Under ideal conditions, evapotranspiration (ET) fluxes derived through the eddy covariance (EC) technique are considered a direct measure of actual ET. Eddy covariance flux measurements provide estimates at a temporal frequency that allows examining sub-daily, daily, and seasonal scale processes and relationships between different surface fluxes. The Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) project has collected micrometeorological and biophysical data to ground-truth new remote sensing tools for fine-tuning vineyard irrigation management across numerous sites since 2013. This rich dataset allows us to quantify the impact of different approaches to estimate daily ET fluxes, while accounting for energy imbalance. This imbalance results from the lack of agreement between the total available energy and turbulent fluxes derived by the EC technique. We found that different approaches to deal with this energy imbalance can lead to uncertainty in daily ET estimates of up to 50%. Over the growing season, this uncertainty can lead to considerable biases in crop water use estimates, which in some cases were equivalent to ~ 1/3rd of the total growing season applied irrigation We analyzed ET uncertainty relative to atmospheric meteorological, stability, and advective conditions, and highlight the importance of recognizing limitations of micrometeorological observational techniques, considered state of the art, to quantify ET for model validation and field-scale monitoring. This study provides a framework to quantify daily ET estimates’ uncertainty and expected reliability when using the eddy covariance technique for ground-truthing or model validation purposes.

Evapotranspiration (ET) is a crucial part of commercial grapevine production in California, and the partitioning of this quantity allows the separate assessment of soil and vine water and energy fluxes. This partitioning has an important role in agriculture since it is related to grapevine stress, yield quality, irrigation efficiency, and growth. Satellite remote sensing-based methods provide an opportunity for ET partitioning at a subfield scale. However, medium-resolution satellite imagery from platforms such as Landsat is often insufficient for precision agricultural management at the plant scale. Small, unmanned aerial systems (sUAS) such as the AggieAir platform from Utah State University enable ET estimation and its partitioning over vineyards via the two-source energy balance (TSEB) model. This study explores the assessment of ET and ET partitioning (i.e., soil water evaporation and plant transpiration), considering three different resistance models using ground-based information and aerial high-resolution imagery from the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). We developed a new method for temperature partitioning that incorporated a quantile technique separation (QTS) and high-resolution sUAS information. This new method, coupled with the TSEB model (called TSEB-2T_Q), improved sensible heat flux (H) estimation, regarding the bias, with around 61% and 35% compared with the H from the TSEB-PT and TSEB-2T, respectively. Comparisons among ET partitioning estimates from three different methods (Modified Relaxed Eddy Accumulation—MREA; Flux Variance Similarity—FVS; and Conditional Eddy Covariance—CEC) based on EC flux tower data show that the transpiration estimates obtained from the FVS method are statistically different from the estimates from the MREA and the CEC methods, but the transpiration from the MREA and CEC methods are statistically the same. By using the transpiration from the CEC method to compare with the transpiration modeled by different TSEB models, the TSEB-2T_Q shows better agreement with the transpiration obtained via the CEC method. Additionally, the transpiration estimation from TSEB-2T_Q coupled with different resistance models resulted in insignificant differences. This comparison is one of the first for evaluating ET partitioning estimation from sUAS imagery based on eddy covariance-based partitioning methods.

Accurate characterization of evapotranspiration (ET) is imperative in water-limited cropping systems such as California vineyards and almond orchards. Satellite-based ET modeling techniques, including the atmosphere–land exchange inverse model (ALEXI) and associated flux disaggregation technique (DisALEXI), have proven reliable in determining field scale ET. However, validation efforts typically focus on ET and omit an evaluation of partitioned evaporation (E) and transpiration (T). ALEXI/DisALEXI is based on the two-source energy balance (TSEB) model, making it uniquely qualified to derive E and T individually. The current study evaluated E and T estimates derived using two formulations of DisALEXI; one based on Priestley-Taylor (DisALEXI-PT) and the other on Penman-Monteith (DisALEXI-PM). The modeled values were validated against partitioned fluxes derived from the conditional eddy covariance (CEC) approach using EC flux towers in three wine grape vineyards and three almond orchards for the year 2021. Modeled estimates were derived using Landsat 8 Collection 2 thermal infrared and surface reflectance imagery as well as Harmonized Landsat and Sentinel-2 surface reflectance datasets as input into DisALEXI. The results indicated that the modeled total ET fluxes were similar between the two methods, but the partitioned values diverged, with DisALEXI-PT overestimating E and slightly underestimating T when compared to CEC estimates. Conversely, DisALEXI-PM agreed better with CEC-derived E and overestimated T estimates under non-advective conditions. Compared to one another, DisALEXI-PM estimated canopy temperatures ~5 °C cooler and soil temperatures ~5 °C warmer than DisALEXI-PT, causing differences in E and T of −2.6 mm day⁻¹ and +2.6 mm day⁻¹, respectively. The evaluation of the iterative process required for DisALEXI indicates DisALEXI-PM ET values converge on ALEXI ET with proportionate adjustments to E and T, while DisALEXI-PT convergence is driven by adjustments to E. The analysis presented here can potentially drive improvements in the modeling framework to provide specific soil and canopy consumptive water use information in unique canopy structures, allowing for improved irrigation and water use efficiencies in these water-limited systems.

Precision irrigation management requires operational monitoring of crop water status. However, there is still some controversy on how to account for crop water stress. To address this question, several physiological, several physiological metrics have been proposed, such as the leaf/stem water potentials, stomatal conductance, or sap flow. On the other hand, thermal remote sensing has been shown to be a promising tool for efficiently evaluating crop stress at adequate spatial and temporal scales, via the Crop Water Stress Index (CWSI), one of the most common indices used for assessing plant stress. CWSI relates the actual crop evapotranspiration ET (related to the canopy radiometric temperature) to the potential ET (or minimum crop temperature). However, remotely sensed surface temperature from satellite sensors includes a mixture of plant canopy and soil/substrate temperatures, while what is required for accurate crop stress detection is more related to canopy metrics, such as transpiration, as the latter one avoids the influence of soil/substrate in determining crop water status or stress. The Two-Source Energy Balance (TSEB) model is one of the most widely used and robust evapotranspiration model for remote sensing. It has the capability of partitioning ET into the crop transpiration and soil evaporation components, which is required for accurate crop water stress estimates. This study aims at evaluating different TSEB metrics related to its retrievals of actual ET, transpiration and stomatal conductance, to track crop water stress in a vineyard in California, part of the GRAPEX experiment. Four eddy covariance towers were deployed in a Variable Rate Irrigation system in a Merlot vineyard that was subject to different stress periods. In addition, root-zone soil moisture, stomatal conductance and leaf/stem water potential were collected as proxy for in situ crop water stress. Results showed that the most robust variable for tracking water stress was the TSEB derived leaf stomatal conductance, with the strongest correlation with both the measured root-zone soil moisture and stomatal conductance gas exchange measurements. In addition, these metrics showed a better ability in tracking stress when the observations are taken early after noon.

Some grapevine rootstocks perform better than others during and after drought events, yet it is not clear how inherent and stress-induced differences in root morphology and anatomy along the length of fine roots are involved in these responses. Using a variety of growing conditions and plant materials, we observed significant differences in root diameter, specific root length (SRL) and root diameter distribution between two commonly used commercial grapevine rootstocks: Richter 110 (110R; drought resistant) and Millardet et de Grasset 101-14 (101-14Mgt; drought sensitive). The 110R consistently showed greater root diameters with smaller SRL and proportion of root length comprised of fine lateral roots. The 110R also exhibited significantly greater distance from tip to nearest lateral, longer white root length, and larger proportion of root length that is white under drought stress. Mapping of fine root cortical lacunae showed similar patterns between the rootstocks; mechanical failure of cortical cells was common in the maturation zone, limited near the root tip, and increased with drought stress for both genotypes; however, lacuna formed under wetter soil conditions in 110R. Results suggest that drought resistance in grapevine rootstocks is associated with thick, limitedly branched roots with a larger proportion of white-functional roots that tend to form lacuna under more mild water deficit, all of which likely favor continued resource acquisition at depth.