Global production and free access to Landsat-scale Evapotranspiration with EEFlux and eeMETRIC

EEFlux (Earth Engine Evapotranspiration Flux) is a version of the METRIC (mapping evapotranspiration at high resolution with internal calibration) application that operates on the Google Earth Engine (EE). EEFlux has a web-based interface and provides free public access to transform Landsat images into 30 m spatial evapotranspiration (ET) data for terrestrial land areas around the globe. EE holds the entire Landsat archive to power EEFlux along with NLDAS/CFSV2 gridded weather data for estimating reference ET. EEFlux is a part of the upcoming OpenET platform (https://openetdata.org/ ) that has leveraged nonprofit funding to provide ET information to all of the lower 48 states for free, as a means to foster water exchange between agriculture, cities and environment (Melton et al., 2020). The METRIC version in OpenET is named eeMETRIC, and includes cloud detection and time integration of ET snapshots into monthly ET estimates. EEFlux and eeMETRIC employ METRIC’s “mountain” algorithms for estimating aerodynamics and solar radiation in complex terrain. Calibration is automated and ET images are computed for download in seconds using EE’s large computational capacity

OpenET : filling a critical data gap in water management for the western United States.

The lack of consistent, accurate information on evapotranspiration (ET) and consumptive use of water by irrigated agriculture is one of the most important data gaps for water managers in the western United States (U.S.) and other arid agricultural regions globally. The ability to easily access information on ET is central to improving water budgets across the West, advancing the use of data-driven irrigation management strategies, and expanding incentive-driven conservation programs. Recent advances in remote sensing of ET have led to the development of multiple approaches for field-scale ET mapping that have been used for local and regional water resource management applications by U.S. state and federal agencies. The OpenET project is a community-driven effort that is building upon these advances to develop an operational system for generating and distributing ET data at a field scale using an ensemble of six well-established satellite-based approaches for mapping ET. Key objectives of OpenET include: Increasing access to remotely sensed ET data through a web-based data explorer and data services; supporting the use of ET data for a range of water resource management applications; and development of use cases and training resources for agricultural producers and water resource managers. Here we describe the OpenET framework, including the models used in the ensemble, the satellite, meteorological, and ancillary data inputs to the system, and the OpenET data visualization and access tools. We also summarize an extensive intercomparison and accuracy assessment conducted using ground measurements of ET from 139 flux tower sites instrumented with open path eddy covariance systems. Results calculated for 24 cropland sites from Phase I of the intercomparison and accuracy assessment demonstrate strong agreement between the satellite-driven ET models and the flux tower ET data. For the six models that have been evaluated to date (ALEXI/DisALEXI, eeMETRIC, geeSEBAL, PT-JPL, SIMS, and SSEBop) and the ensemble mean, the weighted average mean absolute error (MAE) values across all sites range from 13.6 to 21.6 mm/month at a monthly timestep, and 0.74 to 1.07 mm/day at a daily timestep. At seasonal time scales, for all but one of the models the weighted mean total ET is within ±8% of both the ensemble mean and the weighted mean total ET calculated from the flux tower data. Overall, the ensemble mean performs as well as any individual model across nearly all accuracy statistics for croplands, though some individual models may perform better for specific sites and regions. We conclude with three brief use cases to illustrate current applications and benefits of increased access to ET data, and discuss key lessons learned from the development of OpenET

Bias and Other Error in Gridded Weather Data Sets and Their Impacts on Estimating Reference Evapotranspiration

Gridded weather data sets are increasingly used in a variety of hydrologic and agricultural applications due to their complete spatial and temporal coverage. One application of gridded data sets is the estimation of evapotranspiration (ET). Several operational remote sensing (RS) approaches for estimating ET, such as the SEBAL, METRIC and EEFlux models, require estimates of reference ET (ETref), where ETref is expected ET from a hypothetical reference crop of clipped grass or alfalfa. Gridded weather data provide for the computation of ETref in all areas of a remote sensing image, and therefore potentially remove the need for dense weather station data. Given the increasing use of gridded weather data to estimate ETref, this study assessed the quality of gridded weather data estimates of ETref. To accomplish this evaluation, several gridded weather data sets – GLDAS-1, NLDAS-2, CFSv2 operational analysis, GRIDMET, RTMA and NDFD – were compared to weather station data that were considered to represent ‘ground truth’ across the continental United States. The stations were selected to represent reference conditions when possible. The four primary weather variables – near-surface air temperature, vapor pressure, wind speed and shortwave solar radiation - required to compute ETref, plus calculated ETref itself were compared. The application of the same analysis to multiple gridded data sets made comparisons among the gridded data sets possible. Generally, the gridded weather data sets overestimated ETref. This was mainly due to overestimation of air temperature, shortwave radiation and wind speed, and underestimation of vapor pressure. RTMA had the most accurate weather data and the most accurate estimates of ETref due to its assimilation of vast amounts of surface weather data and its continual refinement. Surprisingly, the global data sets, GLDAS and CFSv2, generally performed better than their North American counterparts – NLDAS and GRIDMET. All gridded weather data sets may be useful for estimating ETref and employment in remote sensing ET models provided some procedures for correcting biases are developed. Advisor: Ayse Kili