ECOSTRESS: NASA's Next Generation Mission to Measure Evapotranspiration From the International Space Station
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".