We invite presentations concerning the past, present and future of soil moisture estimation, including remote sensing, field experiments, land surface modelling and data assimilation, machine learning and Cal/Val activities including the use and establishment of fiducial reference measurements (FRMs).
Over the past two decades, the technique of microwave remote sensing has made tremendous progress to provide robust estimates of surface and deeper-layer soil moisture at different scales. From local to landscape scales, several field or aircraft experiments have been organised to improve our understanding of active and passive microwave soil moisture sensing, including the effects of soil roughness, vegetation, spatial heterogeneities, and topography. At continental scales, a series of several passive and active microwave space sensors, including SMMR (1978-1987), AMSR (2002-), ERS/SCAT (1992-2000) provided information on surface soil moisture. Current investigations of L-band passive microwave observations with SMOS (2009-) and SMAP (2015-), and active C-band microwave observations with the Metop/ASCAT series (2006-) and Sentinel-1, enable an accurate quantification of the soil moisture at regional and global scales. Building on the legacy of these mission operational programmes like Copernicus but also novel developments will further enhance our capabilities to monitor soil moisture, and they will ensure continuity of multi-scale soil moisture measurements from agricultural to climate scales. At the same time, research has put a new focus on establishing rigorous guidelines for the installation, calibration, operation, maintenance, and use of in situ soil moisture measurements that are informed by metrological practices, as well as on the development of advanced quality control procedures for an ever growing suit of global in situ soil moisture measurement networks to obtain so-called fiducial reference measurements (FRMs) for soil moisture.
Over the last decade, efficient machine learning-based observation operators have been advocated to assimilate remotely sensed soil moisture observations, such as neural networks and gradient boosting trees.