Hourly Soil Temperature Variability and Estimation at Multiple Depths from Meteorological Data Using Multiple Linear Regression in a Tropical Semi-Arid Region of Burkina Faso

Accurate estimation of soil temperature is essential for understanding land–atmosphere interactions and supporting agricultural management in semi-arid regions. This study focuses on the analysis of hourly soil temperature variability and the performance of Multiple Linear Regression (MLR) models at depths of 10, 20, 30, and 40 cm in a tropical semi-arid soil of Burkina Faso. Using in situ meteorological data collected at the Tanghin (Saaba, Kadiogo) experimental site, the results reveal a progressive attenuation of temperature fluctuations with depth, confirming the buffering effect and thermal inertia of subsurface layers. A clear phase lag between soil and air temperatures was observed, increasing with depth and particularly evident beyond 20 cm, which reflects delayed heat propagation within the soil profile. MLR models performed reasonably well at shallow depths, especially when driven by a minimal set of meteorological predictors (Ta, BP, WS, RH), achieving R2 values up to 0.75 at 10 cm. However, their predictive ability declined with increasing depth and under wet-season conditions, where soil temperature becomes increasingly decoupled from atmospheric drivers. Moreover, the models struggled to reproduce full diurnal cycles, underestimating nighttime cooling and overestimating during the rainy season, likely due to limited sensitivity to soil moisture dynamics. These findings emphasize both the potential and the limitations of MLR in capturing subsurface thermal variability.