Estimation of leaf water potential by thermal imagery and spatial analysis

Canopy temperature has long been recognized as an indicator of plant water status and as a potential tool for irrigation scheduling. In the present study, the potential of using thermal images for an in-field estimation of the water status of cotton under a range of irrigation regimes was investigated. Thermal images were taken with a radiometric infrared video camera. Specific leaves that appeared in the camera field of view were sampled, their LWP was measured and their temperature was calculated from the images. Regression models were built in order to predict LWP according to the crop canopy temperature and to the empirical formulation of the crop water stress index (CWSI). Statistical analysis revealed that the relationship between CWSI and LWP was more stable and had slightly higher correlation coefficients than that between canopy temperature and LIMP. The regression models of LWP against CWSI and against leaf temperatures were used to create LWP maps. The classified LIMP maps showed that there was spatial variability in each treatment, some of which may be attributed to the difference between sunlit and shaded leaves. The distribution of LIMP in the maps showed that irrigation treatments were better distinguished from each other when the maps were calculated from CWSI than from leaf temperature alone. Furthermore, the inclusion of the spatial pattern in the classification enhanced the differences between the treatments and was better matched to irrigation amounts. Optimal determination of the water status from thermal images should be based on an overall view of the physical status as well as on the analysis of the spatial structure. Future study will involve investigating the robustness of the models and the potential of using water status maps, derived from aerial thermal images, for irrigation scheduling and variable management in commercial fields.