Vegetation phenology has a great impact on land-atmosphere interactions like carbon cycling, albedo, and water and energy exchanges. To understand and predict these critical land-atmosphere feedbacks, it is crucial to measure and quantify phenological responses to climate variability, and ultimately climate change. Coarse-resolution sensors such as MODIS and AVHRR have been useful to study vegetation phenology from regional to global scales. These sensors are, however, not capable of discerning phenological variation at moderate spatial scales. By offering increased observation density and higher spatial resolution, the combination of Landsat and Sentinel-2 time series might provide the opportunity to overcome this limitation.

In this study, we analyzed the potential of combined Sentinel-2 and Landsat time series for estimating start of season (SOS) of broadleaf forests across Germany for the year 2018. We tested two common statistical modeling approaches (logistic and generalized additive models using thin plate splines) and the two most commonly used vegetation indices, the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI).

We found strong agreement between SOS estimates from logistic and spline models (r_EVI = 0.86; r_NDVI = 0.65), whereas agreement was higher for EVI than for NDVI (RMSD_EVI = 3.07, RMSD_NDVI = 5.26 days). The choice of vegetation index thus had a higher impact on the results than the fitting method. The EVI-based SOS also showed higher correlation with ground observations compared to NDVI (r_EVI = 0.51, r_NDVI = 0.42). Data density played an important role in estimating land surface phenology. Models combining Sentinel-2A/B, with an average cloud-free observation frequency of 12 days, were largely consistent with the combined Landsat and Sentinel-2 models, suggesting that Sentinel-2A/B may be sufficient to capture SOS for most areas in Germany in 2018. However, in non-overlapping swath areas and mountain areas, observation frequency was significantly lower, underlining the need to combine Landsat and Sentinel-2 for consistent SOS estimates over large areas. Our study demonstrates that estimating SOS of temperate broadleaf forests at medium spatial resolution has become feasible with combined Landsat and Sentinel-2 time series.

植被物候学对诸如碳循环，反照率以及水和能量交换等陆地与大气的相互作用有很大的影响。为了理解和预测这些关键的陆地-大气反馈，至关重要的是测量和量化对气候变化以及最终对气候变化的物候响应。诸如MODIS和AVHRR之类的粗分辨率传感器对于研究从区域尺度到全球尺度的植被物候学非常有用。但是，这些传感器无法识别中等空间尺度上的物候变化。通过提供更高的观测密度和更高的空间分辨率，Landsat和Sentinel-2时间序列的组合可能会提供克服这一限制的机会。

在这项研究中，我们分析了结合Sentinel-2和Landsat时间序列来估计德国2018年阔叶林的季节开始（SOS）的潜力。我们测试了两种常见的统计建模方法（使用稀疏的逻辑和广义加性模型板样条）和两个最常用的植被指数：归一化植被指数（NDVI）和增强植被指数（EVI）。

我们发现从逻辑模型和样条模型得出的SOS估计值之间有很强的一致性（r _EVI  = 0.86； r _NDVI  = 0.65），而EVI的一致性高于NDVI（RMSD _EVI  = 3.07，RMSD _NDVI  = 5.26天）。因此，植被指数的选择比拟合方法对结果的影响更大。与NDVI相比，基于EVI的SOS还显示出与地面观测的更高相关性（r _EVI  = 0.51，r _NDVI = 0.42）。数据密度在估计地表物候方面起着重要作用。Sentinel-2A / B组合模型的平均无云观测频率为12天，在很大程度上与Landsat和Sentinel-2组合模型相吻合，这表明Sentinel-2A / B可能足以捕获美国大部分地区的SOS。德国于2018年。但是，在不重叠的条带地区和山区，观测频率显着降低，强调需要结合Landsat和Sentinel-2进行大范围的一致SOS估计。我们的研究表明，结合Landsat和Sentinel-2时间序列，以中等空间分辨率估算温带阔叶林的SOS已变得可行。