Relationships between soil chemistry, soil moisture, and vegetation indices in grassland ecosystems

The NDVI and PRI vegetation indices (VIs) are widely used to assess grassland condition; however, our understanding of their sensitivity to soil chemical properties and moisture content needs to be expanded. Linking soil chemical properties and water availability to spectral responses increases the reliability of grassland monitoring and management. The present study aimed to analyze two distinct grassland ecosystems, where the spatial heterogeneity of soil chemical parameters and their effects on vegetation indices were investigated.

The study was conducted in 2025 at two research sites, one in the Serbian Rekovac region (RS) and one in the Hungarian Balaton Uplands (HU). Both research sites covered approximately 1 ha. At each site, we collected 32 and 35 soil samples and measured soil chemical parameters (pH, soil organic carbon (SOC), total nitrogen, potassium, and phosphorus) in a laboratory. In the field, we also used non-destructive measurements, including soil elemental compositions using XRF (e.g., Fe, Ca, Cu, As, Pb), soil water content (SWC; Hydrosense II, Campbell Scientific), and vegetation NDVI and PRI values using spectral reflectance sensors (Meter Group) approximately 2 meters above ground. Soil CO₂ emissions were measured using an EGM5 IR analyzer (PP Systems). Data for each sampling point were averaged prior to analysis.

When we compared the two grassland sites, we found significantly higher C to N ratio, total N, K, P, SOC, CaCO₃ concentration, and CO₂ emissions at the HU site, while vegetation NDVI and PRI values were significantly lower (p < 0.05). However, SWC and soil temperature data showed no significant differences (p > 0.05). Given the large number of measured soil chemical parameters, cluster analysis and principal component analysis (PCA) were applied to reduce dimensionality and identify the main factors influencing vegetation indices. Cluster analysis grouped the variables into three distinct clusters, with the Serbian site in one and the Hungarian sites in two. We found that NDVI and PRI values were strongly and negatively correlated with many of the soil chemical parameters (e.g., pH: r = -0.94 and -0.89, SOC: r = -0.86 and -0.83, respectively). Soil CO2 emissions showed only moderate correlations with specific parameters, such as pH, potassium, or C to N ratio (r = 0.50-0.53). SWC, on the other hand, did not show any clear correlations with the main parameters measured.

Our results indicate that variability in vegetation indices was primarily associated with soil chemical gradients rather than soil moisture conditions. Our study showed that local heterogeneity can strongly affect soil chemical data, which in turn affects VIs. Both sites were assessed twice, but further measurements are planned to provide more robust support for our findings.

Acknowledgments: The research was funded by the Sustainable Development and Technologies National Programme of the Hungarian Academy of Sciences (FFT NP FTA) and the 2023-1.2.4-TÉT-2023-0009 project.