Analysing changes in vegetation seasonality of terrestrial ecosystems is important to understand ecological responses to global change. Based on over three decades of observations by the series of Advanced Very High Resolution Radiometer (AVHRR) sensors, the Global Inventory Modelling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) dataset has been widely used for monitoring vegetation trends. However, it is not well known how robust long-term trends in vegetation seasonality derived from GIMMS NDVI are, given inevitable influences from sensor and processing artefacts. Here we analyse long-term seasonality trends in the GIMMS third generation (NDVI3g) record (1982–2013). Changes in vegetation seasonality are decomposed into changes in duration (related to growing season length) and timing (related to peak growing season). We compare seasonality trends from the previous version (NDVI3g v0) with those in the subsequently released version (NDVI3g v1) and, for their common period, with those derived from MODerate Resolution Imaging Spectroradiometer (MODIS) collection 6 NDVI. We find that NDVI3g v0 shows marked seasonality trends for 1982–2013 over more than one-third of the global vegetated area. Long-term trends based on v1 are generally consistent with v0, but v1 shows a strong trend towards earlier timing across the Arctic regions that is absent in v0. NDVI3g v0, v1, and MODIS all point towards an increased duration across the tundra of North Asia and later timing across North Africa. However, several discrepancies are also found between the NDVI datasets. For example, for the North-American tundra, MODIS shows earlier and v0 later timing, while MODIS shows an increased duration and v1 a reduced duration. For North Africa, v0 and v1 exhibit a reduced duration that is absent in MODIS. We conclude that both the primary observations and the subsequent processing can have a marked influence on inferred seasonality trends, and propose that the robustness of trends should be examined and corroborated using alternative data sources wherever possible.

分析陆地生态系统的植被季节性变化对于了解生态系统对全球变化的响应非常重要。基于一系列高级超高分辨率辐射计（AVHRR）传感器的三十多年的观测结果，全球清单建模和制图研究（GIMMS）归一化植被指数（NDVI）数据集已广泛用于监测植被趋势。但是，由于传感器和处理伪像的不可避免影响，从GIMMS NDVI得出的植被季节性的长期趋势有多么健壮，这一点尚不为人所知。在这里，我们分析了GIMMS第三代（NDVI3g）记录（1982年至2013年）中的长期季节性趋势。植被季节的变化分解为持续时间（与生长期有关）和时间（与生长期高峰有关）的变化。我们将先前版本（NDVI3g v0）与后续发行版本（NDVI3g v1）的季节性趋势进行比较，并比较它们在整个时期的季节性变化趋势，这些趋势来自MODerate分辨率成像光谱仪（MODIS）收集的6 NDVI。我们发现，1982年至2013年，NDVI3g v0在全球超过三分之一的植被中显示出明显的季节性趋势。基于v1的长期趋势通常与v0一致，但是v1显示了在v0中没有出现的北极地区更早定时的强烈趋势。NDVI3g v0，v1和MODIS都指向北亚苔原的持续时间增加，而北非的时间更晚。但是，在NDVI数据集之间也发现了一些差异。例如，对于北美苔原，MODIS显示的时间更早，v0的时间更晚，MODIS显示持续时间增加，而v1显示减少时间。对于北非，v0和v1的持续时间减少了，这在MODIS中是不存在的。我们得出的结论是，主要观察结果和后续处理都可能对推断的季节性趋势有显着影响，并建议应尽可能使用替代数据源检查和确认趋势的稳健性。