From local CRNS network observations to meso-scale soil moisture distributions

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In the first phase of Cosmic Sense, we focused on soil moisture (SM) variability at the upper field scale (0.1 - 1 km²) at several study sites (Fendt, Wüstebach, Marquardt). CRNS locations were selected to achieve a balance between maximizing footprint overlap and spatial coverage. Thus, horizontal SM gradients could be identified within and between footprints by assuming some degree of continuity in space, justified by the large sensor footprint in combination with a high network density. For larger spacings of the CRNS sensors, this assumption may, however, not be valid.Bridging this gap between sparsely placed sensors is the main subject of the Smart Coverage module (SC). While continuing the field-scale work of the MC module of the first project phase, we now expand research to larger scales. A key question is: How can we use the CRNS technique to represent spatio-temporal distribution of SM on the meso-scale (~ 10 - 100 km²) where an exhaustive CRNS coverage is not feasible? To that end, we will operate a "smart" combination of small CRNS clusters and individual CRNS probes as a network with a similar number of probes as in the Joint Field Campaigns of the first phase, but with optimized density and locations. We will also consider a range of proxies and supporting measurements that inform us about SM at unmonitored locations, e. g. (1) static landscape attributes known to affect SM (soil texture, geomorphology, land use); (2) remote sensing products (RS) to represent surface SM dynamics and vegetation properties; (3) hydrological models to integrate the meteorological forcing that affects the root zone (RZ); as well as (4) roving observations using ground-based and airborne platforms to quantify SM variability along transects at discrete points in time (RA).Smart coverage refers to the identification of location properties for which the relationship to CRNS-based SM is not yet sufficiently constrained and understood, so that new locations with such properties can be covered by additional CRNS measurements. Another aspect of smart coverage is that we will use the identified relationships to find optimal locations within a stationary CRNS network at the catchment scale such that the overall estimation uncertainty of catchment-scale SM is minimized. These ideas will be explored, together with other research modules, in a Joint Field Campaign (JFC), but also in a "virtual” JFC with synthetic SM and proxy data and simulated CRNS observations.Furthermore, we will explore the integration of new developments into the operation of CRNS networks, such as high sensitivity probes, directional shielding, and local incoming correction. We will also continue our downscaling efforts. Overall, this research module will combine technical improvements, methodological developments, meso-scale data from other modules and extended interpretation capabilities to obtain catchment scale SM from the employment of CRNS in both dense and sparse network settings.