UAV-based stomatal conductance estimation under water stress using the PROSAIL model coupled with meteorological factors

Leaf stomatal conductance (Gs) is an important indicator for measuring crop water stress. Influenced by variation of environmental conditions and growth stages of crops, achieving the reliable and accurate Gs estimation by UAV image is of challenge. Therefore, this study aimed to explore the potential of Gs estimation of winter wheat by UAV-based multispectral imagery based on coupling meteorological factors with the PROSAIL model. Firstly, we set up field experiments with different moisture treatments, acquired the canopy images of winter wheat at different fertility stages using the UAV equipped with a multispectral camera, and acquired meteorological factors (MFs) synchronously. Next, we collected leaf chlorophyll content (Cab), leaf area index (LAI), canopy chlorophyll content (CCC) and Gs. Then, we used PROSAIL model and machine learning models to estimated Gs from UAV-based multispectral images, and the estimation results of Gs at different growth stages were evaluated by coupling MFs. The results showed that, (1) the PROSAIL model successfully retrieved Cab, LAI, and CCC from UAV-based multispectral images, with rRMSE of 0.109, 0.136, and 0.191 respectively, (2) the Cab, LAI and CCC retrieved by PROSAIL model performed well to estimate Gs, with rRMSE of 0.166, 0.150 and 0.130, respectively, (3) the coupling of meteorological factors with the retrieved Cab, LAI, and CCC further enhanced the estimation accuracy of Gs, which is comparable to the results obtained with machine learning models, importantly. The proposed method also enhanced the robustness of estimating Gs at different growth stages. In conclusion, the potential of the Gs estimation with UAV-based multispectral images was proved through the PROSAIL model coupled with meteorological factors, which also provided a technical reference and idea for the assessment of crop water stress.