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Estimating time-dependent vegetation biases in the SMAP soil moisture product

机译：估算液体水分产品中的时间依赖植被偏差

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摘要

Remotely sensed soil moisture products are influenced by vegetation and how it is accounted for in the retrieval, which is a potential source of time-variable biases. To estimate such complex, time-variable error structures from noisy data, we introduce a Bayesian extension to triple collocation in which the systematic errors and noise terms are not constant but vary with explanatory variables. We apply the technique to the Soil Moisture Active Passive (SMAP) soil moisture product over croplands, hypothesizing that errors in the vegetation correction during the retrieval leave a characteristic fingerprint in the soil moisture time series. We find that time-variable offsets and sensitivities are commonly associated with an imperfect vegetation correction. Especially the changes in sensitivity can be large, with seasonal variations of up to 40 %. Variations of this size impede the seasonal comparison of soil moisture dynamics and the detection of extreme events. Also, estimates of vegetationhydrology coupling can be distorted, as the SMAP soil moisture has larger R-2 values with a biomass proxy than the in situ data, whereas noise alone would induce the opposite effect. This observation highlights that time-variable biases can easily give rise to distorted results and misleading interpretations. They should hence be accounted for in observational and modelling studies.
机译：远程感测的土壤水分产品受植被的影响以及如何在检索中占据，这是一种潜在的时变偏差来源。为了估算来自嘈杂数据的这种复杂，时间变量错误结构，我们将贝叶斯扩展引入三重搭配，其中系统错误和噪声术语不是恒定的，而是因解释变量而变化。我们将该技术应用于土壤水分活性（SMAP）土壤水分产品在农田上，假设在检索期间植被校正中的误差留在土壤湿度时间序列中的特征指纹。我们发现时变偏移和敏感性通常与不完美的植被校正有关。特别是灵敏度的变化可能很大，季节性变化高达40％。这种大小的变化阻碍了土壤水分动力学的季节性比较和对极端事件的检测。此外，植被中的植被偶联的估计可以扭曲，因为液体水分具有比原位数据的生物质代理具有更大的R-2值，而单独的噪音会引起相反的效果。该观察结果突出显示时变偏差很容易产生扭曲的结果和误导性解释。因此，他们应该在观察和建模研究中占据。

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