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As a sensitive indicator for climate change, the spring phenology of alpine grassland on the Qinghai–Tibet Plateau (QTP) has received extensive concern over past decade. It has been demonstrated that temperature and precipitation/snowfall play an important role in driving the green-up in alpine grassland. However, the spatial differences in the temperature and snowfall driven mechanism of alpine grassland green-up onset are still not clear. This manuscript establishes a set of process-based models to investigate the climate variables driving spring phenology and their spatial differences. Specifically, using 500 m three-day composite MODIS NDVI datasets from 2000 to 2015, we first estimated the land surface green-up onset (LSGO) of alpine grassland in the QTP. Further, combining with daily air temperature and precipitation datasets from 2000 to 2015, we built up process-based models for LSGO in 86 meteorological stations in the QTP. The optimum models of the stations separating climate drivers spatially suggest that LSGO in grassland is: (1) controlled by temperature in the north, west and south of the QTP, where the precipitation during late winter and spring is less than 20 mm; (2) driven by the combination of temperature and precipitation in the middle, east and southwest regions with higher precipitation and (3) more likely controlled by both temperature and precipitation in snowfall dominant regions, since the snow-melting process has negative effects on the air temperature. The result dictates that snowfall and rainfall should be concerned separately in the improvement of the spring phenology model of the alpine grassland ecosystem.

remote sensing

Spatial Difference between Temperature and Snowfall Driven Spring Phenology of Alpine Grassland Land Surface Based on Process-Based Modeling on the Qinghai–Tibet Plateau