Although satellite-based light use efficiency (LUE) model is widely used to estimate gross primary production (GPP) of terrestrial ecosystems, microwave observations have not been integrated into LUE models. This study developed a new LUE model coupled with a passive microwave vegetation index (Emissivity Difference Vegetation Index, EDVI) for daily GPP estimation. Normalized EDVI (nEDVI), an indicator of canopy-scale leaf development and biomass change, was used as a proxy of fraction of photosynthetically active radiation (FPAR). EDVI-based evaporative fraction (EDVI-EF) under all sky was used to indicate synoptic-scale water stress on LUE. 8-year in-situ measurements from seven flux tower sites (four forests, two grasslands and one croplands) of ChinaFLUX network were used to evaluate the model. We found that nEDVI-based FPAR better captured the short-term variations in daily in-situ GPP (GPP_obs) than other optic-based FPAR schemes. This capability of nEDVI was more noticeable under moderate and heavy cloud cover (Frc) conditions. Validations against daily GPP_obs at all sites showed that EDVI-based GPP (GPP_EDVI) generated an overall small bias of −0.47 gC m⁻² day⁻¹ (−8.1%) and good Taylor score (S) of 0.86 at the daily scale. Better accuracy of GPP_EDVI was found at forests sites with R² of 0.43 to 0.73, bias of-5.29% to 3.03% and S of 0.58 to 0.78, respectively. At a tropical forest site with most frequent cloud cover, the model also well captured the variation in GPP_obs from clear sky to cloudy sky (R² of 0.93) with stable accuracies. Furthermore, the accuracy of daily GPP_EDVI was found to be comparable with global satellite optic MOD17 GPP (GPP_MOD17) and EC-LUE GPP (GPP_ECLUE) from 8-day to yearly scales across the sites. In particular, GPP_EDVI performed generally smaller bias at evergreen broadleaf forests, while both of GPP_MOD17 and GPP_ECLUE were overestimated, suggesting that there could be less saturation for microwave-based LUE model over dense vegetation. Although all three satellite LUE models severely underestimated GPP of crop, GPP_EDVI generated lower bias (−29.8%) than GPP_MOD17 (−66.8%) and GPP_ECLUE (−59.5%). Overall, this study is the first attempt toward the integration of microwave-derived variables into LUE model for daily GPP estimation. The microwave-based LUE model has a potential of mapping spatiotemporally continues daily GPP under various clouds.

尽管基于卫星的光利用效率 (LUE) 模型被广泛用于估算陆地生态系统的初级生产总值 (GPP)，但微波观测尚未被整合到 LUE 模型中。本研究开发了一种新的 LUE 模型，结合被动微波植被指数（发射率差异植被指数，EDVI），用于每日 GPP 估计。归一化 EDVI (nEDVI) 是冠层叶片发育和生物量变化的指标，被用作光合有效辐射 (FPAR) 分数的代表。全天空下基于 EDVI 的蒸发率 (EDVI-EF) 用于指示 LUE 的天气尺度水分胁迫。使用来自 ChinaFLUX 网络的 7 个通量塔站点（4 个森林、2 个草地和 1 个农田）的 8 年原位测量来评估该模型。_obs ) 比其他基于光学的 FPAR 方案。在中度和重度云量 (Frc) 条件下，nEDVI 的这种能力更为明显。对所有站点的每日 GPP_观察的验证表明，基于 EDVI 的 GPP (GPP _EDVI ) 产生了 -0.47 gC m ^-2 天^-1 (-8.1%)的总体小偏差和每天 0.86 的良好泰勒评分 (S)规模。在 R ²为 0.43 至 0.73、偏差为-5.29% 至 3.03% 和 S 分别为 0.58 至 0.78 的森林地点发现GPP _{EDVI 的}准确度更高。在云量最频繁的热带森林地点，该模型还很好地捕捉了从晴空到多云天空的GPP_观测值的变化（R ²0.93) 具有稳定的精度。此外，发现每日 GPP _EDVI的准确性与全球卫星光学 MOD17 GPP (GPP _MOD17 ) 和 EC-LUE GPP (GPP _ECLUE ) 从 8 天到跨站点的年度尺度_相当。特别是，GPP _EDVI在常绿阔叶林中的偏差普遍较小，而 GPP _MOD17和 GPP _ECLUE均被高估，表明基于微波的 LUE 模型在茂密植被上的饱和度可能较低。尽管所有三个卫星 LUE 模型都严重低估了作物的 GPP，但 GPP _EDVI产生的偏差 (-29.8%) 低于 GPP _MOD17 (-66.8%) 和 GPP _ECLUE(-59.5%)。总的来说，这项研究是首次尝试将微波衍生变量整合到 LUE 模型中以进行日常 GPP 估计。基于微波的 LUE 模型具有在各种云下绘制时空连续每日 GPP 的潜力。