Abstract. Vegetation optical depth (VOD) from microwave satellite observations has received much attention in global vegetation studies in recent years due to its relationship to vegetation water content and biomass. We recently have shown that VOD is related to plant productivity, i.e. gross primary production (GPP). Based on this relationship between VOD and GPP we developed a theory-based machine learning model to estimate global patterns of GPP from passive microwave VOD retrievals. The VOD-GPP model generally showed good agreement with site observations and other global data sets in temporal dynamic but tended to overestimate annual GPP across all latitudes. We hypothesized that the reason for the overestimation is the missing effect of temperature on autotrophic respiration in the theory-based machine learning model. Here we aim to further assess and enhance the robustness of the VOD-GPP model by including the effect of temperature on autotrophic respiration within the machine learning approach and by assessing the interannual variability of the model results with respect to water availability. We used X-band VOD from the VOD Climate Archive (VODCA) data set for estimating GPP and used global state-of-the art GPP data sets from FLUXCOM and MODIS to assess residuals of the VOD-GPP model with respect to drought conditions as quantified by the Standardized Precipitation and Evaporation Index (SPEI). Our results reveal an improvement in model performance for correlation when including the temperature dependency of autotrophic respiration. This increase in temporal dynamic is largest for regions outside the tropics. For error and bias, the results are regionally diverse and are compensated in the global average. On interannual time scales, estimates of the VOD-GPP model agree well with GPP from FLUXCOM and MODIS. We further find that the residuals between VOD-based GPP estimates and the other data sets do not significantly correlate with SPEI which demonstrates that the VOD-GPP model can capture responses of GPP to water availability even without including additional information on precipitation, soil moisture or evapotranspiration. However, some regions reveal significant correlations between VOD-GPP residuals with SPEI, which may indicate different plant strategies for dealing with variations in water availability. Overall, our findings support the robustness of global microwave-derived estimates of gross primary production for large-scale studies on climate-vegetation interactions.

中文翻译：

---

温度和水的可获得性对微波衍生的初级总产值的影响

摘要。微波卫星观测的植被光学深度（VOD）由于与植被含水量和生物量的关系，近年来在全球植被研究中备受关注。我们最近发现，视频点播与工厂生产率（即初级生产总值（GPP））有关。基于VOD与GPP之间的这种关系，我们开发了一种基于理论的机器学习模型，用于从无源微波VOD检索中估计GPP的全局模式。VOD-GPP模型通常在时间动态方面与站点观测和其他全局数据集显示出良好的一致性，但往往会高估所有纬度的年度GPP。我们假设，在基于理论的机器学习模型中，高估的原因是温度对自养呼吸缺乏影响。在这里，我们的目标是通过在机器学习方法中纳入温度对自养呼吸的影响以及评估模型结果在水利用率方面的年际变化来进一步评估和增强VOD-GPP模型的鲁棒性。我们使用了来自VOD气候档案（VODCA）数据集的X波段VOD来估计GPP，并使用了FLUXCOM和MODIS的全球最新GPP数据集来评估VOD-GPP模型相对于干旱条件的残差，例如通过标准化降水和蒸发指数（SPEI）进行量化。我们的结果表明，当包括自养呼吸的温度依赖性时，相关模型性能会有所改善。对于热带以外的地区，时间动态的这种增加最大。对于错误和偏见，结果因地区而异，并在全球平均值中得到补偿。在每年的时间尺度上，VOD-GPP模型的估计与FLUXCOM和MODIS的GPP吻合得很好。我们进一步发现，基于VOD的GPP估算值与其他数据集之间的残差与SPEI没有显着相关，这表明VOD-GPP模型可以捕获GPP对水的响应，即使不包含有关降水，土壤湿度或水分的其他信息。蒸散。然而，一些地区揭示了VOD-GPP残差与SPEI之间的显着相关性，这可能表明应对水利用量变化的不同植物策略。总体，