When Should a Satellite Estimate Be Changed? Stress-Testing Neural Corrections for Evapotranspiration

Title:When Should a Satellite Estimate Be Changed? Stress-Testing Neural Corrections for Evapotranspiration

Abstract:Neural residuals can improve satellite evapotranspiration (ET) estimates, but selectors must predict when a correction helps and reject unsupported inputs. We evaluate ten-member models on 16,366 flux-tower observations from 151 stations paired with OpenET, across nine rolling years and five spatial folds. At one held-out station, Gain accepted corrections on all 32 physically invalid records: it predicted a mean benefit of 0.83 mm/day, but the corrections increased mean absolute error by 21.6 mm/day versus OpenET. On spatially held-out unit errors, SupportGain reduced station-macro MAE versus Gain by 0.148 mm/day under wind x3.6 (simultaneous 95% interval, 0.070 to 0.226), with 9.3% acceptance versus Gain's 51.8%; on clean inputs, its 0.006 mm/day advantage had an interval that includes zero. These fault analyses are exploratory; none of 40 preplanned temporal comparisons passed Holm correction, while a separate predeclared cropland contrast found 0.041 mm/day lower station-macro MAE with crop-only training (95% interval, 0.009 to 0.079).

Machine Learning (cs.LG)

arXiv:2609.35314 [cs.LG]

(or arXiv:2609.35314v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.35314 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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