Abstract: Abstract: Field surface temperature is one of the most important parameters for the water/heat exchange between soil, crop and atmosphere, particularly for the remote sensing inversion model of irrigation areas. Among them, the crop canopy temperature and soil surface temperatureare often mixed in the field surface temperature data at the early growth stage, due to the crop growth and development in the row and plant spacing. Continuous observation can normally be implemented using the thermal infrared sensor of the automatic monitoring system. The mean value of monitored temperature data is usually used to replace the temperature at the actual position at present. The mixed temperature data can pose a great challenge to the calculation accuracy of the fine field irrigation model during data processing. In this study, an improved screening was combined with the Logistic crop growth model to accurately partition the massive monitoring data of field surface temperature, considering the Leaf Area Index (LAI), crop canopy height, and the key points of crop growth status. The measured temperature data of maize and sunflower was collected in the Yongji experimental station in Inner Mongolia of China in 2021. The scanning temperature data was obtained using the field monitoring system (CTMS-On line). The screening algorithm was then designed and verified. The field observation data of maize and sunflower was collected in the Jiefangzha irrigation field in 2015, while the maize data was in the Changchun experimental station of Jilin Province from 2018 to 2019. Results showed that: 1) An efficient determination was achieved in the data screening for the surface temperature of sparse vegetation in the fields. A logistic model was used to simulate the key points in the screening algorithm, considering the crop growth indicators of LAI and crop canopy height. 2) Taking the relative error as an example, the optimization ranges of canopy temperature and soil surface temperature were about 10 percentage points, and more than 5 percentage points, compared with the temperature measured by the hand-held thermometer. A higher accuracy of data screening was achieved in the canopy temperature and soil surface temperature acquisition. 3) The correction factor after the screening was then determined, according to the crop planting density and LAI. Among them, the correction factors of crop canopy temperature (0.9) and soil surface temperature (1.1) were selected for the maize. The correction factors for the sunflower were specified as the correction factors of crop canopy temperature of 0.7 and the correction factors of soil surface temperature of 1.2, due to the baseline of maximal LAI of 4. Therefore, one recommendation was proposed to apply the screening in different field situations. Specifically, each increasing value can increase the correction factors of crop canopy temperature by 0.35 and reduce the correction factors of soil surface temperature by 0.18 per increase of sunflower maximal LAI. Therefore, important technical support can be obtained for precision irrigation management for the better performance of field monitoring data. The finding can also provide a strong reference to deal with the field temperature data of sparse vegetation crops. A great contribution can then be made to the precision screening of remote sensing data from unmanned aerial vehicles and satellites.