While the transpiring field will act as hypothesized with the slope and direction of a fitted plane in line with the wind direction, a plane fitted to the fallow field downwind will likely show a slope that is opposite in direction to the wind. The wind carries moist air from the vegetated field onto the fallow field, leading the upwind edge of the fallow field to have higher water vapor concentrations than the edge that is downwind. In the case of the downwind area being another highly transpiring field , the moist, advected air from the upwind field may reduce the transpiration rate of the downwind field at the boundary by decreasing the vapor pressure deficit.The accumulation of water vapor from one field can therefore lead to shifts in vegetation response that are difficult to account for. Figure 4.15C illustrates the scenario where a dry, fallow field is upwind of a transpiring field. If the area upwind of a vegetated field is fallow, we would expect the saturation deficit of the dry advecting air to increase the evaporation rate at the boundary unless the vapor pressure deficit is high enough to initialize stomatal closure . A higher evapotranspiration rate at the upwind side of the field will lessen the expected, observable trend of advection across the field. The transpiration response will be species-dependent. Second, not all fields will interact with the atmosphere in the same ways,container growing raspberries due to differences in aerodynamic roughness, affected by row spacing, plant height, plant size, orientation, and composition. The aerodynamic roughness of a field will influence how effectively and at what height the transpired water vapor will mix with the atmosphere .

Agricultural fields may differ strongly in aerodynamic roughness, and these differences will lead to deviations from the hypothesized water vapor slope and intercept patterns as they vary with crop type. Therefore, we would not expect all fields to show the same relationships between water vapor, wind, and estimated transpiration rates. We would expect aerodynamically rougher surfaces, such as orchards, to generate greater turbulence, show more mixing between ET and the atmosphere, generate mixing higher up in the atmosphere, and show greater coupling with the wind than row crops . Depending on the wind speed, orchards may show higher or lower slopes than row crops if their vapor patterns are more tied to wind patterns. In contrast, shorter and smoother row crops such as alfalfa will be less coupled to the atmosphere . Because crops such as orchards are more closely coupled to the atmosphere, they may be more appropriate to study with water vapor imagery. Therefore, isolating the effects of neighboring fields would be beneficial for field-level water vapor analyses, but this was not logistically possible in our study. The study area is a high-producing agricultural area where most fields are bordered by multiple neighbors of varying GV cover, crop type, size, physical characteristics that influence roughness, and ET rate. Further, without LiDAR data from which physical characteristics such as orientation, height and structure could be obtained, it was not possible to model field-scale differences in aerodynamic roughness in this study. This work has aimed to enhance understanding of the impact of GV fraction, field size, crop type and ET rate on patterns of water vapor, although not enough is yet known to allow for accurate modeling and analysis of all such factors on field level trends in such a diverse landscape.

Temporally, snapshots of water vapor and wind at one point in a day do not encompass smaller-scale patterns that are occurring over the entire day, and these snapshots from one moment will not accurately capture wind-vapor interactions. The patterns of water vapor shown in an acquired image will not be created by wind patterns that occur at the time of acquisition. Rather, water vapor will show patterns of advection over a length of time of minutes to hours prior to image acquisition, dependent upon wind speed, ET, boundary between wind direction and the directionality of a fitted surface of water vapor above a crop field may show large differences when, in actuality, they align with each other. The effect of wind on water vapor at different scales also makes tying an air parcel to the small patch of land at which it originated challenging. In order to determine the time-scale of wind as it relates to water vapor patterns and better be able to account for these effects, a large eddy simulation study would be of value to model water vapor results under a variety of temporal lengths and atmospheric conditions. Spatially, our results support the assumption that advection is happening at different length scales, but analyses of water vapor at these separate scales is complicated by heterogeneity in the landscape that is not evenly distributed. Many field-level trends are likely obscured by larger synoptic differences in water vapor over the study site because factors of the landscape that are likely to influence water vapor cannot be decoupled from patterns of advection across the scene. Within our study area, both crop species and field sizes are not evenly distributed spatially. Generally, fields in the southern part of the study area are larger and those in the northern part are smaller. Additionally, these larger fields in the south are disproportionately planted with nut crops, while citrus fruits and grapes are more common in the northern, smaller fields. Because water vapor slope is affected by field size, and crop species show large differences in average size, the impact of field size on slope cannot be distinguished from the impact of crop ET on slope.

Almond and pistachio trees are high water users, had large fields, but low water vapor slopes. Fruit trees and grapes are lower water users, had small fields, and steep water vapor slopes. Additionally,raspberries for containers the results of this study lead to a new hypothesis that water vapor accumulation on fields may be the dominant process rather than field-level advection, which would cause higher-transpiring crops to have higher water vapor concentration/intercepts. However, this hypothesis could not be tested because scene-scale advection of water vapor obscures potential analyses of water vapor intercept by field size or crop type when the landscape is not evenly distributed. With unequally distributed crops and scene-scale advection, a crop’s water vapor intercept will be factor of its position within the scene, and other factors that are expected to influence the intercept, such as ET rates, cannot be studied in isolation. This finding indicates that water vapor analysis in such a diverse and complex agricultural scene, such as the Central Valley, is very scale dependent as larger scene-level advection confounds analysis of small-scale patterns and vice versa. Future studies that aim to detect ET through water vapor imagery should carefully choose a study site that is of an appropriate scale to measure the type of advection it proposes to detect. In our study area we found trends at the pixel, field and scene scale although these trends muddled analyses at other scales. Future studies might consider a well-mixed study area of an intermediate scale where wind inconsistencies average out.Water vapor imagery shows patterns of vapor that are highly variable through space and time and that hold valuable information about land-atmosphere interactions. Because few studies have used these data for analyses of vapor patterns, there is considerable room for growth of knowledge in this field. To further scientific understanding of water vapor imagery analysis, further studies are necessary to refine observation and quantification of land-surface interactions as the signal is highly complex and is affected by many factors. While water vapor imagery could potentially be used to parameterize models of land-surface interactions, additional studies in equally complex landscapes are necessary to define the conditions and scales at which this imagery can be used. Almost 4,000 AVIRIS images have been collected since 2006 and are available for public download. With such a large repository of data collected at different time points, under varied atmospheric conditions, and over diverse surfaces, future research could tease out the conditions under which interactions can best be observed in a more comprehensive way than this study of three snapshots in time could. Further, with future remote sensing missions such as SBG, which will collect hyperspectral imagery at moderate spatial resolutions and enable column water vapor estimates globally, these data streams can be exploited for comparisons of water vapor over large agricultural areas worldwide. These large archives of water vapor observations can also act as a compliment to models that estimate water vapor and plant water use by providing validation data.

In addition to increasing analysis of similarly complex scenes, future studies would benefit from additional data sources that could to isolate the signal of water vapor and validate its link to the surface. Such controls include on-site continuous wind measurements, flux tower measurements of ET, and/or more spatially comprehensive wind data. On-site wind data and ET measurements at a high temporal resolution would both validate trends seen in the water vapor imagery and assist in pinpointing the appropriate temporal scale and time of day for which this analysis is best suited. A meso scale weather model such as the Weather Research and Forecasting Model would also be an asset to future studies as it could produce a more accurate and spatially comprehensive estimation for wind speeds and directions than interpolation of wind data from weather stations. Although more work is needed in order to refine understanding of the water vapor signal in a complex agricultural environment, the results suggest that this technique could currently be of use for crop water analyses in agricultural areas that experience less variation in crop type, wind, and field size than the Central Valley of California. The Central Valley proved to be an especially challenging study site as it has a large variety of crops and management practices, which will create non-uniform distributions of aerodynamic roughness, ET rates, and landscape structures throughout the scene. Further, with an average field size that is smaller than average in the United States, the water vapor parcels are more difficult to tie to a source field. Therefore, using this imagery for crop ET estimation would be less challenging in an agricultural area that grows only a few crop types and that has a more consistent and central wind direction. In line with this assumption, we suggest that future studies may consider agricultural sites in North American mid-continental agricultural regions with large fields such as Iowa or Nebraska. Beyond advancing our ability to capture patterns of field-level ET with water vapor imagery, this imagery may prove valuable for regional analyses of water transport. There are many challenges associated with linking water vapor to crops at the field-level as outlined above, but the idea behind this work will likely hold at a smaller scale. Lo and Famiglietti found that in the Western United States the irrigation from California has been shown to increase the summer stream flow of the Colorado River by 30 percent. This finding matches with observations of advected moisture in 2013 and 2015 that show increasing water vapor from west to east from the coast to the Sierra Nevada Mountains. These large movements of water vapor have implications for climate change and land use, and call upon the need to increase monitoring of water vapor patterns in areas with large irrigation inputs. Therefore, a study that examines the ability of water vapor imagery to assist in regional water transport assessments could be of high value. The State of California and many other jurisdictions around the world have adopted the goal of reducing greenhouse gas emissions by 80% below 1990 levels by 2050. In addition to other changes throughout the economy, this target is likely to require substantially different urban-development patterns, emphasizing features such as compactness, a greater mixture of land uses, and greater orientation toward pedestrian, bicycle, and public transport . However, relatively little is yet known about what an 80%-GHG-reduction urbanization strategy might look like, how land use, population, and energy-efficiency strategies might interrelate, and what sorts of co-benefits might occur in terms of farmland and open space preservation.