Combining land surface modelling and Earth observations: the key role of soil moisture data to improve estimates of agricultural water uses

Soil moisture is an essential climate variable and the main driver for water exchanges between the land surface and the atmosphere. An accurate knowledge of the soil moisture conditions is also crucial to estimate the amount of water needed or used for agricultural purposes.

As human demand for water is increasing along with extreme drought events, an optimal agricultural management is paramount to cope with a drier and warmer future, e.g. in Mediterranean regions. Thus, the knowledge of soil moisture is central for monitoring agricultural drought, optimizing agricultural water uses (i.e., irrigation) and improving the water cycle and land-atmosphere processes understanding. Nevertheless, the point-based nature and limited spatial coverage of in situ soil moisture observations in conjunction with the poor parameterization of human processes in earth system models (i.e., unmodelled or wrongly modelled irrigation), undermine the ability to accurately monitor and forecast drought events as well as the capacity to safely manage water resources.

Remote sensing observations offer a unique opportunity to fill these gaps as they can directly observe the processes of the plant-soil continuum. Here we provide insights on the value of satellite-based soil moisture and soil moisture-related measurements (i.e, radar backscatter) for land surface models and for agricultural drought research. We will show the utility of both classical coarse-scale and new high resolution observations for a number of applications that span from irrigation estimation, crop yield analysis, improvement of water cycle processes to estimation of small scale soil moisture variability across agricultural and mountaineous European pilot sites.