Previous studies have indicated that gross primary production (GPP) and solar-induced chlorophyll fluorescence (SIF) have a strong linear relationship, and usually exhibit similar spatial and temporal patterns. However, the responses of GPP and SIF to the environment may be different, which will lead to a variant GPP-SIF relationship. To better investigate the impact of the dynamics in GPP-SIF relationship on GPP estimation, we established two GPP models. An inconstant GPP/SIF ratio model (Dynamic-Ratio model, DR model) was first established using meteorological variables and leaf area index (LAI) based on random forest regression algorithm. The model was then used to estimate GPP (referred to as GPP_DR) with different satellite SIF datasets i.e., downscaled fine resolution SIF from the Orbiting Carbon Observatory-2 (GOSIF) and Global Ozone Monitoring Experiment‐2 SIF (downscaled GOME-2 SIF). The second model (SIF-Climate-LAI model, SCL model) was also based on the random forest algorithm but was directly driven by meteorological variables, LAI and SIF data, and no GPP/SIF ratio was used in the model. As a comparison, the linear relationship between GPP and SIF was also established using eddy covariance tower GPP (GPP_EC) and SIF datasets based on linear regression without considering variations of GPP-SIF relationship (Fixed-Ratio model, FR model). Considering the spatio-temporal variations of GPP-SIF relationship can improve the GPP simulation to a certain extent by mitigating the underestimation of peak GPP values. This improvement was found for both DR and SCL models. Owing to the dynamic variations of GPP/SIF ratio and associated uncertainties, the performance of DR model was not as good as that of SCL model. GPP estimation derived from GOSIF matched better with GPP_EC than that from downscaled GOME-2 SIF for DR, SCL and FR models. Our findings suggested that GPP can be better derived from satellite SIF by considering the variations of GPP-SIF relationship.

先前的研究表明，总初级生产量（GPP）和太阳诱导的叶绿素荧光（SIF）具有很强的线性关系，并且通常表现出相似的空间和时间模式。但是，GPP 和 SIF 对环境的反应可能不同，这会导致 GPP-SIF 关系的变异。为了更好地研究 GPP-SIF 关系中的动态对 GPP 估计的影响，我们建立了两个 GPP 模型。基于随机森林回归算法，首先利用气象变量和叶面积指数（LAI）建立了一个不稳定的GPP/SIF比率模型（Dynamic-Ratio model，DR模型）。然后使用该模型估计具有不同卫星 SIF 数据集的 GPP（称为 GPP_DR），即 来自 Orbiting Carbon Observatory-2 (GOSIF) 和全球臭氧监测实验 - 2 SIF（缩小的 GOME-2 SIF）的缩小精细分辨率 SIF。第二种模型（SIF-Climate-LAI 模型，SCL 模型）同样基于随机森林算法，但直接由气象变量、LAI 和 SIF 数据驱动，模型中没有使用 GPP/SIF 比率。作为比较，GPP 和 SIF 之间的线性关系也是使用涡协方差塔 GPP (GPP_EC) 和基于线性回归的 SIF 数据集建立的，而不考虑 GPP-SIF 关系的变化（Fixed-Ratio 模型，FR 模型）。考虑 GPP-SIF 关系的时空变化可以通过减轻对峰值 GPP 值的低估，在一定程度上改善 GPP 模拟。DR 和 SCL 模型都发现了这种改进。由于 GPP/SIF 比率的动态变化和相关的不确定性，DR 模型的性能不如 SCL 模型。对于 DR、SCL 和 FR 模型，来自 GOSIF 的 GPP 估计与 GPP_EC 的匹配比来自缩小的 GOME-2 SIF 的更好。我们的研究结果表明，通过考虑 GPP-SIF 关系的变化，可以更好地从卫星 SIF 推导出 GPP。