Irrigation and other agricultural management practices play a key role in land surface fluxes and their interactions with atmospheric processes. California’s Central Valley agricultural productivity is strongly linked to water availability associated with conveyance infrastructure and groundwater, but greater scrutiny over agricultural water use requires better practices particularly during extended and severe drought conditions. The future of irrigated agriculture in California is expected to be characterized neither by perpetual scarcity nor by widespread abundance. Thus, further advancing irrigation technologies and improving management practices will be key for California’s agriculture sustainability. In this study, we present micrometeorological observations from the Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) project. Daily, seasonal, and inter-seasonal surface flux patterns and relationships across five vineyards over three distinct California wine production regions were investigated. Vineyard actual evapotranspiration showed significant differences at the sub-daily and daily scale when comparisons across wine production regions and varieties were performed. Water use in vineyards in the Central Valley was about 70% greater in comparison to the vineyards at the North Coast area due to canopy size, atmospheric demand, and irrigation inputs. Inter-annual variability of surface fluxes was also significant, even though, overall weather conditions (i.e., air temperature, vapor pressure deficit, wind speed, and solar radiation) were not significantly different. Thus, not only irrigation but also other management practices played a key role in seasonal water use, and given these differences, we conclude that further advancing ground-based techniques to quantify crop water use at an operational scale will be key to facing California’s agriculture present and future water challenges.

High-resolution spatial–temporal root zone soil moisture (RZSM) information collected at different scales is useful for a variety of agricultural, hydrologic, and climate applications. RZSM can be estimated using remote sensing, empirical equations, or process-based simulation models. Machine learning (ML) approaches for evaluating RZSM across numerous spatial–temporal scales are less generalizable than process-based models. However, data-driven ML approaches offer a unique opportunity to develop complex models of soil moisture without making assumptions about the processes governing soil water dynamics in a given study region. In this study, comparisons were made between two models, pySEBAL and EFSOIL, which were based on evaporation fraction (EF) and soil properties, and a data-driven model based on the Random Forest (RF) ensemble algorithm. These approaches were evaluated to demonstrate their capabilities for RZSM estimation. The EF obtained from Landsat images was used after validation with eddy covariance measurements as the major input to all three models, along with other meteorological and soil physical properties. The RF model was trained using in situ soil moisture data from Time Domain Reflectometry (TDR) sensors installed in a vineyard from 2018 to 2020. The predictor variables comprised of meteorological, soil properties, EF, and a vegetation index. The results reveal that there was a strong correlation between the in situ measured soil moisture and the RF predicted soil moisture at all sensor locations. Due to the complexity of the physical processes involved in soil water flow, the empirical models pySEBAL and EFSOIL were unable to reliably predict RZSM values at all monitored locations. The high RZSM predicted by pySEBAL demonstrated the presence of possible bias in the model’s algorithm used to estimate soil moisture. We also demonstrated that ML based on the RF algorithm may be used to predict spatially distributed RZSM when a few soil moisture ground measurements are combined with remote sensing to produce soil moisture maps.

In the context of growing evidence of climate change and the fact that agriculture uses about 70% of all the water available for irrigation in semi-arid areas, there is an increasing probability of water scarcity scenarios. Water irrigation optimization is, therefore, one of the main goals of researchers and stakeholders involved in irrigated agriculture. Irrigation scheduling is often conducted based on simple water requirement calculations without accounting for the strong link between water movement in the root zone, soil–water–crop productivity and irrigation expenses. In this work, we present a combined simulation and optimization framework aimed at estimating irrigation parameters that maximize the crop net margin. The simulation component couples the movement of water in a variably saturated porous media driven by irrigation with crop water uptake and crop yields. The optimization component assures maximum gain with minimum cost of crop production during a growing season. An application of the method demonstrates that an optimal solution exists and substantially differs from traditional methods. In contrast to traditional methods, results show that the optimal irrigation scheduling solution prevents water logging and provides a more constant value of water content during the entire growing season within the root zone. As a result, in this case, the crop net margin cost exhibits a substantial increase with respect to the traditional method. The optimal irrigation scheduling solution is also shown to strongly depend on the particular soil hydraulic properties of the given field site.

A method of soil moisture content prediction is presented using fractional differential generalization of one-dimensional Richards equation for the purposes of decision support in irrigation. We propose to use water head dynamics data gathered in the beginning of growth season to perform a calibration of the model by the meta-heuristic particle swarm algorithm. Van Genuchten model is used to describe water retention curve. The cases of the known values of its coefficients along with their identification from water head dynamics data are considered. The paper presents the results of model verification for both classical Richards equation and its fractional differential generalization. For the conditions of sprinkling irrigation in the south of Ukraine, we estimate the accuracy of modelling the water head dynamics measured by the Watermark sensors and the accuracy of seasonal irrigation rate assessment. The obtained results show that the use of water transport model that take soil fractality into consideration has no effect on assessing seasonal irrigation rate but gives up to 7% increase in accuracy while predicting the time and rate of next irrigation application.