The increase in extreme weather conditions on the one hand and novel technologies on the other hand induce the development of new methods for drought detection. Great potential is expected from remote sensing, which allows to observe planet earth with unprecedented spatial coverage. Since the beginnings, the temporal and spatial resolution have improved considerably and the availability of long-term data records is rising. In near future, it will be possible to derive climatologies based on these records, reflecting the normal state of a parameter within a specific region. This prospect, together with innovative analysis methods and the emergence of artificial intelligence, offers new opportunities to monitor environmental processes. Consequently, also natural hazards can be detected and monitored by means of remote sensing. Regarded as the most complex ones, droughts are responsible for great economic losses. Therefore, their tracking and quantification is of great interest for governments and insurances. Several approaches already exist. Next to the established meteorological-based drought indices, like the Standardized Precipitation Evaporation Index (SPEI), which relies on the observation of precipitation and temperature, others aim on identifying drought through anomalies within the vegetation. One of these vegetation indices is the Leaf Area Index (LAI). Currently, a new approach is taken by tracking drought through satellite-derived soil moisture. One outcome of these efforts is the Soil Water Index (SWI), which has been developed by Wagner et al. in 1998 at the European Commission Joint Research Center (JRC) and further refined at the Technical University of Vienna. The SWI builds the foundation for the calculation of a variety drought indices. Calculated from extrapolated Surface Soil Moisture (SSM), which can be well determined through satellite observations, the SWI provides an estimation for the amount of water stored within the first meter of soil. This zone represents the root-zone of most of the plants and plays an important role when it comes to the vegetations vitality. In this thesis, its potential for drought monitoring over Austria is assessed. Therefore, its derivative, the Soil Water Deficit Index (SWDI), is checked against the SPEI and the LAI. As long as some climatic and topographic preconditions are met, the SWDI show a good performance of detecting agricultural droughts. The SWDI provides drought classes based on the amount of water actually available to plants. Contrary to the SPEI, the SWDI reflects the prevailing soil moisture conditions, which are most relevant for the vegetation. Additionally, because of its absolute scale, it does not depend on long-term climatologies. This is a great advantage compared to other drought indices, which are only meaningful in their climatic context and must be set in relation to the normal conditions within a specific region. Unfortunately, such climatologies require data of at least 30 years, which in many regions is not available yet.