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Agricultural droughts are extreme events which are often a result of interplays between multiple hydro-meteorological processes. Therefore, assessing drought occurrence, extent, duration and intensity is complex and requires the combined use of multiple variables, such as temperature, rainfall, soil moisture (SM) and vegetation state. The benefit of using information on SM and vegetation state is that they integrate information on precipitation, temperature and evapotranspiration, making them direct indicators of plant available water and vegetation productivity. Microwave remote sensing enables the retrieval of both SM and vegetation information, and satellite-based SM and vegetation products are available operationally and free of charge on a regional or global scale and daily basis. As a result, microwave remote sensing products play an increasingly important role in drought monitoring applications. Here, we provide an overview of recent developments in using microwave remote sensing for large-scale agricultural drought monitoring. We focus on the intricacy of monitoring the complex process of drought development using multiple variables. First, we give a brief introduction on fundamental concepts of microwave remote sensing together with an overview of recent research, development and applications of drought indicators derived from microwave-based satellite SM and vegetation observations. This is followed by a more detailed overview of the current research gaps and challenges in combining microwave-based SM and vegetation measurements with hydro-meteorological data sets. The potential of using microwave remote sensing for drought monitoring is demonstrated through a case study over Senegal using multiple satellite- and model-based data sets on rainfall, SM, vegetation and combinations thereof. The case study demonstrates the added-value of microwave-based SM and vegetation observations for drought monitoring applications. Finally, we provide an outlook on potential developments and opportunities.

In a previous study, the topsoil and root zone ASCAT satellite soil moisture data were implemented into three multi-objective calibration approaches of the TUW hydrological model in 209 Austrian catchments. This paper examines the model parametrization in those catchments, which in the validation of the dual-layer conceptual semi-distributed model showed improvement in the runoff simulation efficiency compared to the single objective runoff calibration. The runoff simulation efficiency of the three multi-objective approaches was separately considered. Inferences about the specific location and the physiographic properties of the catchments where the inclusion of ASCAT data proved beneficial were made. Improvements were primarily observed in the watersheds with lower slopes (median of the catchment slope less than 15 per cent) and a higher proportion of farming land use (median of the proportion of agricultural land above 20 per cent), as well as in catchments where the runoff is not significantly influenced by snowmelt and glacier runoff. Changes in the mean and variability of the field capacity parameter FC of the soil moisture regime were analysed. The values of FC decreased by 20 per cent on average. Consequently, the catchments’ water balance closure generally improved by the increase in catchment evapotranspiration during the validation period. Improvements in model efficiency could be attributed to better runoff simulation in the spring and autumn month. The findings refine recommendations regarding when hydrological modelling could consider satellite soil moisture data added to runoff signatures in calibration useful.

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