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.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Improved accuracy of evapotranspiration (ET) estimation, including its partitioning between transpiration (T) and surface evaporation (E), is key to monitor agricultural water use in vineyards, ...especially to enhance water use efficiency in semi-arid regions such as California, USA. Remote-sensing methods have shown great utility in retrieving ET from surface energy balance models based on thermal infrared data. Notably, the two-source energy balance (TSEB) has been widely and robustly applied in numerous landscapes, including vineyards. However, vineyards add an additional complexity where the landscape is essentially made up of two distinct zones: the grapevine and the interrow, which is often seasonally covered by an herbaceous cover crop. Therefore, it becomes more complex to disentangle the various contributions of the different vegetation elements to total ET, especially through TSEB, which assumes a single vegetation source over a soil layer. As such, a remote-sensing-based three-source energy balance (3SEB) model, which essentially adds a vegetation source to TSEB, was applied in an experimental vineyard located in California’s Central Valley to investigate whether it improves the depiction of the grapevine-interrow system. The model was applied in four different blocks in 2019 and 2020, where each block had an eddy-covariance (EC) tower collecting continuous flux, radiometric, and meteorological measurements. 3SEB’s latent and sensible heat flux retrievals were accurate with an overall RMSD ~ 50 W/m
2
compared to EC measurements. 3SEB improved upon TSEB simulations, with the largest differences being concentrated in the spring season, when there is greater mixing between grapevine foliage and the cover crop. Additionally, 3SEB’s modeled ET partitioning (T/ET) compared well against an EC T/ET retrieval method, being only slightly underestimated. Overall, these promising results indicate 3SEB can be of great utility to vineyard irrigation management, especially to improve T/ET estimations and to quantify the contribution of the cover crop to ET. Improved knowledge of T/ET can enhance grapevine water stress detection to support irrigation and water resource management.
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.
► We evaluate a method for mapping field-scale evapotranspiration at daily timesteps. ► Interpolation assumes conservation of actual-to-potential ET between imaging dates. ► The method misses ET ...peaks due to irrigation, but reasonably reproduces seasonal ET. ► Further analyses can help to identify optimal characteristics of mapping missions.
Robust spatial information about environmental water use at field scales and daily to seasonal timesteps will benefit many applications in agriculture and water resource management. This information is particularly critical in arid climates where freshwater resources are limited or expensive, and groundwater supplies are being depleted at unsustainable rates to support irrigated agriculture as well as municipal and industrial uses. Gridded evapotranspiration (ET) information at field scales can be obtained periodically using land–surface temperature-based surface energy balance algorithms applied to moderate resolution satellite data from systems like Landsat, which collects thermal-band imagery every 16days at a resolution of approximately 100m. The challenge is in finding methods for interpolating between ET snapshots developed at the time of a clear-sky Landsat overpass to provide complete daily time-series over a growing season. This study examines the efficacy of a simple gap-filling algorithm designed for applications in data-sparse regions, which does not require local ground measurements of weather or rainfall, or estimates of soil texture. The algorithm relies on general conservation of the ratio between actual ET and a reference ET, generated from satellite insolation data and standard meteorological fields from a mesoscale model. The algorithm was tested with ET retrievals from the Atmosphere–Land Exchange Inverse (ALEXI) surface energy balance model and associated DisALEXI flux disaggregation technique, which uses Landsat-scale thermal imagery to reduce regional ALEXI maps to a finer spatial resolution. Daily ET at the Landsat scale was compared with lysimeter and eddy covariance flux measurements collected during the Bushland Evapotranspiration and Agricultural Remote sensing EXperiment of 2008 (BEAREX08), conducted in an irrigated agricultural area in the Texas Panhandle under highly advective conditions. The simple gap-filling algorithm performed reasonably at most sites, reproducing observed cumulative ET to within 5–10% over the growing period from emergence to peak biomass in both rainfed and irrigated fields.
This paper describes important characteristics of an uncoupled high-resolution land data assimilation system (HRLDAS) and presents a systematic evaluation of 18-month-long HRLDAS numerical ...experiments, conducted in two nested domains (with 12- and 4-km grid spacing) for the period from 1 January 2001 to 30 June 2002, in the context of the International H₂O Project (IHOP_2002). HRLDAS was developed at the National Center for Atmospheric Research (NCAR) to initialize land-state variables of the coupled Weather Research and Forecasting (WRF)–land surface model (LSM) for high-resolution applications. Both uncoupled HRDLAS and coupled WRF are executed on the same grid, sharing the same LSM, land use, soil texture, terrain height, time-varying vegetation fields, and LSM parameters to ensure the same soil moisture climatological description between the two modeling systems so that HRLDAS soil state variables can be used to initialize WRF–LSM without conversion and interpolation. If HRLDAS is initialized with soil conditions previously spun up from other models, it requires roughly 8–10 months for HRLDAS to reach quasi equilibrium and is highly dependent on soil texture. However, the HRLDAS surface heat fluxes can reach quasi-equilibrium state within 3 months for most soil texture categories. Atmospheric forcing conditions used to drive HRLDAS were evaluated against Oklahoma Mesonet data, and the response of HRLDAS to typical errors in each atmospheric forcing variable was examined. HRLDAS-simulated finescale (4 km) soil moisture, temperature, and surface heat fluxes agreed well with the Oklahoma Mesonet and IHOP_2002 field data. One case study shows high correlation between HRLDAS evaporation and the low-level water vapor field derived from radar analysis.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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.
The exchange of carbon between the Earth's atmosphere and biosphere influences the atmospheric abundances of carbon dioxide (CO2) and methane (CH4). Airborne eddy covariance (EC) can quantify ...surface-atmosphere exchange from landscape-to-regional scales, offering a unique perspective on carbon cycle dynamics. We use extensive airborne measurements to quantify fluxes of sensible heat, latent heat, CO2, and CH4 across multiple ecosystems in the Mid-Atlantic region during September 2016 and May 2017. In conjunction with footprint analysis and land cover information, we use the airborne dataset to explore the effects of landscape heterogeneity on measured fluxes. Our results demonstrate large variability in CO2 uptake over mixed agricultural and forested sites, with fluxes ranging from −3.4 0.7 to −11.5 1.6 mol m−2 s−1 for croplands and −9.1 1.5 to −22.7 3.2 mol m−2 s−1 for forests. We also report substantial CH4 emissions of 32.3 17.0 to 76.1 29.4 nmol m−2 s−1 from a brackish herbaceous wetland and 58.4 12.0 to 181.2 36.8 nmol m−2 s−1 from a freshwater forested wetland. Comparison of ecosystem-specific aircraft observations with measurements from EC flux towers along the flight path demonstrate that towers capture ∼30%-75% of the regional variability in ecosystem fluxes. Diel patterns measured at the tower sites suggest that peak, midday flux measurements from aircraft accurately predict net daily CO2 exchange. We discuss next steps in applying airborne observations to evaluate bottom-up flux models and improve understanding of the biophysical processes that drive carbon exchange from landscape-to-regional scales.
•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.
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.
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