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Deriving evapotranspiration is crucial for determining the water requirements of crops and for efficiently allocating water resources for irrigation. Various experiments and methods have proven that earth observation (EO) is a useful tool for estimating evapotranspiration and supporting irrigation and water resource management at different scales. This study presents a framework based on the Penman-Monteith big leaf model and Shuttleworth-Wallace sparse canopy model for estimating the evapotranspiration in irrigated crops with partial and full-canopy conditions. The approach fully utilizes the high-resolution and multi-spectral capabilities of the Sentinel-2 (S2) sensors for the derivation of surface parameters such as hemispherical shortwave albedo(α), Leaf Area Index (LAI), and the water status of the soil-canopy ensemble by using the OPTRAM model. Proposed by Sadeghi [1], the OPTRAM model uses the pixel distribution in the Shortwave Infrared Transformed Reflectance (STR)-NDVI space, where the water content of the soil-canopy system is linearly correlated to the STR index. In detail, the proposed approach estimates the contributions of soil and canopy to the total evapotranspiration by incorporating the OPRAM model to assess the water status of the surface and adjust the resistance terms in the combination equation [2] The results are validated by using Eddy Covariance data collected during the GRAPEX (Grape Remote Sensing Atmospheric Profile Evapotranspiration eXperiment) project [3], T-REX (Tree crop Remote sensing of Evapotranspiration eXperiment) project, and COALA (COpernicus Applications and services for Low impact agriculture in Australia) project [4]. These projects are conducted respectively in irrigated vineyards and almond orchards in California, and in irrigated maize and alfalfa in Australia. [1] Sadeghi, Morteza, Scott B. Jones, and William D. Philpot.: A linear physically-based model for remote sensing of soil moisture using short wave infrared bands. Remote Sensing of Environment 164, 66-76 (2015). [2] D’Urso, G., Bolognesi, S. F., Kustas, W. P., Knipper, K. R., Anderson, M. C., Alsina, M. M., ... & Belfiore, O. R.: Determining evapotranspiration by using combination equation models with sentinel-2 data and comparison with thermal-based energy balance in a California irrigated Vineyard. Remote Sensing, 13(18), 3720 (2021). [3] Kustas, W.P., Anderson, M.C., Alfieri, J.G., Knipper, K., Torres-Rua, A., Parry, C.K., Nieto, H., Agam, N., White, W.A., Gao, F. The grape remote sensing atmospheric profile and evapotranspiration experiment. Bulletin of the American Meteorological Society 2018, 99, 1791-1812. [4] COALA project. https://www.coalaproject.eu/

Precision irrigation management requires operational monitoring of crop water status. However, there is still some controversy on how to account for crop water stress. To address this question, several physiological, several physiological metrics have been proposed, such as the leaf/stem water potentials, stomatal conductance, or sap flow. On the other hand, thermal remote sensing has been shown to be a promising tool for efficiently evaluating crop stress at adequate spatial and temporal scales, via the Crop Water Stress Index (CWSI), one of the most common indices used for assessing plant stress. CWSI relates the actual crop evapotranspiration ET (related to the canopy radiometric temperature) to the potential ET (or minimum crop temperature). However, remotely sensed surface temperature from satellite sensors includes a mixture of plant canopy and soil/substrate temperatures, while what is required for accurate crop stress detection is more related to canopy metrics, such as transpiration, as the latter one avoids the influence of soil/substrate in determining crop water status or stress. The Two-Source Energy Balance (TSEB) model is one of the most widely used and robust evapotranspiration model for remote sensing. It has the capability of partitioning ET into the crop transpiration and soil evaporation components, which is required for accurate crop water stress estimates. This study aims at evaluating different TSEB metrics related to its retrievals of actual ET, transpiration and stomatal conductance, to track crop water stress in a vineyard in California, part of the GRAPEX experiment. Four eddy covariance towers were deployed in a Variable Rate Irrigation system in a Merlot vineyard that was subject to different stress periods. In addition, root-zone soil moisture, stomatal conductance and leaf/stem water potential were collected as proxy for in situ crop water stress. Results showed that the most robust variable for tracking water stress was the TSEB derived leaf stomatal conductance, with the strongest correlation with both the measured root-zone soil moisture and stomatal conductance gas exchange measurements. In addition, these metrics showed a better ability in tracking stress when the observations are taken early after noon., Funding and logistical support for the GRAPEX project were provided by E. & J. Gallo Winery and from the NASA Applied Sciences-Water Resources Program (Grant no. NNH17AE39I). This research was also supported in part by the U.S. Department of Agriculture, Agricultural Research Service. Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.

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., Funding and logistical support for the GRAPEX project were provided by E. & J. Gallo Winery and from the NASA Applied Sciences-Water Resources Program (Grant No. NNH17AE39I). This research was also supported in part by the U.S. Department of Agriculture, Agricultural Research Service.

The impacts of surface heterogeneity and land surface parameterization on the mesoscale processes were studied. Experiments were conducted using the Weather Research and Forecasting (WRF) model coupled with a simple (slab) land surface model (LSM), a relatively complex Noah LSM, and a land data assimilation system (LDAS) with detailed surface fields. Three heterogeneity length scales: 1, 3, and 9 km, were employed to alter land cover and land use. A series of simulations were performed over the U.S. Southern Great Plains during the summer when the soil moisture was abundant. Results indicate that both the land surface parameterizations and fine-scale surface heterogeneity affect the model simulations; and the impact of land surface parameterization is found to be more important, particularly for low frequency ( $$f

The assimilation of L-band surface brightness temperature (Tb) into the land surface model (LSM) component of a numerical weather prediction (NWP) system is generally expected to improve the quality of summertime 2-m air temperature (T2m) forecasts during water-limited surface conditions. However, recent retrospective results from the European Centre for Medium-Range Weather Forecasts (ECMWF) suggest that the assimilation of L-band Tb from the European Space Agency’s (ESA) Soil Moisture Ocean Salinity (SMOS) mission may, under certain circumstances, degrade the accuracy of growing-season 24-h T2m forecasts within the central United States. To diagnose the source of this degradation, we evaluate ECMWF soil moisture (SM) and evapotranspiration (ET) forecasts using both in situ and remote sensing resources. Results demonstrate that the assimilation of SMOS Tb broadly improves the ECMWF SM analysis in the central United States while simultaneously degrading the quality of 24-h ET forecasts. Based on a recently derived map of true global SM–ET coupling and a synthetic fraternal twin data assimilation experiment, we argue that the spatial and temporal characteristics of ECMWF SM analyses and ET forecast errors are consistent with the hypothesis that the ECMWF LSM overcouples SM and ET and, as a result, is unable to effectively convert an improved SM analysis into enhanced ET and T2m forecasts. We demonstrate that this overcoupling is likely linked to the systematic underestimation of root-zone soil water storage capacity by LSMs within the U.S. Corn Belt region.

Estimating the transport of ammonia and particulate matter (PM) from ventilation tunnel fans of poultry houses is needed to develop effective mitigation strategies. However, field measurements are time-consuming and costly. Alternatively, air dispersion models can provide more information under a variety of conditions. Therefore, this study was conducted to modify and to validate the Gaussian plume model to predict poultry house plumes. The most notable modification was the addition of a virtual, emission-releasing point behind the exhaust tunnel fan. The modified model was validated using previously-reported field measurements. The fraction of predictions within a factor of two (FAC2) for both ammonia and PM observations was greatly improved compared with original model. In addition, the model performance was not sensitive to different sampling scenarios. This new model can be applied to other experiments and will be useful in evaluating the effectiveness of mitigation strategies for air pollutant emissions.

Recent advances in leaf fluorescence measurements and canopy proximal remote sensing currently enable the non-destructive collection of rich diurnal and seasonal time series, which are required for monitoring vegetation function at the temporal and spatial scales relevant to the natural dynamics of photosynthesis. Remote sensing assessments of vegetation function have traditionally used actively excited foliar chlorophyll fluorescence measurements, canopy optical reflectance data and vegetation indices (VIs), and only recently passive solar induced chlorophyll fluorescence (SIF) measurements. In general, reflectance data are more sensitive to the seasonal variations in canopy chlorophyll content and foliar biomass, while fluorescence observations more closely relate to the dynamic changes in plant photosynthetic function. With this dataset we link leaf level actively excited chlorophyll fluorescence, canopy proximal reflectance and SIF, with eddy covariance measurements of gross ecosystem productivity (GEP). The dataset was collected during the 2017 growing season on maize, using three automated systems (i.e., Monitoring Pulse-Amplitude-Modulation fluorimeter, Moni-PAM; Fluorescence Box, FloX; and from eddy covariance tower). The data were quality checked, filtered and collated to a common 30 minutes timestep. We derived vegetation indices related to canopy functioning (e.g., Photochemical Reflectance Index, PRI; Normalized Difference Vegetation Index, NDVI; Chlorophyll Red-edge, Clre) to investigate how SIF and VIs can be coupled for monitoring vegetation photosynthesis. The raw datasets and the filtered and collated data are provided to enable new processing and analyses.

Effective measurement of seasonal variations in the timing and amount of production is critical to managing spatially heterogeneous agroecosystems in a changing climate. Although numerous technologies for such measurements are available, their relationships to one another at a continental extent are unknown. Using data collected from across the Long-Term Agroecosystem Research (LTAR) network and other networks, we investigated correlations among key metrics representing primary production, phenology, and carbon fluxes in croplands, grazing lands, and crop-grazing integrated systems across the continental U.S. Metrics we examined included gross primary productivity (GPP) estimated from eddy covariance (EC) towers and modelled from the Landsat satellite, Landsat NDVI, and vegetation greenness (Green Chromatic Coordinate, GCC) from tower-mounted PhenoCams for 2017 and 2018. Overall, our analysis compared production dynamics estimated from three independent ground and remote platforms using data for 34 agricultural sites constituting 51 site-years of co-located time series.Pairwise sensor comparisons across all four metrics revealed stronger correlation and lower root mean square error (RMSE) between end of season (EOS) dates (Pearson R ranged from 0.6 to 0.7 and RMSE from 32.5 to 67.8) than start of season (SOS) dates (0.46 to 0.69 and 40.4 to 66.2). Overall, moderate to high correlations between SOS and EOS metrics complemented one another except at some lower productivity grazing land sites where estimating SOS can be challenging. Growing season length estimates derived from 16-day satellite GPP (179.1 days) were significantly longer than those from PhenoCam GCC (70.4 days, padj

Efficient use of available water resources is key to sustainable viticulture management in California (CA) and other regions with limited water availability in the western US and abroad. This requi...

Landsat-based monitoring of seasonal and near real-time evapotranspiration (ET) in California vineyards is currently challenged by its low temporal revisit period and missing data under cloudy cond...

A new approach is proposed to derive evapotranspiration (E) and irrigation requirements by implementing the combination equation models of Penman–Monteith and Shuttleworth and Wallace with surface parameters and resistances derived from Sentinel-2 data. Surface parameters are derived from Sentinel-2 and used as an input in these models; namely: the hemispherical shortwave albedo, leaf area index and water status of the soil and canopy ensemble evaluated by using a shortwave infrared-based index. The proposed approach has been validated with data acquired during the GRAPEX (Grape Remote-sensing Atmospheric Profile and Evapotranspiration eXperiment) in California irrigated vineyards. The E products obtained with the combination equation models are evaluated by using eddy covariance flux tower measurements and are additionally compared with surface energy balance models with Landsat-7 and -8 thermal infrared data. The Shuttleworth and Wallace (S-W S-2) model provides an accuracy comparable to thermal-based methods when using local meteorological data, with daily E errors < 1 mm/day, which increased from 1 to 1.5 mm/day using meteorological forcing data from atmospheric models. The advantage of using the S-W S-2 modeling approach for monitoring ET is the high temporal revisit time of the Sentinel-2 satellites and the finer pixel resolution. These results suggest that, by integrating the thermal-based data fusion approach with the S-W S-2 modeling scheme, there is the potential to increase the frequency and reliability of satellite-based daily evapotranspiration products.

In vineyards, hourly soil heat flux (SHF) may account for as much as 30% of net radiation. Therefore, inaccurate estimates of SHF may lead to non-negligible errors when quantifying the surface energy balance. The typical canopy height to width ratio of two along with widely spaced rows (row spacing exceeding canopy height), and leaf biomass concentrated in the upper half of the vine canopy result in variable shading conditions producing sharp, sometimes abrupt, differences in SHF between adjacent points within the interrow space. Drip irrigation, which is also a typical practice in many vineyards in semi-arid regions, adds an additional source of variability in the interrow soil moisture which strongly affects SHF. Lastly, the common practice in Californian wine-grape vineyards to plant cover crop in the interrow, forming two distinct areas below the canopy—bare soil and crop cover, further increases SHF variability in the interrow. This small-scale variability challenges the measurement of SHF in such agrosystems. The objective of the research was to assess the micro-scale (within the interrow between two vine rows) spatial variability in SHF, and to determine which of the three variables—non-uniform (in both space and time) shading patterns, non-uniform surface cover (bare soil vs. cover crop) and non-uniform soil water content (due to the drip irrigation)—is the most significant driver for the local heterogeneity. The variability of incoming solar radiation reaching the ground was found to be the primary source for the spatial and temporal variation of SHF once the vine canopy was fully developed. The water content distribution and the grass cover in the interrow both had minor impacts on the spatial and temporal variation in SHF. It was further found that a transect of five equally distributed sensors across the interrow accurately represented the area-average SHF given by the 11-sensor array, particularly during the growing season.

Multiple radiation transfer models with unique clumping indices (a total of five approaches) were evaluated on two Pinot Noir vineyards in Central California over 3 years. In the first approach, a basic clumping index meant for heterogeneous randomly placed clumped canopies was combined with the Campbell and Norman transfer model (C&N–H). The other four approaches, namely, the Campbell and Norman with rectangular hedgerow clumping index (C&N–R), Campbell and Norman with a geometric elliptical hedgerow model (C&N–E), the 4-stream scattering by arbitrary inclined leaves model (4SAIL) with row-crop clumping index, and the discrete anisotropic radiative transfer (DART) models, account for the unique canopy coverage distribution of the vineyard row-structured canopies. Each modeling approach varied in its complexity to predict transmitted solar radiation at ground level and the outputs were compared to solar radiation observed at the surface with an array of pyranometers. All five modeling approaches showed good agreement with the observed values [correlation coefficients (r) ranged from 0.95 to 0.97]. Model performance varied throughout the season due to their sensitivity to canopy growth. Although r values showed good agreement among all approaches, the C&N–E and DART models showed a better “goodness of fit” with lower root mean squared and bias values.

Vineyards in many semi-arid regions globally face limited water resources. Monitoring évapotranspiration (ET) of vineyards is critical for water resource management, but remains difficult due to the complex biophysics of the surfaces. Both measurement and modeling approaches for estimating turbulent water vapor transport rely on implicit assumptions that exchanges occur in a reasonably regular fashion over the time scales generally used for averaging. However, heterogeneous vegetation in semi-arid climates, such as many vineyards, presents inherent factors, including canopy row/row space structure and frequent periods of light wind, unstable conditions, that can create episodic transport characteristics. Eddy covariance data were collected above and within the canopy of two vineyards in the Central Valley of California during the Grape Remote sensing Atmospheric Profile & Evapotranspiration experiment (GRAPEX). The goal was to document and quantify the existence of intermittent turbulence transport of water vapor, and associated episodic canopy venting. These effects were found to correlate with periods light winds and highly unstable/convective conditions. Power and cross-spectra for intermittent periods documented enhancement of low-frequency water vapor exchange events compared to more steady periods, and diminished time scale correlation between humidity within the canopy and above the canopy. Analyses show that intermittent cases can necessitate longer flux-averaging periods (up to 2 h) than more steady conditions. Episodic exchange events were isolated and summed to determine their relative contribution to the overall water vapor flux. Since light wind, unstable conditions are relatively common in many arid vineyard regions, these findings have implications for mechanistic ET models that rely on time-averaged vertical gradients, which implies reasonably steady transport.

sUAS (small-Unmanned Aircraft System) and advanced surface energy balance models allow detailed assessment and monitoring (at plant scale) of different (agricultural, urban, and natural) environments. Significant progress has been made in the understanding and modeling of atmosphere-plant-soil interactions and numerical quantification of the internal processes at plant scale. Similarly, progress has been made in ground truth information comparison and validation models. An example of this progress is the application of sUAS information using the Two-Source Surface Energy Balance (TSEB) model in commercial vineyards by the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment - GRAPEX Project in California. With advances in frequent sUAS data collection for larger areas, sUAS information processing becomes computationally expensive on local computers. Additionally, fragmentation of different models and tools necessary to process the data and validate the results is a limiting factor. For example, in the referred GRAPEX project, commercial software (ArcGIS and MS Excel) and Python and Matlab code are needed to complete the analysis. There is a need to assess and integrate research conducted with sUAS and surface energy balance models in a sharing platform to be easily migrated to high performance computing (HPC) resources. This research, sponsored by the National Science Foundation FAIR Cyber Training Fellowships, is integrating disparate software and code under a unified language (Python). The Python code for estimating the surface energy fluxes using TSEB2T model as well as the EC footprint analysis code for ground truth information comparison were hosted in myGeoHub site https://mygeohub.org/ to be reproducible and replicable.

Recent Arctic warming has led to changes in the hydrological cycle. Circum-Arctic and circumboreal ecosystems are showing evidence of “greening” and “browning” due to temperature warming leading to shrub encroachment, tree mortality and deciduousness. Increases in latent heat flux from increased evapotranspiration rates associated with deciduous-dominated ecosystems may be significant, because deciduous vegetation has extremely high-water use and water storage capacity compared to coniferous and herbaceous plant species. Thus, the impact of vegetation change in boreal ecosystems on regional surface energy balance is a significant knowledge gap that must be addressed to better understand observed trends in water use/availability and tree mortality. To this end, output from a two-source energy balance model (TSEB) with modifications for high latitude boreal ecosystems was evaluated using flux tower measurements and Terra/Aqua MODIS remote sensing data collected over the two largest boreal forest types in Alaska (birch and black spruce). Data under clear and overcast days and from leaf-out to senescence from 2012 to 2016 were used for validation. Using flux tower observations and local model inputs, modifications to the model formulation for soil heat flux, net radiation partitioning, and canopy transpiration were required for the boreal forest. These improvements resulted in a mean absolute percent difference of around 23% for turbulent daytime fluxes when surface temperature from the flux towers was used, similar to errors reported in other studies conducted in warmer climates. Results when surface temperature from Terra/Aqua MODIS estimates were used as model input suggested that these model improvements are pertinent for regional scale applications. Vegetation indices and LAI time-series from the Terra/Aqua MODIS products were confirmed to be appropriate for energy flux estimation in the boreal forest to describe vegetation properties (LAI and green fraction) when field observations are not available. Model improvements for boreal settings identified in this study will be implemented operationally over North America to map surface energy fluxes at regional scales using long time series of remote sensing estimates as part of NOAA’s GOES Evapotranspiration and Drought Information System.

Estimation of surface energy fluxes using thermal remote sensing–based energy balance models (e.g., TSEB2T) involves the use of local micrometeorological input data of air temperature, wind speed, and incoming solar radiation, as well as vegetation cover and accurate land surface temperature (LST). The physically based Two-source Energy Balance with a Dual Temperature (TSEB2T) model separates soil and canopy temperature (T(s) and T(c)) to estimate surface energy fluxes including R(n), H, LE, and G. The estimation of T(s) and T(c) components for the TSEB2T model relies on the linear relationship between the composite land surface temperature and a vegetation index, namely NDVI. While canopy and soil temperatures are controlling variables in the TSEB2T model, they are influenced by the NDVI threshold values, where the uncertainties in their estimation can degrade the accuracy of surface energy flux estimation. Therefore, in this research effort, the effect of uncertainty in T(s) and T(c) estimation on surface energy fluxes will be examined by applying a Monte Carlo simulation on NDVI thresholds used to define canopy and soil temperatures. The spatial information used is available from multispectral imagery acquired by the AggieAir sUAS Program at Utah State University over vineyards near Lodi, California as part of the ARS-USDA Agricultural Research Service’s Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) project. The results indicate that LE is slightly sensitive to the uncertainty of NDVI(s) and NDVI(c). The observed relative error of LE corresponding to NDVI(s) uncertainty was between −1% and 2%, while for NDVI(c) uncertainty, the relative error was between −2.2% and 1.2%. However, when the combined NDVI(s) and NDVI(c) uncertainties were used simultaneously, the domain of the observed relative error corresponding to the absolute values of |ΔLE| was between 0% and 4%.

Validation of surface energy fluxes from remote sensing sources is performed using instantaneous field measurements obtained from eddy covariance (EC) instrumentation. An eddy covariance measurement is characterized by a footprint function / weighted area function that describes the mathematical relationship between the spatial distribution of surface flux sources and their corresponding magnitude. The orientation and size of each flux footprint / source area depends on the micro-meteorological conditions at the site as measured by the EC towers, including turbulence fluxes, friction velocity (u(star)), and wind speed, all of which influence the dimensions and orientation of the footprint. The total statistical weight of the footprint is equal to unity. However, due to the large size of the source area / footprint, a statistical weight cutoff of less than one is considered, ranging between 0.85 and 0.95, to ensure that the footprint model is located inside the study area. This results in a degree of uncertainty when comparing the modeled fluxes from remote sensing energy models (i.e., TSEB2T) against the EC field measurements. In this research effort, the sensitivity of instantaneous and daily surface energy flux estimates to footprint weight cutoffs are evaluated using energy balance fluxes estimated with multispectral imagery acquired by AggieAir sUAS (small Unmanned Aerial Vehicle) over commercial vineyards near Lodi, California, as part of the ARS-USDA Agricultural Research Service’s Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) project. The instantaneous fluxes from the eddy covariance tower will be compared against instantaneous fluxes obtained from different TSEB2T aggregated footprint weights (cutoffs). The results indicate that the size, shape, and weight of pixels inside the footprint source area are strongly influenced by the cutoff values. Small cutoff values, such as 0.3 and 0.35, yielded high weights for pixels located within the footprint domain, while large cutoffs, such as 0.9 and 0.95, result in low weights. The results also indicate that the distribution of modelled LE values within the footprint source area are influenced by the cutoff values. A wide variation in LE was observed at high cutoffs, such as 0.90 and 0.95, while a low variation was observed at small cutoff values, such as 0.3. This happens due to the large number of pixel units involved inside the footprint domain when using high cutoff values, whereas a limited number of pixels are obtained at lower cutoff values.

Surface temperature is necessary for the estimation of energy fluxes and evapotranspiration from satellites and airborne data sources. For example, the Two-Source Energy Balance (TSEB) model uses thermal information to quantify canopy and soil temperatures as well as their respective energy balance components. While surface (also called kinematic) temperature is desirable for energy balance analysis, obtaining this temperature is not straightforward due to a lack of spatially estimated narrowband (sensor-specific) and broadband emissivities of vegetation and soil, further complicated by spectral characteristics of the UAV thermal camera. This study presents an effort to spatially model narrowband and broadband emissivities for a microbolometer thermal camera at UAV information resolution (~0.15 m) based on Landsat and NASA HyTES information using a deep learning (DL) model. The DL model is calibrated using equivalent optical Landsat / UAV spectral information to spatially estimate narrowband emissivity values of vegetation and soil in the 7–14- nm range at UAV resolution. The resulting DL narrowband emissivity values were then used to estimate broadband emissivity based on a developed narrowband-broadband emissivity relationship using the MODIS UCSB Emissivity Library database. The narrowband and broadband emissivities were incorporated into the TSEB model to determine their impact on the estimation of instantaneous energy balance components against ground measurements. The proposed effort was applied to information collected by the Utah State University AggieAir small Unmanned Aerial Systems (sUAS) Program as part of the ARS-USDA GRAPEX Project (Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment) over a vineyard located in Lodi, California. A comparison of resulting energy balance component estimates, with and without the inclusion of high-resolution narrowband and broadband emissivities, against eddy covariance (EC) measurements under different scenarios are presented and discussed.

Evapotranspiration (ET) 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 (sUAS) with sensor technology similar to satellite platforms allows for the estimation of high-resolution ET at plant spacing scale for individual fields. However, while multiple efforts have been made to estimate ET from sUAS products, the sensitivity of ET 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 sUAS imagery for operational applications. In this study, the Two-Source Energy Balance with a dual temperature (TSEB2T) model, which uses remotely sensed soil/substrate and canopy temperature from sUAS imagery, was used to estimate ET 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 AggieAirTM sUAS 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 (GRAPEX). Original spectral and thermal imagery data from sUAS 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 (EC) measurements. Results indicated that the TSEB2T model is only slightly affected in the estimation of the net radiation (Rn) and the soil heat flux (G) at different spatial resolutions, while the sensible and latent heat fluxes (H and LE, respectively) are significantly affected by coarse grid sizes. The results indicated overestimation of H and underestimation of LE values, particularly at Landsat scale (30 m). This refers to the non-linear relationship between the land surface temperature (LST) and the normalized difference vegetation index (NDVI) at coarse model resolution. Another predominant reason for LE reduction in TSEB2T was the decrease in the aerodynamic resistance (Ra), which is a function of the friction velocity ( u * ) 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 sUAS imagery. The results also indicated that the mean LE at field scale is reduced by 10% to 20% at coarser resolutions, while the with-in field variability in LE values decreased significantly at the larger grid sizes and ranged between approximately 15% and 45%. This implies that, while the field-scale values of LE are fairly reliable at larger grid sizes, the with-in field variability limits its use for precision agriculture applications.

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 these effects, very high spatial resolution remote-sensing data from an unmanned aerial vehicle were collected over vineyards in California, as part of the Grape Remote sensing and Atmospheric Profile and Evapotranspiration eXperiment and used in four different TSEB approaches to estimate the component soil and canopy temperatures, and ET partitioning between soil and canopy. Two approaches rely on the use of composite $$T_\mathrm{rad}$$ , and assume initially that the canopy transpires at the Priestley–Taylor potential rate. The other two algorithms are based on the contextual relationship between optical and thermal imagery partition $$T_\mathrm{rad}$$ into soil and canopy component temperatures, which are then used to drive the TSEB without requiring a priori assumptions regarding initial canopy transpiration rate. The results showed that a simple contextual algorithm based on the inverse relationship of a vegetation index and $$T_\mathrm{rad}$$ to derive soil and canopy temperatures yielded the closest agreement with flux tower measurements. The utility in very high-resolution remote-sensing data for estimating ET and E and T partitioning at the canopy level is also discussed.

Evapotranspiration (ET) derived from remote sensing-based models represents ½ hourly to hourly value that is upscaled to a daily scale for practical applications in the fields of agricultural and water management. Several upscaling methods such as Gaussian fitting curve, sine approach, and evaporative fraction approach have been developed to extrapolate remote sensing-based ET values to daily scale by assuming constant daytime ratios (e.g., self-preservation of available energy partitioning). This simple assumption can result in uncertainties in the performance of those methods and can be violated for unstable conditions such as a cloudy day. Studies showed that, for example, diurnal variation of incoming shortwave radiation will change from a Gaussian distribution to a multimodal distribution on a cloudy day. Besides, when remote-sensing ET outputs are directly upscaled to daily scale and compared with eddy covariance measurements, a fixed footprint of eddy covariance is assumed, while the actual eddy covariance footprint is dynamic and changes with wind speed, direction and atmospheric stability. In this study, a new method is proposed to spatially and temporally simulate canopy and soil temperature for each time step (e.g., 1-hour) based on the temperature pattern recorded by IRT temperature sensors and UAV initial temperatures at the specific time of day. Next, the Two-Source Energy Balance (TSEB) model is executed for each time step of the daytime period (usually when net radiation >100 W/m^2) to calculate energy balance components. The integration of TSEB outputs over the daytime period leads to estimations of daily energy balance components. Since cloudy conditions affect temperatures recorded by IRT sensors, the proposed model is not sensitive to weather conditions. In addition, the proposed model physically simulates ET at each time step instead of directly extrapolating ET from a single remote sensing observation and model output This feature solves the limitations of comparing the direct extrapolation methods of instantaneous ET against eddy covariance measurements. The proposed approach is applied on information collected by the Utah State University AggieAir small Unmanned Aerial Systems (sUAS) Program as part of the ARS-USDA GRAPEX Project (Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment) conducted since 2014 over multiple vineyards located in California. The estimated ET values from the TSEB model at hourly time steps and integrated over the daytime period are compared to eddy covariance measurements of ET. Additionally, hourly model output integrated over the daytime period compared to the different upscaling methods are presented and discussed.

The unique vertical canopy structure and clumped plant distribution/row structure of vineyards and orchards creates an environment that is likely to cause the wind profile inside the canopy air space to deviate from how it is typically modelled for most crops. This in turn affects the efficiency of turbulent flux exchange and energy transport as well as their partitioning between the plant canopy and soil/substrate layers. The objective of this study was to evaluate a new wind profile formulation in the canopy air space that explicitly considers the unique vertical variation in plant biomass of vineyards. The validity of the new wind profile formulation was compared to a simpler wind attenuation profile that assumes attenuation through a homogeneous canopy. We evaluated both attenuation models using measurements of wind speed in a vineyard interrow, as well as turbulent flux estimates retrieved from a two-source energy balance model, which uses land surface temperature as the key boundary condition for flux estimation. This is relevant in developing a robust remote sensing-based energy balance modelling system for accurately monitoring vineyard water use or evapotranspiration that can be applied using satellite and airborne imagery for field-to-regional scale applications. These tools are needed in intensive agricultural production regions with arid climates such as the Central Valley of California, which experiences water shortages during extended drought periods requiring an effective water management policy based on robust water use estimates for allocating water resources. Results showed that the new wind profile model improved sensible heat flux estimates (RMSE reduction from 42 to 35 $$\text{W}\,\text{m}^{-2}$$ ) when the vine canopy is in early growth stage resulting in a strongly clumped canopy.

Water-limiting conditions in many California vineyards necessitate assessment of vine water stress to aid irrigation management strategies and decisions. This study was designed to evaluate the utility of a Crop Water Stress Index (CWSI) using multiple canopy temperature sensors and to study the diurnal signature in the stress index of an irrigated vineyard. A detailed instrumentation package comprised of eddy covariance instrumentation, ancillary surface energy balance components, soil water content sensors and a unique multi-canopy temperature sensor array were deployed in a production vineyard near Lodi, CA. The instrument package was designed to measure and monitor hourly growing season turbulent fluxes of heat and water vapor, radiation, air temperature, soil water content directly beneath a vine canopy, and vine canopy temperatures. April 30–May 02, June 10–12 and July 27–28, 2016 were selected for analysis as these periods represented key vine growth and production phases. Considerable variation in computed CWSI was observed between each of the hourly average individual canopy temperature sensors throughout the study; however, the diurnal trends remained similar: highest CWSI values in morning and lowest in the late afternoon. While meteorological conditions were favorable for plant stress to develop, soil water content near field capacity due to frequent irrigation allowed high evapotranspiration rates resulting in downward trending CWSI values during peak evaporative demand. While the CWSI is typically used to evaluate plant stress under the conditions of our study, the trend of the CWSI suggested a lowering of plant water stress as long as there was adequate soil water available to meet atmospheric demand.

California is among the largest wine-producing regions in the world. It is also a region with limited water resources. To ensure that scarce water resources are used effectively, the ongoing Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) project seeks to improve irrigation management within vineyards by providing remote sensing-based tools that can monitor ET across the continuum from sub-vineyard to regional scales. This study, which was conducted as a part of the GRAPEX project, compares the surface fluxes collected over a pair of vineyards separated by approximately 1 km from 2013 to 2017 to better understand the role of environmental conditions in controlling evapotranspiration. A comparison of the meteorological conditions, which include wind speed, wind direction, air temperature, water vapor pressure, and atmospheric pressure, showed there was no statistically meaningful difference in the measurements of these quantities either between the two vineyards or year to year. In contrast, the comparison of the surface fluxes, and in particular the sensible heat (H) and latent heat (λE) fluxes, showed that there were large inter-site and inter-annual differences. On average, during the growing seasons, H differed by 28 W m−2, while λE differed by 32 W m−2. With coefficients of determination (r2) in excess of 0.90, the differences in the surface fluxes can be largely explained by differences in leaf area index (LAI) and soil moisture content. Since these quantities are, in turn, dependent on vineyard management practices, this work highlights the importance of management decisions for ensuring that limited water resources are used effectively.

Vineyards’ canopy architecture and row structure pose unique challenges in modeling the radiation partitioning and energy exchange between the vine canopy and the interrow area. The vines are often pruned and manipulated to be strongly clumped, while mechanical harvesting requires wide rows, often with vine height to vine spacing ratio > 1. This paper estimates the intercepted radiation by the canopy, and the effect of this interception on the below canopy surface energy balance and evapotranspiration (ET). Measurements were conducted in an east–west oriented vineyard in CA during intensive observation periods as part of the grape remote sensing atmospheric profile and evapotrnspiration eXperiment (GRAPEX). Below canopy incoming shortwave radiation was measured at multiple positions across the interrow, and the surface energy balance/ET below the vine rows was measured for only one growing season (in 2015) using three micro-Bowen ratio (MBR) systems. These MBR systems were deployed across the interrow, in the north, center, and south of the interrow. A significant spatial and temporal variability in radiation was observed since the vines were not significantly pruned or manipulated and thus grew randomly into the interrow. However, when averaged across the interrow using the radiation sensor array, the values appeared to give reliable mean radiation extinction conditions that agreed with model estimates. The variation in the surface energy fluxes were dominated by the amount of transmitted radiation, while soil moisture was a second order effect. Daily estimates of ET from the three micro-Bowen ratio systems, weighted by their respective representative sampling area, yielded estimates similar to values computed by the correlation-based flux partitioning method, which utilizes high-frequency eddy covariance data measured above the canopy.

In recent years, the deployment of satellites and unmanned aerial vehicles (UAVs) has led to production of enormous amounts of data and to novel data processing and analysis techniques for monitoring crop conditions. One overlooked data source amid these efforts, however, is incorporation of 3D information derived from multi-spectral imagery and photogrammetry algorithms into crop monitoring algorithms. Few studies and algorithms have taken advantage of 3D UAV information in monitoring and assessment of plant conditions. In this study, different aspects of UAV point cloud information for enhancing remote sensing evapotranspiration (ET) models, particularly the Two-Source Energy Balance Model (TSEB), over a commercial vineyard located in California are presented. Toward this end, an innovative algorithm called Vegetation Structural-Spectral Information eXtraction Algorithm (VSSIXA) has been developed. This algorithm is able to accurately estimate height, volume, surface area, and projected surface area of the plant canopy solely based on point cloud information. In addition to biomass information, it can add multi-spectral UAV information to point clouds and provide spectral-structural canopy properties. The biomass information is used to assess its relationship with in situ Leaf Area Index (LAI), which is a crucial input for ET models. In addition, instead of using nominal field values of plant parameters, spatial information of fractional cover, canopy height, and canopy width are input to the TSEB model. Therefore, the two main objectives for incorporating point cloud information into remote sensing ET models for this study are to (1) evaluate the possible improvement in the estimation of LAI and biomass parameters from point cloud information in order to create robust LAI maps at the model resolution and (2) assess the sensitivity of the TSEB model to using average/nominal values versus spatially-distributed canopy fractional cover, height, and width information derived from point cloud data. The proposed algorithm is tested on imagery from the Utah State University AggieAir sUAS Program as part of the ARS-USDA GRAPEX Project (Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment) collected since 2014 over multiple vineyards located in California. The results indicate a robust relationship between in situ LAI measurements and estimated biomass parameters from the point cloud data, and improvement in the agreement between TSEB model output of ET with tower measurements when employing LAI and spatially-distributed canopy structure parameters derived from the point cloud data.

The Evaporative Stress Index (ESI) quantifies temporal anomalies in a normalized evapotranspiration (ET) metric describing the ratio of actual-to-reference ET (fRET) as derived from satellite remote sensing. At regional scales (3–10 km pixel resolution), the ESI has demonstrated the capacity to capture developing crop stress and impacts on regional yield variability in water-limited agricultural regions. However, its performance in some regions where the vegetation cycle is intensively managed appears to be degraded due to spatial and temporal limitations in the standard ESI products. In this study, we investigated potential improvements to ESI by generating maps of ET, fRET, and fRET anomalies at high spatiotemporal resolution (30-m pixels, daily time steps) using a multi-sensor data fusion method, enabling separation of landcover types with different phenologies and resilience to drought. The study was conducted for the period 2010–2014 covering a region around Mead, Nebraska that includes both rainfed and irrigated crops. Correlations between ESI and measurements of maize yield were investigated at both the field and county level to assess the potential of ESI as a yield forecasting tool. To examine the role of crop phenology in yield-ESI correlations, annual input fRET time series were aligned by both calendar day and by biophysically relevant dates (e.g. days since planting or emergence). At the resolution of the operational U.S. ESI product (4 km), adjusting fRET alignment to a regionally reported emergence date prior to anomaly computation improves r2 correlations with county-level yield estimates from 0.28 to 0.80. At 30-m resolution, where pure maize pixels can be isolated from other crops and landcover types, county-level yield correlations improved from 0.47 to 0.93 when aligning fRET by emergence date rather than calendar date. Peak correlations occurred 68 days after emergence, corresponding to the silking stage for maize when grain development is particularly sensitive to soil moisture deficiencies. The results of this study demonstrate the utility of remotely sensed ET in conveying spatially and temporally explicit water stress information to yield prediction and crop simulation models.

The eddy covariance method is regularly used for measuring gas fluxes over agricultural fields and natural ecosystems. For many applications, it is desirable to partition the measured fluxes into constitutive components: the water vapor flux into transpiration and direct evaporation components, and the carbon dioxide flux into photosynthesis and respiration components. The flux variance similarity (FVS) partitioning method is based on flux variance similarity relationships and correlation analyses of high-frequency eddy covariance data ( Scanlon and Sahu, 2008 , Scanlon and Kustas, 2010 , Scanlon and Kustas, 2012 ). The FVS method is relatively complex computationally, and that complexity has likely been an impediment to greater use and testing of the procedure. In this work, we present a new algebraic solution to the key computational task in the partitioning algorithm, which significantly simplifies the FVS method. We also introduce Fluxpart, a free and open source Python 3 module that implements the FVS partitioning procedure. Example flux partitioning calculations are presented.

Daily evapotranspiration (ETd) plays a key role in irrigation water management and is particularly important in drought-stricken areas, such as California and high-value crops. Remote sensing allows for the cost-effective estimation of spatial evapotranspiration (ET), and the advent of small unmanned aerial systems (sUAS) technology has made it possible to estimate instantaneous high-resolution ET at the plant, row, and subfield scales. sUAS estimates ET using “instantaneous” remote sensing measurements with half-hourly/hourly forcing micrometeorological data, yielding hourly fluxes in W/m2 that are then translated to a daily scale (mm/day) under two assumptions: (a) relative rates, such as the ratios of ET-to-net radiation (Rn) or ET-to-solar radiation (Rs), are assumed to be constant rather than absolute, and (b) nighttime evaporation (E) and transpiration (T) contributions are negligible. While assumption (a) may be reasonable for unstressed, full cover crops (no exposed soil), the E and T rates may significantly vary over the course of the day for partially vegetated cover conditions due to diurnal variations of soil and crop temperatures and interactions between soil and vegetation elements in agricultural environments, such as vineyards and orchards. In this study, five existing extrapolation approaches that compute the daily ET from the “instantaneous” remotely sensed sUAS ET estimates and the eddy covariance (EC) flux tower measurements were evaluated under different weather, grapevine variety, and trellis designs. Per assumption (b), the nighttime ET contribution was ignored. Each extrapolation technique (evaporative fraction (EF), solar radiation (Rs), net radiation-to-solar radiation (Rn/Rs) ratio, Gaussian (GA), and Sine) makes use of clear skies and quasi-sinusoidal diurnal variations of hourly ET and other meteorological parameters. The sUAS ET estimates and EC ET measurements were collected over multiple years and times from different vineyard sites in California as part of the USDA Agricultural Research Service Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). Optical and thermal sUAS imagery data at 10 cm and 60 cm, respectively, were collected by the Utah State University AggieAir sUAS Program and used in the Two-Source Energy Balance (TSEB) model to estimate the instantaneous or hourly sUAS ET at overpass time. The hourly ET from the EC measurements was also used to validate the extrapolation techniques. Overall, the analysis using EC measurements indicates that the Rs, EF, and GA approaches presented the best goodness-of-fit statistics for a window of time between 1030 and 1330 PST (Pacific Standard Time), with the Rs approach yielding better agreement with the EC measurements. Similar results were found using TSEB and sUAS data. The 1030–1330 time window also provided the greatest agreement between the actual daily EC ET and the extrapolated TSEB daily ET, with the Rs approach again yielding better agreement with the ground measurements. The expected accuracy of the upscaled TSEB daily ET estimates across all vineyard sites in California is below 0.5 mm/day, (EC extrapolation accuracy was found to be 0.34 mm/day), making the daily scale results from TSEB reliable and suitable for day-to-day water management applications.

The health of the Chesapeake Bay ecosystem has been declining for several decades due to high levels of nutrients and sediments largely tied to agricultural production systems. Therefore, monitoring of agricultural water use and hydrologic connections between crop lands and Bay tributaries has received increasing attention. Remote sensing retrievals of actual evapotranspiration (ET) can provide valuable information in support of these hydrologic modeling efforts, spatially and temporally describing consumptive water use by crops and natural vegetation and quantifying response to expansion of irrigated area occurring with Bay watershed. In this study, a multi-sensor satellite data fusion methodology, combined with a multi-scale ET retrieval algorithm, was applied over the Choptank River watershed located within the Lower Chesapeake Bay region on the Eastern Shore of Maryland, USA to produce daily 30-m resolution ET maps. ET estimates directly retrieved on Landsat satellite overpass dates have high accuracy with relative error (RE) of 9%, as evaluated using flux tower measurements. The fused daily ET time series have reasonable errors of 18% at the daily time step - an improvement from 27% errors using standard Landsat-only interpolation techniques. Annual water consumption by different land cover types was assessed, showing reasonable distributions of water use with cover class. Seasonal patterns in modeled crop transpiration and soil evaporation for dominant crop types were analyzed, and agree well with crop phenology at field scale. Additionally, effects of irrigation occurring during a period of rainfall shortage were captured by the fusion program. These results suggest that the ET fusion system will have utility for water management at field and regional scales over the Eastern Shore. Further efforts are underway to integrate these detailed water use datasets into watershed-scale hydrologic models to improve assessments of water quality and inform best management practices to reduce nutrient and sediment loads to the Chesapeake Bay.

Current approaches to scheduling crop irrigation using reference evapotranspiration (ET 0 ) recommend using a dual-coefficient approach using basal (K cb ) and soil (K e ) coefficients along with a stress coefficient (K s ) to model crop evapotranspiration (ET c ), [e.g. ET c = (K s *K cb + K e )*ET 0 ]. However, determining K s , K cb , and K e from the combined evapotranspiration (ET) is challenging, particularly for K e , 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 (10–20 Hz) 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 K c consistent with their height (>4 m). The results showed differences in K e , with the sub-surface drip-irrigated sugarcane having a low K e (0.1). There was no significant relationship (r 2 cb *K s , indicating that there was no stress (K s = 1), while the peach orchard showed mid-season declines in K cb *K s when VWC declined below 0.2. Partitioning of K c into K cb and K e resulted in a better regression (r 2 = 0.43) between the Normalized Differential Vegetation Index (NDVI) and K cb in sugarcane than between NDVI and K c (r 2 = 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 K c , K e , K s , and K cb .

Volatilization represents a significant loss pathway for many pesticides, herbicides and other agrochemicals. One common method for measuring the volatilization of agrochemicals is the flux-gradient method. Using this method, the chemical flux is estimated as the product of the vertical concentration gradient and a turbulent-transfer coefficient (eddy diffusivity). For computational simplicity, the evaluation height needed to calculate the eddy diffusivity is typically approximated as either the geometric or logarithmic mean. Both of these estimation methods are based on simplifying assumptions and can be a significant source of error, particularly when the separation distance between the measurement heights is large. Using data collected over an eight-year period at the USDA-ARS OPE3 experimental watershed, this study compared fluxes of metolachlor, a commonly-used herbicide, computed using the approximated evaluation heights with those calculated using the exact evaluation height. While it was found that the primary factor influencing the accuracy of the flux estimates using the approximate evaluation heights was atmospheric stability, errors in the estimate of the evaluation height can result in significant (>10%) errors in the flux estimates. Based on these results, it is recommended that the exact evaluation height be used with the flux-gradient technique.

The ability to regionally monitor crop progress and condition through the growing season benefits both crop management and yield estimation. In the United States, these metrics are reported weekly at state or district (multiple counties) levels by the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) using field observations provided by trained local reporters. However, the ground data collection process supporting this effort is time consuming and subjective. Furthermore, operational crop management and yield estimation efforts require information with more granularity than at the state or district level. This paper evaluates remote sensing approaches for mapping crop phenology using vegetation index time-series generated by fusing Landsat and MODIS (Moderate Resolution Imaging Spectroradiometer) surface reflectance imagery to improve temporal sampling over that provided by Landsat alone. The case study focuses on an agricultural region in central Iowa from 2001 to 2014. Our objectives are 1) to assess Landsat-MODIS data fusion results over cropland; 2) to map crop phenology at 30 m resolution using fused surface reflectance data; and 3) to identify the relationships between remotely sensed crop phenology metrics and the crop progress stages reported by NASS. The results show that detailed spatial and temporal variability in vegetation development across this landscape can be identified using the fused Landsat-MODIS data. The mean difference (bias) in Normalized Difference Vegetation Index (NDVI) between actual Landsat observations and the fused Landsat-MODIS data, generated for Landsat overpass dates, is in the range of − 0.011 to 0.028 for every year. The derived phenological metrics show distinct features for different crops and natural vegetation at field scales. Strong correlations are observed between remotely sensed phenological stages, based on NDVI curve inflection points, and the observed crop physiological growth stages from the NASS Crop Progress (CP) reports. The green-up dates detected from remote sensing data typically occurred during crop vegetative stages when 2–4 leaves were developed for both corn and soybeans, or about 1–3 weeks after the reported emergence dates when the plant were first visible to ground-based observers. Despite being a lagging indicator, remotely sensed green-up can be used effectively to backcast emergence, e.g. as input to spatially distributed crop models. The differences in green-up date between corn and soybean were 8–10 days, consistent with the offset in emergence dates reported by NASS at district level. The reported harvest dates were typically about 2–3 weeks after the dormancy stage was detected via remote sensing for corn and about 1–2 weeks for soybeans. This suggests that probable harvest times for individual fields may be predicted 1–3 weeks ahead using remote sensing data. The results suggest that crop phenology and certain growth stages at field scales (30 m spatial resolution) can be linked and mapped by integrating imagery from multiple remote sensing platforms.

In viticulture, deficit irrigation strategies are often implemented to control vine canopy growth and to impose stress at critical stages of vine growth to improve wine grape quality. To support deficit irrigation scheduling, remote sensing technologies can be employed in the mapping of evapotranspiration (ET) at the field to sub-field scales, quantifying time-varying vineyard water requirements and actual water use. In the current study, we investigate the utility of ET maps derived from thermal infrared satellite imagery over a vineyard in the Central Valley of California equipped with a variable rate drip irrigation (VRDI) system which enables differential water applications at the 30 × 30 m scale. To support irrigation management at that scale, we utilized a thermal-based multi-sensor data fusion approach to generate weekly total actual ET (ETa) estimates at 30 m spatial resolution, coinciding with the resolution of the Landsat reflectance bands. Crop water requirements (ETc) were defined with a vegetative index (VI)-based approach. To test capacity to capture stress signals, the vineyard was sub-divided into four blocks with different irrigation management strategies and goals, inducing varying degrees of stress during the growing season. Results indicate derived weekly total ET from the thermal-based data fusion approach match well with observations. The thermal-based method was also able to capture the spatial heterogeneity in ET over the vineyard due to a water stress event imposed on two of the four vineyard blocks. This transient stress event was not reflected in the VI-based ETc estimate, highlighting the value of thermal band imaging. While the data fusion system provided valuable information, latency in current satellite data availability, particularly from Landsat, impacts operational applications over the course of a growing season.

Land-surface temperature retrieved from thermal infrared (TIR) remote sensing has proven to be a valuable constraint in surface energy balance models for estimating evapotranspiration (ET). For optimal utility in agricultural water management applications, frequent thermal imaging (