We invite presentations concerning the past, present and future of soil moisture estimation, including remote sensing, multi-sensor data fusion, field experiments, land surface modelling and data assimilation, machine learning, and Cal/Val activities, in particular concerning the establishment and use of Fiducial Reference Measurements (FRMs).

Microwave remote sensing has made tremendous progress to provide robust estimates of surface and subsurface soil moisture at different scales. From local to landscape scales, several field or aircraft experiments have been organized 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 passive and active microwave space sensors, such as SMMR (1978-1987), AMSR (2002-), and ERS/SCAT (1992-2000) provided information on surface soil moisture. Current investigations of L-band passive observations with SMOS (2009-) and SMAP (2015-), active C-band observations with the Metop/ASCAT series (2006-) and Sentinel-1 (2014-), L- and S-band observations from the NISAR mission (2025-), and bistatic L-band radar observations from GNSS-R missions such as HydroGNSS (2025-) enable an accurate quantification of the soil moisture at regional and global scales at ever higher spatial resolution.

Operational programmes like Copernicus but also novel developments, such as the upcoming CIMR and ROSE-L, 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, and operation 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.

Lastly, machine learning has opened up new opportunities for soil moisture retrieval, downscaling, data fusion, gap filling and data assimilation. For example, data assimilation systems have been complemented with machine learning-based observation operators to assimilate remotely sensed soil moisture using techniques such as neural networks and gradient boosting trees.