发表机构
Irrigant（Irrigant）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过压力测试神经校正选择器，发现Gain在无效输入上过度接受，而SupportGain在空间外推时更稳健，但需谨慎对待探索性结果。
AI 中文摘要
神经残差可以改进卫星蒸散量（ET）估算，但选择器必须预测何时校正有帮助并拒绝不支持的输入。我们在与OpenET配对的151个站点的16,366个通量塔观测上评估了十成员模型，跨越九个滚动年份和五个空间折。在一个留出站点上，Gain接受了所有32个物理无效记录上的校正：它预测平均收益为0.83毫米/天，但校正使平均绝对误差相对于OpenET增加了21.6毫米/天。在空间留出单元误差上，SupportGain在风x3.6下将站点宏观MAE相对于Gain降低了0.148毫米/天（同时95%区间，0.070至0.226），接受率为9.3%，而Gain为51.8%；在干净输入上，其0.006毫米/天的优势区间包含零。这些故障分析是探索性的；40个预计划的时间比较中没有一个通过Holm校正，而一个单独的预声明农田对比发现，仅作物训练使站点宏观MAE降低0.041毫米/天（95%区间，0.009至0.079）。
英文摘要
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).
Comments11 pages
