Detecting Inter-Annual Variations in the Phenology of Evergreen Conifers Using Long-Term MODIS Vegetation Index Time Series
Detecting Inter-Annual Variations in the Phenology of Evergreen Conifers Using Long-Term MODIS Vegetation Index Time Series
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DOI：
10.3390/rs9010049
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发表时间：
2017-01
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通讯作者：
Laura Ulsig;C. Nichol;K. Huemmrich;D. Landis;E. Middleton;A. Lyapustin;I. Mammarella;J. Levula;A. Porcar-Castell
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作者：
Laura Ulsig;C. Nichol;K. Huemmrich;D. Landis;E. Middleton;A. Lyapustin;I. Mammarella;J. Levula;A. Porcar-Castell
植被物候的长期观测可用于监测陆地生态系统对气候变化的响应。卫星遥感通过对植被指数（例如归一化植被指数（NDVI））进行时间序列分析，提供了观察物候事件的最有效手段。本研究调查了与植被光利用效率相关的光化学反射指数 (PRI) 的潜力，以提高常绿针叶林中基于 MODIS 的物候估计的准确性。生长季开始和结束的时间（SGS 和 EGS）源自长达 13 年的 PRI 和 NDVI 时间序列，基于 MAIAC（多角度实施大气校正）处理的 MODIS 数据集和标准 MODIS NDVI 产品数据。得出的日期通过生态系统生产力地面通量塔测量的物候估计进行了验证。 MAIAC 时间序列与地面估计的 SGS 之间存在显着相关性（R2 = 0.36-0.8），这是值得注意的，因为之前的研究发现很难从卫星数据观察常绿植被的年际物候变化。噪声相当大的 NDVI 产品无法准确预测 SGS，并且无法从任何时间序列成功导出 EGS。虽然从地面数据得出的 SGS 与 PRI 之间的总体关系最强，但 MAIAC NDVI 与 SGS 的相关性更一致（在所有情况下 R2 > 0.6）。结果表明，PRI 可以作为春季季节转换的有效指标，但是，还需要开展额外的工作来确认观察到的关系，并进一步探索 MODIS PRI 在检测物候方面的有用性。
Long-term observations of vegetation phenology can be used to monitor the response of terrestrial ecosystems to climate change. Satellite remote sensing provides the most efficient means to observe phenological events through time series analysis of vegetation indices such as the Normalized Difference Vegetation Index (NDVI). This study investigates the potential of a Photochemical Reflectance Index (PRI), which has been linked to vegetation light use efficiency, to improve the accuracy of MODIS-based estimates of phenology in an evergreen conifer forest. Timings of the start and end of the growing season (SGS and EGS) were derived from a 13-year-long time series of PRI and NDVI based on a MAIAC (multi-angle implementation of atmospheric correction) processed MODIS dataset and standard MODIS NDVI product data. The derived dates were validated with phenology estimates from ground-based flux tower measurements of ecosystem productivity. Significant correlations were found between the MAIAC time series and ground-estimated SGS (R2 = 0.36–0.8), which is remarkable since previous studies have found it difficult to observe inter-annual phenological variations in evergreen vegetation from satellite data. The considerably noisier NDVI product could not accurately predict SGS, and EGS could not be derived successfully from any of the time series. While the strongest relationship overall was found between SGS derived from the ground data and PRI, MAIAC NDVI exhibited high correlations with SGS more consistently (R2 > 0.6 in all cases). The results suggest that PRI can serve as an effective indicator of spring seasonal transitions, however, additional work is necessary to confirm the relationships observed and to further explore the usefulness of MODIS PRI for detecting phenology.