Abstract: Thermal infrared imaging can be expected to rapidly and cost-saving monitor the soil moisture content in the large-scale farmland. But it is still unclear on the impact of lighting conditions on thermal infrared images. This study aims to explore the impact of direct sunlight or shadow occlusion of the crop canopy and soil on the UAV thermal infrared remote sensing, in order to diagnose the crop water stress and monitor soil moisture content. Summer corn with different irrigation treatments was taken as the research object. The thermal infrared images were divided into four parts: illuminated canopy, shaded canopy, illuminated soil, and shaded soil. The light temperature was extracted from the higher temperature, whereas, the shadow temperature was assumed as the lower temperature. Temperature extraction was used to calculate the 11:00, 13:00, and 15:00 canopy temperature difference (difference between canopy temperature and atmospheric temperature, ΔT), crop water stress index (CWSI), and evaporative fraction (ratio of latent heat flux to effective energy, EF). A comparison was made on three changes in the monitoring effect of the index on soil moisture content after using light temperature (ΔT_L, CWSI_L, EF_L) and shadow temperature (ΔT_S, CWSI_S, EF_S) at different times. The results show that: 1) There was the variation in the monitoring effects of the three indices over time. The EF monitoring effect was better at 11:00 and 15:00, the crop water stress index monitoring effect was better at 13:00. There was the less change in the ΔT monitoring over time. The temperature index should be selected for monitoring soil moisture content using field conditions and the time of flying the drone; 2) The monitoring effect was improved the most at 11:00 in the jointing period, after distinguishing light temperature and shadow temperature. The R²of EF, EF_S, and EF_L were 0.54, 0.65, and 0.78, respectively. The R² of CWSI, CWSI_S, and CWSI_L were 0.47, 0.64, and 0.70, respectively. There was no significant light temperature in the period of tasseling and filling, whereas, the index monitoring effect was significantly reduced using shadow temperature. There was the largest decrease in the CWSI_S at 13:00, compared with the CWSI, where the R² decreases were 0.11 and 0.06, respectively. Therefore, it was very necessary to choose the clear and cloudless weather and avoid cloudy days, when shooting thermal infrared images. Furthermore, the impact of lighting conditions on thermal infrared images was also change with the growth period; 3) The best monitoring soil moisture content was obtained using EF_L at 11:00 in the jointing and tasseling period, and CWSI at 13:00 in the filling period, where the R² of predicting soil moisture content were 0.75, 0.75, and 0.89, respectively. In addition, the soil moisture content was also dominated the monitoring effect. When the soil volumetric moisture content was around 0.23, both EF_L and CWS_I shared the more accurate prediction. Different time points, light temperature and shade temperature were utilized to analyze the monitoring effect of three temperature indexes on soil moisture content. The better monitoring time and index were determined in the three growth periods of summer corn. The finding can provide a strong reference for the UAV thermal infrared monitoring of soil moisture content.