Adjusting Diurnal Error in In-Situ Soil Moisture Measurements via Fourier Time-Filtering Using Land Surface Model Datasets

Abstract. Soil moisture (SM) measurements obtained via dielectric-based sensors are widely used in hydrological and climate studies. However, these measurements exhibit significant temperature sensitivity due to the Maxwell–Wagner polarization effect, causing an unrealistic diurnal cycle having spurious daytime peaks. This study introduces a Fourier transform-based method to correct such temperature-induced errors using physically consistent diurnal patterns from land surface model (LSM) reanalysis datasets (ERA5-Land and MERRA-2). The proposed approach adjusts the spectral power of the SM diurnal cycle to align with model-derived patterns constrained by conservation of mass, resulting in physically realistic SM behavior—peaking in the morning and decreasing during the daytime due to evapotranspiration. Validation against non-dielectric reference sensors indicates that the adjusted SM measurements are significantly improved. The diurnal correlation between SM and soil temperature shifts from predominantly positive to negative, particularly evident in regions with large diurnal temperature ranges and dry climates. Furthermore, applying this method to flux tower observations improves the characterization of land–atmosphere interactions by depicting the energy-limited process at sub-daily timescales, where increased incoming radiation during the daytime drives enhanced latent heat flux and subsequently reduces SM. Overall, this Fourier transform-based adjustment enhances the verity of in-situ soil moisture observations, promoting accurate sub-daily analyses of soil moisture dynamics and enabling improved understanding of land–atmosphere coupling processes.