Abstract Vegetation phenology is widely acknowledged to be a sensitive indicator of the response of ecosystems to climate change, and phenological shifts have been shown to exert substantial impacts on ecosystem function, biodiversity, and carbon budgets at multiple scales. Therefore, long-term records of the phenology of the vegetated land surface are critical in exploring the biological response to environmental change at regional to global scales. Land surface phenology (LSP) from satellite observations has been extensively used to monitor the dynamics of terrestrial ecosystems in the face of a changing climate. Here we introduce and describe the global land surface phenology (GLSP) product derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) data at a gridded spatial resolution of 500 m. This new product will provide continuity for the Moderate Resolution Imaging Spectroradiometer (MODIS) GLSP product that has been produced on an operational basis since 2001. The VIIRS GLSP algorithm uses daily VIIRS Nadir BRDF (Bidirectional Reflectance Distribution Function)-Adjusted Reflectance (NBAR) data as the primary input to calculate the two-band enhanced vegetation index (EVI2) at each 500 m pixel. The temporal EVI2 trajectory is modeled using a hybrid piecewise logistic function to track the seasonal vegetation development, detect phenological transition dates, calculate the magnitude of vegetation greenness development, and characterize the confidence of phenology detections. The VIIRS GLSP algorithm has been implemented across the contiguous United States, and the resulting phenological metrics have been evaluated through comparisons with species-specific field phenological observations, Landsat phenology retrievals, and the MODIS phenology detections. The results demonstrate that the VIIRS GLSP metrics are of high quality and are in a good agreement with the other independent satellite and field observations. The results also indicate that the uncertainty in the VIIRS GLSP retrievals is primarily associated with missing high quality observations in VIIRS EVI2 time series.

中文翻译：

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VIIRS地表物候产品的生成与评价

摘要 植被物候被广泛认为是生态系统对气候变化响应的敏感指标，物候变化已被证明在多个尺度上对生态系统功能、生物多样性和碳收支产生重大影响。因此，植被地表物候的长期记录对于探索区域到全球范围内环境变化的生物响应至关重要。来自卫星观测的地表物候 (LSP) 已被广泛用于监测面对气候变化的陆地生态系统的动态。在这里，我们介绍和描述了从可见红外成像辐射计套件 (VIIRS) 数据以 500 m 的网格空间分辨率导出的全球地表物候 (GLSP) 产品。这种新产品将为自 2001 年以来一直在运营基础上生产的中分辨率成像光谱仪 (MODIS) GLSP 产品提供连续性。 VIIRS GLSP 算法使用每日 VIIRS Nadir BRDF（双向反射分布函数）-调整反射率 (NBAR) 数据作为计算每 500 m 像素的双波段增强植被指数 (EVI2) 的主要输入。使用混合分段逻辑函数对时间 EVI2 轨迹进行建模，以跟踪季节性植被发展、检测物候过渡日期、计算植被绿度发展的幅度并表征物候检测的置信度。VIIRS GLSP 算法已在美国本土实施，并且通过与特定物种的野外物候观测、Landsat 物候检索和 MODIS 物候检测进行比较，对由此产生的物候指标进行了评估。结果表明，VIIRS GLSP 指标具有高质量，并且与其他独立的卫星和现场观测结果吻合良好。结果还表明，VIIRS GLSP 反演的不确定性主要与 VIIRS EVI2 时间序列中缺少高质量观测有关。