Investigating the global representativeness of the ESA CCI RZSM product

Root-Zone Soil Moisture (RZSM) is critical for understanding hydrological processes, predicting droughts, and improving agricultural yield forecasts. As RZSM cannot be directly measured by satellites, a simple Exponential Filter (EF) model is applied to the ESA Climate Change Initiative (CCI) Soil Moisture (SM) product to derive a global satellite-based RZSM product. The EF model uses a single parameter, T, to smooths and temporally delay the surface SM signal to estimate RZSM at three depth layers (0–10 cm, 10–40 cm, and 40–100 cm). Previously, the T parameter was defined with vertical variability only (one value per depth layer), and was otherwise assumed to be globally constant. The T parameter is calibrated for each RZSM layer based on in-situ observations of the International Soil Moisture Network (ISMN). Consequently, the resulting RZSM product is strongly influenced by the in-situ data used for calibration. Given that ISMN stations are unevenly distributed globally, the calibrated T parameter may not be fully representative at the global scale.

To investigate how the choice of ISMN stations influences the optimal T per depth layer, pre-filtered ISMN stations were first characterized according to land cover class (ESA CCI), climate class (Köppen-Geiger classification) and soil texture (USDA soil triangle). Based on these characteristics, station selection methods were defined to better match the global distributions of the characterization variables. The resulting T values were then compared to those without deliberate station selection.

This method did not result in major changes to the T parameter across the different station selection methods, demonstrating the stability of the EF model. However, the effectiveness of station selection is constrained by the spatial coverage of the ISMN, with some land cover, climate and soil texture classes represented by few or no stations. This limited coverage leads to deviations from the corresponding global distributions. Increasing numbers of ISMN stations and improved representativeness of the limiting classes in the future calls for further research.