Models
OpenET provides satellite-based estimates of the total amount of water that is transferred from the land surface to the atmosphere through the process of evapotranspiration (ET). This is also referred to as ‘actual ET’, since it represents an estimate of the actual amount of ET that occurred. OpenET provides ET data from multiple satellite-driven models, and also calculates a single “ensemble value” from those models. The models currently included are shown in the table below. All of the models included in the OpenET ensemble have been used by government agencies with responsibility for water use reporting and management in the western U.S., and some models are widely used internationally. All models currently use Landsat satellite data to produce data at a spatial resolution of 30 meters by 30 meters (0.22 acres per pixel), as well as gridded weather variables including solar radiation, air temperature, humidity, and wind speed, and in some cases, precipitation.
Models currently included in OpenET:
| Model Acronym | Model Name | Primary References |
|---|---|---|
| ALEXI/DisALEXI | Atmosphere-Land Exchange Inverse model/ALEXI disaggregation | Anderson et al., 2007; Anderson et al., 2018 |
| METRIC | Mapping Evapotranspiration at high Resolution with Internalized Calibration model |
Allen et al., 2005; Allen et al., 2007 |
| PT-JPL | Priestley-Taylor Jet Propulsion Laboratory | Fisher et al., 2008 |
| SEBAL | Surface Energy Balance Algorithm for Land | Bastiaanssen et al., 1998 |
| SIMS | Satellite Irrigation Management Support | Melton et al., 2012; Pereira et al., 2020 |
| SSEBop | Operational Simplified Surface Energy Balance | Senay et al., 2014; Senay et al., 2018 |
The majority of the models that make up the OpenET ensemble are based on full or simplified implementations of the surface energy balance (SEB) approach. The SEB approach accounts for the energy used to transform liquid water in plants and soil into vapor that is released to the atmosphere. The SEB approach relies on satellite measurements of surface temperature and surface reflectance combined with other key land surface and weather variables to estimate components of the energy balance—net radiation, sensible heat flux, ground heat flux, and latent heat flux or ET. METRIC, geeSEBAL, and DisALEXI estimate each component of the energy balance using optical (i.e., short-wave) and thermal (i.e., long-wave) data, whereas SSEBop and PT-JPL are simplified approaches in which certain components of the energy balance are not estimated or are calculated using a set of simplifying assumptions. SIMS relies on surface reflectance data and crop type information to compute ET as a function of canopy density using a crop coefficient approach for agricultural lands.
Input Datasets
OpenET relies entirely on publicly available satellite, meteorological, crop type, land use, and soil data as inputs to the ET models. OpenET does not use or distribute private or proprietary data.
Landsat is currently the primary satellite dataset used within the OpenET platform. The Landsat program, a joint program of the U.S. Geological Survey and NASA, provides the longest continuous space-based record of Earth’s land in existence, dating back to 1972 for optical data and to 1982 for thermal data. Landsat is the only operational satellite that combines thermal and optical data at the spatial resolution needed to assess water use and water rights, which is often at the level of individual agricultural fields. Multiple models implemented within the OpenET framework also integrate data from GOES, Sentinel-2, Suomi NPP, Terra, Aqua and other satellites to produce ET data at a range of spatial and temporal scales.
Along with satellite-based observations of the Earth’s surface, OpenET also uses weather station measurements across the country that are integrated into assimilation systems to produce spatially distributed or gridded weather datasets, such as gridMET, Spatial CIMIS, DAYMET, PRISM, and NLDAS. These datasets are used within the OpenET platform for various model parameters and variables, such as atmospheric stability, net radiation, and surface air temperature gradients.
One of the primary variables derived from gridded weather data is the reference ET, which is the amount of ET from a reference surface, typically a well-watered grass or alfalfa crop. OpenET uses reference ET data that is calculated using the American Society of Civil Engineers (ASCE) Standardized Penman-Monteith equation for a grass reference surface, and usually notated as ‘ETo’. Reference ET data are used to support the calculation of actual ET in between Landsat satellite overpasses, which occur every eight days (excepting cloud cover) with two Landsat satellites in orbit. First, the fraction of reference ET for each satellite overpass date is calculated by dividing the satellite ET on the overpass date by the reference ET. Fraction of reference ET values are then linearly interpolated on a daily timestep for all days in between clear satellite overpass dates, one image pixel at a time, and are then multiplied by the daily reference ET values to calculate a daily time series of actual ET for every pixel. These per-pixel daily time series of actual ET are then aggregated to monthly and annual time periods. The fraction of reference ET is interpolated in time because it tends to change in proportion to changes in vegetation, similar to its equivalent, the widely used crop coefficient. Daily reference ET is then used to reintroduce the impacts of day to day changes in weather on actual ET rates.
For California, OpenET uses Spatial CIMIS meteorological datasets generated by the California Department of Water Resources to compute ASCE grass reference ET. For other states, OpenET calculates ASCE grass reference ET using meteorological inputs from the gridMET dataset. To ensure that gridded reference ET data are representative of agricultural weather conditions, nearly 800 weather stations located in agricultural areas are filtered, quality controlled and then used by OpenET for bias correction of gridded reference ET calculated from gridMET within these areas. Bias correction of calculated reference ET accounts for effects of deviations in gridMET wind speed, solar radiation, humidity and air temperature data from local weather station data, as well as for aridity biases that can exist in gridded weather data sets. Weather station data are passed through rigorous quality assurance and quality control procedures following ASCE and FAO guidelines prior to calculation of reference ET.
Ancillary data used by the OpenET platform include crop type information from the U.S. Department of Agriculture (USDA) and state agencies, USDA soils data, U.S. Geological Survey (USGS) digital elevation models, USGS land use classifications, active irrigated lands datasets, and manually digitized agricultural field boundaries from USDA, state agencies, and research groups.
The OpenET science teams are currently evaluating data pre-processing and time integration techniques to further improve the accuracy of ET estimates, as well as exploring the accuracy improvements associated with having more frequent thermal and optical satellite imagery. Driving all models with community-selected and more frequent datasets for pre-processing and time integration techniques has increased the accuracy and consistency and reduced the range of ET estimates among the different models.
Compute and Software Resources
OpenET uses Google Earth Engine for computation, storage and visualization of image data, and to support data requests submitted to the Application Programming Interface (API). Google Earth Engine is a parallel cloud-computing and environmental monitoring platform. Most model input image datasets used in OpenET are accessed via the Google Earth Engine data catalog, where image collections are continuously and operationally updated with minimal latency (approximately 1-16 days), bypassing the need for continually downloading and processing dozens of data streams on local compute resources. Google Earth Engine helps overcome many of the data storage, processing, and operational limitations commonly encountered by researchers, practitioners, and stakeholders.
Google Earth Engine is used to spatially average ET and other spatial data to predefined boundaries (e.g. states, counties, watersheds, irrigation districts, agricultural field boundaries). Spatial averages are stored in a large geodatabase that is connected to an API and open source raster and vector tiling software. This framework supports rapid response to data queries, as well as spatial and temporal visualizations of ET and associated variables (e.g., NDVI, reference ET, fraction of reference ET) via a lightweight web mapping and data visualization application.
Model Accuracy and Intercomparison
OpenET is conducting the largest satellite-based field-scale ET model accuracy and intercomparison assessment to date. The accuracy assessment is being conducted using four different sets of benchmark “in-situ” or ground-based ET estimates:
1. Over 150 AmeriFlux, USGS, USDA, and university-deployed eddy covariance stations located across the continental U.S., and within a variety of land uses and vegetation types.
2. Four USDA precision weighing lysimeters.
3. Hundreds of publicly available state agency and farmer groundwater pumping records.
4. Water balance estimates of ET for hundreds of unimpaired watersheds in the western U.S.
Benchmark ground-based datasets have gone through extensive quality assurance and quality control and are further described below.
Eddy Covariance Station Data – The primary dataset for the intercomparison study has been collected from eddy covariance networks that measure the exchanges of carbon dioxide, water vapor, and energy between the land surface and the atmosphere. These network stations are important because they provide ground-based ET datasets for specific locations with known land use and vegetation types. These datasets are derived from micrometeorological measurements used to calculate water and energy fluxes from the land surface. We’ve developed and applied an automated screening process to identify outliers, fill gaps in the instrument measurement record, perform energy balance closure and review of eddy covariance data following data processing procedures established by the FLUXNET organization. We have also reviewed the sites using aerial imagery and wind speed and direction data from the station to ensure that the location is representative of the surrounding vegetation and land surface characteristics, and developed daily and monthly dynamic station fetch areas for sampling and intercomparing satellite and ground-based ET datasets using a two-dimensional Flux Footprint Prediction system.
Groundwater Pumping Records – Comparisons against groundwater pumping records provide complementary information for assessing satellite-based ET data for more agricultural fields than can be obtained from micrometeorological measurements alone. Comparing groundwater pumping records to satellite-based ET offers insights into differences between the volume of water that is pumped for irrigation and the consumptive use of water through ET, and is also proving useful for identifying erroneous meter readings and pumping estimates.
Watershed Water Balances – Finally, contrasting satellite-based ET estimates against watershed-scale water balance ET estimates is useful to test the hypothesis that random errors tend to cancel and results from different models will more closely converge when data are aggregated over larger areas. While watershed water balances can have higher uncertainty than other comparison approaches, they are complementary and useful for our broader intercomparison effort, and are commonly used in the peer-review literature for assessing regional scale satellite-based ET estimation.
Previous Comparisons to Ground-based Data
While this intercomparison effort is an important step for OpenET, given that the models are being applied across a broad range of geographies and land cover types, it is worth noting that individual models have been extensively assessed at local to global scales. See this list of peer-reviewed publications that highlight these intercomparison assessments.
[LIST OF LINKS]
Calculating the Ensemble
In most cases, the models used in OpenET produce a range of ET values, with some models biased high and others low relative to the ground-based ET datasets. In this case, the ensemble value from the models works to offset some of these biases, leading to the likelihood of a more accurate estimate of actual ET than any one model may provide on its own across the full diversity of crops and vegetation types in the western U.S and elsewhere. There may, however, be instances when a subset of models is truly and consistently more accurate. In those cases, the ensemble value may be weighted or based more heavily on those models, or may even exclude particular models for specific regions or vegetation types.
OpenET encourages users to apply their own discretion and knowledge in accessing and applying the data, and in choosing whether to use the ensemble value or a subset of the models. To provide additional guidance when desired, the OpenET team is undertaking two efforts:
1. Developing a best practices manual, based in large part on the intercomparison effort, to help users decide whether to use the ensemble value or one model. When complete, we will provide a link here and on the home page.
2. Assessing the ensemble value across different geographies and landscapes, based on the team’s best understanding of quantitative approaches to reduce the range of results down to what is likely to be the most accurate number.
Model Updates and Improvements
In the course of developing and validating fully automated implementations of the six models within OpenET on Google Earth Engine, a number of model improvements have been made. While the full details of each model are contained within the citations provided, brief summaries of the key modifications for each model are provided below. For a description of known issues and ongoing improvements for each model, please refer to the description of Known Issues.
ALEXI/DisALEXI (version 0.0.27)
DisALEXI was recently ported to Google Earth Engine as part of the OpenET framework, and refinement is still ongoing. The baseline ALEXI/DisALEXI model structure is described by Anderson et al. (2012, 2018). DisALEXI uses Landsat thermal and optical imagery to spatially disaggregate continental-scale, 4-km resolution ET estimates from ALEXI (Anderson et al. 2007)—an energy balance algorithm based on morning land surface temperature rise data obtained over the U.S. from the GOES geostationary satellite network.
Results from Phase I of the OpenET Accuracy Assessment and Intercomparison study suggested a few modifications to the baseline ALEXI/DisALEXI modeling system that were implemented in Phase II. First, a wet bias in arid grasslands in the western U.S. led to modification of the soil resistance formulation in the Two-Source Energy Balance (TSEB) land-surface representation (Norman et al., 1995) used in ALEXI, building on the findings of Kustas et al., (2016). However, Phase II results indicate that this adjustment must be tempered in future improvements; for example, in arid grassland pixels containing small fractions of irrigated pasture or croplands.
In addition, an empirical aridity correction was applied to the Priestley-Taylor (PT) coefficient used in the TSEB initial estimate of potential canopy transpiration (Agam et al., 2010). This correction increases the transpiration flux under conditions of high vapor pressure deficit in pixels where the TSEB does not detect canopy stress. In the near future, the PT correction approach described below for PT-JPL will be adopted in both ALEXI and DisALEXI to better capture effects of advective enhancement on ET from irrigated crops in arid climates.
Additional future refinements will include the slope and aspect corrections to solar radiation load used in eeMETRIC, which should improve disaggregation in regions of high topographic variability.
eeMETRIC (version 0.20.15)
eeMETRIC applies the traditional METRIC calibration process of Allen et al. (2007; 2015) and Irmak (Kilic) et al. (2013), where a singular relationship between the near surface air temperature difference (dT) and delapsed land surface temperature (TsDEM) is used to estimate sensible heat flux (H) and is applied to each Landsat scene. Automated selection of the hot and cold pixels for an image generally follows a statistical isolation procedure described by Allen et al. (2013a). The calibration of H in eeMETRIC utilizes alfalfa reference ET calculated from the NLDAS gridded weather dataset using a fixed 15% reduction in computed reference ET to account for known biases in the gridded data set (Blankenau et al., 2020). The fixed reduction does not impact the calibration accuracy of eeMETRIC and mostly reduces impacts of boundary layer buoyancy correction.
The identification of candidates for pools of hot and cold pixels has evolved in the eeMETRIC implementation of METRIC. The new automated calibration process incorporates the combination of methodologies and approaches that stem from two development branches of EEFlux (Allen et al., 2015). The first branch focused on improving the automated pixel selection process using standard lapse rates for land surface temperature (LST) without any further spatial delapsing. The second branch incorporated a secondary spatial delapsing of LST as well as changes to the pixel selection process. The final, combined approach is described by ReVelle, Kilic and Allen (2021a, b).
Application of the METRIC algorithm near coasts and in mountainous terrain can benefit from special treatment of LST where cool ocean temperatures may influence the relationship between LST and sensible heat flux with distance from the coast, and where lapsing of LST in mountains can vary with location or time of year. Recent efforts by ReVelle et al. (2021a) developed a two-dimensional planar delapsing surface over Landsat images to remove spatial trends in LST that may be related to distance from an ocean, impacts of steep terrain, or frontal weather activity that can cause spatial variation in equilibrium LST. Application of the secondary planar delapsing tends to help equalize LST to represent only the effects that are associated strictly with sensible heat fluxes.
eeMETRIC employs the aerodynamic-related functions in complex terrain (mountains) developed by Allen et al. (2013b) to improve estimates for aerodynamic roughness, wind speed and boundary layer stability as related to estimated terrain roughness, position on a slope and wind direction. These functions tend to increase estimates for H (and reduce ET) on windward slopes and may reduce H (and increase ET) on leeward slopes.
Other METRIC functions employed in eeMETRIC that have been added since the descriptions provided in Allen et al. (2007 and 2011) include reduction in soil heat flux (G) in the presence of organic mulch on the ground surface, use of an excess aerodynamic resistance for shrublands, use of the Perrier function for trees identified as forest (Allen et al., 2018; Santos et al., 2012) and aerodynamic estimation of evaporation from open water rather than using energy balance (Jensen and Allen 2016; Allen et al., 2018). These functions and other enhancements to the original METRIC model are described in the most current METRIC users manual (Allen et al., 2018). eeMETRIC uses the atmospherically corrected surface reflectance and LST from Landsat Collection 2, with fallback to Collection 1 when needed for near real-time estimates.
geeSEBAL (version 0.2.1)
Implementation of geeSEBAL was recently completed within the OpenET framework. An overview of the current geeSEBAL version can be found in Laipelt et al. (2021), which is based on the original algorithms developed by Bastiaanssen et al. (1998). The OpenET geeSEBAL implementation uses LST data from Landsat Collection 2, in addition to NLDAS and gridMET datasets as instantaneous and daily meteorological inputs, respectively. The automated statistical algorithm to select the hot and cold endmembers is based on a simplified version of the Calibration using Inverse Modeling at Extreme Conditions (CIMEC) algorithm proposed by Allen et al. (2013), where quantiles of LST and the normalized difference vegetation index (NDVI) values are used to select endmember candidates in the Landsat domain area. The cold and wet endmember candidates are selected in well vegetated areas, while the hot and dry endmember candidates are selected in the least vegetated cropland areas. Based on the selected endmembers, geeSEBAL assumes that in the cold and wet endmember all available energy is converted to latent heat (with high rates of transpiration), while in the hot and dry endmember all available energy is converted to sensible heat. Finally, estimates of daily evapotranspiration are upscaled from instantaneous estimates based on the evaporative fraction, assuming it is constant during the daytime without significant changes in soil moisture and advection.
Based on the results from the OpenET Accuracy Assessment and Intercomparison study, the OpenET geeSEBAL algorithm was modified as follows: (i) the simplified version of CIMEC was improved by using additional filters to select the endmembers, including the use of the USDA Cropland Data Layer (CDL) and filters for NDVI and albedo; (ii) corrections to LST for endmembers based on antecedent precipitation; (iii) definition of NLDAS wind speed thresholds to reduce model instability during the atmospheric correction; and, (iv) improvements to estimate daily net radiation, using FAO-56 as reference (Allen et al., 1998).
Overall, geeSEBAL performance is dependent on topographic, climate, and meteorological conditions, with higher sensitivity and uncertainty related to hot and cold endmember selections for the CIMEC automated calibration, and lower sensitivity and uncertainty related to meteorological inputs (Laipelt et al., 2020 and 2021). The geeSEBAL team is currently working to reduce these uncertainties and increase model accuracy for multiple climate conditions and complex topography. Some of the future improvements to geeSEBAL will include: (i) corrections of LST based on slope, aspect, environmental lapse rate, and incident solar radiation; (ii) improvements to endmember selection and the relationship between the near surface air temperature difference (dT) in arid and semiarid climates, where uncertainties in ET estimates are related to elevated LST in bare soil and sparsely vegetated areas.
PT-JPL (version 0.2.1)
The core formulation of the PT-JPL model within the OpenET framework has not changed from the original formulation detailed in Fisher et al. (2008). However, enhancements and updates to model inputs and time integration for PT-JPL were made to take advantage of contemporary gridded weather datasets, provide consistency with other models, improve open water evaporation estimates, and account for advection over crop and wetland areas in semiarid and arid environments. These changes include the use of Landsat surface reflectance and thermal radiation for calculating net radiation, photosynthetically active radiation, plant canopy and moisture variables, and use of NLDAS, Spatial CIMIS, and gridMET weather data for estimating insolation and ASCE reference ET. Similar to the implementation of other OpenET models, estimation of daily and monthly time integrated ET is based on the fraction of ASCE reference ET. Open water evaporation is estimated following a surface energy balance approach of Abdelrady et al. (2016) that is specific for water bodies by accounting for water heat flux as opposed to soil heat flux.
Priestley-Taylor (PT) evapotranspiration was originally developed to represent wet environment ET (ETw) or advection-free potential ET where the vapor pressure deficit (VPD) between the surface and atmosphere approaches equilibrium (Priestley and Taylor, 1972). True advection-free, equilibrium conditions rarely occur in the natural environment, therefore the PT alpha coefficient was developed to account for additional nonequilibrium, advection-based vapor fluxes. On average, alpha was shown to equal approximately 1.26 for wet environments; however, values can fall well above or below the original 1.26 alpha value depending on environmental conditions such as soil moisture, VPD, and vegetation cover (Jensen et al., 1990; Agam et al, 2010; Engstrom et. al., 2002).
To improve PT-JPL ET estimates for croplands, wetlands, and riparian areas in arid and semiarid environments where advection is prevalent, a PT alpha adjustment layer was developed based on the ratio of ASCE reference ET and Priestley-Taylor ETw, following principles of the complementary relationship of evaporation (Kahler and Brutsaert, 2006; Szilagyi, 2007; Huntington et al., 2011). ASCE reference ET and ETw estimates were developed using the gridMET dataset. The alpha adjustment layer was calculated as the ratio of the growing average bias corrected ASCE reference ET to the growing season average ETw. Growing seasons were defined for each gridMET grid cell based on cumulative growing degree days of daily average air temperature from January 1 and killing frost thresholds of 300 C and -2 C, respectively. Adjustment factors were limited to a minimum of 0.79 and maximum of 2 to account for uncertainty in weather data and model performance in locations with extreme climate and weather (e.g. Death Valley, coastal regions). Adjusted alpha values range from 1 to 2.5, and fall within the range of alpha values previously reported across arid and humid settings (Priestley and Taylor, 1972; McAneney and Itier, 1996; Weiß and Menzel, 2008; Yang et al., 2009; Tabari and Talaee, 2011). Application of adjusted alpha values were limited to cropland, wetland, and riparian areas as defined by the USDA cropland data layer (CDL).
Results from Phase II of the OpenET Accuracy Assessment and Intercomparison study indicated that application of the PT alpha adjustment layer increased overall accuracy of PT-JPL for cropland, wetland, and riparian areas, however, ET estimates generally remain near or below the ensemble average for these areas. Future enhancements to PT-JPL will include the slope and aspect corrections to short and longwave radiation and LST, similar to eeMETRIC, and further improvements to the PT alpha adjustment layer.
SIMS (version 0.0.20)
The primary change from the most recent SIMS publication (Pereira et al., 2020) for implementation in OpenET is the integration of a gridded soil water balance model to account for soil evaporation following precipitation events. Results of the Phase I intercomparison and accuracy assessment showed that SIMS generally performed well for cropland sites during the growing season, but had a persistent low bias during the winter months or other time periods with frequent precipitation. This result was anticipated, since the reflectance-based approach used by SIMS is not sensitive to soil evaporation. To correct for this underestimation, a soil water balance model based on FAO-56 (Allen et al., 1998) was implemented on Google Earth Engine and driven with gridded precipitation data from gridMET to estimate soil evaporation coefficients. These coefficients were then combined with the basal crop coefficients calculated by SIMS to estimate total crop evapotranspiration. Full documentation of the SIMS model, current algorithms, and details and equations used in the soil water balance model are included in the SIMS user manual.
Results from Phase II of the OpenET Accuracy Assessment and Intercomparison study indicated that this change improved SIMS estimates of total actual ET and increased the overall accuracy of SIMS for cropland sites. However, the reflectance-based approach used by SIMS assumes well-irrigated conditions, and SIMS will still have a positive bias for deficit irrigated crops and croplands with short-term or intermittent crop water stress. At present, SIMS is only implemented for croplands, and future research will extend the vegetation density-crop coefficient approach used within SIMS to other land cover types.
SSEBop (version 0.1.5)
The Operational Simplified Surface Energy Balance (SSEBop) model by Senay et al. (2013, 2017) is a thermal-based simplified surface energy model for estimating actual ET based on the principles of satellite psychrometry (Senay 2018). The OpenET SSEBop implementation uses land surface temperature (Ts) from Landsat (Collection 2 Level-2 Science Products) with key model parameters (cold reference, Tc, and surface psychrometric constant, 1/dT) derived from a combination of climatological average (1980-2017) daily maximum air temperature (Ta, 1-km) from Daymet, and net radiation data from ERA-5. Notable model enhancements include parameterization for elevation lapse rate in complex topography and model c factor calibration using a spatially dynamic 5-km gridded implementation. In SSEBop, when Ts cools faster than Ta in complex topography and elevation, additional environmental lapse rate (ELR) correction is necessary to adjust for these differences. Daily Ta data is corrected using an ELR constant (0.005 K/m) to adjust for lapse rate differences between air and surface temperature values at high elevations and sharp rises in topographically complex regions. Operationally, sub-scene localized c factor values are determined based on the ratio between Ts and Ta, filtered by NDVI and reduced to the value representing the cold, wet surface at each 5-km grid cell. Spatial smoothing techniques are employed to create a gradually varying c factor surface for each satellite overpass image before calculating ET at Landsat resolution.
References
Abdelrady, A., Timmermans, J., Vekerdy, Z. and Salama, M., 2016. Surface energy balance of fresh and saline waters: AquaSEBS. Remote sensing, 8(7), p.583.
Agam, N., Kustas, W.P., Anderson, M.C., Norman, J.M., Colaizzi, P.D., Howell, T.A., Prueger, J.H., Meyers, T.P. and Wilson, T.B., 2010. Application of the Priestley–Taylor approach in a two-source surface energy balance model. Journal of Hydrometeorology, 11(1), pp.185-198.
Allen, R.G., Tasumi, M., Morse, A. and Trezza, R., 2005. A Landsat-based energy balance and evapotranspiration model in Western US water rights regulation and planning. Irrigation and Drainage Systems, 19(3-4), pp.251-268.
Allen, R.G., Tasumi, M. and Trezza, R., 2007. Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)—Model. Journal of Irrigation and Drainage Engineering, 133(4), pp.380-394.
Allen, R., Irmak, A., Trezza, R., Hendrickx, J.M., Bastiaanssen, W. and Kjaersgaard, J., 2011. Satellite‐based ET estimation in agriculture using SEBAL and METRIC. Hydrological Processes, 25(26), pp.4011-4027.
Allen, R.G., Morton, C., Kamble, B., Kilic, A., Huntington, J., Thau, D., Gorelick, N., Erickson, T., Moore, R., Trezza, R. and Ratcliffe, I., 2015. EEFlux: A Landsat-based evapotranspiration mapping tool on the Google Earth Engine. In 2015 ASABE/IA Irrigation Symposium: Emerging Technologies for Sustainable Irrigation-A Tribute to the Career of Terry Howell, Sr. Conference Proceedings (pp. 1-11). American Society of Agricultural and Biological Engineers.
Allen, R.G., Burnett, B., Kramber, W., Huntington, J., Kjaersgaard, J., Kilic, A., Kelly, C. and Trezza, R., 2013. Automated calibration of the metric‐landsat evapotranspiration process. JAWRA Journal of the American Water Resources Association, 49(3), pp.563-576.
Allen, R.G., Trezza, R., Kilic, A., Tasumi, M. and Li, H., 2013. Sensitivity of Landsat‐scale energy balance to aerodynamic variability in mountains and complex terrain. JAWRA Journal of the American Water Resources Association, 49(3), pp.592-604.
Allen, R.G., Trezza R., Tasumi M., Robison C.,Kjaersgaard J., Kilic, A., 2018. METRIC – Mapping evapotranspiration at high resolution using internalized calibration – Applications manual for Landsat satellite imagery. University of Idaho. Version 3.02, 2018. p.187
Allen, R.G., Pereira, L.S., Raes, D. and Smith, M., 1998. Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. Fao, Rome, 300(9), p.D05109.
Anderson, M.C., Norman, J.M., Mecikalski, J.R., Otkin, J.A. and Kustas, W.P., 2007. A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 1. Model formulation. Journal of Geophysical Research: Atmospheres, 112(D10).
Anderson, M.C., Kustas, W.P., Alfieri, J.G., Gao, F., Hain, C., Prueger, J.H., Evett, S., Colaizzi, P., Howell, T. and Chávez, J.L., 2012. Mapping daily evapotranspiration at Landsat spatial scales during the BEAREX’08 field campaign. Advances in Water Resources, 50, pp.162-177.
Anderson, M., Gao, F., Knipper, K., Hain, C., Dulaney, W., Baldocchi, D., Eichelmann, E., Hemes, K., Yang, Y., Medellin-Azuara, J. and Kustas, W., 2018. Field-scale assessment of land and water use change over the California Delta using remote sensing. Remote Sensing, 10(6), p.889.
Bastiaanssen, W.G., Menenti, M., Feddes, R.A. and Holtslag, A.A.M., 1998. A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation. Journal of Hydrology, 212, pp.198-212.
Blankenau, P.A., Kilic, A. and Allen, R., 2020. An evaluation of gridded weather data sets for the purpose of estimating reference evapotranspiration in the United States. Agricultural Water Management, 242, p.106376.
Engstrom, R.N., Hope, A.S., Stow, D.A., Vourlitis, G.L. and Oechel, W.C., 2002. PRIESTLEY‐TAYLOR ALPHA COEFFICIENT: VARIABILITY AND RELATIONSHIP TO NDVI IN ARCTIC TUNDRA LANDSCAPES 1. JAWRA Journal of the American Water Resources Association, 38(6), pp.1647-1659.
Fisher, J.B., Tu, K.P. and Baldocchi, D.D., 2008. Global estimates of the land–atmosphere water flux based on monthly AVHRR and ISLSCP-II data, validated at 16 FLUXNET sites. Remote Sensing of Environment, 112(3), pp.901-919.
Huntington, J.L., Szilagyi, J., Tyler, S.W. and Pohll, G.M., 2011. Evaluating the complementary relationship for estimating evapotranspiration from arid shrublands. Water Resources Research, 47(5).
Irmak (Kilic), A., Ratcliffe, I., Ranade, P., Hubbard, K.G., Singh, R.K., Kamble, B. and Kjaersgaard, J., 2011. Estimation of land surface evapotranspiration with a satellite remote sensing procedure. Great plains research, pp.73-88.
Jensen M.E and Allen R.G., 2016. Evaporation, evapotranspiration, and irrigation water requirements. ASCE Manual of Practice 70, 2nd edn. American Society of Civil Engineers, Reston, VA. 744 p.
Jensen, M.E., Burman, R.D. and Allen, R.G., 1990. Evapotranspiration and irrigation water requirements. ASCE Manuals and Reports on Engineering Practices No. 70, New York, 332 p.
Kahler, D. M. and Brutsaert, W., 2006. Complementary relationship between daily evaporation in the environment and pan evaporation. Water resources research, 42(5).
Laipelt, L., Kayser, R.H.B., Fleischmann, A.S., Ruhoff, A., Bastiaanssen, W., Erickson, T.A. and Melton, F., 2021. Long-term monitoring of evapotranspiration using the SEBAL algorithm and Google Earth Engine cloud computing. ISPRS Journal of Photogrammetry and Remote Sensing, 178, pp.81-96.
Laipelt, L., Ruhoff, A.L., Fleischmann, A.S., Kayser, R.H.B., Kich, E.D.M., da Rocha, H.R. and Neale, C.M.U., 2020. Assessment of an automated calibration of the SEBAL algorithm to estimate dry-season surface-energy partitioning in a forest–savanna transition in Brazil. Remote Sensing, 12(7), p.1108.
Melton, F.S., Johnson, L.F., Lund, C.P., Pierce, L.L., Michaelis, A.R., Hiatt, S.H., Guzman, A., Adhikari, D.D., Purdy, A.J., Rosevelt, C. and Votava, P., 2012. Satellite irrigation management support with the terrestrial observation and prediction system: A framework for integration of satellite and surface observations to support improvements in agricultural water resource management. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(6), pp.1709-1721.
McAneney, K.J. and Itier, B., 1996. Operational limits to the Priestley-Taylor formula. Irrigation Science, 17(1), 37-43.
Pereira, L.S., Paredes, P., Melton, F., Johnson, L., Wang, T., López-Urrea, R., Cancela, J.J. and Allen, R.G., 2020. Prediction of crop coefficients from fraction of ground cover and height. Background and validation using ground and remote sensing data. Agricultural Water Management, 241, p.106197.
Priestley, C.H.B. and Taylor, R.J., 1972. On the assessment of surface heat flux and evaporation using large-scale parameters. Monthly weather review, 100(2), 81-92.
ReVelle, P., A. Kilic and R. Allen. 2021a. Updated Calibration Description: Spatial Delapsing. OpenET documentation. University of Nebraska-Lincoln and University of Idaho. 9 p.
ReVelle, P., A. Kilic and R. Allen. 2021b. Updated Calibration Description: Automated Pixel Selection Method. University of Nebraska-Lincoln and University of Idaho. 13 p.
Santos, C., Lorite, I.J., Allen, R.G. and Tasumi, M., 2012. Aerodynamic parameterization of the satellite-based energy balance (METRIC) model for ET estimation in rainfed olive orchards of Andalusia, Spain. Water Resources Management, 26(11), pp.3267-3283.
Senay, G.B., Bohms, S., Singh, R.K., Gowda, P.H., Velpuri, N.M., Alemu, H. and Verdin, J.P., 2013. Operational evapotranspiration mapping using remote sensing and weather datasets: A new parameterization for the SSEB approach. JAWRA Journal of the American Water Resources Association, 49(3), pp.577-591.
Senay, G.B., Schauer, M., Friedrichs, M., Velpuri, N.M. and Singh, R.K., 2017. Satellite-based water use dynamics using historical Landsat data (1984–2014) in the southwestern United States. Remote Sensing of Environment, 202, pp.98-112.
Senay, G.B., 2018. Satellite psychrometric formulation of the Operational Simplified Surface Energy Balance (SSEBop) model for quantifying and mapping evapotranspiration. Applied Engineering in Agriculture, 34(3), pp.555-566.
Szilagyi, J., 2007. On the inherent asymmetric nature of the complementary relationship of evaporation. Geophysical Research Letters, 34(2).
Tabari, H. and Talaee, P. H., 2011. Local calibration of the Hargreaves and Priestley-Taylor equations for estimating reference evapotranspiration in arid and cold climates of Iran based on the Penman-Monteith model. Journal of Hydrologic Engineering, 16(10), 837-845.
Weiß, M. and Menzel, L., 2008. A global comparison of four potential evapotranspiration equations and their relevance to stream flow modelling in semi-arid environments. Advances in Geosciences, 18, pp.15-23.
Yang, H., Yang, D., Lei, Z., Sun, F. and Cong, Z., 2009. Variability of complementary relationship and its mechanism on different time scales. Science in China Series E: Technological Sciences, 52(4), pp.1059-1067.