwide. This open problem is the main driver of the European Space Agency’s (ESA) Irrigation+ project, whose main goals are: (i) the development of methods and algorithms to detect, map, and quantify irrigation at different spatial scales, (ii) the production of satellite-derived irrigation products, and (iii) the assessment of the impacts of irrigation on society and science. This study presents a comparison between two different methodologies, developed within the Irrigation+ project, aimed at estimating irrigation water amounts over different test sites: a satellite-based approach, only relying on satellite data, and a data assimilation approach, developed within the NASA Land Information System (LIS) framework, which integrates land surface modelling and remote sensing (hereafter model-based approach). The satellite-based method, namely the Soil-Moisture-based (henceforth SM-based) inversion approach, relies on the backwards estimation of irrigation rates by exploiting satellite soil moisture and evapotranspiration data as an input. The method has been implemented with two Sentinel-1-derived soil moisture products: RT1 Sentinel-1, whose spatial resolution is 1 km, and S2MP, produced at a plot scale by merging Sentinel-1 Synthetic Aperture Radar (SAR) data with Sentinel-2 observations. On the other hand, the model-based approach investigates possible benefits, in terms of irrigation quantification, provided by the assimilation of 1-km spatial resolution Sentinel-1 backscatter into the Noah-MP land surface model coupled with an irrigation scheme. The assimilation updates soil moisture and vegetation via a calibrated backscatter forward operator which is represented by a Water Cloud Model. Results shed a light over the irrigation information contained in the high-resolution Sentinel-1 soil moisture products. In addition, the comparison of different methodologies is useful to understand the limits and potential of each, thus highlighting the existing gaps to be filled in order to obtain reliable irrigation estimates.