Evaluating the precise grapevine water stress detection using unmanned aerial vehicles and evapotranspiration-based metrics

Authors: BURCHARD-LEVINE, V - The Institute Of Agricultural Sciences; NIETO, H - The Institute Of Agricultural Sciences; Kustas, William - Bill; GUERRA, J - The Institute Of Agricultural Sciences; BORRA, I - The Institute Of Agricultural Sciences; DORADO, J - The Institute Of Agricultural Sciences; MESIAS-RUIZ, G - The Institute Of Agricultural Sciences; McKee, Lynn; PENA, J - The Institute Of Agricultural Sciences

Submitted to: Irrigation Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/7/2024
Publication Date: 4/29/2024
Citation: Burchard-Levine, V., Nieto, H., Kustas, W.P., Guerra, J., Borra, I., Dorado, J., Mesias-Ruiz, G., McKee, L.G., Pena, J. 2024. Evaluating the precise grapevine water stress detection using unmanned aerial vehicles and evapotranspiration-based metrics. Irrigation Science. https://doi.org/10.1007/s00271-024-00931-9.
DOI: https://doi.org/10.1007/s00271-024-00931-9

Interpretive Summary: Precision irrigation management requires accurate quantification of crop water status to adequately manage irrigation practices within available water resources, particularly in water–limited regions. While unmanned aerial vehicles (UAV) have shown great promise to improve water management in crops such as vineyards through high resolution imagery, there still remains large uncertainties to accurately quantify water requirements especially through physically based models. This study evaluated the potential of UAV imagery to estimate evapotranspiration (ET) and alternative crop water stress indices to better monitor and detect irrigation requirements in an experimental vineyard near Madrid, Spain where three different irrigation treatment levels were implemented. The two-source energy balance (TSEB) model using UAV imagery was applied and found to produce reliable ET estimates and stress indices compared to ground measurements. These results demonstrate the utility of a physically based model TSEB to estimate ET and enhance the detection of vine water stress for precision irrigation management of vineyards.

Technical Abstract: Precise irrigation management requires accurate quantification of crop water status in order to adequately manage irrigation practices within available water resources. While unmanned aerial vehicles (UAV) have shown great promise to improve water management in crops such as vineyards through high resolution imagery, there still remains large uncertainties to accurately quantify water requirements especially through physically-based methods. Notably, thermal remote sensing has been shown to be a promising tool to evaluate water stress at different scales, most commonly through the Crop Water Stress Index (CWSI). This work aimed to evaluate the potential of a UAV payload to estimate evapotranspiration (ET) and alternative crop water stress indices to better monitor and detect irrigation requirements in vineyards. The study was implemented in an experimental vineyard near Madrid, Spain where three irrigation treatments were implemented to impose an irrigation regime, based on the crop coefficient (Kc) approach of the FAO56 method, to maintain weekly Kc to 0.2 (stressed), 0.4 (control) and 0.8 (over-irrigated) of reference ET. The two-source energy balance (TSEB) has been robustly applied in different crops and scales (tower, airborne, satellite) to estimate ET and related energy fluxes, including partitioning the fluxes between vegetation and soil sources. In this study, both the original Priestley-Taylor initialized TSEB (TSEB-PT) and the dual temperature TSEB (TSEB-2T), which takes advantage of high-resolution imagery to discriminate canopy and soil temperatures, were implemented and evaluated against an eddy-covariance (EC) tower measurement. In addition, simultaneous to UAV overpasses, in-situ physiological measurements such as stomatal conductance (gs), leaf ('leaf ) and stem ('stem) water potential, were collected as in-situ proxies of water stress. Different variants of the CWSI and alternative metrics that take advantage of the partitioned ET from TSEB, such as Crop Transpiration Stress Index (CTSI) and the Crop Stomatal Stress Index (CSSI), were also evaluated to test their relationship with these in-situ physiological indicators. Both TSEB-PT and TSEB-2T modelled fluxes compared well against EC measurements (rRSMD = 12-40%) and their respective CWSI related similarly to in-situ measurements ('leaf: pspearman ~ 0.4; 'stem: pspearman ~ 0.55). On the other hand, stress indicators using canopy fluxes (i.e. CTSI and CSSI) were much more effective when using TSEB-2T ('leaf: pspearman = 0.43; 'stem: pspearman = 0.61) compared to TSEB-PT ('leaf: pspearman = 0.18; 'stem: pspearman = 0.49), revealing important differences in the ET partitioning between model variants. These results demonstrate the utility of physically-based models to estimate ET and partitioned canopy fluxes, which can enhance the detection of vine water stress.