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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., Funding and logistical support for the GRAPEX project were provided by E. & J. Gallo Winery and from the NASA Applied Sciences-Water Resources Program (Grant no. NNH17AE39I). This research was also supported in part by the U.S. Department of Agriculture, Agricultural Research Service. Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.

Water conservation efforts for California’s agricultural industry are critical to its sustainability through severe droughts like the current one and others experienced over the last two decades. This is most critical for perennial crops, such as vineyards and orchards, which are costly to plant and maintain and constitute a significant fraction of the regional water use. It is no longer feasible to access groundwater for irrigation to replace deficit surface water resources during drought due to a significant overdraft of aquifers and new regulation limiting its use. To achieve significant water savings, the actual crop water use or evapotranspiration (ET) needs to be mapped from field to regional scales on a daily basis. This can only be achieved using remote sensing-based models, particularly thermal-based energy balance models that are sensitive to deficit irrigation conditions. The two-source energy balance (TSEB) model has been successfully applied over vineyards in California, but challenges still remain. In particular, much of the irrigated cropland in the California Central Valley is affected by advection of hot dry air masses from surrounding non-irrigated areas and the TSEB model appears to need modifications to adequately estimate ET under such conditions, as well as the partitioning between evaporation and transpiration. This study investigates the application of the TSEB model, using local observations in a vineyard having significant advection. Four versions of the transpiration algorithm in TSEB are applied and evaluated with tower eddy covariance measurements spanning 4 growing seasons. The results suggest the performance of the original transpiration algorithm based on Priestley–Taylor used in TSEB is satisfactory in all but the most extreme advective conditions, while a transpiration algorithm based on Shuttleworth–Wallace with a canopy resistance formula, which relates maximum stomata conductance to vapor pressure deficit (VPD), performs well in all cases. These modifications have potential for improving regional applications of the TSEB model in support of water management in the Central Valley., Funding and logistical support for the GRAPEX project were provided by E. & J. Gallo Winery and from the NASA Applied Sciences-Water Resources Program (Grant No. NNH17AE39I). This research was also supported in part by the U.S. Department of Agriculture, Agricultural Research Service.

The partitioning of ecosystem evapotranspiration and carbon dioxide fluxes into their plant and ground components is a critical research priority to better understand the water cycle and ecosystem function. Despite advances in different measurement techniques and partitioning models in the last decades, much is still unknown regarding the importance of different components of H2O and CO2 fluxes in ecosystems. In this work, we compare three partitioning methods that are based on analysis of conventional high frequency eddy-covariance (EC) data: the flux variance similarity method, the modified relaxed eddy accumulation methods, and the conditional eddy covariance method. First, we test these methods using fields experimental data, comparing them to other reference measurements for the components fluxes (gas chambers and leaf levels measurements). Subsequently, we develop a novel approach for simulating these fluxes in large eddy simulations and apply it to further probe the performance, assumptions, and relative skill of the three methods. The findings allow us to recommend partitioning best practices for their implementation, and to develop methods for the joint analyses of the various approaches.