The peak of the growing season (POS) is known to play an important role in regulating the interannual variability of terrestrial carbon sequestration. Recent research has developed several models for POS estimation; however, a comprehensive understanding of the predictive ability of these models remains lacking, especially taking various biomes and satellite data from different sensors into account. Using data from 54 eddy covariance (EC) flux sites (434 site-years in total) from the FLUXNET2015 dataset, we extracted POS from the normalized difference vegetation index (NDVI), derived from two sensors (MODIS and SPOT-VGT), using four different methods. We then compared the model outputs when data from the different sensors were used, and across different biomes. Our results show that the model predictions correlated weakly (R² < 0.4) with the flux-based POS when multiple biomes were considered together. However, the performance of the models varied significantly between the models, the sensors that provided the data, and different biomes. Firstly, the more recently proposed methods did not perform as expected, and some of them performed even worse than the commonly used approach. Secondly, POS modeled from MODIS data performed slightly better than that from SPOT-VGT data. Thirdly, when the models are combined, they can reliably estimate POS for grasslands, deciduous broadleaf forests, and open shrublands, but not necessarily for other biomes. Lastly, our results indicate that NDVI-based POS is not a good proxy of flux-based POS. The study suggests that both biomes and sensor properties should be taken into account when estimating POS, and a rigorous validation is necessary before different models are implemented at regional, or larger scales. Therefore, this study provides insights that are helpful for improving our understanding of the impacts of algorithms, sensors, and biomes on model estimates of POS.

众所周知，生长季高峰（POS）在调节陆地碳汇的年际变化方面发挥着重要作用。最近的研究开发了几种 POS 估计模型；然而，仍然缺乏对这些模型的预测能力的全面了解，尤其是考虑到来自不同传感器的各种生物群落和卫星数据。使用来自 FLUXNET2015 数据集的 54 个涡度协方差 (EC) 通量站点（总共 434 个站点年）的数据，我们从来自两个传感器（MODIS 和 SPOT-VGT）的归一化差异植被指数 (NDVI) 中提取 POS，使用四种不同的方法。然后，当使用来自不同传感器的数据以及跨不同生物群落时，我们比较了模型输出。我们的结果表明模型预测的相关性很弱（R²< 0.4) 与基于通量的 POS 一起考虑多个生物群落时。然而，模型的性能在模型、提供数据的传感器和不同的生物群落之间存在显着差异。首先，最近提出的方法没有达到预期的效果，其中一些方法的性能甚至比常用的方法还要差。其次，从 MODIS 数据建模的 POS 表现略好于从 SPOT-VGT 数据建模的 POS。第三，当模型结合起来时，它们可以可靠地估计草原、落叶阔叶林和开阔灌木地的 POS，但不一定适用于其他生物群落。最后，我们的结果表明基于 NDVI 的 POS 不是基于通量的 POS 的良好代理。该研究表明，在估计 POS 时，应同时考虑生物群落和传感器特性，在区域或更大规模实施不同模型之前，需要进行严格的验证。因此，这项研究提供的见解有助于提高我们对算法、传感器和生物群落对 POS 模型估计的影响的理解。