Mapping canopy conductance and CWSI in olive orchards using high resolution thermal remote sensing imagery

The characterization of field properties related to water in heterogeneous open canopies is limited by the lack of spatial resolution of satellite-based imagery and by the physical constraints of point observations on the ground. We apply here models based on canopy temperature estimated from high resolution airborne imagery to calculate tree canopy conductance (Gc) and the crop water stress index (CWSI) of heterogeneous olive orchards. The Gc model requires the simulation of net radiation (Rn) and the aerodynamic resistance (ra) as a function of windspeed and canopy structure. In both cases, the Rn and ra models were tested against measurements and published data for olive orchards. Modeled values of Gc of trees varying in water status correlated well with Gc estimates obtained from stomatal conductance measurements in the same trees. The model used to calculate the Crop Water Stress Index (CWSI) took into account, not only the vapor pressure deficit but the Rn and the windspeed as well, parameters known to affect the temperature differences between the air and the tree canopy. The calculated CWSI for water deficit and well irrigated olive trees correlated with the water potential measured on the same trees. The methodology applied in this manuscript was used to validate the estimation of theoretical baselines needed for the CWSI calculations, comparing against traditional empirical baseline determination. High resolution thermal imagery obtained with the Airborne Hyperspectral Scanner (AHS), and from an Unmanned Aerial Vehicle (UAV) for two years was used to map Gc and CWSI of an olive orchard where different irrigation treatments were applied. The methodology developed here enables the spatial analysis of water use within heterogeneous orchards, and the field characterization of water stress, leading to potential applications in the improvement of orchard irrigation management using high resolution thermal remote sensing imagery.