Information about the models, input datasets, and compute resources used by OpenET is provided below, along with a summary of the approach used in calculation of the OpenET ensemble value. Information about the OpenET Intercomparison and Accuracy Assessment is available here.
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 over a specified time period. 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 ET data at a spatial resolution of 30 meters by 30 meters (0.22 acres per pixel). Additional inputs include gridded weather variables such as solar radiation, air temperature, humidity, wind speed, and in some cases, precipitation.
Models currently included in OpenET:
|Model Acronym||Model Name||Primary References|
|Atmosphere-Land Exchange Inverse / Disaggregation of the Atmosphere-Land Exchange Inverse||
Anderson et al., 2007;
Anderson et al., 2018;
|Google Earth Engine implementation of the Mapping Evapotranspiration at high Resolution with Internalized Calibration model||
Allen et al., 2005;
Allen et al., 2007;
Allen et al., 2011
|Google Earth Engine implementation of the Surface Energy Balance Algorithm for Land||
Bastiaanssen et al., 1998;
Laipelt et al., 2021
|Priestley-Taylor Jet Propulsion Laboratory||Fisher et al., 2008|
|Satellite Irrigation Management Support||
Melton et al., 2012;
Pereira et al., 2020
|Operational Simplified Surface Energy Balance||
Senay et al., 2013;
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, which is the energy consumed through 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.
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 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 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 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 cover, 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.
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 0.0.32)
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 (a description of the OpenET Accuracy Assessment and Intercomparison study is available here). 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 some regions; for example, in arid grassland pixels containing small fractions of irrigated pasture or croplands. Further refinement has been implemented in the current version, leading to more reasonable fluxes in these ecoregions
Time-varying misregistration of GOES-based inputs to ALEXI between GOES series had resulted in in spatial artifacts in DisALEXI in the previous collection, particularly along strong moisture discontinuities (e.g. borders between irrigated land area and dry grasslands). Registration has been improved and regularized in time, reducing these artifacts in the current version.
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. In addition, a Penman-Monteith version of the TSEB is under evaluation and will be implemented in future collections pending satisfactory performance.
eeMETRIC (version 0.20.26)
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 0.2.2)
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, LST 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., 2021 and Kayser et al., 2022). To reduce uncertainties related to complex terrain, we added some improvements to correct LST and global (incident) radiation on the surface (including the environmental lapse rate, elevation slope and aspect) to represent the effects of topographic features on the model’s endmember selection algorithm and ET estimates.
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 to improve global (incident) radiation on complex terrain; (ii) improvements to the hot and cold endmembers selection to estimate the near surface and air temperature difference (dT) in arid and temperate climates with dry summers, where uncertainties in ET estimates are related to elevated LST in bare soil and sparsely vegetated areas; (iii) separate solutions for open water evaporation, including a better representation of the heat transferred to the water column.
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 0.1.0)
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. In addition, a modest positive bias was frequently observed in the SIMS data for periods with low or sparse vegetative cover. To correct for this bias, updates were made to the equations that calculate the minimum basal crop coefficient, to allow lower minimum basal crop coefficient values to be achieved. 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 these changes 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 0.2.6)
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. This model implementation uses the Google Earth Engine processing framework for connecting key SSEBop ET functions and algorithms together when generating both intermediate and aggregated ET results. A detailed study and evaluation of the SSEBop model across CONUS (Senay et al., 2022a) informs both cloud implementation and assessment for water balance applications at broad scales. Notable model (v0.2.6) enhancements and performance against previous versions include additional compatibility with Landsat 9 (launched Sep 2021), global model extensibility, and improved parameterization of SSEBop using the Forcing and Normalizing Operation (Senay et al., 2022b) to better estimate ET in all landscapes and all seasons regardless of vegetation cover density, thereby improving model accuracy by avoiding extrapolation of Tc to non-calibration regions.
Calculating the OpenET Ensemble Value
Differences in model physics, assumptions, and input data result in a range of ET estimates from the ensemble of models included in OpenET. The use of multi-model ensembles is a common practice within the climate science, hydrology, and decision-making communities. For many applications, it has been shown previously that when estimates from an ensemble of models are combined, they yield estimates that are, on average, equally or more accurate than any individual model (Thompson, 1977; Branzei et al., 2001; Kirtman et al., 2014; Arsenault et al., 2015). In addition to improved accuracy, the use of a single estimate calculated from an ensemble of ET models reduces confusion about which ET model to use, provides a path toward acceptance and consistency, and is useful for identifying both model outliers and potential errors in ground-based ET datasets. In cases where ET estimates vary substantially, legitimate questions around model accuracy and which model is “the best” can present significant barriers to the operational use and adoption of satellite-based ET data. A key objective of OpenET is to provide a single ET estimate for each location and time step, calculated from an ensemble of six models, while making individual model results available to provide transparency and support assessment and increased understanding of uncertainties. The use of a single ET value calculated from the ensemble of models can reduce barriers to use and adoption of remotely-sensed ET for a wide range of water management applications.
Many multi-model ensemble averaging approaches exist, ranging from the simple arithmetic average, weighted average, to stochastic Bayesian model averaging. Each approach has strengths and weaknesses related to simplicity, speed, accuracy, and ease of operational implementation. The optimal approach ideally addresses most, if not all, of these factors. Limitations due to small sample size, outliers, and overfitting also need to be considered.
For OpenET, a simple yet robust approach was chosen where the single ensemble ET estimate is computed at monthly time steps as the simple arithmetic average after outlier ET estimates are removed. Outlier ET estimates are detected and removed using the Median Absolute Deviation (MAD) method initially developed by Carl Friedrich Gauss, and more recently rediscovered and popularized by Hampel (1974) and Leys et al. (2013). The MAD is a measure of scale, or spread of the data, based on the median of the absolute deviations from the median of the distribution. Huber (1981) describes the method as “the single most useful ancillary estimate of scale” since it overcomes many limitations of more common standard deviation and interquartile approaches for identifying outliers. A refinement was added to the MAD outlier detection approach to account for the small size of the OpenET ensemble of models. Rather than exclude all models that may be flagged as outliers, a minimum of four models was always retained to calculate the single ensemble value. This approach still consistently eliminates outliers in most settings, while also taking advantage of an ensemble of models to improve the accuracy of ET estimates, especially for desert areas during the warm season where many, but not all models commonly estimate ET at or near zero.
From close inspection of the ensemble average, median, and individual model ET estimates, both spatially and temporally, it is clear that all models can produce erroneous ET estimates, and that these errors include both random and systematic errors. These erroneous ET estimates are often easily identified as outliers relative to the ensemble average and median. In some instances, however, the ‘outlier’ ET estimates may be the more correct estimate, though comparison against data collected from 148 eddy covariance stations shows that this is a rare occurrence. In other cases, the range of model results is large enough that the MAD approach fails to detect and remove outliers. Results from application of the MAD approach, using a threshold of plus-or-minus two times the MAD to eliminate outliers, indicate that it is rare that more than one model is dropped within cropland areas. Where one or more models are dropped within cropland areas, these models are usually estimating significantly lower ET than the majority. These limited instances mostly occur in arid to semi-arid regions where advection plays an important role in the land surface energy balance. In mountainous and complex terrain, one or more models are commonly dropped due to generation of ET estimates at extreme ends of the ensemble range, likely due to differences in model physics and assumptions for these regions. In rainfed arid and semi-arid grasslands and desert regions with low vegetation cover, it is common that two models are dropped due to complexities in estimating and accounting for precipitation and soil water balances, and accurately representing the land surface energy balance when ET is exceptionally low, or near zero.
There are some circumstances in which the MAD approach fails to detect outliers. When the range of modeled ET is large relative to the ensemble median, the utility of the MAD outlier detection approach (and others) is limited, and models with systematic biases may not be flagged as outliers and removed prior to calculation of the ensemble average. As a result, it is possible in some regions for models with local or regional systematic biases to be included in the calculation of the OpenET ensemble value.
Based on the OpenET team’s experience, and results of the intercomparison and accuracy assessment to date, the ensemble average value appears to provide the most reliable and stable estimate of ET for expansive regions with well-watered crops, and for many natural land cover types. Examples include most of California’s Central Valley and Delta, and most agricultural regions in the Midwest. However, from the limited number of cropland in-situ flux stations located in arid and semi-arid environments, it is evident that some models have a systematic low bias for smaller agricultural areas in arid regions, and the MAD outlier filtering approach does not filter outliers as desired due to the large range in model estimates. This can result in a low bias for the ensemble ET value. These areas are often indicated by a wide range of ET estimates across the ensemble of ET models for the majority of fields within a region. Over the coming months, the OpenET team will continue to conduct additional research in these more challenging settings and develop a Best Practices Manual that will provide more region and application specific guidance. Note that the ensemble value is likely to evolve in the coming year as the team conducts additional research and designs more region-specific approaches for calculation of the ensemble ET value. We strongly encourage users to rely upon their knowledge of local conditions in applying the ensemble ET value, or selecting a single model or subset of models for use in their application. When the Best Practices Manual is complete, it will be made prominently available on the OpenET website
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 following overpass), 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.
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.
Arsenault, R., Gatien, P., Renaud, B., Brissette, F., & Martel, J. L. (2015). A comparative analysis of 9 multi-model averaging approaches in hydrological continuous streamflow simulation. Journal of Hydrology, 529, 754-767.
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.
Brânzei, R., Tijs, S. and Timmer, J., 2001. Collecting information to improve decision-making. International Game Theory Review, 3(01), pp.1-12.
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.
Fritsch, J. M., Hilliker, J., Ross, J., & Vislocky, R. L. (2000). Model consensus. Weather and Forecasting, 15(5), 571-582.
Hampel, F.R., 1974. The influence curve and its role in robust estimation. Journal of the American Statistical Association, 69(346), pp.383-393.
Huber, P.J. and Ronchetti, E.M., 1981. Robust statistics. John Wiley & Sons. New York, 1(1).
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).
Kayser, R.H., Ruhoff, A., Laipelt, L., de Mello Kich, E., Roberti, D.R., de Arruda Souza, V., Rubert, G.C.D., Collischonn, W. and Neale, C.M.U., 2022. Assessing geeSEBAL automated calibration and meteorological reanalysis uncertainties to estimate evapotranspiration in subtropical humid climates. Agricultural and Forest Meteorology, 314, p.108775.
Kirtman, B.P., Min, D., Infanti, J.M., Kinter, J.L., Paolino, D.A., Zhang, Q., Van Den Dool, H., Saha, S., Mendez, M.P., Becker, E. and Peng, P., 2014. The North American multimodel ensemble: phase-1 seasonal-to-interannual prediction; phase-2 toward developing intraseasonal prediction. Bulletin of the American Meteorological Society, 95(4), pp.585-601.
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.
Leys, C., Ley, C., Klein, O., Bernard, P. and Licata, L., 2013. Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median. Journal of Experimental Social Psychology, 49(4), pp.764-766.
McAneney, K.J. and Itier, B., 1996. Operational limits to the Priestley-Taylor formula. Irrigation Science, 17(1), 37-43.
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.
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.
Senay, G. B., Friedrichs, M., Morton, C., Parrish, G. E., Schauer, M., Khand, K., … & Huntington, J. (2022a). Mapping actual evapotranspiration using Landsat for the conterminous United States: Google Earth Engine implementation and assessment of the SSEBop model. Remote Sensing of Environment, 275, 113011.
Senay, G.B., G. Parrish, M. Schauer, M. Friedrichs, K. Khand, O. Boiko, S. Kagone, R. Dittmeier, S. Arab, and Lei Ji. (2022b). Improving the Operational Simplified Surface Energy Balance evapotranspiration model using the Forcing and Normalizing Operation. Remote Sensing. Under Preparation.
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.
Thompson, P.D., 1977. How to improve accuracy by combining independent forecasts. Monthly Weather Review, 105(2), pp.228-229.
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.