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Wine grape quality and quantity are affected by vine growing conditions during critical phenological stages. Field observations of vine growth stages are too sparse to fully capture the spatial variability of vine conditions. In addition, traditional grape yield prediction methods are time consuming and require large amount grape samples. Remote sensing data provide detailed spatial and temporal information regarding vine development that is useful for vineyard management. In this study, Landsat surface reflectance products from 2013 and 2014 were used to map satellite-based Normalized Difference Vegetation Index (NDVI) and leaf area index (LAI) over two Vitis vinifera L. cv. Pinot Noir vineyards in California, USA. The spatial correlation between grape yield maps and the interpolated daily time series (LAI and NDVI) was quantified. NDVI and LAI were found to have similar performance as a predictor of spatial yield variability, providing peak correlations of 0.8 at specific times during the growing season, and the timing of this peak correlation differed for the two years of study. In addition, correlations with maximum and seasonal-cumulative vegetation indices were also evaluated, and showed slightly lower correlations with the observed yield maps. Finally, the within-season grape yield predictability was examined using a simple strategy in which the relationship between grape yield and vegetation indices were calibrated with limited ground measurements. This strategy has a strong potential to improve the accuracy and efficiency of yield estimation in comparison with traditional approaches used in the wine grape growing industry.

Pesticide volatilization is a significant loss pathway that may have unintended consequences in nontarget environments. Field-scale pesticide volatilization involves the interaction of a number of complex variables. There is a need to acquire pesticide volatilization fluxes from a location where several of these variables can be held constant. Accordingly, soil properties, tillage practices, surface residue management, and pesticide formulations were held constant while fundamental information regarding metolachlor volatilization (a pre-emergent pesticide) was monitored over a five-year period as influenced by meteorological variables and soil water content. Metolachlor vapor concentrations were measured continuously for 120 h after each application using polyurethane foam plugs in a logarithmic profile above the soil surface. A flux gradient technique was used to compute volatilization fluxes from metolachlor concentration profiles and turbulent fluxes of heat and water vapor (as determined from eddy covariance measurements). Differences in meteorological conditions and surface soil water contents resulted in variability of the volatilization losses over the years studied. The peak volatilization losses for each year occurred during the first 24 h after application with a maximum flux rate in 2001 (1500 ng m(-2) s(-1)) associated with wet surface soil conditions combined with warm temperatures. The cumulative volatilization losses for the 120-hour period following metolachlor application varied over the years from 5 to 25% of the applied active ingredient, with approximately 87% of the losses occurring during the first 72 h. In all of the years studied, volatilization occurred diurnally and accounted for between 43 and 86% during the day and 14 and 57% during the night of the total measured loss. The results suggest that metolachlor volatilization is influenced by multiple factors involving meteorological, surface soil, and chemical factors.

Irrigation in the Central Valley of California is essential for successful wine grape production. With reductions in water availability in much of California due to drought and competing water use interests, it is important to optimize irrigation management strategies. In the current study, we investigate the utility of satellite-derived maps of evapotranspiration (ET) and the ratio of actual to reference ET (fRET) based on remotely sensed land surface temperature (LST) imagery for monitoring crop water use and stress in vineyards. The Disaggregated Atmosphere Land EXchange Inverse (ALEXI/DisALEXI) surface energy balance model, a multi-scale ET remote sensing framework with operational capabilities, is evaluated over two Pinot noir vineyard sites in central California that are being monitored as part of the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). A data fusion approach is employed to combine ET timeseries retrievals from multiple satellite platforms to generate estimates at both the high spatial (30m) and temporal (daily) resolution required for field-scale irrigation management. Comparisons with micrometeorological data indicate reasonable model performance, with mean absolute errors of 0.59 mm d-1 in ET at the daily timestep and minimal bias. Values of fRET agree well with tower observations and reflect known irrigation. Spatiotemporal analyses illustrate the ability of ALEXI/DisALEXI/data fusion package to characterize heterogeneity in ET and fRET both within a vineyard and over the surrounding landscape. These findings will inform the development of strategies for integrating ET mapping time series into operational irrigation management framework, providing actionable information regarding vineyard water use and crop stress at the field and regional scale and at daily to multiannual timescales.

The thermal-based Two Source Energy Balance (TSEB) model partitions the evapotranspiration (ET) and energy fluxes from vegetation and soil components providing the capability for estimating soil evaporation (E) and canopy transpiration (T). However, it is crucial for ET partitioning to retrieve reliable estimates of canopy and soil temperatures and net radiation, as the latter determines the available energy for water and heat exchange from soil and canopy sources. These two factors become especially relevant in row crops with wide spacing and strongly clumped vegetation such as vineyards and orchards. To better understand part of this research was conducted thanks to the MC-COFUND Talentia Program

a b s t r a c tAgricultural productivity has increased in the Texas High Plains at the cost of declining water tables, putting at risk the sustainability of the Ogallala Aquifer as a principal source of water for irrigated agriculture. This has led area producers to seek alternative practices that can increase water use efficiency (WUE) through more careful management of water. One potential way of improving WUE is by reducing soil evaporation (E), thus reducing overall evapotranspiration (ET). Before searching for ways to reduce E, it is first important to quantify E and understand the factors that determine its magnitude. The objectives of this study were (1) to quantify E throughout part of the growing season for irrigated cotton in a strongly advective semi-arid region; (2) to study the effects of LAI, days after irrigation, and measurement location within the row on the E/ET fraction; and (3) to study the ability of microlysimeter (ML) measures of E combined with sap flow gage measures of transpiration (T) to accurately estimate ET when compared with weighing lysimeter ET data and to assess the E/T ratio. The research was conducted in an irrigated cotton field at the Conservation & Production Research Laboratory of the USDA-ARS, Bushland, TX. ET was measured by a large weighing lysimeter, and E was measured by 10 microlysimeters that were deployed in two sets of 5 across the interrow. In addition, 10 heat balance sap flow gages were used to determine T. A moderately good agreement was found between the sum E + T and ET (SE = 1 mm or $10% of ET). It was found that E may account for >50% of ET during early stages of the growing season (LAI < 0.2), significantly decreasing with increase in LAI to values near 20% at peak LAI of three. Measurement location within the north-south interrows had a distinct effect on the diurnal pattern of E, with a shift in time of peak E from west to east, a pattern that was governed by the solar radiation reaching the soil surface. However, total daily E was unaffected by position in the interrow. Under wet soil conditions, wind speed and direction affected soil evaporation. Row orientation interacted with wind direction in this study such that aerodynamic resistance to E usually increased when wind direction was perpendicular to row direction; but this interaction needs further study because it appeared to be lessened under higher wind speeds.

Particularly in light of California’s recent multiyear drought, there is a critical need for accurate and timely evapotranspiration (ET) and crop stress information to ensure long-term sustainability of high-value crops. Providing this information requires the development of tools applicable across the continuum from subfield scales to improve water management within individual fields up to watershed and regional scales to assess water resources at county and state levels. High-value perennial crops (vineyards and orchards) are major water users, and growers will need better tools to improve water-use efficiency to remain economically viable and sustainable during periods of prolonged drought. To develop these tools, government, university, and industry partners are evaluating a multiscale remote sensing–based modeling system for application over vineyards. During the 2013–17 growing seasons, the Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) project has collected micrometeorological and biophysical data within adjacent pinot noir vineyards in the Central Valley of California. Additionally, each year ground, airborne, and satellite remote sensing data were collected during intensive observation periods (IOPs) representing different vine phenological stages. An overview of the measurements and some initial results regarding the impact of vine canopy architecture on modeling ET and plant stress are presented here. Refinements to the ET modeling system based on GRAPEX are being implemented initially at the field scale for validation and then will be integrated into the regional modeling toolkit for large area assessment.

Abstract. Thermal and multispectral remote sensing data from low-altitude aircraft can provide high spatial resolution necessary for sub-field ( ≤ 10 m) and plant canopy (≤ 1 m) scale evapotranspiration (ET) monitoring. In this study, highresolution (sub-meter-scale) thermal infrared and multispectral shortwave data from aircraft are used to map ET over vineyards in central California with the two-source energy balance (TSEB) model and with a simple model having operational immediate capabilities called DATTUTDUT (Deriving Atmosphere Turbulent Transport Useful To Dummies Using Temperature). The latter uses contextual information within the image to scale between radiometric land surface temperature (T R ) values representing hydrologic limits of potential ET and a non-evaporative surface. Imagery from 5 days throughout the growing season is used for mapping ET at the sub-field scale. The performance of the two models is evaluated using tower-based measurements of sensible (H ) and latent heat (LE) flux or ET. The comparison indicates that TSEB was able to derive reasonable ET estimates under varying conditions, likely due to the physically based treatment of the energy and the surface temperature partitioning between the soil/cover crop inter-row and vine canopy elements. On the other hand, DATTUTDUT performance was somewhat degraded presumably because the simple scaling scheme does not consider differences in the two sources (vine and inter-row) of heat and temperature contributions or the effect of surface roughness on the efficiency of heat exchange. Maps of the evaporative fraction (EF = LE/(H + LE)) from the two models had similar spatial patterns but different magnitudes in some areas within the fields on certain days. Large EF discrepancies between the models were found on 2 of the 5 days (DOY 162 and 219) when there were significant differences with the tower-based ET measurements, particularly using the DATTUTDUT model. These differences in EF between the models translate to significant variations in daily water use estimates for these 2 days for the vineyards. Model sensitivity analysis demonstrated the high degree of sensitivity of the TSEB model to the accuracy of the T R data, while the DATTUTDUT model was insensitive to systematic errors in T R as is the case with contextual-based models. However, it is shown that the study domain and spatial resolution will significantly influence the ET estimation from the DATTUTDUT model. Future work is planned for developing a hybrid approach that leverages the strengths of both modeling schemes and is simple enough to be used operationally with high-resolution imagery.