This study investigated the relationship between spectral reflectance, derived from a thermal sensor mounted on an Unmanned Aerial Vehicle (UAV), and soil moisture gradients. The research was based on the premise that soil moisture significantly influences its spectral response, particularly in the visible and infrared regions, impacting the absorption and reflection of electromagnetic radiation. High-spatial-resolution remote sensing data were used to monitor variations in soil moisture. The methodology involved acquiring thermal and multispectral imagery over an experimental area with laboratory-identified moisture gradients. An analysis of the importance of moisture predictive variables was performed using machine learning techniques, such as the Random Forest algorithm, due to its robustness against noise and its ability to non-parametrically assess variable relevance. The performance of the soil moisture prediction model was evaluated using statistical metrics such as predictive correlation and Root Mean Square Error (RMSE). The results indicated a significant correlation between thermal sensor readings and soil moisture levels, demonstrating the approach's capability to distinguish different water saturation conditions. The analysis of spectral variable importance confirmed the sensitivity of specific electromagnetic spectrum bands to moisture variations. The investigation highlighted the importance of precise sensor calibration to ensure the consistency and comparability of data acquired at different times or environmental conditions, a critical factor for analyzing temporal changes and evaluating the effectiveness of management practices.