Abstract: Abstract: Surface temperature can be one of the most important indicators to monitor the soil water deficit situation. However, the surface temperature depends mainly on solar radiation, meteorological factors, and crop growth state. The low accuracy of soil water content estimation cannot fully meet the requirements of precision irrigation, especially in the morning and evening. Fortunately, unmanned aerial vehicles (UAVs) with thermal infrared remote sensing can be expected to rapidly extract the surface temperature in recent years. In this study, an accurate and rapid detection of soil water content was proposed to clarify the effect of environmental factors and maize growth on the surface temperature using UAVs with thermal infrared remote sensing. Four gradients of irrigation treatment were set as severe water stress (T1), moderate water stress (T2), mild water stress (T3), and adequate irrigation (T4), whereas, the soil water content was controlled by the field capacity of 40%-50%, 50%-65%, 65%-80%, and 80%-100%. The layout of 12 plots was completely random, where each irrigation treatment was carried out with three replicates. Specifically, the jointing and sampling periods of summer corn growth were set on July 27, August 2, August 8, and August 10, 2020. Thermal infrared sensors were then used to obtain the field surface temperature at 09:00, 11:00, 13:00, 15:00, and 17:00 daily. The soil moisture content was collected simultaneously at a depth of 0-20 cm in the test area. Firstly, a systematic analysis was performed on the surface temperature at various time in the irrigation treatments. Secondly, three factors were selected as total solar radiation, meteorological parameters, and crop growth status. The influence of three factors on the surface-air temperature difference was then quantified using the solar height angle, vegetation coverage, as well as the pressure difference between saturated water and air. Thirdly, the comprehensive adjustment temperature was calculated using the surface temperature of 13:00. A monitoring model of soil water content was then constructed. Finally, the applicability of the monitoring model was verified at the silking and blistering stages. The results show that: 1) There was a negative correlation between the surface temperature in the different irrigation treatments and the soil water content at the same time. The daily change of the surface temperature in the same irrigation treatment presented a negative skewed distribution trend of fast warming in the morning and slow cooling in the afternoon. 2) The linear correlation coefficients were 0.509, 0.948, and -0.659, respectively, for the four power roots of the sine value in the solar height and surface-air temperature difference, the saturated water-gas pressure difference and surface-air temperature difference, as well as the vegetation coverage and surface-air temperature difference. 3) The coefficients of determination of the monitoring model using the integrated temperature increased from 0.230 to 0.771, and the normalized root mean square errors were reduced from 18.8% to 10.3%, respectively, compared with the model using surface temperature. 4) The comprehensive adjusted temperature was used to monitor the soil water content of other growth periods. The coefficient of determination increased from 0.238 to 0.831, and the normalized root mean square error was reduced from 18.9% to 9.5%, indicating the strong applicability in different growth periods of the maize growing season. This finding can provide a strong reference to accurately monitor the soil water deficit using the UAV thermal infrared remote sensing.