Key Points:• ECOSTRESS is a state-of-the-art combination of thermal bands, spatial and temporal resolutions, and measurement accuracy and precision • Data from 82 eddy covariance sites were coalesced concurrently with the first year of ECOSTRESS for Stage 1 validation • Clear-sky ET from ECOSTRESS compared well against a wide range of eddy Abstract The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) was launched to the International Space Station on 29 June 2018 by the National Aeronautics and Space Administration (NASA). The primary science focus of ECOSTRESS is centered on evapotranspiration (ET), which is produced as Level-3 (L3) latent heat flux (LE) data products. These data are generated from the Level-2 land surface temperature and emissivity product (L2_LSTE), in conjunction with ancillary surface and atmospheric data. Here, we provide the first validation (Stage 1, preliminary) of the global ECOSTRESS clear-sky ET product (L3_ET_PT-JPL, Version 6.0) against LE measurements at 82 eddy covariance sites around the world. Overall, the ECOSTRESS ET product performs well against the site measurements (clear-sky instantaneous/time of overpass: r 2 = 0.88; overall bias = 8%; normalized root-mean-square error, RMSE = 6%). ET uncertainty was generally consistent across climate zones, biome types, and times of day (ECOSTRESS samples the diurnal cycle), though temperate sites are overrepresented. The 70-m-high spatial resolution of ECOSTRESS improved correlations by 85%, and RMSE by 62%, relative to 1-km pixels. This paper serves as a reference for the ECOSTRESS L3 ET accuracy and Stage 1 validation status for subsequent science that follows using these data.

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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.

The SMAP (Soil Moisture Active Passive) mission provides global surface soil moisture product at 36 km resolution from its L-band radiometer. While the coarse resolution is satisfactory to many applications there are also a lot of applications which would benefit from a higher resolution soil moisture product. In this paper the SMAP radiometer-based soil moisture product was downscaled to 1 km using MODIS (Moderate Resolution Imaging Spectroradiometer) data, and validated against airborne data from the PALS (Passive Active Lband System) instrument. The downscaling approach uses MODIS land surface temperature and normalized difference vegetation index to construct soil evaporative efficiency, which is used to downscale the SMAP soil moisture. The algorithm was applied to one SMAP pixel during the SMAP Validation Experiment 2015 (SMAPVEX15) in a semiarid study area for validation of the approach. The results showed that the approach had reasonable skill (root mean square difference of 0.053 m3/m3 for 1-km resolution and 0.037 m3/m3 for 3-km resolution) in resolving high resolution soil moisture features within the coarse scale pixel. The success benefits from the fact that the surface temperature in this region is controlled by soil evaporation, the topographical variation within the chosen pixel area is relatively moderate and the vegetation density is relatively low over most parts of the pixel. The analysis showed that the combination of the SMAP and MODIS data under these conditions can result in a high resolution soil moisture product with an accuracy suitable for many applications.

This analysis is the first global validation of the Moderate Resolution Imaging Spectroradiometer (MODIS)‐derived near‐surface air temperature and dew point estimates, which both serve as crucial input data in models of energy, water, and carbon exchange between terrestrial ecosystems and the atmosphere. By hypsometrically interpolating the MOD07 Level‐2 atmospheric profile product to surface pressure level, we obtained near‐surface air temperature and dew point observations at 5 km pixel resolution. We compared these daily data, retrieved over a 14‐year record, to corresponding measurements from 109 ground meteorological stations (FLUXNET). Our results show strong agreement between satellite and in situ near‐surface air temperature measurements (R2 = 0.89, root‐mean‐square error = 3.47°C, and bias = −0.19°C) and dew point observations (R2 = 0.76, root‐mean‐square error = 5.04°C, and bias = 0.79°C) with insignificant differences in error across climate zones. This validation is among the earliest assessments of the reprocessed, crosstalk‐corrected Collection 6.1 Terra MODIS data and provides support for widespread applications of near‐surface atmospheric data.