ABSTRACT High‐resolution time series recorded by soil water dielectric sensors are often affected by other processes. Dielectric soil matric potential sensors, such as the TEROS 21, exhibit temperature sensitivity, resulting in deviations of readings from the true value due to soil temperature fluctuations. However, methods for correcting this effect remain limited. The objective of this study was to create a straightforward, scale‐based statistical approach to mitigate the influence of daily temperature oscillations on matric potential dielectric readings. The temperature sensitivity correction function (TSCF) identifies, quantifies, and smooths diurnal fluctuations caused by soil temperature dynamics. We provide a detailed description of the algorithm, including freely accessible and easily transferable computer codes suitable for implementation in any programming language. The method was developed for a main dataset collected by TEROS 21 and validated using two additional datasets from sensors MPS‐2 and TEROS 21, all installed in volcanic ash soils in southern Chile. The diurnal deviations reached up to approximately ±720 and ±1440 kPa at suction head values larger than 1500 kPa in the main and validation datasets, respectively. Using wavelet analysis to describe the characteristic scales in the evaluated data, the global wavelet spectrum and the wavelet coherence analysis demonstrated that TSCF effectively removes diurnal temperature effects while preserving soil matric potential dynamics. Additionally, key statistical properties (i.e., mean, median, and the first and third quartiles) were conserved across all datasets. Finally, we assessed the impact of correcting diurnal deviations on soil water retention properties, including soil water content time series.
Peña et al. (Sun,) studied this question.