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A Simple Method for Detecting Phenological Change From Time Series of Vegetation Index

机译：一种从植被指数时间序列中检测物候变化的简单方法

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Remote sensing is a valuable way to retrieve spatially continuous information on vegetation phenological changes, which are widely used as an indicator of climate change. We propose a simple method called weighted cross-correlogram spectral matching—phenology (CCSM-P), which combines CCSM and a weighted correlation system, for detecting vegetation phenological changes by using multiyear vegetation index (VI) time series. In experiments with simulated enhanced VI (EVI) for various scenarios, CCSM-P exhibited high accuracy and robustness to noise and the potential to capture long-term phenological change trends. For a temperate grassland in northern China, CCSM-P retrieved more reasonable vegetation spring phenology from Moderate Resolution Imaging Spectroradiometer (MODIS) EVI images than the MODIS phenology product (MCD12Q2). When validated against field phenological observations in five of the AmeriFlux Network sites in the U.S. (four deciduous broadleaf forest sites and a closed shrublands site), and a cropland site in China, CCSM-P exhibited mean absolute differences (MADs) ranging from 2 to 10 days (median: 4.2 days), whereas MAD of non-CCSM methods showed larger variations, ranging from 5 to 58 days (median: 21.3 days). This is because CCSM-P integrates field phenological observations. Compared with non-CCSM methods, which are widely used to identify phenological events, CCSM-P is more accurate and less dependent on prior knowledge (thresholds or predefined functions), which indicates its effectiveness and applicability for detecting year-to-year variations and long-term change trends in phenology, and should facilitate more reliable assessments of phenological changes in climate change studies.
机译：遥感是一种检索关于植被物候变化的空间连续信息的有价值的方法，该信息被广泛用作气候变化的指标。我们提出了一种简单的方法，称为加权互相关图谱匹配物候（CCSM-P），它结合了CCSM和加权相关系统，用于通过多年植被指数（VI）时间序列检测植被物候变化。在针对各种场景的模拟增强VI（EVI）的实验中，CCSM-P表现出较高的准确性和鲁棒性，并具有捕获长期物候变化趋势的潜力。对于中国北方的温带草原，CCSM-P从中等分辨率成像光谱仪（MODIS）EVI图像中检索的植被春季物候比MODIS物候产品（MCD12Q2）更合理。在美国的五个AmeriFlux网络站点（四个落叶阔叶林站点和一个封闭的灌木丛站点）以及中国的农田站点进行野外物候观察验证时，CCSM-P的平均绝对差（MAD）为2到2。 10天（中位数：4.2天），而非CCSM方法的MAD显示较大的差异，范围从5到58天（中位数：21.3天）。这是因为CCSM-P集成了现场物候观测。与广泛用于识别物候事件的非CCSM方法相比，CCSM-P更加准确，并且对先验知识（阈值或预定义功能）的依赖程度更低，这表明了该方法在检测逐年变化和变化时的有效性和适用性。物候方面的长期变化趋势，应有助于在气候变化研究中更可靠地评估物候变化。

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