This study aimed to understand which vegetation indices (VIs) are an ideal proxy for describing phenology and interannual variability of Gross Primary Productivity (GPP) in short-rotation coppice (SRC) plantations. Canopy structure- and chlorophyll-sensitive VIs derived from Sentinel-2 images were used to estimate the start and end of the growing season (SOS and EOS, respectively) during the period 2016–2018, for an SRC poplar (Populus spp.) plantation in Lochristi (Belgium). Three different filtering methods (Savitzky–Golay (SavGol), polynomial (Polyfit) and Harmonic Analysis of Time Series (HANTS)) and five SOS- and EOS threshold methods (first derivative function, 10% and 20% percentages and 10% and 20% percentiles) were applied to identify the optimal methods for the determination of phenophases. Our results showed that the MEdium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) had the best fit with GPP phenology, as derived from eddy covariance measurements, in identifying SOS- and EOS-dates. For SOS, the performance was only slightly better than for several other indices, whereas for EOS, MTCI performed markedly better. The relationship between SOS/EOS derived from GPP and VIs varied interannually. MTCI described best the seasonal pattern of the SRC plantation’s GPP (R² = 0.52 when combining all three years). However, during the extreme dry year 2018, the Chlorophyll Red Edge Index performed slightly better in reproducing growing season GPP variability than MTCI (R² = 0.59; R² = 0.49, respectively). Regarding smoothing functions, Polyfit and HANTS methods showed the best (and very similar) performances. We further found that defining SOS as the date at which the 10% or 20% percentile occurred, yielded the best agreement between the VIs and the GPP; while for EOS the dates of the 10% percentile threshold came out as the best.

中文翻译：

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使用从Sentinel-2图像得出的遥感指数估算短轮伐人工林的总初级生产力（GPP）物候。

这项研究旨在了解哪种植被指数（VIs）是描述短轮转小灌木林（SRC）人工林总物力和年生产力变化的理想代理。来自Sentinel-2图像的冠层结构敏感和叶绿素敏感的VI被用于估计SRC杨树（杨）的生长季节的开始和结束（分别为SOS和EOS）。spp。）人工林（比利时）。三种不同的滤波方法（Savitzky-Golay（SavGol），多项式（Polyfit）和时间序列的谐波分析（HANTS））以及五种SOS和EOS阈值方法（一阶导数，10％和20％百分比以及10％和20应用百分位数（％）来确定测定表位的最佳方法。我们的结果表明，中等分辨率成像光谱仪（MERIS）陆地叶绿素指数（MTCI）在确定SOS和EOS日期方面最符合GPP物候，这是从涡度协方差测量得出的。对于SOS，性能仅略好于其他几个指标，而对于EOS，MTCI的性能明显更好。源自GPP的SOS / EOS与VI之间的关系每年都会变化。三年合并后= ² = 0.52）。但是，在极端干旱的2018年期间，叶绿素红色边缘指数在繁殖生长季GPP变异性方面的表现略好于MTCI（R ² = 0.59; R ² = 0.49）。关于平滑功能，Polyfit和HANTS方法表现出最好的（非常相似）性能。我们进一步发现，将SOS定义为发生10％或20％百分位数的日期，将在VI和GPP之间达成最佳协议。而对于EOS，最佳百分比是10％阈值的日期。