Soil Moisture Characterization from UAV-Based Optical and Thermal Infrared (TIR) Images

Slope stability is highly sensitive to soil moisture variation, especially in the case of highway slopes of central MS, typically constructed on expansive clay. Traditional borehole monitoring techniques do not offer a complete picture of the soil moisture variation throughout the slopes. In contrast to high-altitude, satellite-based remote sensing data, uncrewed aerial vehicles (UAVs) with optical cameras and thermal sensors offer higher resolution, data-loaded pixels. Furthermore, UAV flights can be precisely timed to provide ambient sunlight exposure, and the freedom to acquire photos when desired is an added advantage. These advantages make the UAV-based imagery particularly suited for capturing reliable soil moisture variation data within the vadose zone of highway slopes. To this end, in this study, a UAV equipped with FLIR’s Zenmuse XT2 dual-sensor payload unit was used to capture temporally spaced optical and TIR images for a highway slope in central Mississippi (MS). The radiometric thermal infrared (TIR) images were post-processed, and pixel temperature values (°F) were obtained across the slope’s soil surface. The surface temperature readings were then correlated with ground truth soil moisture content (SMC). The ground truth SMC data were obtained through near-surface soil sampling and in situ moisture sensors in the vadose zone. A single-parameter regression model was developed to predict soil moisture from diurnal land surface temperature (LST) variations. Similarly, red, blue, and green (RGB) pixel values were extracted from the optical images and calibrated to obtain an RGB vs. SMC model with suitable fitting parameters. The results of this study are promising and set the stage to implement a time-efficient and non-contact site characterization method to capture soil moisture content variations within the vadose zone.