Recent soil moisture products have not yet targeted profile soil moisture estimates derived from cosmic-ray neutron sensors (CRNS) despite their unique footprint. CRNS have a capability to integrate measurements over a volume of ~300 m in radius and ~30 cm in depth, surpassing other in-situ techniques. This extraordinary coverage presents a distinct advantage for gauging plant water availability, which provides valuable information for managing water resources, particularly in farming and land-use planning. To extend the sensor’s benefits from station to raster scale, the combination of high-resolution remote sensing data with artificial intelligence models is being pursued. This can offer significant advantages in improving the vertical accuracy of satellite-based soil moisture products.

Related literature

Schrön, Martin, et al. "Improving calibration and validation of cosmic-ray neutron sensors in the light of spatial sensitivity." Hydrology and Earth System Sciences 21.10 (2017): 5009–5030. → Vertically weighting point-based in-situ soil moisture data

Wagner, Wolfgang, Guido Lemoine, and Helmut Rott. "A method for estimating soil moisture from ERS scatterometer and soil data." Remote sensing of environment 70.2 (1999): 191–207. → Exponentially filtering surface soil moisture data

Zreda, Marek, et al. "Measuring soil moisture content non‐invasively at intermediate spatial scale using cosmic‐ray neutrons." Geophysical research letters 35.21 (2008). → Introduction to using CRNS in environmental sciences