ECOSTRESS: NASA's Next Generation Mission to Measure Evapotranspiration From the International Space Station

Abstract

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.

Impact of the Revisit of Thermal Infrared Remote Sensing Observations on Evapotranspiration Uncertainty—A Sensitivity Study Using AmeriFlux Data

“…Moreover, optical sensors (from Visible to TIR) require cloud-free observations to derive surface properties; microwave radiometers may provide information about surface temperature in all weather conditions, but at spatial resolutions too coarse for surface heterogeneity detection. To improve the temporal sampling of current TIR observations and ET estimates at high spatial resolution, the NASA ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) [18][19][20] was deployed to the International Space Station on 29 June 2018. ECOSTRESS is providing multispectral thermal infrared data to measure the Earth surface temperature at a spatial resolution of 40 × 70 m. The spaceborne system has an average revisit time of four days over 90% of the contiguous United States (CONUS) at varying times of day depending on the latitude [21].…”

Capturing the Diurnal Cycle of Land Surface Temperature Using an Unmanned Aerial Vehicle

“…When considering data from the Landsat series of satellites, which offer some of the highest thermal resolutions available (≈ 10 2 m), the 16-day return interval limits potential applications. With the recent launch of the National Aeronautics and Space Administration (NASA) Venture class ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS), which was successfully deployed onboard the International Space Station (ISS) in July 2018, retrieval of diurnal temperature is a nascent possibility, with imagery now being collected at different times of the day (albeit not on the same day) at a spatial resolution of 38 × 69 m [34]. At the other end of the spatiotemporal scale, geostationary platforms, such as the Geostationary Operational Environmental Satellites (GOES) and Meteosat Second Generation (MSG), offer a temporal sampling of 15 min, providing excellent insight into diurnal variability that is only offset by their coarse spatial resolution (≈10 3 m).…”

Competing effects of soil fertility and toxicity on tropical greening

“…These approaches include, for example, multi-variate regression, artificial neural networks, random forest, principal components analysis, and structural equation modeling. We have used these approaches extensively in previous analyses [102][103][104] . In this analysis, however, the data distribution statistical requirements for these approaches were not always satisfied, and, while the results largely reinforced what we already found, they often minimized interesting relationships with the micronutrients.…”