Abstract：
Non-linear trend detection in Earth observation time series has become a standard method to characterize changes in terrestrial ecosystems. However, results are largely dependent on the quality and consistency of the input data, and only few studies have addressed the impact of data artifacts on the interpretation of detected abrupt changes. Here we study non-linear dynamics and turning points (TPs) of temperate grasslands in East Eurasia using two independent state-of-the-art satellite NDVI datasets (CGLS v3 and MODIS C6) and explore the impact of water availability on observed vegetation changes during 2001-2019. By applying the Break For Additive Season and Trend (BFAST01) method, we conducted a classification typology based on vegetation dynamics which was spatially consistent between the datasets for 40.86 % (459,669 km2) of the study area. When considering also the timing of the TPs, 27.09 % of the pixels showed consistent results between datasets, suggesting that careful interpretation was needed for most of the areas of detected vegetation dynamics when applying BFAST to a single dataset. Notably, for these areas showing identical typology we found that interrupted decreases in vegetation productivity were dominant in the transition zone between desert and steppes. Here, a strong link with changes in water availability was found for >80 % of the area, indicating that increasing drought stress had regulated vegetation productivity in recent years. This study shows the necessity of a cautious interpretation of the results when conducting advanced characterization of vegetation response to climate variability, but at the same time also the opportunities of going beyond the use of single dataset in advanced time-series approaches to better understanding dryland vegetation dynamics for improved anthropogenic interventions to combat vegetation productivity decrease.
摘要：
对地观测时间序列中的非线性趋势检测已成为表征陆地生态系统变化的标准方法。然而,结果在很大程度上取决于输入数据的质量和一致性，只有少数研究解决了数据伪影对检测到的突变的解释的影响。在这里，我们使用两个独立的最先进的卫星NDVI数据集（CGLSv3和MODISC6）研究了东欧亚大陆温带草原的非线性动态和转折点（TP），并探讨了水可获得性对观测到的植被变化的影响2001-2019年。通过应用加性季节和趋势中断(BFAST01)方法，我们基于植被动态进行了分类类型，该分类类型在41％（459,669km2）的研究区域的数据集之间在空间上是一致的。在考虑TP的时机时，27%的像素在数据集之间显示一致的结果，这表明，当将BFAST应用于单个数据集时，需要对>2/3的检测到的植被动态区域进行仔细的解释。值得注意的是,对于这些显示相同类型的地区，我们发现在沙漠和草原之间的过渡带中，植被生产力的中断下降占主导地位。这里,在>80%的地区发现了与水供应变化的密切联系，表明近年来干旱胁迫加剧对植被生产力有调节作用。这项研究表明，在对植被对气候变化的响应进行高级表征时，必须对结果进行谨慎的解释。但与此同时，也有机会超越在先进的时间序列方法中使用单一数据集，以更好地了解旱地植被动态，以改善人为干预措施，以防止植被生产力下降。
