Abstract
Land surface phenology (LSP), as a precise bio-indicator that responds to climate change, has received much attention in fields concerned with climate change and ecology. Yet, the dynamics of LSP changes in the Qinling Mountains (QMs)—A transition zone between warm-temperate and north subtropical climates with complex vegetation structure—under significant climatic environmental evolution are unclear. Here, we analyzed the spatiotemporal dynamics of LSP for different vegetation types in the QMs from 2001 to 2019 and quantified the degree of influence of meteorological factors (temperature, precipitation, and shortwave radiation), and soil (temperature and moisture), and biological factors (maximum of NDVI and middle date during the growing season) on LSP changes using random forest models. The results show that there is an advanced trend (0.15 days/year) for the start of the growing season (SOS), a delayed trend (0.24 days/year) for the end of the growing season (EOS), and an overall extended trend (0.39 days/year) for the length of the growing season (LOS) in the QMs over the past two decades. Advanced SOS and delayed EOS were the dominant patterns leading to a lengthened vegetation growing season, followed by a joint delay of SOS and EOS, and the latter was particularly common in shrub and evergreen broadleaved forests. The growth season length increased significantly in western QMs. Furthermore, we confirmed that meteorological factors are the main factors affecting the interannual variations in SOS and EOS, especially the meteorological factor of preseason mean shortwave radiation (SWP). The grass and crop are most influenced by SWP. The soil condition has, overall, a minor influence the regional LSP. This study highlighted the specificity of different vegetation growth in the QMs under warming, which should be considered in the accurate prediction of vegetation growth in the future.
Highlights
Vegetation phenology is the seasonal timing of lifecycle events, such as leaf emergence, flowering, leaf coloration and fall, and it has become an important topic in the field of climate and ecology as a sensitive and precise indicator that is responsive to climate warming [1,2]
The earliest end of the growing season (EOS) occurred in GL, with a mean EOS of 287 ± 11 days, and the latest occurred in evergreen broadleaved forest (EBF), with a mean EOS of 295 ± 12 days (Figure 4c)
This study used the phenology metrics of vegetation in the Qinling Mountains (QMs) extracted from satellite normalized difference vegetation index (NDVI) data to analyze the spatiotemporal trends of land surface phenology (LSP) during 2001–2019, and to identify the dominant growth patterns of different vegetation types during the growing season
Summary
Vegetation phenology is the seasonal timing of lifecycle events, such as leaf emergence, flowering, leaf coloration and fall, and it has become an important topic in the field of climate and ecology as a sensitive and precise indicator that is responsive to climate warming [1,2]. Shifts in spring and autumn vegetation phenology caused by climate warming can differentially alter the length of the growing season, which affects carbon, water, and energy exchange between terrestrial ecosystems and the atmosphere [3,4,5]. 2021, 13, 4538 and drivers of phenology among different vegetation types to improve phenology models and enrich our understanding of the carbon cycle of terrestrial ecosystem. With the application of remote sensing in monitoring vegetation phenology, we traditionally use the term land surface phenology (LSP) to denote the dynamic variations in vegetation land surface as observed from satellite imagery [10]. Satellite-derived LSP metrics are usually focused on the start (SOS) and end (EOS) of growing seasons [11]
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