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The fate of the terrestrial biosphere is highly uncertain given recent and projected changes in climate. This is especially acute for impacts associated with changes in drought frequency and intensity on the distribution and timing of water availability. The development of effective adaptation strategies for these emerging threats to food and water security are compromised by limitations in our understanding of how natural and managed ecosystems are responding to changing hydrological and climatological regimes. This information gap is exacerbated by insufficient monitoring capabilities from local to global scales. Here, we describe how evapotranspiration (ET) represents the key variable in linking ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources, and highlight both the outstanding science and applications questions and the actions, especially from a space‐based perspective, necessary to advance them. ET science and applications have significantly advanced across a wide array of fields over the past several decadesCritical outstanding ET‐based research and applied science questions from local to global scales remain due to deficiencies in our observational capabilitiesNational and international research priorities should include ET‐focused satellite observational investments and programs

The Budyko hypothesis provides a first‐order estimate of water partitioning into runoff (Q) and evapotranspiration (E). Observations, however, often show significant departures from the Budyko curve; moreover, past improvements to Budyko curve tend to lose predictive power when migrated between regions or to small scales. Here to estimate departures from the Budyko curve, we use hydrologic signatures extracted from Gravity Recovery And Climate Experiment (GRACE) terrestrial water storage anomalies. The signatures include GRACE amplitude as a fraction of precipitation (A/P), interannual variability, and 1‐month lag autocorrelation. We created a group of linear models embodying two alternate hypotheses that departures can be predicted by (a) Taylor series expansion based on the deviation of physical characteristics (seasonality, snow fraction, and vegetation index) from reference conditions and (b) surrogate indicators covarying with E, e.g., A/P. These models are fitted using a mesoscale USA data set (HUC4) and then evaluated using world data sets and USA basins <1 ×105km2. The model with A/Pcould reduce error by 50% compared to Budyko itself. We found that seasonality and fraction of precipitation as snow account for a major portion of the predictive power of A/P, while the remainder is attributed to unexplained basin characteristics. When migrated to a global data set, type b models performed better than type a. This contrast in transferability is argued to be due to data set limitations and catchment coevolution. The GRACE‐based correction performs well for USA basins >1000 km2and, according to comparison with other global data sets, is suitable for data fusion purposes, with GRACE error as estimates of uncertainty. GRACE amplitude can predict departure of water partitioning from the Budyko curveGRACE‐based models have better transferability than physical‐factor‐based modelsOur global product is comparable to other ET products and can be useful in data fusion

Although numerous evapotranspiration (ET) estimates have been developed from remote sensing measurements, a diurnally resolved product with high spatial resolution (<100 m) is still lacking, which is critically needed for agricultural and ecological monitoring. The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) provides, for the first time, ET at 70 m resolution with diurnal samplings, enabling a step toward filling this data gap. This study developed the first framework to resolve the full diurnal cycle of ET and Land Surface Temperature (LST) from sporadic ECOSTRESS measurements. We first constructed 70 m diurnal LST utilizing a diurnal temperature cycle (DTC) model that fused ECOSTRESS and Geostationary Operational Environmental Satellite (GOES) LST. Next, we derived 70 m diurnal ET from the diurnally resolved LST, along with ancillary meteorological and reflectance data sets, using the Priestley‐Taylor Jet Propulsion Laboratory (PT‐JPL) algorithm. This framework was tested in two regions: A relatively homogeneous semiarid grassland landscape (centered at the US‐Seg Ameriflux site) and a heterogeneous humid cropland landscape (centered at US‐ARM). Our diurnally resolved LST and ET successfully reproduced the spatial patterns in the native ECOSTRESS measurements during overpasses for both landscapes (LST: r> 0.97, RMSE ∼2°C; ET: r> 0.99, and RMSE = 1–7 W m−2). The constructed time series also well captured the temporal variability of the in situ measurements (especially under clear‐sky conditions) at both US‐Seg and US‐ARM, albeit performance varying with availability and quality of ECOSTRESS samples. This study sets the stage for testing and applying our framework to broader climates, biomes, and landscapes toward eventually generating diurnally resolved 70 m global operational LST/ET products to enhance ecological/agricultural applications. We developed a framework to construct the clear‐sky diurnal cycle of ECOSTRESS land surface temperature (LST) and evapotranspiration (ET)The diurnally resolved 70 m LST/ET successfully reproduced the fine spatial variation in the native ECOSTRESS measurementsThe diurnally resolved LST/ET well captured the diurnal variation of independent in situ measurements, especially on clear‐sky conditions We developed a framework to construct the clear‐sky diurnal cycle of ECOSTRESS land surface temperature (LST) and evapotranspiration (ET) The diurnally resolved 70 m LST/ET successfully reproduced the fine spatial variation in the native ECOSTRESS measurements The diurnally resolved LST/ET well captured the diurnal variation of independent in situ measurements, especially on clear‐sky conditions

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 LEmeasurements 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: r2= 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. ECOSTRESS is a state‐of‐the‐art combination of thermal bands, spatial and temporal resolutions, and measurement accuracy and precisionData from 82 eddy covariance sites were coalesced concurrently with the first year of ECOSTRESS for Stage 1 validationClear‐sky ET from ECOSTRESS compared well against a wide range of eddy covariance sites, vegetation classes, climate zones, and times of day

Here, we evaluate basin‐scale evapotranspiration (ET) estimates for eleven major river basins in the contiguous United States against a water balance approach with Gravity Recovery and Climate Experiment (GRACE) satellite observations. The relatively precise measurements of large‐scale changes in water mass from GRACE are used to estimate the storage rate term in the terrestrial water budget and consequently provide an estimate, with propagated uncertainty, of basin‐aggregated ET from mass conservation. We apply GRACE‐based ET to two modeling systems (NLDAS‐2 and GLDAS‐2.1) comprised of five land surface models and three remote sensing‐based products (MOD16, PT‐JPL, and FLUXCOM) for 2003 to 2014. Both the land surface model‐based and remote sensing‐based ET are persistently lower than GRACE‐based ET in all eleven basins tested. We also find that interannual variability is greater for GRACE‐ET than the model and remote sensing products, and this is attributed to precipitation variability. GRACE‐based estimates of ET are used to evaluate modeled, remote sensing, and empirical ET products at the basin scale in the contiguous United StatesLong‐term mean modeled and observation‐based ET is persistently smaller in magnitude compared to GRACE‐based ETInterannual variability of GRACE‐based ET is greater than in modeled and remote sensing‐based products