Soil Moisture Estimation Using Thermal Image and Ambient Temperature

Accurate soil moisture (SM) estimation is vital for various applications, including agriculture, ecology, and water resource management. This study presents a novel approach for noninvasive SM estimation using thermal imaging and ambient temperature data. A low-altitude thermal sensing camera was employed to capture alluvial soil surface temperature variations under controlled moisture conditions. Analysis revealed a strong linear relation between thermal image temperature and ambient temperature at constant moisture levels. Crucially, the intercept of this linear relationship was found to be directly proportional to SM, enabling the development of an estimation model. To enhance accuracy, a two-phased approach was implemented: first, thermal images were classified as “wet” or “dry,” based on mean pixel intensity; then, a linear model tailored to the “wet” category was applied for moisture estimation. This method demonstrated 83.6% accuracy in estimating SM across a range of moisture conditions, highlighting the potential of thermal imaging and the presented methodology as a valuable tool for efficient and noninvasive SM monitoring.