Accurate monitoring of crop transpiration (T) is essential for saving and managing irrigation water resources. Ground and UAV (Unmanned Aerial Vehicle) thermal infrared (TIR) images have high enough resolution to meet the needs of precision agriculture. At present, they are mostly used to assess crop water stress instead of to monitor T, especially through UAV TIR images. And the pixel scale effect on T estimation based on ground (T-ground) and UAV (T-UAV) images remains less explored. In this study, the diurnal T of regulated deficit-irrigated corn and soybean was estimated using a three-temperature (3T) model paired with ground and UAV TIR remote sensing images. This method was verified with hydrogen-oxygen stable isotopes measurements, and the pixel scale effect on estimating T with it was analyzed by comparing the LST (Land Surface Temperature) and T derived from ground and UAV TIR images. The results show that the method can accurately estimate T with a high coefficient of determination (R², 0.84–0.88) and a low root mean square error (RMSE, 0.099–0.104 mm/h) and mean absolute percentage error (MAPE, 12.33 %–12.58 %). Contrary to LST, T-ground and T-UAV decreased as the water stress increased, and their peaks appeared around 15:00. During the experimental period, the mean T of full-irrigated, medium-irrigated, and low-irrigated corn was around 0.72, 0.63, and 0.59 mm/h, respectively, and that of well-irrigated and non-irrigated soybean was 0.77 and 0.27 mm/h, respectively. As the pixel scale increased, the T became slightly underestimated, and the scale effect increased with the complexity of the farmland. This happened mainly because the larger pixel scale of UAV images leads to more mixed pixels and greater soil-vegetation interaction, resulting in overestimation of the canopy temperature. But the T-UAV was highly consistent with the T-ground, with an R², a RMSE, and a MAPE of 0.60–0.89, 0.06–0.14 mm/h, and 7.39 %–15.51 %, respectively. Therefore, the scale effect on estimating T is acceptable, especially when the canopy is more closed. The proposed method is concluded to be feasible for use in estimating T over heterogeneous farmland.

准确监测作物蒸腾量 (T) 对于节约和管理灌溉水资源至关重要。地面和 UAV（无人机）热红外 (TIR) 图像具有足够高的分辨率，可以满足精准农业的需求。目前，它们主要用于评估作物水分胁迫，而不是监测 T，尤其是通过无人机 TIR 图像。而基于地面 (T-ground) 和无人机 (T-UAV) 图像的像素尺度对 T 估计的影响仍然很少被探索。在这项研究中，使用三温 (3T) 模型与地面和无人机 TIR 遥感图像配对，估计了调节亏缺灌溉玉米和大豆的昼夜 T。该方法已通过氢氧稳定同位素测量验证，通过比较 LST（地表温度）和来自地面和无人机 TIR 图像的 T，分析了像素尺度对估计 T 的影响。结果表明，该方法能够准确估计T，具有较高的决定系数(R², 0.84–0.88) 和低均方根误差 (RMSE, 0.099–0.104 mm/h) 和平均绝对百分比误差 (MAPE, 12.33 %–12.58 %)。与LST相反，T-ground和T-UAV随着水分胁迫的增加而减少，它们的峰值出现在15:00左右。试验期间，全灌、中灌和低灌玉米的平均 T 分别为 0.72、0.63 和 0.59 mm/h，而良好灌溉和未灌溉大豆的平均 T 为 0.77 和分别为 0.27 毫米/小时。随着像素尺度的增加，T变得略微被低估，尺度效应随着农田的复杂程度而增加。这主要是因为无人机图像的较大像素尺度导致更多混合像素和更大的土壤-植被相互作用，从而导致冠层温度被高估。^{如图 2 所示}，RMSE 和 MAPE 分别为 0.60–0.89、0.06–0.14 mm/h 和 7.39 %–15.51 %。因此，估计 T 的尺度效应是可以接受的，尤其是当冠层更封闭时。得出的结论是，所提出的方法可用于估计异质农田上的 T。