Estimation of crop transpiration and its scale effect based on ground and UAV thermal infrared remote sensing images

Abstract

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 (Tground) 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.