•FAO-56 dual crop coefficients (Kcb, Ke, Ks) are used in many agricultural water models.•Kcb, Ke, and Ks are difficult to independently determine.•We determined Kcb, Ke, and Ks by re-analyzing eddy ...covariance (EC) data.•Method can be used to re-assess existing EC datasets to constrain Kcb, Ke, and Ks.
Current approaches to scheduling crop irrigation using reference evapotranspiration (ET0) recommend using a dual-coefficient approach using basal (Kcb) and soil (Ke) coefficients along with a stress coefficient (Ks) to model crop evapotranspiration (ETc), e.g. ETc=(Ks*Kcb+Ke)*ET0. However, determining Ks, Kcb, and Ke from the combined evapotranspiration (ET) is challenging, particularly for Ke, and a new method is needed to more rapidly determine crop coefficients for novel cultivars and cultivation practices. In this study, we partition eddy covariance ET observations into evaporation (E) and transpiration (T) components using correlation structure analysis of high frequency (10–20Hz) observations of carbon dioxide and water vapor (Scanlon and Sahu, 2008) at three irrigated agricultural sites. These include a C4 photosynthetic-pathway species (sugarcane—Sacharum officinarum L.) and a C3 pathway species (peach—Prunus persica) under sub-surface drip and furrow irrigation, respectively. Both sites showed high overall Kc consistent with their height (>4m). The results showed differences in Ke, with the sub-surface drip-irrigated sugarcane having a low Ke (0.1). There was no significant relationship (r2<0.05) between root zone soil volumetric water content (VWC) in sugarcane and observed Kcb*Ks, indicating that there was no stress (Ks=1), while the peach orchard showed mid-season declines in Kcb*Ks when VWC declined below 0.2. Partitioning of Kc into Kcb and Ke resulted in a better regression (r2=0.43) between the Normalized Differential Vegetation Index (NDVI) and Kcb in sugarcane than between NDVI and Kc (r2=0.11). The results indicate the potential for correlation structure flux partitioning to improve crop ET coefficient determination by improved use of eddy covariance observations compared to traditional approaches of lysimeters and microlysimeters and sap flow observations to determine Kc, Ke, Ks, and Kcb.
•Crop energy and CO2 fluxes are modified behind a single wind turbine.•Turbines slightly enhance daytime CO2 drawdown and double nighttime CO2 respiration.•Turbines increase simultaneous nighttime ...canopy mixing of heat and momentum.•Flux perturbations by single turbines indicate negligible impact on crop yield.
The Crop Wind-Energy Experiment (CWEX) provides a platform to investigate the effect of wind turbines and large wind farms on surface fluxes of momentum, heat, moisture, and carbon dioxide (CO2). In 2010 and 2011, eddy covariance flux stations were installed between two lines of turbines at the southwest edge of a large Iowa wind farm from late June to early September. We report changes in fluxes of momentum, sensible heat, latent heat, and CO2 above a corn canopy after surface air had passed through a single line of turbines. In 2010, our flux stations were placed within a field with homogeneous land management practices (same tillage, cultivar, chemical treatments). We stratify the data according to wind direction, diurnal condition, and turbine operational status. Within these categories, the downwind–upwind flux differences quantify turbine influences at the crop surface. Flux differences were negligible in both westerly wind conditions and when the turbines were non operational. When the flow is perpendicular (southerly) or slightly oblique (southwesterly) to the row of turbines during the day, fluxes of CO2 and water (H2O) are enhanced by a factor of five in the lee of the turbines (from three to five turbine diameter distances downwind from the tower) as compared to a west wind. However, we observe a smaller CO2 flux increase of 30–40% for these same wind directions when the turbines are off. In the nighttime, there is strong statistical significance that turbine wakes enhance upward CO2 fluxes and entrain sensible heat toward the crop. The direction of the scalar flux perturbation seems closely associated to the differences in canopy friction velocity. Spectra and co-spectra of momentum components and co-spectra of heat also demonstrate nighttime influence of the wind turbine turbulence at the downwind station.
Water is already a limited resource in California, and meeting the competing water needs, there will be only more challenges in the coming decades. Thus, sustaining the production of wine grapes, ...which are among the highest value specialty crops in the state, requires water to be used efficiently as possible. At the same time, improving irrigation management in vineyards requires spatially distributed information regarding vine water use or evapotranspiration (ET) at the sub-field scale that can only be collected via remote sensing. However, due to their unique canopy structure, current remote sensing models may not accurately describe the underlying turbulent exchange controlling ET from vineyards. To address that knowledge gap, this study investigates the vertical turbulent structure over a vineyard in the Central Valley of California. Using data from a profile of sonic anemometers (2.5 m, 3.75 m, 5 m, and 8 m, above the surface) collected during 2017 as a part of the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX), this study characterized the relationship between the turbulent flow at different heights using spectral analysis. It was found that the turbulent structure is strongly influenced by the underlying canopy. It also showed that the characteristics of the vertical structure differ significantly from what would be expected over other types of crops because of the unique configuration of vineyards, i.e., the concentration of the biomass in the upper part of the canopy and wide inter-row spacing. As a result, surface energy balance modeling using remote sensing data will likely require modifications to formulations of the turbulent energy exchange of the inter-row-canopy system with the lower atmosphere to reliably estimate vine ET. An example of this effect is shown for the mean wind profile which deviates from predicted profile using classical Monin–Obukhov similarity theory (MOST) used in remote sensing-based energy balance models resulting in errors in heat flux exchange which in turn affects modeled ET.
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.
In agriculture, leaf area index (LAI) is an important variable that describes occurring biomass and relates to the distribution of energy fluxes and evapotranspiration components. Current LAI ...estimation methods at subfield scale are limited not only by the characteristics of the spatial data (pixel size and spectral information) but also by the empiricity of developed models, mostly based on vegetation indices, which do not necessarily scale spatiality (among different varieties or planting characteristics) or temporally (need for different LAI models for different phenological stages). Widely used machine learning (ML) algorithms and high-resolution small unmanned aerial system (sUAS) information provide an opportunity for spatial and temporal LAI estimation addressing the spatial and temporal limitations. In this study, considering both accuracy and efficiency, a point-cloud-based feature-extraction approach (Full Approach) and a raster-based feature-extraction approach (Fast Approach) using sUAS information were developed based on multiple growing seasons (2014–2019) to extract and generate vine-scale information for LAI estimation in commercial vineyards across California. Three known ML algorithms, Random Forest (RF), eXtreme Gradient Boosting (XGB), and Relevance Vector Machine (RVM), were considered, along with hybrid ML schemes based on those three algorithms, coupled with different feature-extraction approaches. Results showed that the hybrid ML technique using RF and RVM and the Fast Approach with 9 input variables, called RVM-RF
Fast
model, performs better than others in a visual and statistical assessments of the generated LAI being also computationally efficient. Furthermore, using the generated LAI products in the quantification of energy balance using the two-source energy balance Priestley-Taylor version (TSEB-PT) model and EC tower data, the results indicated excellent estimation of net radiation (Rn) and latent heat flux (LE), good estimation of surface heat flux (G), and poor estimation of sensible heat flux (H). Additionally, TSEB-PT sensitivity analysis performed by regenerating LAI maps based on the generated LAI map (from − 15% of the original LAI map to + 15% with a 5% gap) showed that LAI uncertainty had a major impact on G, followed by evapotranspiration partitioning (T/ET), H, LE, and Rn. When considering the annual growth cycle of grapevines, the impact of LAI uncertainty on the T/ET in the veraison stage was larger than in the fruit set stage.
Here we demonstrate a novel method to physically integrate radiometric surface temperature (TR) into the Penman‐Monteith (PM) formulation for estimating the terrestrial sensible and latent heat ...fluxes (H and λE) in the framework of a modified Surface Temperature Initiated Closure (STIC). It combines TR data with standard energy balance closure models for deriving a hybrid scheme that does not require parameterization of the surface (or stomatal) and aerodynamic conductances (gS and gB). STIC is formed by the simultaneous solution of four state equations and it uses TR as an additional data source for retrieving the “near surface” moisture availability (M) and the Priestley‐Taylor coefficient (α). The performance of STIC is tested using high‐temporal resolution TR observations collected from different international surface energy flux experiments in conjunction with corresponding net radiation (RN), ground heat flux (G), air temperature (TA), and relative humidity (RH) measurements. A comparison of the STIC outputs with the eddy covariance measurements of λE and H revealed RMSDs of 7–16% and 40–74% in half‐hourly λE and H estimates. These statistics were 5–13% and 10–44% in daily λE and H. The errors and uncertainties in both surface fluxes are comparable to the models that typically use land surface parameterizations for determining the unobserved components (gS and gB) of the surface energy balance models. However, the scheme is simpler, has the capabilities for generating spatially explicit surface energy fluxes and independent of submodels for boundary layer developments.
Key Points:
Reintroducing radiometric surface temperature into Penman‐Monteith (PM) model
Holistic surface moisture availability framework to constrain the PM equation
Numerical estimation of Priestley‐Taylor parameter
Evapotranspiration (
) is a key variable for hydrology and irrigation water management, with significant importance in drought-stricken regions of the western US. This is particularly true for ...California, which grows much of the high-value perennial crops in the US. The advent of small Unmanned Aerial System (
) with sensor technology similar to satellite platforms allows for the estimation of high-resolution
at plant spacing scale for individual fields. However, while multiple efforts have been made to estimate
from
products, the sensitivity of
models to different model grid size/resolution in complex canopies, such as vineyards, is still unknown. The variability of row spacing, canopy structure, and distance between fields makes this information necessary because additional complexity processing individual fields. Therefore, processing the entire image at a fixed resolution that is potentially larger than the plant-row separation is more efficient. From a computational perspective, there would be an advantage to running models at much coarser resolutions than the very fine native pixel size from
imagery for operational applications. In this study, the Two-Source Energy Balance with a dual temperature (
) model, which uses remotely sensed soil/substrate and canopy temperature from
imagery, was used to estimate
and identify the impact of spatial domain scale under different vine phenological conditions. The analysis relies upon high-resolution imagery collected during multiple years and times by the Utah State University
program over a commercial vineyard located near Lodi, California. This project is part of the USDA-Agricultural Research Service Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (
). Original spectral and thermal imagery data from
were at 10 cm and 60 cm per pixel, respectively, and multiple spatial domain scales (3.6, 7.2, 14.4, and 30 m) were evaluated and compared against eddy covariance (
) measurements. Results indicated that the
model is only slightly affected in the estimation of the net radiation (
) and the soil heat flux (
) at different spatial resolutions, while the sensible and latent heat fluxes (
and
, respectively) are significantly affected by coarse grid sizes. The results indicated overestimation of
and underestimation of
values, particularly at Landsat scale (30 m). This refers to the non-linear relationship between the land surface temperature (
) and the normalized difference vegetation index (
) at coarse model resolution. Another predominant reason for
reduction in
was the decrease in the aerodynamic resistance (
), which is a function of the friction velocity F
) that varies with mean canopy height and roughness length. While a small increase in grid size can be implemented, this increase should be limited to less than twice the smallest row spacing present in the
imagery. The results also indicated that the mean
at field scale is reduced by 10% to 20% at coarser resolutions, while the with-in field variability in
values decreased significantly at the larger grid sizes and ranged between approximately 15% and 45%. This implies that, while the field-scale values of
are fairly reliable at larger grid sizes, the with-in field variability limits its use for precision agriculture applications.
Remote sensing offers the capability of observing an object without being in contact with the object. Throughout the recent history of agriculture, researchers have observed that different ...wavelengths of light are reflected differently by plant leaves or canopies and that these differences could be used to determine plant biophysical characteristics, e.g., leaf chlorophyll, plant biomass, leaf area, phenological development, type of plant, photosynthetic activity, or amount of ground cover. These reflectance differences could also extend to the soil to determine topsoil properties. The objective of this review is to evaluate how past research can prepare us to utilize remote sensing more effectively in future applications. To estimate plant characteristics, combinations of wavebands may be placed into a vegetative index (VI), i.e., combinations of wavebands related to a specific biophysical characteristic. These VIs can express differences in plant response to their soil, meteorological, or management environment and could then be used to determine how the crop could be managed to enhance its productivity. In the past decade, there has been an expanded use of machine learning to determine how remote sensing can be used more effectively in decision-making. The application of artificial intelligence into the dynamics of agriculture will provide new opportunities for how we can utilize the information we have available more effectively. This can lead to linkages with robotic systems capable of being directed to specific areas of a field, an orchard, a pasture, or a vineyard to correct a problem. Our challenge will be to develop and evaluate these relationships so they will provide a benefit to our food security and environmental quality.
Watershed scale soil moisture estimates are necessary to validate current remote sensing products, such as those from the Advanced Microwave Scanning Radiometer (AMSR). Unfortunately, remote sensing ...technology does not currently resolve the land surface at a scale that is easily observed with ground measurements. One approach to validation is to use existing soil moisture measurement networks and scale these point observations up to the resolution of remote sensing footprints. As part of the Soil Moisture Experiment 2002 (SMEX02), one such soil moisture gaging system in the Walnut Creek Watershed, Iowa, provided robust estimates of the soil moisture average for a watershed throughout the summer of 2002. Twelve in situ soil moisture probes were installed across the watershed. These probes recorded soil moisture at a depth of 5 cm from June 29, 2002 to August 19, 2002. The sampling sites were analyzed for temporal and spatial stability by several measures including mean relative difference, Spearman rank, and correlation coefficient analysis. Representative point measurements were used to estimate the watershed scale (∼25 km) soil moisture average and shown to be accurate indicators with low variance and bias of the watershed scale soil moisture distribution. This work establishes the validity of this approach to provide watershed scale soil moisture estimates in this study region for the purposes of satellite validation with estimation errors as small as 3%. Also, the potential sources of error in this type of analysis are explored. This study is a first step in the implementation of large-scale soil moisture validation using existing networks such as the Soil Climate Analysis Network (SCAN) and several Agricultural Research Service watersheds as a basis for calibrating satellite soil moisture products, for networks design, and designing field experiments.
Remote sensing-based models are the most viable means of collecting the high-resolution spatially distributed estimates of evaporative water loss needed to manage irrigation and ensure the effective ...use of limited water resources. However, due to the unique canopy structure and configuration of vineyards, these models may not be able to adequately describe the physical processes driving evapotranspiration from vineyards. Using data collected from 2014 to 2016 as a part of the Grape Remote sensing Atmospheric Profile and Evapotranspiration Experiment (GRAPEX), the twofold objective of this study was to (1) identify the relationship between the roughness parameters, zero-plane displacement height (
d
o
) and roughness length for momentum (
z
o
), and local environmental conditions, specifically wind direction and vegetation density and (2) determine the effect of using these relationships on the ability of the remote sensing-based Two-Source Energy Balance (TSEB) model to estimate the sensible (
H
) and latent (
λE
) heat fluxes. Although little variation in
d
o
was identified during the growing season, a well-defined sigmoidal relationship was observed between
z
o
and wind direction. When the output from a version of the TSEB model incorporating these relationships (TSEB
VIN
) was compared to output from the standard model (TSEB
STD
), there were large changes to the roughness parameters, particularly
z
o
, but only modest changes in the turbulent fluxes. When the output from TSEB
VIN
was compared to that of a version using a parameterization scheme representing open canopies (TSEB
OPN
), the mean absolute difference between the estimates of
d
o
and
z
o
were 0.44 m and 0.25 m, respectively. While these values represent differences in excess of 45%, the turbulent fluxes differed by just 13 W m
−2
or 10%, on average. The results suggest that the TSEB model is largely insensitive to changes in the roughness parameters for the range in roughness values evaluated in this study. This also suggests that the requirement for highly accurate roughness values has limited utility in the application of the TSEB model in vineyard systems. Since there is no significant advantage to using the more complex TSEB
OPN
and TSEB
VIN
models, it is recommended that the standard model be used.
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